The DataWeave Blog

Category: Strategy

  • The Importance of Pricing Parity for Brands

    The Importance of Pricing Parity for Brands

    With bricks-and-mortar stores steadily increasing their online presence, the balancing act of pricing online and in-store is now more important and complex than ever. Companies spend years building brands and brand equity. Yet, a misplaced or poorly executed pricing strategy to handle both online and offline pricing can erode that equity with consumers very quickly.

    This problem is not new. It first started when Clubs like Costco and Sam’s started popping up in the 80’s. Suddenly, brands had to figure out a way to balance Club and Grocery pricing while taking advantage of a new, fast-growing channel. The biggest difference between now and then is that consumers now can check prices within seconds on their phone.

    So, how do you avoid losing your brand equity while ensuring price parity across online and offline channels?

    The key areas to consider are:

    1. Product Mix

    Do you have a broad enough mix of product sizes and case configurations for each channel? To maximize your sales and minimize your price disruption, reviewing your supply chain and product mix to ensure you are able to deliver value to both online and offline retailers is critical. Each channel is looking for ways to improve and maximize your brand sales. If you do not give them the right size and case configuration to enable them to increase margins, you will end up relying disproportionately on trade spend (dollars a brand spends with a retailer to promote products) to do so, or find your product on page 212 of every search.

    Examples of this strategy can be seen with companies offering only “bundled” items such as 12 cans or a large case on online marketplaces, while other retailers offer individual cans for purchase. This allows your online partners to make up margin by shipping a full case and not going through the process of breaking down a case and shipping single units. Also, this allows bricks-and-mortar retailers to have a sharper price point to lure consumers into the store. This strategy has played out well for many brands as they dealt with the rise of Club stores and can be played successfully in e-commerce as well, benefiting all parties.

    2. Price Lists

    Do you have harmonized price lists that do not favor one channel over another? If you do not, you are likely subsidizing the higher list cost in a channel with trade spend, which is highly inefficient. A single price list that provides an adequate price slope between the various sizes across your product range will maximize your ability to manage both channel pricing and brand equity.

    The single largest mistake brands tend to make is thinking that offering “net price” price lists to online marketplaces will benefit them while they use trade dollars in bricks-and-mortar stores to cater to EDLP (Everyday low price) customers. This approach is quite inefficient in many ways, and consumes valuable time and resources that can otherwise be better utilized. Having a single price list with the same price offered to all retailers allows for a more manageable and equitable pricing environment. It also enables a more profitable distribution of trade spend across the most effective areas to invest in for each retailer.

    I have worked with two brands in the past – one that managed two separate price lists and one that we implemented as a single-standard. While the one with the single price list saw sales grow and trade spend remain constant, the other saw trade spend double in just two years as it got caught in a scenario of always having to placate one side of the equation or the other.

    3. Trade Spend

    Today’s brands need to focus on a balanced trade spend strategy to address each channel’s unique needs. Using trade spend with online retailers can be tricky, as the channel is usually assumed to be the lowest priced anyway. Still, it can be used to drive traffic and offset supply chain costs, in order to ensure sufficient margins for the retailer, which will keep you off the CRAP (Can’t Realize A Profit) lists. Meanwhile, as JC Penny quickly learned when it made the disastrous shift to EDLP, consumers still want in-store discounts and sales.

    The best approach I have worked with is to set a single dead net price inclusive of all trade. For example, if your product’s standard list cost is $6.80 and you have a dead net price for promotions (or EDLP) of $5.40, then all retailers – online and bricks-and-mortar – are on equal footing. The only variance in the price for consumers will be the margin each operator chooses to take. This approach is not without issues, as you have to apply all elements of trade spend (such as ad fees, etc.) to the promotional unit costs to ensure you are truly capturing the dead net cost of the retailer.

    Still, the advantage of utilizing this approach is that when a retailer complains about the price another is offering to consumers, the conversation turns to margins being taken and not the cost of the product. At the very least, this approach provides a common ground on which to have a constructive conversation with all retailers.

    So why does this all matter so much to a brand?

    The road to selling online is littered with disaster and missed expectations for sales. Most manufacturers that jumped to online sales without considering pricing quickly learned that abandoning one channel for another does not lead to increased sales. Conversely, we have seen a few brands go from online only to in-store as well. These brands seem to have learned from the others’ mistakes and rarely will you find price variances between the online and offline channels. Instead, you tend to see these brands growing, as online consumers start experiencing the brand in-store.

    A Business To Community study by Larisa Bedgood in 2019 showed that “lower price” was second to only “convenience” for why consumers shop online, while 51% of consumers said that the biggest drawback to shopping online was not being able to touch and feel the product. Brands that are able to bridge the gap and provide consumers with the convenience of online while also showing up well in-store at the right price point will be able to break out of the stagnate 1-2% (if they are lucky) growth most CPG companies are experiencing. If online selling is growing 40-50% a year, why are these companies only managing brand declines and flat growth? I believe it is mainly due to the lack of a proper pricing parity strategy for the two channels along with a lack of actionable e-commerce data.

    Brands that do not focus on all three areas listed above often find themselves in a constant churn of conversations with retailers on all sides, which will typically lead to either online marketplaces or bricks-and-mortar stores deprioritizing the brand in promotions or search. Finding and setting a level playing field will allow for a balanced trade spend and growth for brands on both platforms, while also enabling a brand to break out of the net 1% growth that is plaguing a lot of CPG brands today.

    Outside of deploying basic pricing principles for your brand, I would also suggest early and strong investments in data, systems and people to monitor your brand’s health and pricing. Many brands jumped online without any way to monitor the consumer conversation around the brand or the pricing of the brand online. Not having the tools and resources in place to do this can lead to a quick and long-lasting erosion of brand equity and sales. Most, if not all, large manufacturers have subscribed to POS data for years and fully understand how to analyze this data. But the world has shifted. If your organization has not invested in digital shelf analytics, you may be driving blind and unaware that your brand is losing equity, which equals losing consumers and sales.

    Using a combination of pricing principles and e-commerce data mining tools will help you maintain price parity and brand relevance, while keeping you from becoming the last brand of choice for consumers, regardless of where they shop.

  • Compete Profitably in Retail: Leveraging AI-Powered Competitive Intelligence at Massive Scale

    Compete Profitably in Retail: Leveraging AI-Powered Competitive Intelligence at Massive Scale

    AI is everywhere. Any retailer worth his salt knows that in today’s hyper-competitive environment, you can’t win just by fighting hard – you have to do it by fighting smart. The solution? Retailers are turning to AI in droves.

    The problem is that many organizations regard AI as a black box of sorts – where you can throw all your data (the digital era’s blessing that feels like a curse) in at one end and have miraculously meaningful output appearing out the other. The reality of how AI works, however, is a lot more complex. It takes a lot of work to make AI work for you – and then to derive value out of it.

    Image Source: https://xkcd.com/1838

    Following the advent of the digital era, businesses across industries, particularly retail, were left grappling with massive amounts of internal data. To make things worse, this data was unstructured and siloed, making it difficult to process effectively. Yet, businesses learned to leverage simple analytics to extract relevant data and insights to affect smarter decisions.

    But just as that happened, the e-commerce revolution stirred things up again. As businesses of all shapes, sizes, and types moved online, they suddenly became a whole lot more vulnerable to other players’ movements than they were just about a decade ago, when buyers rarely visited more than one store before they made a purchase. In other words, retailers are now operating in entire ecosystems – with consumers evaluating a number of retailers before making a purchase, and a disproportionate number of players vying for the same consumer mindshare and share of wallet.

    Thus, external data from the web – the largest source of data known to man at present – is becoming critical to business’ ability to compete profitably in the market.

    Competing profitably in the digital era: Can AI help?

    As organizations across industries and geographies increasingly realized that their business decisions were affected by what’s happening around them (such as competitors’ pricing and merchandize decisions), they started shifting away from their excessive obsession with internal data, and began to look for ways to gather external data, integrate it with their internal data, and process it all in entirety to derive wholesome, meaningful insights.

    Simply put, harnessing external data consistently and on a large scale is the only way for businesses to gain a sustainable competitive advantage in the retail market. And the only way to practically accomplish that is with the help of AI. Many global giants are already doing this – they’re analyzing loads of external data every minute to take smarter decisions.

    That said, though, what you need to know is that all this data, while publicly available and therefore accessible, is massive, unstructured, noisy, scattered, dynamic, and incomplete. There’s no algorithm in the world that can start working on it overnight to churn out valuable insights. AI can only be effective if enormous amounts of training data is constantly fed back into it, coaxing it to get better and more astute each time. However, given the scarcity of readily available training datasets, limited and unreliable access to domain-specific data, and the inconsistent nature of the data itself, a majority of AI initiatives have ended up in a “garbage in, garbage out” loop that they can’t break out of.

    What you need is the perfect storm

    At DataWeave, we understand the challenge of blindly dealing with data at such a daunting scale. We get that what you need is a practical way to apply AI to the abundant web data out there and generate specific, relevant, and actionable insights that enable you to make the right decisions at the right time. That’s why we’ve developed a system that runs on a human-aided-machine-intelligence driven virtuous loop, ensuring better, sharper outcomes each time.

    Our technology platform includes four modules:

    1. Data aggregation: Here, we capture public web data at scale – whatever format, size, or shape it’s in – by deploying a variety of techniques.

    2. AI-driven analytics: Since the gathered data is extremely raw, it’s cleaned, curated, and normalized to remove the noise and prepare it for the AI layer, which then analyzes the data and generates insights.

    3. Human-supervised feedback: Though AI is getting smarter with time, we see that it’s still far from human cognitive capabilities – so we’ve introduced a human in the loop to validate the AI-generated insights, and use this as training data that gets fed back to the AI layer. Essentially, we use human intelligence to make AI smarter.

    4. Data-driven decision-making: Once the data has been analyzed and the insights generated, they can either be used as it to drive decision-making, or then integrated with internal data for decision-making at a higher level.

    With intelligent, data-backed decision-making capabilities, you can outperform your competitors

    Understandably, pricing is one of the most popular applications of data analytics in retail. For instance, a leading, US-based online furniture retailer approached us with the mission-critical challenge of pricing products just right to maximize sell-through rates as well as gross margin in a cost-effective and sustainable manner. We matched about 2.5 million SKUs across 75 competitor websites using AI and captured pricing, discounts, and stock status data every day. As a result, we were able to affect an up to 30% average increase in the sales of the products tracked, and up to a 3x increase in their gross margin.

    DataWeave’s powerful AI-driven platform is essentially an engine that can help you aggregate and process external data at scale and in near-real time to manage unavoidably high competition and margin pressures by enabling much sharper business decisions than before. The potential applications for the resulting insights are diverse – ranging from pricing, merchandize optimization, determination of customer perception, brand governance, and business performance analysis.

