The DataWeave Blog

Tag: Assortment Intelligence

How Healthy is Your Assortment?
In 2025, both consumers and retailers continue to prioritize better health – albeit with evolving definitions and expectations.

The pandemic fundamentally transformed how consumers approach wellness, with this shift becoming entrenched in shopping behaviors years later. As shopping habits have permanently altered, retailers now face increased pressure to rapidly adapt their assortments with in-demand health and wellness products that enhance customer experience across various channels – online and offline.

Let’s explore how leading retailers are keeping consumers – and their own bottom lines – healthy by responding effectively to market trends to drive online sales and market share.

Health & Wellness Influence The Product Mix Across Categories

Consumption habits have changed dramatically since the onset of the pandemic. A McKinsey study shows that 82% and 73% of US, and UK consumers respectively now consider health & wellness a top priority. Typically shoppers adjust grocery shopping and meal planning at the start of the year, with many focusing on fresh, organic, and nutrient-rich foods.

The influential health and wellness mega-trend spans diverse retail channels, including grocery, pharmacy and mass. It extends across numerous categories like:
- Food and beverage (natural, organic, vegan, plant-based food)
- Health and personal care
- Beauty
- Cleaning products
- Fitness equipment
- Athleisure (apparel)
- Consumer electronics like health wearables.
Today’s health movement is so powerful and compelling that retailers have revised their business strategies to better serve health-conscious consumers. For instance, drugstores are reinventing themselves as healthcare destinations, with CVS and Kroger expanding into personalized care delivery and value-based clinics to enhance their health offerings.

Major retailers like Amazon, Walmart, and Target report robust sales in health and wellness categories. For example, Walmart saw a 4.6% increase in comparable sales in early 2024, driven significantly by grocery, consumables, and health-related products.

New product categories are gaining traction:
- Functional foods and beverages are seeing unprecedented growth, with Target launching over 2,000 wellness items in the category, including exclusive products priced under $10.
- Personalized nutrition and mental health products are surging, including tailored dietary solutions and stress-reducing items.
- Health wearables and wellness tech continue to rise in popularity, with over 150 new wellness tech items launched at Target this year, including innovative red-light therapy devices.
- Transparency and sustainability certifications like organic, non-GMO, and vegan labels are increasingly driving purchasing decisions.
- Clinically proven benefits offered by health & wellness products are gaining traction among Gen Z.
Retail’s Survival Of The Fittest Moves Online

As the omnichannel retail sector continues to grow, more shoppers now make purchase decisions within minutes using just a few clicks rather than physically visiting brick-and-mortar stores. In some cases, AI agents like Operator from Chat-GPT or Gemini (Google’s Chatbot) even make personalized, curated lists and reduce the time taken to make purchase decisions. Traditional retail paradigms are rapidly becoming obsolete as consumers grow savvier, more empowered, and better informed than ever before.

To stay competitive, more retailers are embracing AI-driven data insights to adjust their assortments to reflect consumer demand for health and wellness products.

According to industry experts, data insights have emerged as a critical retail strategy that continues to gain momentum. This is because retailers can no longer afford to guess how to approach their omnichannel strategy. They need the accuracy, clarity, and efficiency of data insights to guide their assortment and pricing decisions to outmaneuver competitors, maximize sales, and win market share as shopping evolves online.

Among its retail best practices, Bain & Company recommends retailers “lead with superior assortments that use a customer-centric lens to reduce complexity and increase space for the products customers love.” Insights can help retailers discover the optimal mix of national brands, private labels, limited-time offers, and value-added bundles.

“Lead with superior assortments …
increase space for the products consumers love“
~ Bain & Company

Determining the optimal mix of products also includes bestsellers and unique items that help retailers distinguish their offerings. Assortment insights help retail executives track competitors’ assortment changes and spot gaps in their own product assortment to adapt to emerging consumer trends and in-demand products.

