The DataWeave Blog

Tag: Highlights

  • Prime Day Sale: Unraveling the Highs and Lows of Amazon’s Flagship Event

    Prime Day Sale: Unraveling the Highs and Lows of Amazon’s Flagship Event

    Another year, another round of media frenzy, and another set of records broken.

    In only three years, Amazon’s Prime Day has evolved into one of the landmark sale events of the shopper’s calendar. Reports indicate that this year’s sale made a major splash, raking in over $4.2 billion in sales — a 33% increase compared to last year. Also, the retail behemoth shipped over 100 million products during the 36-hour sale. Amazon stated that they “welcomed more new Prime members on July 16 than on any other previous day in Prime history.”

    The much talked about website outage added some spice and drama to the proceedings during the first hour. However, this was fixed quickly.

    This year is also the first Prime Day with Whole Foods, Amazon’s most expensive acquisition, giving US shoppers unprecedented incentives to shop at the physical stores of the grocery retailer.

    However, Prime Day is not just about the US, but a truly global event. In India, as part of its promotions for Prime Day, Amazon leveraged VR to have people experience the products in their true form factor at select malls.

    At DataWeave, our proprietary data aggregation and analysis platform enabled us to keep an eye on the pricing and discounts of products during the sale. We tracked Amazon.com, Amazon.co.uk, and Amazon.in before (14th July) and during the sale (16th July) and monitored several product types in Electronics, Men’s Fashion, Women’s Fashion and Furniture categories. We captured information on the price, brand, rank on the category page, whether Prime was offered or not, etc. and analyzed the top 200 ranks in each product type listing page. To best indicate the additional value to shoppers during the sale, we focused our analysis only on additional discounts on products between the 14th and 16th of July.

    Scrutinizing the data yielded some rather interesting insights:

    Amazon UK was more aggressive with its discounts than the US and India across most categories, with Furniture being the only exception (highest discounts in the US).

    In the US, Women’s Fashion observed the steepest discounts (12%), though there were discounts available on a larger number of Men’s Fashion products (5% additional discount on 20% of products).

    While disparity between discounts on Prime products vs non-Prime was quite evident, it was surprisingly low for many categories. In fact, the Electronics category in the UK and the Furniture category in India witnessed sharper discounts for non-Prime products than Prime.

    Top categories by additional discount include Women’s Handbags, Sports Shoes, and Pendrives in the US, Sunglasses and Tablets in the UK, and Women’s Tops, Men’s Jeans, Women’s Sunglasses, and Refrigerators in India. Top brands include Nike, Amazon Essentials, Sandisk, and 1home in the US, Oakley, Toshiba, Belledorm, and rfiver in the UK, and Adidas, Sony, UCB, and Red Tape in India.

    As indicated in the following infographic, some of the most discoverable brands during the sale include Canon, Apple, Nike and Casio in the US, Sandisk, Amazon, Levi’s, and Ray Ban in the UK, and Nikon, UCB, Whirlpool, and HP in India. Discoverability here is measured as a combination of the number of the brand’s products in the top 100 ranks and the average rank of all products of the brand. Also in the infographic, is a set of products with high additional discounts during the sale.

     

    Amazon’s competitors though aren’t ones that simply roll with the punches.

    Flipkart, Amazon’s largest competitor in India (recently acquired by Walmart), announced its own Big Shopping Days sale between July 16 and July 19. On Prime Day, the company joined in with some attractive offers:

    • 8%, 10%, and 7% additional discounts on 11%, 29%, and 16% of Electronics, Men’s Fashion, and Women’s Fashion categories, respectively.
    • 35% off on Perfect Homes 3-seater Sofa
    • 27% additional discount on Acer Predator Helios Gaming Laptop
    • 25% additional discount on Sandisk 16GB Pen Drive

    Propelling the Amazon Flywheel

    While Amazon clearly benefits in the short-term with this sale, the long-term effect of feeding its famous flywheel is evident as well.

    Amazon’s flywheel is a framework through which the company looks to build a self-feeding platform that accelerates growth over time. Attractive discounts and a broad selection of products improves customer experience, which increases traffic to the website, which attracts more merchants on its platform, who in turn broaden the selection of available products.