    If you’d like to learn more about our unique approach to AI-driven competitive intelligence in retail, reach out to us for a demo today!

  • 6 Smart Pricing Strategies for eCommerce Success

    6 Smart Pricing Strategies for eCommerce Success

    Over the last decade, the proliferation of e-commerce and the consequent surge in competitiveness among retailers has brought focus to one of the most critical drivers of success in online retail: pricing. According to McKinsey, an average 1% increase in price can translate into an 8.7% increase in operating profits (with the assumption that there’s no loss of volume). Yet, the company estimates that up to 30% of pricing decisions fail to provide the best price – every year. That’s a potential impact of millions in lost revenue for most modern-day retailers, a fact only made worse by the irony that in today’s times of automation and big data, there’s no shortage of intelligence to facilitate the best decision-making.

    What you need is the ability to gather and rationalize all the data out there – of competitor prices, price perceptions, market dynamics, buyer behavior, etc. – in good time to price your products just right for maximum margin and revenue. The best part? Effective product pricing contributes significantly toward fostering a great customer experience, too.

    Once you have your intel in place, there are plenty of eCommerce pricing strategies to choose from – it’s only a matter of identifying the metrics that matter the most to your business goals. That said, there are several models that have gained widespread popularity and acceptance over the years, like the following six:

    1) Introductory pricing

    This is a common marketing strategy used in the e-commerce space, where you draw consumer focus to a newly launched product or service, or the fact that you’re a new entrant in a market. There are two ways to do this – one is to start with steep discounts (particularly during sale events, and often in partnership with the consumer brand) with the aim of winning over more market share. At the other end is the strategy of setting relatively high initial prices. This works best for “exclusive offer” or “limited edition” opportunities; for instance, the opportunity to be the first to own the latest iPhone model.

    2) Cost-linked pricing

    In this method, you calculate how much it costs to sell a product and add a pre-determined margin to the final cost. In the world of online retail, product cost amounts to a lot more than the mere sum of manufacturing costs. For instance, it includes the procurement, labor, software, sales and marketing, shipping, and overhead costs that contribute to the total cost of housing it as long as it’s unsold. Therefore, all these costs need to be factored when determining the final product price. While the advantages of this model are its simplicity and the promise of guaranteed returns for each product sold, the flip side is that it doesn’t factor in the competitive landscape. The trick, therefore, lies in finding the balance between higher margin and sell-through rates, particularly given the aggressively competitive nature of online retail.

    3) Competitive pricing

    Today’s digitally savvy customers are forever comparing prices across several websites in the quest for the lowest prices. In fact, price is among the most critical factors that influences purchase decisions across products as well as categories. The competitive eCommerce pricing strategy, therefore, determines product price based on how the same products are priced by various competitors. While this model allows you to modify prices as frequently as necessary to drive efficient pricing and maximize revenue and margin, the complexity lies in ensuring consistent access to competitor prices, particularly in today’s highly dynamic e-commerce environment. DataWeave’s Pricing Intelligence platform helps eCommerce businesses overcome this challenge by helping them identify price improvement opportunities based on timely competitive intelligence at a massive scale.

    4) Dynamic pricing

    This model takes into account competitor prices, demand, and inventory levels, which are set up as triggers for automated pricing rules. While this results in sustained competitiveness, it requires a price optimization model that determines the optimal price in real-time response to fluctuations in demand and competitive prices – all the time ensuring alignment with your business goals. In other words, this model allows you to ensure consistently competitive yet optimized prices, thus acquiring and retaining a competitive edge in the market.

    5) Price perception management

    The company most famous for following this strategy is Amazon. The retail giant frequently identifies its most popular products and offers its largest discounts on them, often undercutting competitors. In other words, in this model, you “invest” in customer acquisition through excessively aggressive discounts on a select group of products – following which, you can cross-sell or up-sell other higher-priced products. Thus, you boost your perceived value to customers. Another way to drive a positive perception is to display discounted products at higher ranks on featured listings. For instance, in a recent study that we conducted, we found that 9 out of 10 leading US retailers’ top 50 ranked products (in each category) were significantly cheaper than the rest of their products.

    6) Bundle pricing

    The principle for this model is simple. You sell a number of the same products (or a range of complementary ones) for a combined, economical price. This is different from customers adding products individually to their cart as it works on the consumer psyche, which is more likely to favor a purchase that offers considerable perceived value. Thus, not only are you offering enhanced value to your customers (and in turn improving overall customer experience), you’re also actually increasing sales. Bundle pricing works best for products that are likely to involve repeat purchases (such as batteries, cereal boxes, or socks), and also for those that may need accessories (for instance, a food processor with various attachments). However, for bundle pricing to be effective, it’s also important to understand how your competitors are bundling their products.

    Granted, it isn’t easy to identify the perfect pricing strategy for you. As customers increasingly engage with you at every stage of their decision-making process and market dynamics become exceedingly complex, pricing as a function has to keep pace. As a retailer, your objective is to unearth the actionable insights hidden in your big data and leverage the resulting opportunities to drive the maximum possible revenue and margin – without getting lost in the flood.

  • Retailers Adopt Aggressive Private Label Pricing Strategies in CPG

    Retailers Adopt Aggressive Private Label Pricing Strategies in CPG

    Nine out of 10 leading retailers price their private label products lower than the average prices of their respective categories, reveals the latest DataWeave study, drafted in collaboration with SunTrust Robinson Humphrey The study reveals that an increasing number of retailers are viewing private label brands as a way to ensure sustained profitability.

    “As the CPG space reels under intense competition, a number of retailers are doubling down on private labels to capture valuable additional margin. For instance, Kroger, Walmart, and Amazon Fresh have a higher degree of private label penetration than the other retailers we analyzed,” said Karthik Bettadapura, Co-founder & CEO at DataWeave. “Our study unveils several such key insights covering product assortment & distribution patterns, price perception, and private label dynamics, revealing a clear snapshot of the disruptive transformations sweeping across the US CPG landscape.”

    Other key findings from the report, which tracked and analyzed 450,000 products across 10 leading retailers and 10 ZIP codes each, include the following:

    • Product assortment is emerging as a driver that’s as critical as pricing when it comes to customer retention. Target, H-E-B, and Kroger have a head start here, offering the largest product assortments among the retailers analyzed.
    • A sharp assortment strategy customized to local tastes and preferences is key to sustaining and enhancing customer satisfaction. Albertsons, Walmart, and Amazon Fresh lead here, revealing a higher focus on localized assortments.
    • “Home” and “Beauty & Personal Care” categories lead the distribution of private label products across retailers. The focus on these categories echoes a similar focus among national brands as well. These categories have the highest overall brand concentration, with around 4,000 brands each.

    To download the entire report, click here.

  • Decoding Alibaba’s Singles Day Sales

    Decoding Alibaba’s Singles Day Sales

    An average of $11.7 million per second was the rate at which Alibaba clocked $1 billion in sales during the first 85 seconds of Singles’ Day. As Alibaba’s annual sale event continues to grow in scale, referring to it as a global retail phenomenon is an understatement. Alibaba closed the day having shipped 1.04 billion express packages based on sales of merchandize worth 213.5 billion yuan ($30.67 billion).

    This performance shredded any lingering concerns analysts may have harbored about the prospects of this year’s sale, given the international backdrop of the ongoing trade skirmish between the US and China.

    Along with attractive discounts across a range of product categories, Singles’ Day also promised an integrated experience fusing entertainment, digital and shopping, in stark contrast to other large global sale events like Black Friday, which focus predominantly on discounts.

    At DataWeave, we set out to investigate if all the hype resulted in actual price benefits to the shoppers and how the various categories and brands performed in terms of sales during the event. To do this, we leveraged our proprietary data aggregation and analysis platform to capture a range of diverse data points on Tmall Global, covering unit sales (reported by the website) and pricing associated with Tmall Global’s major categories over the Singles’ Day period.

    Our Methodology

    We captured 5 separate snapshots of data from Tmall.com during the period between October 25 and November 14, encompassing over 15,000 unique products each time, across 15 product categories.

    To calculate the average discount rate, we considered the percentage difference between the maximum retail price and the available price of each product. We also looked at the additional discount rate, for which we compared the available price during Singles’ Day to the available price from before the sale. This metric reflects the truest value to the shopper during Singles’ Day in terms of price.

    Our AI-powered technology platform is also capable of capturing prices embedded in an image. For example, the offer price of ¥4198 was extracted accurately from the accompanying image by our algorithms and attributed as the available price while ¥100 from the same image was ignored.

    This technology was employed across hundreds of products using DataWeave’s proprietary Computer Vision technology.

    Domestic Appliances and Digital/Computer Categories Powered Turnover

    The Domestic Appliances and Digital/Computer categories dominated the Singles Day Sale in terms of absolute sales turnover. This isn’t surprising, since the average order value for these categories are typically much higher compared to the other categories analyzed.

    What clearly stands out in the above infographic is that the two largest categories in terms of sales turnover had average additional discounts of only 2 per cent and 0 per cent — a rather surprising insight. In general, with the exceptions of Women’s skincare, Men’s skincare, and Women’s bags (11 per cent, 10 per cent, and 9 per cent respectively), all other categories saw low additional discounts during Singles’ Day.

    However, the absolute discounts across the board were consistently high, with only Luggage (6 per cent), Digital/Computer (9 per cent) and Women’s wear (12 per cent) staying significantly below the 20 per cent mark. In fact, eight categories enjoyed absolute discounts greater than 30 per cent.

    Among common categories between Men and Women, the Men clocked more sales in Men’s wear, shoes, and bags. Only skincare proved to be an exception, where Women’s skincare generated twice the turnover of their Men’s equivalent.

    The Infants category was another intriguing sector to emerge during the sale. Both Diapers (38 per cent) and Infant’s Formula (25 per cent) were substantially discounted, despite only receiving low additional discounts of 2 per cent and 0 per cent respectively – indicating aggressive pricing strategies in this category even during non-sale time periods.

    The biggest takeaway from our analysis is the lack of any correlation between sales turnover and additional discounts, or even the absolute discounts.

    International Brands Make Gains

    International brands continue to penetrate the Chinese market showing up amongst the Top 5 brands of 13 of the 16 categories on sale.

    In the Diaper category, Pampers delivered nearly twice the sales turnover of its next biggest competitor. As expected, Apple and Huawei battled it out for honors in the Digital/Computer category although Xiaomi enjoyed pleasing results, nearly matching Huawei’s sales to go with its sales leadership of the Domestic Appliances category. Local brands, though, swept the Domestic Appliances, Furniture and Women’s Wear categories.