Why Effective Assortment Planning Matters

Assortment planning sits at the heart of retail success, directly influencing profitability, customer satisfaction, and competitive differentiation. In today’s health-conscious market, getting your assortment right means:
- Meeting Customer Expectations: Today’s health-conscious consumers expect relevant, high-quality products that match their wellness goals. A well-planned assortment signals that a retailer understands its customers’ evolving needs.
- Optimizing Inventory Investment: Strategic assortment planning ensures capital is allocated to products with the highest return potential while minimizing investments in slow-moving items.
- Creating Competitive Advantage: A distinctive assortment that includes popular health and wellness products alongside unique offerings helps retailers stand out in a crowded marketplace.
- Reducing Lost Sales: Effective assortment planning minimizes the risk of stockouts on high-demand health and wellness items, preventing customers from shopping elsewhere.
- Supporting Omnichannel Strategies: Well-executed assortment planning ensures consistency across physical and digital touchpoints, creating a seamless customer experience.
- Improving Operational Efficiency: A thoughtfully curated assortment reduces complexity throughout the supply chain, from procurement to warehouse management to in-store operations.
As health and wellness continues to drive consumer spending, retailers who excel at assortment planning can capitalize on these trends more effectively than their competitors, turning market insights into tangible business results.

AI-Powered Assortment Analytics Driving Retail Success

The synergy of AI and data analytics into retail assortment planning is changing how businesses approach inventory management. Retailers using AI-driven predictive analytics have achieved a 36% SKU reduction while increasing sales by 1-2%, showcasing the efficiency of data-driven approaches according to a McKinsey report.

Retailers face several challenges that can hinder strategic assortment planning:
- Limited Understanding of Competition: Retailers struggle to gain comprehensive insights into their product assortments relative to competitors, often lacking visibility into their strengths and weaknesses across categories.
- Data Overload: Assortment planning involves handling vast amounts of data, making it challenging for category managers to extract actionable insights without user-friendly tools and visualization.
- Cross-Channel Consistency: With omnichannel retailing, ensuring consistency across physical stores, e-commerce, and other channels is complex. Misalignment can lead to customer dissatisfaction and loss of loyalty.
- Adapting to Changing Market Trends: Identifying top-selling products and tracking consumer preferences is challenging. Balancing the right mix of products is crucial; without analytics, retailers risk lost sales or excess slow-moving inventory.
- Scalability and Efficiency: As retailers expand into new markets or categories, scaling their assortment planning processes efficiently becomes a challenge. Legacy systems and manual methods often fail to support the agility needed for quick decision-making at scale.
DataWeave’s Assortment Analytics helps retailers address these challenges by providing a robust, easy-to-use platform that delivers actionable insights into product assortments and competitive positioning. With AI-driven, contextual insights and alerts, retailers can effortlessly identify high-demand, unique products, capitalize on catalog strengths, optimize pricing and promotions, improve stock availability, and refine assortments to maintain a competitive edge.

Beyond Data: Actionable Insights That Drive Results

DataWeave’s platform provides a comprehensive, insight-led view into assortments through several key dimensions:
- Stock Insights: Monitor stock changes across retailers to stay updated on availability.
- Category and Sub-Category Insights: Analyze assortment changes, identify newly introduced or discontinued categories, and track leading retailers in specific segments.
- Brand Insights: Identify newly introduced, missing, or discontinued brands, as well as leading brands within chosen categories.
- Product Insights: Identify bestsellers and evaluate their impact on your portfolio, analyzing pricing and promotions.
- Personalized Recommendations: Receive suggestions tailored to your behavior and user profile to refine decision-making.
- User-Configured Alerts: Stay informed with alerts designed to highlight significant changes or opportunities.
The platform addresses data overload by providing an intuitive, insight-driven view of your assortment. Category managers gain a comprehensive, bird’s-eye perspective of key changes within specified timeframes, allowing them to focus on what matters most.