    Sale events like Prime Day create the sort of hype needed to draw a lot of traffic to Amazon’s website, generating momentum that has a compounding effect on Amazon’s growth. Not surprisingly, more than half of the people surveyed in the US by Cowen last December said they lived in a household with at least one Prime subscription.

    As Amazon’s stock traded at an all time high following Prime Day, it’s only a matter of time before the company becomes the world’s first trillion dollar company.

    Check us out, if you’re interested in learning more about our technology and how we provide Competitive Intelligence as a Service to retailers and consumer brands.

  • How to Win the Coveted Amazon Buy Box | DataWeave

    How to Win the Coveted Amazon Buy Box | DataWeave

    Did you know that over 80% of purchases on Amazon.com is via the buy box?

    While Amazon is all the rage today, raking in 43% of all eCommerce dollars, thousands of merchants on the online marketplace look to seize every opportunity to attract shoppers and drive sales each day. And for these merchants, getting on the buy box is more than half the battle won.

    Recently, Forbes.com published our study of how online merchants can plot their strategy to win the buy box. In this article, we’ll explore some of the key takeaways from this study.

    What is the Amazon buy box?

    The buy box is the section on the right side of Amazon’s product page, where shoppers can add items for purchase to their cart. Since multiple merchants often offer the same product, they compete to win the buy box spot on the product page, which is where customers typically begin the purchasing process — a huge competitive advantage.

    How can merchants win the buy box spot?

    At DataWeave, we aggregate and analyze billions of data points from the Web to deliver Competitive Intelligence as a Service to retailers and consumer brands. Using our proprietary technology platform, we aggregated data for a large sample of products in the mobile phones, clothing, shoes and jewelry categories on Amazon and collected information on all merchants (over 700 in number) selling these product over a period of 10 days.

    We looked closely at several factors that could possibly impact the choice for the buy box:

    • Was Amazon a merchant or not?
    • The effective price (list price + shipping charges — offer/cashback amount) — after all, a common assumption is that the lowest priced merchant has the best chance of winning.
    • Were Prime benefits offered?
    • The quality of review ratings
    • The stock status
    • The number of products offered by a merchant

    We parsed through the data to unearth some interesting insights and found that some factors influenced the move to the buy box spot more than others.

    We see that when Amazon is a merchant, it’s twice as likely to win the buy box compared to other merchants. Further analysis revealed that for around 95% of instances where Amazon was a merchant but was NOT the in the buy box, Amazon was selling at a price 20% greater than the minimum price.

    When the effective price is the lowest, relative to other merchants, the chances of the merchant winning the buy box increased 2.5-fold. Essentially, for the set of merchants who were the lowest priced for each product, only 26% of them won the buy box.

    Merchants who provided Prime benefits to shoppers were 3.5 times more likely to win compared to other merchants. And lastly, if the percentage of positive reviews for a merchant are decreasing over time, the merchant is 5X less likely to win. All other factors analyzed failed to yield statistically significant results.

    Interestingly, no single factor played an overwhelming role in influencing the buy box criteria. So, with the help of statistical modelling, which considers and weighs all factors, we better understood the relationship between all factors, and traced a path for merchants to win the buy box.

    The Cheat Sheet

    While it isn’t quite possible to develop a fool proof framework, the following flowchart can act as a fairly useful guide.

     

    Clearly, the path to the buy box is not a straightforward one.

    If Amazon itself is a merchant for a product, chances of other merchants winning the buy box are low (35%). However, if a merchant is looking to compete with Amazon for the buy box spot, offering Prime benefits is key (82% probability). Without offering Prime, chances of winning the buy box are almost negligible, even if the merchant is the lowest priced. It’s interesting to note that when Amazon does occupy the buy box spot, it’s the lowest priced in 79% of the cases.

    When Amazon is not a merchant for a product, and competition is only between third-party merchants, offering Prime benefits is still the most influential factor (78%). When Prime isn’t offered, the price is the primary determinant of the buy box merchant (86%).

    Evidently, reducing the price is not always the best course of action. It appears that offering Prime benefits has the biggest impact on a merchant’s chances of winning the buy box, across various scenarios.

    However, it’s important to keep in mind that moving up the “merchant ladder” is a gradual process, based on how merchants perform consistently over time.

    If you’re interested to learn more about DataWeave’s technology, and how we help retailers and consumer brands optimize their online strategies, visit our website!

  • 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!