    The challenge posed by Chinese brands was illustrated by Nike’s spot in the second place in the highly competitive Men’s Shoes category after Anta.

    International brands topped only five of the 16 categories and Top 3 positions in ten categories. Still, there’s a growing presence of international brands in China’s eCommerce.

    Gillette won handsomely over its competition in the Personal Care category while Skechers enjoyed a similar result in Women’s Shoes, racking up nearly twice the retail sales of its nearest competitor. Another category dominated by international brands was the Women’s Cosmetics category where international brands accounted for 4 of the Top 5 brands.

    Similarly, Samsonite’s acquisition of American Tourister gave it two top 5 brands in the Luggage category. Other global brands to make the cut during the Singles’ Day sale included L’Oréal, Canada’s Hershel, Playboy, South Korea’s Innisfree and Japan’s Uniqlo.

    It’s Not All About Price On Singles’ Day

    The dramatic rise in shopping during Singles’ Day is not driven solely by price reductions. Alibaba’s commitment to its “New Retail” strategic model has led the Chinese giant to channel its impressive resources to focus on bringing together the online elements of its business with the more traditional offline aspects of its retail distribution. This is combined with entertainment to create a larger story based around the shopper’s overall “experience” rather than just driving “attractive prices” as a short-term retail hook.

    Alibaba is betting big on erasing the line between online and offline and its futuristic vision of structuring retail around the way people actually want to shop. Based on the consistently impressive results of Singles’ Day year after year, “New Retail” has a promising future.

    If you wish to know more about how DataWeave aggregates data from online sources to provide actionable insights to retailers and consumer brands, check out our website!

  • CEO Speak: Serving the US Market, Hiring the Right Talent, And More

    CEO Speak: Serving the US Market, Hiring the Right Talent, And More

    Recently, Karthik Bettadapura, Co-founder & CEO at DataWeave, was interviewed by Vishal Krishna, Business Editor at YourStory, in the Bay Area, California. They discussed DataWeave’s focus on the US market, challenges that retailers face today, DataWeave’s technology platform and hiring practices, and more.

    The following is a transcript of the interview.

    (The transcript has been edited for clarity and brevity)

    Vishal Krishna (VK): You left India to come and conquer America, why is that?

    Karthik Bettadapura (KB) : Just a bit of history — we started in 2011 and product development and research was based in Bangalore, and still is. At the end of the first 5 years, we realized that we built great technology, but we were not able to scale beyond a certain point [in India]. If we had to build a growing business, we had to look at other markets as well.

    VK: Quickly, can you tell me what DataWeave does?

    KB: We provide Competitive Intelligence to retailers and customer brands. We work with some of the largest brands and retailers out there and we provide them with analyses to compete profitably.

    VK: You said you had marque clients in India, yet you didn’t want to stay there because you wouldn’t have scaled beyond a particular point. Why is that?

    KB :The ticket size in India is still on the lower side. If you must build a sustainable business, you need access to a much larger customer base and we found that in the US.

    VK: Let’s start from the basics. What are a few things that a startup should decide to do when coming to America?

    KB: A few things:

    • A good understanding of the market
    • Learn fast about the market
    • Build a team here, or a have a team here already doing some work initially
    • Consider how your team back in India will go about doing things in your absence
    • The last one is about your own personal journey. I was so used to walking into an office and interacting with people. You come here, and you are all alone!

    VK: It’s a lonely journey. Doors don’t open all that easily and you’ve got to hustle. Why?

    KB: For people here, you are an unknown entity. Why should they be trusting someone who does not have enough customers here or has not raised money here? We had two US-based customers when we came in. It’s an uphill task to ensure that customers trust you.

    VK: Who was the first customer you personally met here and why was that meeting so important?

    KB: The first customer I met here was a large, big box retailer, and the meeting was primarily focused around why they should trust us — how can they know that we would survive and serve them, as well as how we are better than some of the other guys out there.

    VK: Can you tell us what DataWeave does for US retailers?

    KB: For retailers, we provide competitive intelligence, primarily around pricing optimization and assortment analytics. In the US, a lot of retailers are shutting shop and filing for bankruptcy.

    VK: Yeah, we saw Sears go through something like that.

    KB: The reasons fall broadly into 3 categories:

    • They failed to compete profitably with a lot of these new age businesses.
    • The new age retailers offer superior customer experience. They have figured out a better assortment/product strategy.
    • The third one is ‘Price’ — price is such an important feature.
      What we do is help these retailers optimize their strategies around pricing, assortment and promotions, eventually enabling them to compete profitably.

    VK: Typically, customers pay you on the outcome, pricing, license or subscription?

    KB: It’s a subscription-based model. There is a one-time setup fee and an ongoing subscription fee.

    VK: So you plug into their data management system?

    KB: Yes, but we can also have our product sit independently. Sitting out of their internal systems is a benefit for us as we don’t have to get into the entire loop of integrations into their internal systems right from Day 1. We prove our product works and then we integrate with their systems.

    VK: How do you integrate? Is the CIO your target?

    KB: No, we don’t sell to the CIO world. We sell to analytics, pricing, and merchandising teams.

    VK: Can pricing alone give retailers a competitive edge?

    KB: Yes, pricing is a big lever that retailers use. For example, last holiday season’s sale, Amazon and Walmart made 120 million price changes in just 2–3 days.

    VK: So they change the prices so dynamically to compete with each other. Is this price war coming to India?

    KB: It is happening in India already.

    VK: How much data can DataWeave’s infrastructure ingest?

    KB: We are a global platform — we have customers across the globe, not just the US or India. So, on a daily basis, we process data on around 120 million products.

    VK: Talk a little bit on R&D quickly. Do you have your marketing team in the US?

    KB: We have marketing teams in the US and India.

    VK: And the engineering team?

    KB: The engineering team is in Bangalore.

    VK: For people who want to work in your company, what kind of talent are you looking for?

    KB: We look at 4 broad talent areas:

    • One is in the world of data acquisition, which addresses issues like how data can be aggregated from thousands of websites and millions of pages on an ongoing basis, and how this data can be stored.
    • The second area is on what kind of insights can be generated using this data. This could be done using text analytics, image analytics, and other technologies. This includes process optimization, in terms of building efficient and scalable systems.
    • The third area is on how well the data can be represented if we have a customer who wants 60–70 million data points to be consumed on a weekly basis.
    • And the last area is on data modeling — what kind of insights can we eventually give to the customer? And, when I say insights, I mean specific actions.

    VK: You want people who can handle massive scale and for that they should be good at linear regression.

    KB: We value people who write good code. We primarily work in Python, and we use a lot of optimization techniques in the middle of the stack to help us scale.

    VK: Would you do something for supermarkets?

    KB: Absolutely. The largest offline supermarket in India is our customer.

    VK: So what can you do for supermarkets?

    KB: Offline retailers across the world are facing something that’s called showrooming. This is when a shopper walks up to a store, looks at and feels a product, then searches online to see it’s available at a better price. So we have retailers who are wary of this phenomenon. We also have retailers who are wary of diminishing customer loyalty. So they have to constantly ensure that they are priced better in the market and are not losing customers because of [online] pricing.

    VK: How powerful are your algorithms?

    KB: There is a dedicated team that works on our algorithms. These fall into several buckets. One is pure data scale algorithms — how do you build systems which ensure that you are able to efficiently query them in real time and get the desired output. The second one is — how do you keep improving your machine learning algorithms. For example, computer vision algorithms, text analytics algorithm, etc. The third — how do you keep experimenting effectively.

    VK: What role can an MBA degree holder play in DataWeave?

    KB: We have people who hold MBA degrees and are working in customer success, delivery management, marketing, and sales.

    VK: Do you spend time in training?

    KB: You do have some lead time if you are a fresher, but if you are a lateral hire, its expected that you keep the ball rolling. They should be able to learn and learn fast — learning is more important than knowing. So, we give a lot of importance to people who can learn and pick up things quickly – about our product, handling customer objections, etc.

    *

    Watch the whole video here or check out DataWeave’s website to know more about how we use data engineering and artificial intelligence to enable retailers and brands to compete profitably in the age of eCommerce.

  • Evolution of Amazon’s US Product Assortment

    Evolution of Amazon’s US Product Assortment

    As with many other product categories, Amazon has made a significant incursion in Apparel — a key battleground category in retail today. Recently, DataWeave once more collaborated with Coresight Research, a retail-focused research firm to publish an in-depth report revealing insights on Amazon’s approach to its US fashion offerings.

    Since our initial collaborative report in February this year, we have witnessed some seismic shifts in the category at both the brand and the product-type level.

    Research Methodology

    We aggregated our analytical data on more than 1 million women’s and men’s clothing products listed on Amazon.com in two stages:

    Firstly, we identified all brands included in the Top 500 featured product listings for each product subcategory in both the Women’s Clothing and Men’s Clothing sections featured on Amazon Fashion (e.g., the top 500 product listings for women’s tops and tees, the top 500 product listings for men’s activewear, etc.). We believe these Top 500 products reflect around 95 percent of all Amazon.com’s clothing sales. This represents 2,782 unique brands.

    We then aggregated the data on all product listings within the Women’s Clothing and Men’s Clothing sections for each of those 2,782 brands. This generated a total of 1.12 million individually listed products. This expansive list forms the basis for our highlights of the report.

    Third-Party Seller Listings Are Rising Sharply

    We identified a total of 1.12 million products across men’s and women’s clothing — a significant increase of 27.3 percent in the seven months between February and September 2018. The drivers of this sharp spike are third-party seller listings. In contrast, the report indicates only a 2.2 percent rise in first-party listings over the same period, compared to a 30.5 percent jump in third-party listings.

    In addition, Amazon has listed just 11.1 percent of all clothing products for sale, with third-party sellers offering the remaining 88.9 percent — an indication of the strength of Amazon’s open marketplace platform.

    A Major Brand Shift On Amazon Fashion Is Underway

    In just over six short months, major brand shifts on Amazon Fashion have taken place. The number of Nike listings has plummeted by 46 percent, driven by a slump in third-party listings following Amazon’s new partnership with Nike — a story recently covered by Quartz. Limited growth in Nike clothing first-party listings failed to compensate for this decline.

    Gildan’s spike in total product listings appears to be fueled by increased first-party listings off a low base. Calvin Klein’s 2017 agreement to supply Amazon with products appears to be driving the Calvin Klein brand’s double-digit uptick in first-party listings on Amazon Fashion.

    Aéropostale’s decline appears to be entirely driven by a drop in its third-party listings. The brand itself is not listed as a seller on Amazon.com.