Preparing for the Future of Retail Health

To avoid supply chain bottlenecks, inventory shortages, and out-of-stock scenarios, retailers are strategically using data insights to anticipate fluctuations in demand and proactively plan how to manage disruptions that could affect their assortments.

For variety that satisfies consumers’ diverse product needs, retailers are using data insights to determine whether to collaborate with nimble suppliers to promptly fill any gaps.

To further strengthen their assortments’ attractiveness, retailers are using AI-powered pricing analytics to offer the right product at the right price. These analytics help retailers know exactly how they compare to rivals’ pricing moves with relevant data so they can keep up with market fluctuations and stay competitive by earning consumer engagement, sales, and trust.

To Conclude…

Like nourishing habits that improve consumers’ health, data insights improve retailers’ e-commerce health. Advanced assortment and pricing analytics, powered by artificial intelligence, help retailers make better decisions faster to boost their agility, outmaneuver rivals, and fuel online growth.

In a retail landscape where consumer preferences for health and wellness continue to evolve rapidly, the retailers who thrive will be those who leverage data and AI to understand, anticipate, and meet these changing demands with the right products at the right time. Reach out to us to know more.
July 12, 2024
Why Strategic Competitive Insights Are Key to Optimizing Your Product Assortment
For retailers, the breadth and relevance of their product assortment are critical for success. Amid a crowded market filled with countless products clamoring for consumer attention, retailers must find innovative ways to distinguish themselves. While pricing undeniably impacts purchasing decisions, the diversity and distinctiveness of a retailer’s product range can provide a crucial competitive advantage.

Creating an attractive and profitable assortment that resonates with your target audience requires more than intuition; it demands deep insights into both your own and your competitors’ offerings. A well-curated assortment aligned with current trends can drive higher conversions and foster customer loyalty. However, achieving this perfect balance is a formidable challenge without the right insights.

This is where a data-driven strategy becomes essential, enabling you to curate a product mix that captivates and converts.

However, retailers often encounter significant challenges when attempting to strategically plan their assortments:
- Limited Competitive Insights: Gaining a clear understanding of your competitors’ assortment strengths and weaknesses across various categories is challenging. Without this visibility, it’s difficult to know where you have an edge or where you might be falling behind.
- Tracking Demand Patterns: Identifying top-sellers and monitoring shifts in consumer demand can be a struggle. Without the ability to easily detect trends or changes in demand, you risk missing opportunities to stock trending items.
Attempting to navigate these challenges manually is not only arduous but also susceptible to substantial errors.

How Assortment Analytics Solutions Help

The ideal Assortment Analytics solution must offer a fact-based approach to:
- Identify Strengths and Weaknesses: Understand how your assortment measures up against the competition.
- Stay Trend-Responsive: Keep your product mix fresh and aligned with the latest consumer trends.
- Boost Conversions: Create a relatively unique, customer-focused assortment that enhances conversions.
Many retailers attempt to analyze competitor assortments using manual, in-house methods, which inevitably leads to significant blind spots:
- Variations in product classifications and taxonomies across competitors make meaningful comparisons challenging.
- Gathering complete and accurate data across a vast competitive landscape is difficult.
- Inconsistent titles and descriptions hinder reliable product matching without AI assistance.
- Capturing and comparing detailed product attributes efficiently is nearly impossible without advanced tools.
To overcome these challenges, retailers need a scalable, accurate Assortment Analysis solution designed specifically for the complexities of modern retail needs.

DataWeave’s Assortment Analytics Solution

DataWeave addresses these challenges by providing retailers with a robust platform to gain actionable insights into their product assortments and the competitive landscape. Leveraging advanced analytics and AI-driven algorithms, Assortment Analytics empowers retailers to make informed assortment management decisions, optimize their product offerings, and stay competitive.