  • Dataweave – CherryPy vs Sanic: Which Python API Framework is Faster?

    Dataweave – CherryPy vs Sanic: Which Python API Framework is Faster?

    Rest APIs play a crucial role in the exchange of data between internal systems of an enterprise, or when connecting with external services.

    When an organization relies on APIs to deliver a service to its clients, the APIs’ performance is crucial, and can make or break the success of the service. It is, therefore, essential to consider and choose an appropriate API framework during the design phase of development. Benefits of choosing the right API framework include the ability to deploy applications at scale, ensuring agility of performance, and future-proofing front-end technologies.

    At DataWeave, we provide Competitive Intelligence as a Service to retailers and consumer brands by aggregating Web data at scale and distilling them to produce actionable competitive insights. To this end, our proprietary data aggregation and analysis platform captures and compiles over a hundred million data points from the Web each day. Sure enough, our platform relies on APIs to deliver data and insights to our customers, as well as for communication between internal subsystems.

    Some Python REST API frameworks we use are:

    • Tornado — which supports asynchronous requests
    • CherryPy — which is multi-threaded
    • Flask-Gunicorn — which enables easy worker management

    It is essential to evaluate API frameworks depending on the demands of your tech platforms and your objectives. At DataWeave, we assess them based on their speed and their ability to support high concurrency. So far, we’ve been using CherryPy, a widely used framework, which has served us well.

    CherryPy

    An easy to use API framework, Cherrypy does not require complex customizations, runs out of the box, and supports concurrency. At DataWeave, we rely on CherryPy to access configurations, serve data to and from different datastores, and deliver customized insights to our customers. So far, this framework has displayed very impressive performance.

    However, a couple of months ago, we were in the process of migrating to python 3 (from python 2), opening doors to a new API framework written exclusively for python 3 — Sanic.

    Sanic

    Sanic uses the same framework that libuv uses, and hence is a good contender for being fast.

    (Libuv is an asynchronous event handler, and one of the reasons for its agility is its ability to handle asynchronous events through callbacks. More info on libuv can be found here)

    In fact, Sanic is reported to be one of the fastest API frameworks in the world today, and uses the same event handler framework as nodejs, which is known to serve fast APIs. More information on Sanic can be found here.

    So we asked ourselves, should we move from CherryPy to Sanic?

    Before jumping on the hype bandwagon, we looked to first benchmark Sanic with CherryPy.

    CherryPy vs Sanic

    Objective

    Benchmark CherryPy and Sanic to process 500 concurrent requests, at a rate of 3500 requests per second.

    Test Setup

    Machine configuration: 4 VCPUs/ 8GB RAM.
Network Cloud: GCE
Number of Cherrypy/Sanic APIs: 3 (inserting data into 3 topics of a Kafka cluster)
Testing tool : apache benchmarking (ab)
Payload size: All requests are POST requests with 2.1KB of payload.

    API Details

    Sanic: In Async mode
Cherrypy: 10 concurrent threads in each API — a total of 30 concurrent threads
Concurrency: Tested APIs at various concurrency levels. The concurrency varied between 10 and 500
Number of requests: 1,00,000

    Results

    Requests Completion: A lower mean and a lower spread indicate better performance

     

    Observation

    When the concurrency is as low as 10, there is not much difference between the performance of the two API frameworks. However, as the concurrency increases, Sanic’s performance becomes more predictable, and the API framework functions with lower response times.

    Requests / Second: Higher values indicate faster performance

    Sanic clearly achieves higher requests/second because:

    • Sanic is running in Async mode
    • The mean response time for Sanic is much lower, compared to CherryPy

    Failures: Lower values indicate better reliability

    Number of non-2xx responses increased for CherryPy with increase in concurrency. In contrast, number of failed requests in Sanic were below 10, even at high concurrency values.

    Conclusion

    Sanic clearly outperformed CherryPy, and was much faster, while supporting higher concurrency and requests per second, and displaying significantly lower failure rates.

    Following these results, we transitioned to Sanic for ingesting high volume data into our datastores, and started seeing much faster and reliable performance. We now aggregate much larger volumes of data from the Web, at faster rates.

    Of course, as mentioned earlier in the article, it is important to evaluate your API framework based on the nuances of your setup and its relevant objectives. In our setup, Sanic definitely seems to perform better than CherryPy.