    Amazon Is Rebalancing Its Apparel Portfolio and Switching Its Focus from Sportswear To Suits

    As its Fashion footprint rapidly matures, Amazon now appears to be rebalancing its portfolio with strong growth being shown in listings for formal categories such as suits and away from sportswear. We recorded a 98.6 percent increase in listings of women’s suits and blazers complemented by a 52.2 percent rise in men’s suit and sports coat listings between February and September 2018.

    Generic “Non-Brands” Are Surging On Top 25 Brands List

    Over the past six months, low-price generic brands have made major inroads into Amazon’s listings. Four unknown “brands” captured the top positions on the list of brands offered on Amazon Fashion. The WSPLYSPJY, Cruiize and Comfy brands appear to be shipped directly to customers from China.

    Source: Coresight/DataWeave (Amazon Fashion: Top 25 Brands’ Number of Listings, February 2018 vs. September 2018)

     

    Source: Coresight/DataWeave (Amazon Fashion: Top 25 Brands’ Number of Listings, February 2018 vs. September 2018)

    WSPLYSPJY alone accounts for fully 8.6 percent of Amazon men’s and women’s clothing listings. Cruiize accounts for a further 3.2 percent of listings while Comfy chips in another 3.1 percent.

    Amazon Appears To be Executing A Strategic Pivot

    Amazon’s fashion offering is fast maturing. We saw substantial growth in the number of listings for more formal categories. The realignment in third-party listings by Nike together with increased first-party listings for Calvin Klein and Gildan appear to be driven by alliances with Amazon.

    Simultaneously, ultralow-price generic clothing items delivered on order from China have inundated the “Most-Listed Products” rankings. Third parties now represent nearly 90 percent of Amazon Fashion’s offering.

    While Amazon Fashion shoppers enjoy a wider choice than they did even six months ago, we believe a stronger emphasis on first-party listings would grow the products eligible for Prime delivery. This tactic could strengthen Amazon Fashion’s long-term appeal as a shopping destination.

    If you’re interested in DataWeave’s technology, and how we aggregate data from online sources to provide unique and comprehensive insights on eCommerce products and pricing, check us out on our website!

  • Inside India’s eCommerce Battle: Attractive Offers Usher In The Festive Season

    Inside India’s eCommerce Battle: Attractive Offers Usher In The Festive Season

    It’s festival season in India again and shoppers took advantage of aggressive cutthroat competition between Indian online retailers to drive sales to unprecedented highs.

    All the major Indian eCommerce websites including, Amazon, Flipkart, Myntra, and Shopclues opted to go head to head by holding their first sale event this season over 4 to 5 days starting on the 10th of October. Still, as industry reports indicate, one retailer came out on top during this event — an insight supported by our analysis as well.

    A New Battleground

    The highlight this year was seeing how the announcement of global retail colossus Walmart’s acquisition of Flipkart would impact the sale events. The acquisition was the most influential development in India’s eCommerce sector, and it has transported a decades-long U.S. rivalry between Amazon and Walmart to Indian soil. As a result, this year’s sale event held out the promise of more attractive pricing and vast product selection for India’s consumers than ever before.

    Industry analysts estimate that the sale generated a cumulative Rs 15,000 crore in sales over the spread of the five sale days, a whopping outcome. In 2018, this translated into around a 64 per cent year-on-year growth outcome compared to the USD 1.4 billion (around Rs 10,325 crore) generated by the 2017 sales.

    The DataWeave Analysis

    At DataWeave, we analyzed the performance of each of the major eCommerce platforms including Amazon, Flipkart, Myntra, Paytm, and Shopclues. For each eCommerce website, we aggregated data on the Top 500 ranked products for over 40 product types spread across 6 product categories (Electronics, Men’s & Women’s Fashion, Furniture, Haircare, Skincare).

    We focused our analysis on only the additional discounts offered during the sale and compared them to prices prior to the sale, to reflect the true value of the sale to India’s shoppers.

     

    The battle of the discounts was led primarily by Flipkart and Amazon. Flipkart’s average additional discounts by category actually exceeded Amazon’s in three out of six categories, and it discounted more products that Amazon across all categories.

    Clearly, the focus for all e-tailers was skewed towards the main battlegrounds of Electronics and Fashion, compared to mainstream FMCG categories such as Hair and Skin Care. However, this is not surprising given FMCG functions on rather skinny margins.

    Across retailers, the Men’s and Women’s Fashion categories were the most aggressively discounted, attracting both the highest additional discounts and the highest percentage of products with additional discounts.

    The Furniture category too was an interesting battleground between Amazon and Flipkart, attracting attractive discounts on a wide range of products, particularly in Flipkart’s case.

    Prospective shoppers in search of relatively more expensive clothing products on discount during the sale would have established Myntra as their ideal destination, as it carried more premium products on discount during the sale, relative to all its competitors. For shoppers in search of an electronics bargain though, they would have done well to opt for Flipkart.

    Shoppers may have found some interesting deals on Paytm Mall too, especially in Men’s Fashion, while Shopclues largely held itself back from any dramatic price reductions.

    While Myntra capitalized on its niche though aggressive discounting in the Fashion category, most of the discounting action revolved unsurprisingly around Amazon and Flipkart. To drill down for a more complete understanding of just how the Amazon and Flipkart discounted their products, we conducted a more detailed follow-on analysis.

    We normalized additional discounts and popularity using a scale of 1 to 10 and plotted each product on a chart to analyze its distribution characteristics. Popularity was calculated as a combination of the average review rating and the number of reviews posted. Products with a popularity score of zero, as well as zero additional discounts were excluded from this analysis.

     

    The most obvious insight yield through this analysis is how Flipkart elected to distribute its additional discounts across a larger range of discount percentages. By contrast, Amazon went all in on the more limited range of products it decided to provide additional discounts on. This is a strategy we have seen Amazon adopt previously.

    One other intriguing insight is Flipkart’s decision to go for a much higher distribution of products falling below a popularity score of 0.5 compared to Amazon. Amazon’s strategy resulted in more of its discounted products having a higher popularity score, relative to Flipkart, albeit only by a comparatively minor amount. However, a shopper’s chances of buying a popular, positively reviewed product at a lower price were higher on Amazon than Flipkart during this sale.

    Achieving a Consistent Competitive Edge

    Flipkart claims to have recorded a 70 per cent plus share of entire Indian e-commerce market in the 4 day-BBD’18 sales. Flipkart further claimed to have cornered an 85 per cent share in the online Fashion category together with a 75 per cent share in the Electrical category’s large appliances during the sale. This includes a contribution by Flipkart’s subsidiary Myntra.

    As these numbers reflect, Amazon still has some way to go to entrench itself in the Fashion category of the Indian market. However, Amazon appears content to continue its surgical discounting philosophy.

    Overall, this year witnessed an impressive participation by Tier II and Tier III Indian city consumers — a sign of things to come in Indian online retail.

    With increasing competitive pressure, retailers simply cannot adopt discounting and product selection strategies in isolation and be successful. Having access to up to date insights on competitors’ products dynamically during the day is emerging as key to ensuring they’re able to sustain their lowest priced strategy for appropriate products. These insights are also proving critical in identifying gaps in their product assortment, which can hamper customer conversion and retention.

    During sale events, modern retailers need to rely on highly granular competitive insights on an hourly basis (or even more frequently) to inform their pricing and product strategies to ensure they consistently maintain a competitive edge for the consumer’s wallet. And while access to reliable competitive intelligence is critical, true value can only be derived when it gets integrated with a retailer’s core business and decision-making processes, such as assortment management, promotions planning, pricing strategies, etc.

    DataWeave’s Competitive Intelligence as a Service helps global retailers do just this by providing timely, accurate, and actionable competitive pricing and product insights, at massive scale. Check out our website to find out more!

  • Evaluating the Influence of Learning Models

    Evaluating the Influence of Learning Models

    Natt Fry, a renowned thought leader in the world of retail and analytics, published recently an article expounding the value and potential of learning models influencing business decision-making across industries over the next few years.

    He quotes a Wall Street Journal article (paywall) published by Steven A. Cohen and Matthew W. Granade who claim that, “while software ate the world the past 7 years, learning models will ‘eat the world’ in the next 7 years.”

    The article defines a learning model as a “decision framework in which the logic is derived by algorithm from data. Once created, a model can learn from its successes and failures with speed and sophistication that humans usually cannot match.”

    Narrowing this down to the world of retail, Natt states, “if we believe that learning models are the future, then retailers will need to rapidly transform from human-learning models to automated-learning models.”

    This, of course, comes with several challenges, one of which is the scarcity of easily consumable data for supervised learning algorithms to get trained on. This scarcity often results in a garbage-in-garbage-out situation and limits the ability of AI systems to improve in accuracy over time, or to generate meaningful output on a consistent basis.

    Enabling Retailers Become More Model-Driven
    As a provider of Competitive Intelligence as a Service to retailers and consumer brands, DataWeave uses highly trained AI models to harness and analyze massive volumes of Web data consistently.

    Far too often, we’ve seen traditional retailers rely disproportionately on internal data (such as POS data, inventory data, traffic data, etc.) to inform their decision-making process. This isn’t a surprise, as internal data is readily accessible and likely to be well structured.

    However, if retailers can harness external data at scale (from the Web — the largest and richest source of information, ever), and use it to generate model-driven insights, they can achieve a uniquely holistic perspective to business decision-making. Also, due simply to the sheer vastness of Web data, it serves as a never-ending source of training data for existing models.

    DataWeave’s AI-based model to leverage Web data

     

    Web data is typically massive, noisy, unstructured, and constantly changing. Therefore, at DataWeave, we’ve designed a proprietary data aggregation platform that is capable of capturing millions of data points from complex Web and mobile app environments each day.

    We then apply AI/ML techniques to process the data into a form that can be easily interpreted and acted on. The human-in-the-loop is an additional layer to this stack which ensures a minimum threshold of output accuracy. Simultaneously, this approach feeds information on human-driven decisions back to the algorithm, thereby rendering it more and more accurate with time.

    Businesses derive the greatest value when external model-based competitive and market insights are blended with internal data and systems to generate optimized recommendations. For example, our retail customers combine competitor pricing insights provided by our platform with their internal sales and inventory data to develop algorithmic price optimization systems that maximize revenue and margin for millions of products.

    This way, DataWeave enables retailers and consumer brands to utilize a unique model-based decision framework, something that will soon be fundamental (if not already) to business decision-making across industry verticals and global regions.

    As AI-based technologies become more pervasive in retail, it’s only a matter of time before they’re considered merely table stakes. As summarized by Natt, “going forward, retailers will be valued on the completeness of the data they create and have access to.”

    If you would like to learn more about how we use AI to empower retailers and consumer brands to compete profitably, check out our website!