Armed with our insights, retailers can lead with their strengths and stock unique and in-demand products in their assortment. Further, by recognizing the strengths in their product catalog, they can craft effective pricing strategies and optimize their logistics, creating a more competitive and appealing shopping experience for their customers. Here are a few capabilities of DataWeave’s solution:

In-Depth Competitive Analysis Across Retailers

The solution offers detailed competitive analysis, revealing insights into competitors’ assortments. It maps competitor products to a common taxonomy, making comparisons accurate and meaningful. Retailers can visualize assortments at granular levels like category, sub-category, and product type.

The data for these insights is collected at configurable intervals, typically monthly or quarterly, and is consumed not only via dashboard summaries but also raw data files to enable in-depth analysis. Retailers have the flexibility to choose specific competitors, brands, products, and categories for tracking, allowing for a tailored and strategic approach to assortment optimization.

Brand and Category Views to Assess Your Portfolio

The solution provides a comprehensive evaluation of your product assortment through brand and category views. In brand views, your portfolio is assessed against competitors at the brand level, highlighting:
- Newly Introduced Brands: Insights into recently introduced brands, revealing shifts in the brand landscape.
- Absence or Limited Presence: Identification of brands lacking representation or with minimal presence compared to competitors, indicating areas for improvement.
- Strong Presence and Exclusivity: Recognition of brands where you excel, including exclusive offerings, showcasing your competitive edge.
Identifying Top-Selling Competitive Products To Boost Assortment Strategy

Beyond just comparing assortment numbers, the DataWeave solution surfaces insights into which competitor products are actually performing well. It equips category and assortment managers with indicators that assess competitor products in terms of their popularity and shelf velocity.

It analyzes metrics like pricing fluctuations, ratings, customer reviews, search rankings, and replenishment rates to pinpoint hot sellers you may want to stock. With these insights, merchandizing managers can pinpoint top-selling products among competitors, enabling informed decisions to enhance their assortment in comparison.

Sophisticated Attribute Tagging and Analysis

Using AI-powered attribute tagging, the solution simplifies granular product analysis within specific categories. An Apparel retailer, for instance, can filter the data to compare assortments based on attributes like material, pattern, color, etc.

Retailers can select attributes relevant to their products and gain detailed insights. These custom filter attributes dynamically populate the panel, facilitating targeted data exploration. Category and merchandizing managers can delve into critical details swiftly, enabling strategic decision-making and comprehensive competitive analysis within their categories.

You can capitalize on opportunities by stocking in-demand, on-trend items and address assortment gaps quickly. At the same time, you can double down on your strengths by enhancing your exclusive or top-performing product sets.

In summary, DataWeave’s Assortment Analytics solution provides an invaluable competitive edge. The insights enable evidence-based decisions to attract more customers, encourage bigger baskets, and maximize the value of every assortment choice.

To learn more, read our detailed product guide here or get on a exploratory call with one of our experts today!
June 13, 2024
Why Localized, Store-Specific Pricing and Availability Insights is Critical for Consumer Brands
Brands are becoming increasingly proficient in monitoring and refining their presence on online marketplaces, utilizing Digital Shelf Analytics to gather and analyze data on their online performance. These tools offer invaluable insights into enhancing visibility, adjusting pricing strategies, and improving content quality on eCommerce sites.

Yet, as the retail landscape shifts towards a more integrated omnichannel approach, it’s crucial for brands, particularly those in CPG, to apply similar strategies to their offline channels. For brands that count physical stores among their primary sales channels, gaining localized insights is key to boosting in-store sales performance.

Collecting shelf data from offline channels presents more challenges than online. Traditional methods, such as physical store visits, often fall short in reliability, timeliness, scale, and level of coverage.

However, the world of eCommerce provides a solution. As part of the effort to facilitate options like buy-online-pickup-in-store (BOPIS) for shoppers, major retailers make store-specific product details available online. Consumers often go online and select their nearest store to make purchases digitally before choosing a fulfillment option like picking up at the store or direct delivery. Aggregating this store-level information offers brands critical insights into pricing and inventory across a vast network of stores, enabling them to make informed decisions that improve pricing strategies and supply chain efficiency, thus minimizing stockouts in crucial markets.