    What do you think? Let me know your thoughts in the comments section below.

    If you’re curious to know more about DataWeave’s technology platform, check out our website, and if you wish to join our team, check out our jobs page!

     

  • Myntra Leads End of Year Promotions in Fashion

    Myntra Leads End of Year Promotions in Fashion

    Following three back-to-back mega-sale events leading up to Diwali, India’s eCommerce companies once again opened the discount floodgates heralding Christmas and New Year. This time around, Fashion was the battleground category of focus for Indian e-retailers.

    Myntra launched its End of Reason Sale held between 22nd and 25th December. eCommerce behemoth Amazon too announced its own grand Amazon Fashion Wardrobe Refresh Sale on the same days, while Flipkart hit the market with its End of Year Bonanza held on the 24th and 25th of December. Paytm and Snapdeal held sale events as well, starting 23rd December. All competing sale events promised consumers up to 80 percent discounts across a range of products, especially in Fashion.

    At DataWeave, we analyzed and reported on the competing pricing strategies of Amazon, Flipkart, Myntra, Paytm, and Snapdeal. In the following infographic, we look specifically only at additional discounts offered on the top 500 ranked products of over 15 product types during the sale, compared to those before the sale events went live.

    Myntra Gets Aggressive

    Myntra elected to discount over 84 percent of its Top 500 ranked Fashion products encompassing each product category, with an average additional discount percentage of over 25 percent offered during the sale.

    A prime example of this discounting approach was the sports shoe segment, which received an aggressive additional discount of 28 percent on over 93 percent of the Top 500 ranked sports shoes. Similarly, Myntra’s additional discounts ranged from between 22 percent and 25 percent across most product types, including T-shirts, Shirts, Handbags, Jeans, Skirts, Sunglasses, and Watches. The fashion e-retailer’s private label brands enjoyed attractive reductions in prices, which include Hrx and Roadster, along with other brands like Red Tape, Nike, and Puma.

    Amazon Discounts To A Different Beat

    Amazon discounted 35 percent of its Top 500 ranked Fashion products in each product type, with an average additional discount percentage of 12.5 percent during the sale. Given Amazon’s track record of dynamic pricing, this was relatively conservative.

    Overall, additional discounts on Amazon ranged between 4 percent and 16 percent across all product types in Fashion. Top brands discounted on Amazon included Adidas, Fastrack, Hush Puppies and Ray-Ban.

    Flipkart Joins The Party

    Flipkart too joined the End of Year discount action with several attractively positioned offers, exceeding those featured on Amazon. Flipkart discounted over 65 percent of its Top 500 ranked Fashion products in each product type, with an average additional discount percentage of over 14 percent during the sale.

    Additional discounts promoted on Flipkart ranged between 8 percent and 22 percent across all Fashion product types, while some of the top discounting brands included Dkny, Metronaut and United Colors of Benetton.

    Conspicuously, other Indian e-retailers like Paytm and Snapdeal chose not to join in the price war. Snapdeal, especially, has consistently offered only moderate additional discounts during recent sale events, choosing to focus more on other areas of improving the user experience for their shoppers.

    Strategic Focus On Profitability

    In contrast to the profit-sapping Diwali sale season, characterized by steep discounts across all product categories, this end of year sale was more concentrated, largely honing in on Fashion. From a strategic and shareholder perspective, limiting the discounting action to Fashion insulated the retailers’ bottom line from another major profit hit.

    Myntra determinedly reaffirmed its leadership status in the Fashion category, with its highly aggressive discounting strategy. This was well received by shoppers, who spent a staggering ₹5 crore in only the first five minutes of the sale.

    Flipkart opted to double down this time around with attractive offers on its own eCommerce platform as well. The e-retailer, currently locked in a battle with Amazon for leadership in India’s eCommerce sector, had acquired Myntra in 2014 in a bid to strengthen its position in the fashion category.

    Amazon, intriguingly, opted for a more conservative approach to its end of year sale than we are used to witnessing from the eCommerce giant. As we enter the new year, and kickstart yet another cycle of aggressive e-retail promotions in India, there will be ample opportunities to see if this is evidence of a rethink in Amazon’s approach to pricing in India.

    If you’d like to know more about DataWeave’s technology, and how we provide Competitive Intelligence as a Service to retailers and consumer brands, check out our website!