    Read Natt’s article in full below:

    Steven A. Cohen and Matthew W. Granade published a very interesting article in the Wall Street Journal on August 19, 2018 — https://www.wsj.com/articles/models-will-run-the-world-1534716720

    Their premise is that while software ate the world (Mark Andreessen essay in 2011, “Why Software is Eating the World”) the past 7 years, learning models will “eat the world” in the next 7 years.

    A learning model is a decision framework in which the logic is derived by algorithm from data. Once created, a model can learn from its successes and failures with speed and sophistication that humans usually cannot match.

    The authors believe a new, more powerful, business opportunity has evolved from software. It is where companies structure their business processes to put continuously learning models at their center.

    Amazon, Alibaba, and Tencent are great examples of companies that widely use learning models to outperform their competitors.

    The implications of a model-driven world are significant for retailers.

    Incumbents can have an advantage in a model-driven world as they already have troves of data.

    Going forward retailers will be valued on the completeness of the data they create and have access to.

    Retailers currently rely on the experience and expertise of their people to make good decisions (what to buy, how much to buy, where to put it, etc.).

    If we believe that learning models are the future then retailers will need to rapidly transform from human-learning models to automated-learning models, creating two significant challenges.

    First, retailers have difficulty in finding and retaining top learning-model talent (data scientists).

    Second, migrating from human-based learning models to machine-based learning models will create significant cultural and change management issues.

    Overcoming these issues is possible, just as many retailers have overcome the issues presented by the digital age. The difference is, that while the digital age has developed over a 20 year period, the learning-model age will develop over the next 7 years. The effort and pace of change will need to be much greater.

  • Amazon’s Fashion & Apparel Product Assortment | DataWeave

    Amazon’s Fashion & Apparel Product Assortment | DataWeave

    Apparel remains one of the key battleground categories in retail today, and like in most other product categories, Amazon has made significant in-roads here. Beyond expanding the range of product offerings and brands in its marketplace, Amazon has also launched several private label brands in this vertical and looked to drive more sales as a first-party seller.

    Recently, DataWeave collaborated with Coresight Research, formerly known as Fung Global Retail & Technology, a retail-focused research arm of Li & Fung Group, to publish an in-depth report revealing Amazon’s strategic approach to product assortment in its fashion and apparel category.

    In this blog post, we’ll summarize some interesting insights into Amazon’s strategy from the report. For an in-depth and detailed view, check out the original article at — “Amazon Apparel: Who Is Selling What? An Exclusive Analysis of Nearly 1 Million Clothing Listings on Amazon Fashion”

    Research Methodology

    Our analysis focused on several critical areas, including the presence of Amazon’s private label, the demarcation between Amazon as a seller and its third-party sellers and the top brands and categories in women and men’s apparel.

    We aggregated data from Amazon.com in two stages:

    Firstly, we identified brands with a meaningful presence in Amazon’s clothing offering by identifying all brands included in the top 500 ranks of featured product listings for each product type in the Women’s Clothing and Men’s Clothing sections on Amazon (e.g., the Top 500 product listings for women’s tops and tees, the Top 500 product listings for men’s activewear, and so on.). This generated a total of 2,798 unique brands.

    Secondly, we aggregated our data on all product listings within the Women’s Clothing and Men’s Clothing sections for each of the 2,798 brands identified previously. This returned a total of 881,269 individually listed products. This extensive list forms the basis for the highlights in Coresight’s report.

    Coresight’s Analysis — Some Interesting Insights

    Strategically, Amazon remains heavily reliant on its third-party sellers in the clothing category. In total, just 13.7 percent of women’s and men’s clothing products featured on Amazon Fashion are listed for sale by Amazon itself (first-party sales), while third-party sellers account for 86.3 percent of listings.

    In womenswear, third-party sellers account for 85.7 percent of listings, while in menswear, they account for 87.1 percent of listings. Moreover, Amazon appears to be focusing its first-party clothing inventory on the higher-value categories. Clearly, the retailer’s reliance on third-party sellers underscores its opportunity to grow its sales of apparel volumes by bringing more of its current inventory in-house.

    The analysis found 834 Amazon private-label products on Amazon website, equivalent to 0.1 percent of all clothing available through Amazon Fashion. The company’s private labels appear to be clustered tightly in specific clothing categories.

    Womenswear brand Lark & Ro is by far the biggest of Amazon’s apparel private labels, as measured by the number of items.

    Nike is the most-listed brand on Amazon Fashion, with 16,764 listed products spanning womenswear and menswear. Lower-price brands such as Gildan and Hanes also rank very highly in terms of the number of products listed.

    Value-positioned brands that have traditionally focused on wholesaling to retailers, such as Gildan and Hanes, also rank very highly in terms of the number of products listed.

    What is clear is that currently, Amazon’s clothing listings are highly diluted, with no one major brand dominating the listings.

    Interestingly, casualwear and activewear clearly lead Amazon’s category rankings. Women’s tops and tees are the most heavily listed clothing category on Amazon Fashion, with 138,001 products listed.

    Men’s shirts, which includes a large number of casual shirts together with polo shirts and some T-shirts, comes in second, with 109,043 products listed. Echoing the prominence of the global Nike and Adidas brands on the Amazon website, activewear has achieved a centre of gravity status as a category, accounting for 76,930 men’s activewear products and 51,992 women’s activewear products listed on the site.

    Several Opportunities for Growth

    Amazon Fashion remains heavily dependent on third-party sellers. It’s a fair assumption that more first-party listings would attract greater numbers of shoppers, especially Amazon Prime members. Amazon’s private-label ranges represent another potential lever for growth.

    Also, the 30 most-listed brands on Amazon Fashion comprise 30 percent of all clothing products listed on the website, while just 189 brands have more than 1,000 products each listed on the website.

    This data indicates the presence of major growth opportunities across the board, be it Amazon private label brands, Amazon as a seller, and for several mid-range clothing brands.

    If you’re interested in DataWeave’s technology, and how we aggregate data from the Web to provide unique and comprehensive insights on eCommerce products and pricing, check us out on our website!

  • What Retailers Can Learn from the Lowe’s Board Announcement

    What Retailers Can Learn from the Lowe’s Board Announcement

    Last Friday, Reuters published, “Home Improvement chain Lowe’s said it has nominated two independent board members and plans to add a third following “constructive” talks with hedge fund D.E. Shaw Group, which has taken an activist stake.”

    It was reported that D.E. Shaw Group had utilized available external data to identify quantifiable opportunities to grow sales by several billion dollars and to reduce costs significantly.

    A question that comes immediately to mind is, “Why didn’t Lowe’s utilize this same available external data themselves?”

    Is it because Lowe’s and many other retailers spend their time focusing on internally generated data, rather than looking at available external data, or better yet, combining available external data with their internal data?

    There are huge opportunities to drive incremental sales, margins and profits through leveraging external data, like competitive intelligence data produced by firms like DataWeave.

    There are huge opportunities to drive incremental store sales, margins, and profits through leveraging digital data to drive better store specific assortments, prices and promotions by providing relevant local digital data to store executives using solutions by firms like Radius8.

    I expect to see more Lowe’s-like announcements in the near future as investment firms realize there are very substantial, untapped financial opportunities within retail.

  • Walmart’s Online Pricing Analysis | DataWeave

    Walmart’s Online Pricing Analysis | DataWeave

    In an increasingly competitive retail landscape and facing intense margin pressures, improving the profitability of online commerce is a growing area of focus for all retailers.

    When Amazon acquired Whole Foods in August, several media outlets and analysts speculated whether there would be a slashing of prices across the board. Instead, Amazon lowered prices only on those items that it knew would drive increased traffic to the stores, resulting in a 25% increase in footfall the first 30 days after the acquisition closed.

    (Read also: Amazon’s Whole Foods Pricing Strategy Revealed)

    Disrupting the Status Quo

    Walmart has now announced a shift in its online pricing to draw more shoppers to purchase from its brick-and-mortar stores and save on shipping costs.

    Sarah Nassauer wrote an interesting article for the Wall Street Journal recently, outlining Walmart’s online pricing strategy and its approach to pricing its products differently between its online and offline stores.

    Sarah reports, “Walmart wants to charge customers more to buy some products online than in stores, part of the company’s efforts to boost profits and drive store traffic as it competes with Amazon.”

    What’s interesting is Walmart’s move to display the lower offline store prices on its website for some grocery products, nudging shoppers to drive down to the nearest Walmart store.

    Again, Walmart did not raise prices for all items but only a few, select food and household items, “including boxes of Kraft Macaroni & Cheese, Colgate toothbrushes and bags of Purina dog food, according to people familiar with the matter and comparisons between online and in-store prices.”

    The article goes on to state that, “[T]he move is unusual for Walmart, which has long honed an ‘everyday low price’ message and has worked to keep online prices at least as low as shoppers find in its 4,700 U.S. stores. Walmart e-commerce workers responsible for product sales have been instructed to boost profits along with sales, according to the people familiar with the situation, and are ‘no longer obligated to follow store pricing.’”

    This move indicates a greater focus on online-to-offline (O2O) strategies by the world’s largest retailer in an effort to cut down on the crippling costs of transport operations and logistics. According to a cost analysis by consultants Spend Management Experts, “A $1.28 box of Kraft Macaroni & Cheese could cost a big retailer around $10 to ship from Chicago to Atlanta, depending on how remote the buyer’s address is . . . A smaller retailer would likely pay about double.”

    With this news, the days of providing the same price online and in stores are over, setting a precedent and reflecting important differences in costs and competitor capabilities.

    But how did Walmart know which items to focus on for lowering (or raising) prices?

    Cutting-Edge Competitive Intelligence Solutions

    Did Walmart pick items at random or guess? Not likely. With recent enhancements in competitive intelligence and data analysis solutions, the era of guesswork, gut-fuelled decisions, and manual number crunching is over.

    In today’s digital economy, actionable competitive intelligence has become a critical component in the transformation of retail. Retailers like Amazon and Walmart use competitive insights to identify categories and items that show the greatest potential for increased shopper interest, sales, and profits, to adjust their prices.

    Competitive intelligence providers like DataWeave provide unique, AI-driven, competitive insights and business recommendations by harnessing and analyzing competitive data from the Web.

    When retailers link these competitive insights and data to their internal pricing and inventory systems, they create a powerful engine that marries internal and external forces to produce highly accurate assortment, pricing, and promotion recommendations, all in near real-time.

    As retailers like Walmart experiment with their pricing and merchandizing across channels, they have come to rely on modern retail technology solutions that continue to evolve to help them reduce operational complexities and yield higher ROI.

  • Top 5 Drivers of Successful eCommerce | DataWeave

    Top 5 Drivers of Successful eCommerce | DataWeave

    Retail has undergone a dramatic transformation over the last decade. Once dominant retailers are today being given a run for their money amid a gradual decline in mall traffic and sharply growing consumer preference for shopping online.