Further, as consumers increasingly seek flexibility in how they receive their purchases—be it through in-store pickup, delivery, or shipping—brands need to adeptly monitor pricing and availability for these different fulfilment options. Such granular insight empowers brands to adapt swiftly and maintain a competitive edge in today’s dynamic retail environment.

Why does monitoring pricing and availability data across stores matter to brands?
- Hyperlocal Competitive Strategy: This allows brands to adjust their pricing strategies based on regional competition. By understanding the local market, brands can decide whether to position themselves as cost leaders or premium offerings. In particular, this is indispensable for Net Revenue Management (NRM) teams.
- Targeted Marketing Initiatives: Understanding regional price and availability enables brands to customize their marketing efforts for specific markets. By aligning their strategies with local demand trends and inventory levels, brands can more effectively engage their target audiences.
- Efficient Inventory Management: By keeping a close eye on store-level data, brands can better manage their stock, ensuring high-demand products are readily available while minimizing the risk of overstocking or running out of stock.
- Minimum Advertised Price (MAP) Monitoring: While brands cannot directly control retail pricing, staying updated on pricing trends helps them adjust their MAP to reflect the competitive landscape, consumer expectations, cost considerations, and regional differences. A strategic approach to MAP management supports brand competitiveness and profitability in a fluctuating market.
DataWeave’s Digital Shelf Analytics solutions equip brands with the necessary data and insights to do all of the above.

DataWeave’s Digital Shelf Analytics is location-aware

DataWeave’s Digital Shelf Analytics platform stands out with its sophisticated location-aware capabilities, enabling the aggregation and analysis of localized pricing, promotions, and availability data. Our platform defines locations using a range of identifiers, including latitudes and longitudes, ZIP codes, or specific stores, and can aggregate this data for particular states or regions.

The strength of the platform lies in its robust data collection and processing framework, which operates seamlessly across thousands of stores and regions. This system is designed to operate at configurable intervals—daily, weekly, or monthly—allowing brands to keep a vigilant eye on product availability, pricing strategies, and delivery timelines based on the selected fulfillment option.

Unlike many other providers, who may provide limited insights from a sample of stores, our solution delivers exhaustive analytics from every storefront. This comprehensive approach grants brands a strategic edge, facilitating efficient inventory tracking, precise pricing adjustments, and rapid responses to fluctuating market dynamics. It cultivates brand consistency and loyalty by enabling brands to adapt proactively to the changing landscape.

Aggregated store-level digital shelf insights via DataWeave

In the summarized view shown above, a brand can track how its various products are positioned across stores and retailers like Walmart, Amazon, Meijer, and others in the US.

Using DataWeave, brands can easily see important metrics like availability levels, prices, and other metrics across these stores gaining immediate visibility without having to physically audit them. the brand can track the same metrics for products across competitor brands and inform its own pricing, stock, and assortment decisions.

Store-level availability insights

We provide a comprehensive view of product availability, highlighting the distribution of out-of-stock (OOS) scenarios across various retailers and pinpointing the availability status throughout a brand’s network of stores. This capability enables swift identification of widespread availability issues, offering a bird’s-eye view of where shortages are most pronounced. By simply hovering over a specific location, detailed information about stock status and pricing for individual stores becomes accessible.

Such insights are crucial for brands to adapt their strategies, mitigate risks, and ensure they meet consumer needs despite the ever-changing retail ecosystem.

Store-level pricing insights

Retailers often adopt different pricing strategies to deal with margin pressure, local competition, and surplus stock. Grasping these pricing dynamics at a hyperlocal level enables brands to tailor their strategies effectively to maintain a competitive edge.

Our platform offers an in-depth look at how prices vary among retailers, across different stores, and throughout various regions. This analysis reveals the nuanced pricing tactics employed by retailers on a regional scale.