    Surfing this online retail wave is Internet behemoth Amazon, which is raking in 43% of all new eCommerce dollars, leaving other retailers floundering in its wake.

    As it unfolded, this transformation has unleashed changes across many areas of retail, a phenomenon that’s been well documented by industry commentators in the media. Some of these shifts include:

    Customer preferences: Customers today are spoilt for choice, both in terms of being able to quickly and easily compare product prices across websites, as well as consistently driving the demand for new and unique products from retailers.

    Hyper-personalization: With shoppers increasingly relying on mobile apps, highly personalized shopping experiences are becoming the new normal.

    Delivery: e-Retailers are competing on faster home deliveries, stretching themselves to guarantee same day delivery, or even (as in the case of hyper-local grocery retailers) within a few hours. Drones, anyone?

    Payment Modes: Even the more tactical aspects of retail, like payment modes, have been forced to evolve. Starting with cash-on-delivery, this trend quickly spread to embrace card payments and digital wallets. These initiatives have posed significant technological and security challenges for retailers.

    As with a forced move in chess, traditional retailers have had to evolve and embrace changes like the ones listed above, in order to survive the incredibly cutthroat world of modern retail. Similar challenges exist for up-and-coming eCommerce companies as well.

    However, many pundits and retailers alike often forget that doing even simple, time-tested things correctly can go a long way in forging an effective competitive position, helping win both market share and customer affections. While digital transformation has altered how these strategies were routinely executed, the fundamentals remain as relevant today as they ever were.

    1. Smarter Pricing

    With 80 percent of first-time shoppers comparing products prior to buying, the need for an eCommerce website to offer competitive pricing has become a mandatory cost-of-entry capability. While dynamic pricing poses a challenge for e-retailers to stay competitive, it also presents them with an opportunity to track their competitors’ pricing and exploit that information to optimize their own pricing.

    However, e-retailers today are frequently forced to perform millions of price-changes every day in the eternal quest to either offer the lowest price or entrench a calculated premium price perception among shoppers.

    For instance, as far back as Christmas season 2014, Amazon is estimated to have made a total of 80 million price changes per day. Similarly, today’s hyper-local grocery retailers offer differentiated and targeted prices for shoppers living in specific zip codes.

    To achieve price controls on this level of scale demands sophisticated automated tracking of competitor pricing to facilitate timely, data-driven dynamic pricing decisions. This has, today, become a table stakes requirement.

    2. Variety and Depth of Product Range

    If customers cannot find what they are looking for on a website, all other aspects of how an eCommerce operator optimizes their retail strategy falls by the wayside.

    A website’s success remains dependent largely on it being able to cater effectively to the needs, wants and desires of its target audience. Simply put, a website offering a mammoth product range may still end up failing compared to a small niche website with a limited but highly targeted assortment that understands closely its customer’s sweet spot.

    However, with millions of products on offer online all day every day, gathering and harvesting deep insights into a competitor’s assortment mix can appear daunting. Include dynamically changing product assortments and different product taxonomies into the standard research mix, and many who lack access to automated competitive intelligence systems find themselves struggling to find the expertise required to gather and summarize this information in an actionable form.

    3. Customer Centricity

    Today, customers demand to be heard. As competitive pricing becomes an expected cost of doing business, retailers will need to place greater support resources and more effective processes to resolve customer problems and complaints in a timely fashion at the heart of their customer service model.

    Following the online social revolution, 9 out of 10 retail customers now expect a consistent response across all social media channels.

    Successful companies like Zappos, Best Buy and Amazon have been quick to understand this significant shift in customer preferences. These retailers have demonstrated their willingness to go the extra mile by establishing a robust, scalable omni-channel support structure.

    The level of this commitment can be seen in Amazon’s recent vision statement announcement, “Amazon today boasts of one of the most responsive omni-channel customer support and Zappos takes pride in sending a personalized response to customer queries. We seek to become Earth’s most customer centric company.” This aggressive customer centric sentiment drives a stake in the ground for all competing eCommerce companies’ to match via their customer service strategy.

    4. Superior Customer Experience

    While bricks and mortar retail stores continue to attract customers by enabling shoppers to touch, feel and test items before they purchase, online and omni-channel retailers have channelized their efforts into increasingly refining their web user experience.

    Several studies reveal it takes only a couple of seconds for a website visitor to decide whether to stay on or leave a website. Aspects such as visual design, ease of use, content attractiveness, website loading time and pervasive calls to action (CTA) are a few of the key user experience parameters that influence visitors to stay on a website.

    eCommerce sites such as Zara, Graze, Asos, and Amazon offer attractively organized and clutter-free designs, which are visually engaging and easy to navigate. While these design elements help them keep their customers engaged, it’s their disciplined focus on content that stimulates visitor conversions.

    Detailed product descriptions and high-quality images are helping these eCommerce sites educate their customers about their products while simultaneously boosting their website’s SEO ranking, helping it attract and engage still more online visitors.

    Complementing the online retailing revolution are substantial efforts by omni-channel retailers to optimize O2O (online to offline) strategies designed to bring together the best of both worlds — the discoverability of online, with the touch-and-feel of an offline environment.

    5. Optimized Promotional Strategies

    With so many options for a shopper to choose from in an increasingly cluttered and competitive online retailing environment, attracting new customers and entrenching customer loyalty is an ongoing challenge. Strategic online promotions are emerging as an effective technique in solving the customer recruitment and retention dilemma. Online promotions if executed effectively are doing wonders for generating inbound website traffic.

    However, for online promotions to be effective, it is critical for e-retailers to understand their competitor’s strategy if they are going to be able to sustain their competitiveness. Key questions to answer in this context are, what brands are they promoting more than others? For how long? At what frequency?

    Keeping a keen eye on and reacting to competitors’ promotions is a key aspect to designing effective online promotions. Being able to exploit this competitive intelligence not only boosts their own sales volumes but erodes that of their competitors as well.

    Competitive Intelligence As A Service

    Having understood the far-reaching impact of these evergreen drivers of eCommerce success, we at DataWeave work with omni-channel and online retailers to provide Competitive Intelligence as a Service and help them evaluate and optimize their strategic approach across the eCommerce landscape.

    If you’re interested in DataWeave’s solutions and would like to learn more about how we help retailers and brands optimize their retail strategies, visit our website!

  • Analysis of Target’s Discount Strategy

    Analysis of Target’s Discount Strategy

    Earlier this year, we witnessed Amazon and Walmart going head to head in a CPG goods price war of fluctuating intensity that soon rippled out to embrace the entire grocery industry.

    This further intensified with Amazon’s takeover of Whole Foods and the Whole Foods’ subsequent announcement hinting at significant discounts toward the end of August.

    (Read Also: Amazon’s Whole Foods Pricing Strategy Revealed)

    Soon, Target announced it was lowering prices on literally “thousands of items.” As Mark Tritton, Target executive vice president and the chief merchandising officer put it, “We want our guests to feel a sense of satisfaction every time they shop at Target.”

    To drive home the seriousness of their intent, Target nominated grocery staples such as cereal, paper towels, milk, eggs, baby formula, razors and bath tissue and vowed to, “eliminate more than two-thirds of their price.”

    At DataWeave, we focused our proprietary data aggregation and analysis platform on Target’s reported price reduction. Our team acquired data on the prices of over 160,000 products listed by Target across 12 zip-codes selected at random. The platform then took two snapshots. Firstly, between 23rd August and 30th August which included the Whole Foods’ price reduction (to study any possible reactions on price) and, secondly, between the 6th September and 13th September, which included Target’s discount strategy announcement.

    Of the categories Target identified as priorities for its discount strategy, only baby products, cereals, and Milk & Eggs displayed significant price drops. This price discounting effect varies, however, across brands in each category. In cereals, while KIND (30.4%) and Purely Elizabeth (24%) displayed high discounts, Apple Jacks, Corn Pops, and Krave more surprisingly increased their prices by up to 25% each.

    Similarly, in the Milk & Eggs category, Price’s (13.6%) and Coffee-Mate (10%) exemplified hefty discounts, while Moon Cheese and Challenge Butter increased their prices by 33% and 48% respectively in the same time period. By comparison, Razors and Paper Towels showed no price changes whatsoever across the review period.

    Interestingly, we observed greater price-change activity coinciding with the time of the Whole Foods’ announcement (between 23rd and 30th of August) than the later time period. Once again, however, no definite price discounting pattern emerged from the study, indeed the team found discount rates fluctuated significantly across categories.

    Looking across the spectrum of CPG categories pricing, we saw significant, sustained variation across both categories and zip-codes.

    Beauty products showed a 2 percent discount on average although this varied by zip-code, fluctuating between a 7 percent discount and an actual 10 percent price increase. F&B showed a 2 percent price increase, which jumped to 10 percent in some zip-codes. Personal care displayed a 2.5 percent increase on average, varying anywhere between an 8 percent discount and a 10 percent price increase. Baby products surprisingly recorded a 4 percent price increase on average during the study.

    So, What Does This All Mean?

    Based on our analysis, Target’s pricing strategy appears to be a combination of very closely concentrated discounting, complemented by selective price increases. Is discounting more a perception than a reality at this stage of the CPG cycle?

    Aggressive price discounting has never been a decisive factor in successfully building Target’s consumer franchise. However, given the current trading environment and the continued pressure applied by competitive omni-channel strategies, which has seen a host of new entrants elbowing their way into the market, we anticipate price will continue to play a prominent role in retailing.

    We suspect, based on evidence we gathered, that price discounts are more a highly targeted weapon in the fight for market share than a broadsword slashing of prices across the board. As Target’s CEO Brian Cornell noted during an earnings call, the company experienced “a meaningful increase in the percent of our business done at regular price and a meaningful decline in the percent on promotion.”

    If you’re interested in DataWeave’s data aggregation and analysis technology, and would like to learn more about how we help retailers and brands build and maintain a competitive edge, visit our website.

  • Amazon’s Whole Foods Pricing Strategy Analysis | DataWeave

    Amazon’s Whole Foods Pricing Strategy Analysis | DataWeave

    Amazon.com, America’s retail behemoth, dominated headlines in August when it completed its acquisition of Whole Foods in early August 2017. Having officially taken control of the up-market grocer, which focuses on premium quality produce, market observers and consumers alike are eagerly awaiting Amazon’s pricing strategy analysis.

    At the heart of Amazon.com’s seemingly unstoppable growth trajectory is the company’s ability to understand consumers, complemented by deep insights into buying cycles and purchase decisions and preferences. It also helps that Amazon.com boasts one of the planet’s mightiest marketing and publicity machines.

    Is Amazon.com About To Launch A Grocery Price War?

    Reports of Amazon.com dropping Whole Foods prices by up to 43 percent quickly made splashes across the news media. Given Jeff Bezos has been quoted in the past as saying, “your margin is our opportunity”, an aggressive promotional campaign to achieve dominance for its new Whole Foods acquisition was anticipated by some commentators.

    These sentiments ignited fears of a profit-sapping price war, immediately hit stock prices in the cutthroat grocery industry, which survives on famously thin margins. Memories of Amazon.com’s impact on US department store profitability quickly surfaced with analysts pointing to Walmart’s revenue/market share plunge from 26 percent in 2005 to just 11 percent in 2016 when the sector came under sustained pressure from Amazon.com.

    How Deep Are Amazon.com’s Price Cuts Really?

    At DataWeave, a Competitive Intelligence as a Service provider for retailers and brands, we put Amazon.com’s actual Whole Foods discounts under the microscope. The resulting careful analysis of price discounts revealed quite a different story to the one initially featured in the media. Scrutiny by our proprietary data aggregation and analysis platform showed the drop in retail grocery prices was minimal to almost negligible, depending on the category.

    In delivering near-real-time competitive insights to retailers and brands, we acquire and compile large volumes of data from the Web on an ongoing basis. A key differentiator is our ability to aggregate data down to a zip-code level.

    Our analysis of Amazon.com’s reported drop in prices was based on data acquired for 13 zip-codes distributed across the country and selected at random. Our platform compared market prices by zip code valid between 23rd August and 30th August.

    Each zip code indicated the overall average discount offered varied between 0.20 percent and -0.20 percent. When the discounts at a category-level were separated out, the discounts available to customers per category varied between -6.8 percent (an actual price increase) and 6.1 percent.

    Moving on to the “Fill the Grill” category, discounts again were modest, varying between -5.6 percent (another price increase) and 6.1 percent across the zip codes analyzed.

    This aligns with Amazon.com’s recognized preference for basing its strategy on competing on breadth and depth of product assortment rather than pure pricing discounts at the checkout.

    Some Sunshine For Foodies

    There was some good news for shoppers looking for higher discounts. Amongst those products attracting a higher discount were:

    • Belton Farm Oak Smoked Cheddar Cheese: 50 percent
    • Beemster Premium Dutch Cheese: 50 percent
    • Heritage Store Black Castor Oil: 50 percent
    • Organic French Lentils: 45 percent
    • Vibrant Health Pro Matcha Protein: 40 percent
    • Hass Avocado: 50 percent (confined to one zip-code).

    Final Word

    Amazon.com’s marketing engine is renowned for skillfully nurturing consumer price perceptions of the giant retail website as being the lowest priced retailer. We kept a keen eye on Amazon’s pricing these past weeks, and unearthed a carefully conceived and executed Whole Foods pricing campaign, which is yet another example of their market shaping expertise at work.

    If you’re intrigued by DataWeave’s technology and would like to learn more about how we help retailers and brands build and maintain a competitive edge, please visit our website!

  • The Role of Competitive Intelligence in Modern Retail

    The Role of Competitive Intelligence in Modern Retail

    When retailers today look to compete in the cutthroat world of online commerce, they face several challenges unique to the nature of modern retail. It is now significantly harder for retailers to benchmark their pricing, assortment, and promotions against their competition, as the online world is highly dynamic and significantly more complex than before.

    Trends like the growing adoption of mobile shopping apps, the rising influence of customer reviews in buying behavior, hyperlocal e-commerce websites differentiating themselves by fulfilling deliveries in a matter of hours — the list goes on — have only added to this complexity.

    However, this complexity also presents an opportunity for retailers to incorporate layers of external competitive information into their merchandising strategies to deliver more value to customers and personalize their experience.

    Vipul Mathur, Chief Branding and Merchandising Officer at Aditya Birla Online Fashion, recently published an article highlighting some of the areas in which Competitive Intelligence providers like DataWeave can strategically influence modern merchandising.

    “The consumer is often driven by the aesthetics of a product, more so in the fashion and lifestyle industries than others. Hence, the choices of buyers are hard to interpret. However, innovative modern technologies are helping us understand these decisions,” says Vipul.

    He provides an example of how using AI-based tools (like DataWeave’s) to unearth the sentiments behind thousands of online reviews can help retailers better channel and message their online promotions.

    “Deciphering the consumers’ comments and converting them into tangible insights is incredible proof of the refinement possible with data analysis tools. It’s like knowing that consumers are delighted by the quality of the soles of a pair of Adidas running shoes. Using this, marketing communication can be modified to highlight this specific product feature,” explains Vipul.

    And it’s not just merchandising. This data can percolate across multiple functions in retail, enabling greater efficiency in operations. “If we have data on the best-selling styles across websites, including other attributes like pricing, region/locality (through pin-code mapping), and possibly even rate of sales, it’s up to our supply-chain systems to ensure that the supply is in accordance with demand.”

    DataWeave’s Retail Intelligence offers global retailers and e-commerce websites with these benefits and more. Our AI-powered technology platform aggregates and analyzes vast volumes of online competitive data and presents them in an easily consumable and actionable form, aiding quick, data-driven merchandising decisions.

    “DataWeave, our partner, has helped us refine our merchandising decisions, saving cost and creating value,” sums up Vipul.

    Read the entire article here, and if you’re intrigued by what DataWeave can do for retail businesses and wish to learn more, visit our website!

     

  • Advantage Flipkart: The Motives Behind Acquiring eBay India

    Advantage Flipkart: The Motives Behind Acquiring eBay India

    Flipkart recently acquired eBay’s India business in an announcement that made a huge splash across the country. With Flipkart already having acquired Myntra and Jabong, and talks of a Snapdeal acquisition picking up steam, this level of consolidation comes clearly as a direct response to internet behemoth Amazon’s aggressive expansion strategies in India.

    With this acquisition play, Flipkart stands to gain primarily on two fronts.

    eBay’s Seller Network

    Firstly, eBay has built a strong network of authorized and highly-rated global sellers, something that Flipkart can leverage to drive increased sales and market share.

    Per Flipkart’s announcement to the press — “Flipkart and eBay have signed an exclusive cross-border trade agreement, as a result of which customers of Flipkart will gain access to the wide array of global inventory on eBay, while eBay’s customers will have access to unique Indian inventory provided by Flipkart sellers. Thus, sellers on Flipkart will now have an opportunity to expand their sales globally.”

    At DataWeave, we ran our proprietary data aggregation and analysis algorithms over eBay’s websites and unearthed some interesting numbers about their seller network.

    eBay.com has a global network of 17,361 sellers, 41% of whom ship to India. Therefore, this acquisition opens the door for Flipkart to gain access to over 7000 global eBay.com sellers who ship to India — a huge boost to the range of products Flipkart can host on its platform.

    Additionally, a sizable chunk — 14% — of eBay.in sellers ship to international destinations. This provides Flipkart with opportunities to expand its reach globally.

    The other, rather lesser known advantage that Flipkart stands to gain from this acquisition is in the refurbished and pre-owned goods space.

    The Emergence of Refurbished and Pre-Owned Goods

    The market for refurbished and pre-owned products is estimated to be between $15 billion and $20 billion globally, with exponential growth forecast for the near future.

    Part of the reason for growth in this segment is it yields higher returns on investment for retailers. While a retailer typically earns 3–5% margin by selling a new smartphone, refurbished smartphones fetch 7–8% margin, and pre-owned smartphones 9–10%.

    The Hidden Advantage

    eBay has established itself over the years as a reliable source of refurbished and pre-owned products, with impressive levels of authentication and warranties. We did a quick analysis of eBay.in, Flipkart, and Amazon to identify their relative strengths in this space.

    Unsurprisingly, Flipkart has close to zero refurbished or pre-owned products hosted on their website. With Amazon, 12% of mobile phones and 9% of Books on their website are refurbished or pre-owned, the largest selling categories in this space.

    eBay.in, though, has a significant share of these products across categories — 95% of books & magazines, 36% of mobile phones, and 28% of televisions — a substantial portion of eBay’s business.

    With this acquisition, Flipkart can now take a gigantic step into the relatively more profitable and exponentially growing refurbished and pre-owned products space. It will also be a strong competitive differentiator for the company as they go head to head with Amazon in India.

    While the refurbished and pre-owned goods space poses a series of advantages for retailers, it sits well with consumer preferences as well, drawing more shoppers, and retaining existing ones.

    Influence of Shopping Behavior on Product Assortment

    Refurbished and pre-owned products provide consumers with attractive alternatives, both in terms of price and variety. Shoppers today explore and research new, pre-owned and refurbished products, all at the same time, and compare prices across e-commerce websites before deciding on a purchase.

    As a result, comprehensive product assortments across price ranges and attributes drive higher engagements, traffic and improve customer conversion and retention rates, as they cater to a more diverse set of consumers.

    For modern retailers, this reinforces the importance of investing in tools that enable to them to identify high-value gaps in their assortment and plug them. To achieve this, they need up-to-date, accurate data, at scale, on the assortments of their competitors.

    DataWeave’s Assortment Intelligence solution is designed to give retailers near-real-time insights on competing retailers’ product mix and suggests product additions to retailer catalogs.

    Click here to know more about how Assortment Intelligence can help your retail business manage assortment efficiently and profitably.

     

  • How to Survive the Loss of Brick & Mortar Retail Stores

    How to Survive the Loss of Brick & Mortar Retail Stores

    For years, the consumer electronics chain Radioshack has endeavored to stay alive in our ever-changing world. Despite their efforts, they have filed for bankruptcy for the second time, in as many years. As of now, the company is closing 200 of their 1,500 stores, slightly more than 13% of their locations

    This one-time retail “giant” isn’t alone on the path of reduction in force. Macy’s has announced that they will close 63 stores, and Sears will lock their doors for the final time on 150 of their stores this fiscal year.

    Brands too are feeling the heat. Ralph Lauren recently announced the closure of an unspecified number of stores (including its Polo store on Fifth Avenue, New York City), and a reduction in its workforce.

    The internet is impacting brick and mortar sales the way that Sears Roebuck and Montgomery Ward catalog mail order sales impacted the general store at the turn of the last century.

    Online Retail Plays the Spoiler

    The disruption of the retail industry following the onset of e-commerce is largely due to the change in shopping behavior. Shoppers today can sit at home and compare multiple retailers before making a purchase. This has a significant impact on consumer expectations and how retailers do business today.

    Smartphone apps make comparing prices, and downloading coupons simple. So, we now see e-retailers compete tooth-and-nail on price, and even willing to take the “loss leader” route to drive adoption. Consequently, consumers expect rock bottom prices. Many brick-and-mortar retailers like Walmart have responded by simply matching online prices.

    While there are tens of thousands of e-commerce companies in the world today, this disruption is led primarily by the behemoth of global retail — Amazon.

     

    The Torchbearer of Modern Retail

    Amazon’s retail business strategy rests on three pillars: price perception, broad assortments, and customer experience.

    Price has long been the primary driving factor in retail. Therefore, there is need to optimize price efficiently to drive revenue and margins. What Amazon has smartly done is to drive the perception among shoppers that the company is always the lowest priced, even though it’s untrue. They do this by ensuring they are the lowest priced in the top 20% selling SKUs by volume. The resulting perception among consumers is a key differentiator.

    Also, to deliver superior customer experience compared to competing retailers, Amazon ensures high quality of online catalogs, provides a wide selection of products, and offers fast shipping to a broad coverage area, at no additional cost.

    When you factor in the Amazon Prime service, consumers have become spoiled with receiving their purchases within 48 hours. Sunday deliveries, and scheduling within the hour means buyers are in the driving seat.

    Some of Amazon’s competitors are following suit. Mega box stores like Costco, in an endeavor to meet their customers’ desire for options, are partnering with Google Express to provide fast delivery of household items, apparel, electronics, pantry staples such as bread and cereal, and more.

    The message is clear — today’s brick-and-mortar retailers need to have an omni-channel approach to retail, and an online presence if they are to stay competitive and relevant. However, this move has its fair share of obstacles –

    The Challenge of Moving Online

    Brick-and-mortar retailers moving online are confronted with several questions that carry more weight today than they used to in the past:

    • How do I deliver a high-quality shopping experience?
    • How can I drive price perception among shoppers?
    • What products do I promote and when?
    • What product assortment do I build to drive sales and retention?
    • How do I manage my logistics to reduce shipping cost and time?

    Traditional retailers looked largely at only internal data — like POS data, product sell-through rates, inventory, etc. to answer these questions. Today, it is mission-critical for retailers to absorb and utilize external competitive data as well — and here lies the problem. When you are benchmarking yourself against the competition online, it is that much harder, as it’s more dynamic and significantly more complex than before.

    For example, Forbes estimated that through Christmas season in 2014, Amazon made a total of 80 million price changes per day to stay competitive. These are extraordinary numbers, and reflect how dynamic online retail is, and its contrast to traditional retail.

    Retailers today have no choice but to automate as much as possible, so they can make quick, timely merchandising decisions and keep pace with modern e-retail. Retail technology providers like DataWeave have stepped in to meet this demand.

    DataWeave’s Retail Intelligence

    At DataWeave, we enable retailers gain a competitive advantage in the online world by providing Competitive Intelligence as a Service. We do this by harnessing public information on the competition, structuring it, and presenting it in a form that is easily consumable and actionable, enabling easy, automated decision-making.

    Our AI-based technology platform facilitates smarter pricing decisions by providing retailers with price change (increase and decrease) opportunities as they occur. Retailers can also plug gaps in their product portfolio by identifying opportunities to expand their assortments. In addition, they can benchmark their shipping speed and cost against competition, to enhance customer experience. And there’s more where these come from!

    Click here to find out more about how we can help modern retailers stay competitive in the online world.

     

  • Smart Practices for Pricing Products

    Smart Practices for Pricing Products

    Top pricing strategies for online retailers

    “When it comes to retail markets, law of one price is no law at all” — Hal Varian

    Hal Varian, in his seminal paper “A Model of Sales”, further remarks that most retail markets are instead characterized by a rather large degree of price dispersion.

    Do you know how much your products are worth? How low are you willing to price an item to compete with another ecommerce retailer?

    Today, online retail has become increasingly competitive. If you are priced higher than your competitors, you may end up losing customers who are sensitive to prices. With the advent of highly competitive pricing tools, winning the online pricing war is an uphill task. Having a differentiated competitive strategy is critical to your e-commerce success.

    We bring to you a list of smart practices that we have seen being played out across online retailers in 10 countries that we actively monitor and analyze.

    Analyzing Competitor Prices And Stock Availability

    Product pricing is one of the largest driver of profitability. So you know who your main competitors are, but do you know how they are priced? Compare prices and stock availability of products that are popular across all your competitors and do the same for products that are popular at your store. If you know that certain products are “not in stock”, you know you need not discount. Look at products that are popular across competition and know your price position. Try for an opportunity to increase prices without losing your price position. However, for products popular on your store, you may want to stay competitive.

    Knowing Price Variations

    You get the right price, and then it’s not right anymore. That’s the story of online retail. But when you are equipped with the knowledge of price variations on popular marketplaces, it gives you an idea of where the market is heading. This, in turn, will help you adjust your prices to get the consumers. For instance, Amazon changed prices of more than 50% of their products in Hair Care category more than once in a week including ~20% of the products at least 4 times in the same week.

    Product Bundling

    A marketer of a successful product may bundle a new or less successful product with its stronger product to edge its way into a new market. This allows you to charge a unique, competitive price that can’t be copied by others. If you realize that you may not be able to compete on direct discounts, bundle products together and offer them at a lower price. You can either bundle in multiples of the same product or pack different products together. One of the more famous examples of this is Microsoft’s bundling of various software applications. In the onsite retail space, for example, on a particular day we noticed ~400+ combo offers from SnapDeal in the camera & accessories category whereas PayTM has ~200+ combo offers and Amazon has ~3000+ combo offers in the same category. Similarly, in hair care category we observed significant variance in combo offers across marketplaces (~900+ by Amazon, ~250+ by PayTM and ~100 by SnapDeal on a specific day). We also noticed that marketplaces have varied number of products sold in packs across different brands (~2500 in Amazon, ~800+ in PayTM and ~500 in Snapdeal on a specific day).

    Shipping Fees & Delivery Time

    Free shipping attracts customers to e-commerce platforms like a moth to a flame. Monitor shipping fees across competition for products you are interested in. There will be cases where your competitor is pricing a product at a lower price than you, but does not offer free shipping. That is your signal to promote your platform.

    Price Match Guarantees

    Price match is an easy way for customers to save money on their day-to-day purchases. During Black Friday sales in the US, a lot of popular stores go for the price match guarantee feature to drive sales. It’s a smart trick to let your customers show you the lowest price and then match them accordingly.

    No Discounts On Unique Products

    No matter how much you dress it up, cutting prices hurts. It might be unavoidable, but you can get rid of discounts on unique products. When you analyze gaps and strengths of your catalog and realize that there are products that are available only on your store, why would you need to provide discounts? So, for instance, it seems that only Flipkart is carrying Icon LaserJet Pro Black Toner currently and it is being sold at 75% discount. Unless the objective is to get rid of the inventory, this product could be priced higher. Another example is, Nikon Coolpix S1100PJ Point & Shoot Camera is out of stock with most of the key marketplaces. Hence if anyone gets this replenished, this should not be discounted. Similarly, unique brands in hair care category, say LeModish, is sold primarily on PayTM. So, PayTM could look at reducing discount for this brand.

    Don’t Price Above Market Rate

    Some retailers price products above the market rate (MRP / MSRP) so that they can show substantial discounts. But your customers are smart and research well. If they realize that this is not really ‘low price’, you may end up losing them.

    Dynamic Pricing

    This is one trend you should definitely follow. Constantly monitor competitor prices and drop or increase prices whenever you see an opportunity. This process is highly tech-driven, so ensure that you work with a vendor who provides the same or you have the in-house capability to do this in a sustained and scalable manner.

    There are multiple product strategies that have to be considered, including cross-border commerce and highly spread out markets like SEA where there exists a lot more C2C marketplaces. However, as with many things in ecommerce, one size does not fit all. Combine the powers of your service and price to drive your bottom line and emerge as an undisputed leader in the retail space.

    Note: This article has been previously published on Inc42 and on Indian Retailer.

    DataWeave Retail Intelligence provides competitive intelligence solution to retailers. DataWeave’s solution is both language and geography agnostic and is built for significant scale

  • Benefits of Competitive Marketing Intelligence | DataWeave

    Benefits of Competitive Marketing Intelligence | DataWeave

    In the aggressive business of online retail every detail you know about your competitor gives you an edge over them. To help you stay ahead of your competition we have designed a series of blog posts that familiarize you with competitive intelligence and equip you to get maximum mileage out of competitive intelligence tools. This is the first post of the series.

    Let’s begin at the beginning.

    What is Competitive Intelligence?

    Competitive intelligence (CI) is the gathering of publicly-available information about an enterprise’s competitors and the use of that information to gain a business advantage.

    Competitive marketing intelligence helps managers and executives to make data-driven decisions both in the short term, as well as formulate medium to long term strategy.

    Why is Competitive Intelligence important?

    Competitive marketing intelligence is critical because it helps businesses stay ahead of the competition by:

    1. Augmenting one’s experience and instincts with hard data and analyses on a regular basis
    2. Delivering reasonable assessments of one’s own business vis-a-vis competitors’ businesses
    3. Identifying and alerting new business opportunities as well as threats
    4. Helping shape short term and long term strategies to grow and consolidate one’s business

    How does Competitive Intelligence help achieve the core objectives of retail business?

    Retail is a particularly competitive sector. Given the volume of transactions that happen in the retail sector, even a slight improvement in metrics has a huge impact. Thus, competitive Intelligence has a direct effect on the bottom line. It helps in the following ways:

    > Improve margins

    This is a result of optimized pricing of products. Knowing the competitors pricing goes a long way in pricing your products right and improving margins. With Competitive Intelligence on your side, you can take pricing decisions backed by data.

    > Reduce customer acquisition costs

    By improving your assortment mix more users looking for products that your site offers become your users. This helps reduce customer acquisition costs. This also helps in retaining existing customers

    > Optimize marketing spend

    Competitive Intelligence brings more clarity and sharper objectives for the marketing team. You get good indicators which products/categories your competitors are promoting, and which new brands/categories they have introduced. This helps streamline and optimize your market spend.

    This is where DataWeave comes in. DataWeave provides Competitive Intelligence for retailers, brands, and manufacturers. DataWeave is built on top of huge amounts of product data to provide features such as: pricing opportunities (and changes), assortment intelligence, gaps in catalogs, reporting and analytics, and tracking promotions, and product launches.

    DataWeave is powered by distributed data crawling and processing engines that enables serving millions of data points around products data refreshed on a daily basis. This data is presented through dashboards, notifications, and reports. PriceWeave brings the ability to use BigData in compelling ways to retailers.

    DataWeave lets you track any number of products across any categories against your competitors. If you wish to try this out, just book a free discovery call with us.

    In the next few posts, we will dig deeper into DataWeave and introduce its major features. We will also talk about how each of these features help you in improving your business metrics.