For example, brands might see that some retailers, like Kroger and Walmart in the chart below, maintain consistent pricing across their outlets, demonstrating a uniform pricing strategy. In contrast, others, such as Meijer and Shoprite, might adjust their prices to match local market conditions, indicating a more localized approach to pricing.

With DataWeave, brands can dive deeper into the pricing landscape of a specific retailer, examining a price map that provides detailed information on pricing at the store level upon hovering over a given location.

By presenting a historical analysis of average selling prices across different retailers, we equip brands with the insights needed to understand past pricing strategies and anticipate future trends, helping them to strategize more effectively in an ever-evolving market.

Digital Shelf Analytics that work for both eCommerce and brick-and-mortar store data

While established brands have made strides in gathering online pricing and availability data through Digital Shelf Analytics solutions, integrating comprehensive insights from both brick-and-mortar and eCommerce channels often remains a challenge.

DataWeave stands out for its capacity to collect data across diverse digital platforms, including desktop sites, mobile sites, and mobile applications. This capability ensures that omnichannel brands can have a holistic view of their pricing, promotional, and inventory strategies across all locations and digital landscapes.

Leveraging localized Digital Shelf Analytics to understand the intricacies of pricing and availability at the store level allows brands to fine-tune their approaches, swiftly adapt to local market shifts, and uphold a unified brand presence across the digital and offline spheres. This strategic agility places them in a favorable competitive position, enhancing customer satisfaction and trust, which are crucial for sustained success.

Know more about DataWeave’s Digital Shelf Analytics here.

Schedule a call with a specialist to see how it can work for your brand.
April 3, 2024
Valentine’s Day eCommerce Insights

Access to these types of real-time digital marketplace insights can enable retailers and brands to make strategic decisions and help drive profitable growth in an intensifying competitive environment. Be sure to reach out to our Retail Analytics experts for access to more details regarding the above analysis.

February 11, 2022
2021 Cost-Push Inflationary Trends Ran Rampant, Impacting Holiday Discounts

Business has been anything but usual this holiday season, especially in the digital retail world. The holiday hustle and bustle historically seen in stores was once again occurring online, but not as anticipated given the current strength of consumer demand and the reemergence of COVID-19 limiting in-store traffic. While ‘Cyber Weekend’, Thanksgiving through Cyber Monday, continues to further its importance to retailers and brands, this year’s performance fell short of expectation due to product shortages and earlier promotions that pulled forward holiday demand.

Holiday promotions were seen beginning as early as October in order to compete with 2020 Prime Day sales, but discounting, pricing and availability took an opposite direction from usual. This shift influenced our team to get a jump start on our 2021 digital holiday analysis to assess how drastic the changes were versus 2020 activity, and to understand how much of this change has been influenced by inflationary pressures and product scarcity.

Scarcity Becomes a Reality

Our initial analysis started by reviewing year-over-year product availability and pricing changes from January through September 2021, leading up to the holiday season, as detailed in our 2021 Cyber Weekend Preliminary Insights blog. We reviewed popular holiday categories like apparel, electronics, and toys, to have a broad sense of notable trends seen consistently throughout various, applicable marketplaces. What we found was a consistent decline in product availability over the last six months compared to last year, alongside an increase in prices.

Although retailers significantly improved stock availability in November and early December 2021, even digital commerce giants like Amazon and Target were challenged to maintain consistent product availability on their website as seen below. While small in magnitude, there is also a declining trend occurring again closer toward the end of our analysis period, post Cyber Weekend, across all websites included in our analysis.

Source: Commerce Intelligence – Product Availability insights for Home & Garden, Jewelry & Watches, Clothing & Shoes, Bed N Bath, Lighting & Ceiling Fans categories

Greater Discounts, Higher Prices?

With inflation at a thirty-nine year high, retailers and manufacturers have realized they can command higher prices without impacting demand as consumers have shown their willingness to pay the price, especially when threatened by product scarcity. Our assessment is that while some products and categories have responded drastically, manufacturers’ suggested retail prices (MSRPs) have increased nearly seven percent on average from January to December 2021. MSRP adjustments are not taken lightly either, as this is an indication increased prices will be part of a longer-term shift in product strategy.

Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com & Target.com each month in 2021 comparing price increases from January 2021 base

Our 2021 pre-Cyber Weekend analysis reviewed MSRP changes for select categories (Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion) on Amazon and Target.com, and found around forty-eight percent of products on Amazon and thirty-five percent of products on Target.com have increased their MSRPs year-over-year, but kept pre-holiday discount percentages the same.

Looking more specifically as to what year-over-year changes occurred on Black Friday in 2021, we observed MSRPs increasing across the board for all categories at various magnitudes. This indicates why 2021 discounts appeared to be greater than or equivalent to 2020 for many categories, when in reality consumers paid a higher price than they would have in 2020 for the same items.

Source: Commerce Intelligence – MSRP Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Black Friday November 27th, 2021, versus average MSRP pricing for the same SKU count from November 20-26th 2021

On Amazon.com, categories like health & beauty have already increase MSRPs by a much greater percentage and magnitude versus Target.com leading up to and during Black Friday 2021, while other categories like furniture have increased MSRPs evenly on average across both retail websites. The below chart cites a few specific examples of year-over-year SKU-level MSRP, promotional price, and discount changes within found within the electronics, furniture, fashion, and health & beauty categories.

Source: Commerce Intelligence – MSRP Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Black Friday November 27th, 2021, versus average MSRP pricing for the same SKUs on Black Friday November 26th, 2020.

Fewer, but Deeper Discounts

From October through early November 2021, fewer products were discounted compared to this same period in 2020, and the few that were saw much deeper discounts apart from the home improvement category. The most extreme example we saw in discounts offered was within furniture where only three percent of SKUs were on discount in 2021 compared to twenty-six percent in 2020. Interestingly, the magnitude of discount was also higher pre-Cyber Weekend 2021 versus 2020, but this trend was not exclusive to furniture and was also seen within electronics, health & beauty, and home improvement.

Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com & Target.com Pre-Black Friday average selling price during November 20-26th 2021 versus average selling price from November 13-19th 2021 compared to Pre-Black Friday average selling price during November 19-25th 2020 versus average selling price from November 12-18th, 2020.

Within the furniture category, the subcategories offering the greatest number of SKUs with price decreases on Black Friday 2021 were rugs by a wide margin, followed by cabinets, bed and bath, and entertainment units, but the magnitude of discounts offered were all under twenty percent.

Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus average pricing for the same SKUs from Pre-Black Friday November 20-26th 2021 and Black Friday November 26th, 2020, versus average pricing for the same SKUs from Pre-Black Friday November 19th-25th 2020

Accounting for this phenomenon could have been retailers’ attempts to clear inventory for SKUs which hadn’t sold even during the period of severe supply chain shortages. With more products selling at higher prices this year, retailers were also able to use fewer SKUs with greater discounts to attract buyer in hopes of filling their digital baskets with more full-priced goods, helping to protect margins heading in to Cyber Weekend. Scarcity threats also encouraged consumers to buy early, even when not on promotion, to ensure they would have gifts in time for the holidays.

The same trends seen pre-Cyber Weekend 2021 were also seen on Black Friday with a year-over-year decrease in the percentage of SKUs offered on discount versus 2020, and steeper price reductions for the discounted products which can also be attributed to the increase in MSRPs.

Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus average pricing for the same SKUs from Pre-Black Friday November 20-26th 2021 and Black Friday November 26th, 2020, versus average pricing for the same SKUs from Pre-Black Friday November 19th-25th 2020

2021 Black Friday Price Increases?

We all know Black Friday is all about price reductions, discounts and deals and so it’s rare to see actual price increases, yet for Black Friday 2021, trends ran counter to this. We observed price increases across all categories for around thirteen to nineteen percent of SKUs, with an average price increase of around fifteen percent in 2021 versus an average of only two percent in 2020.

Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus pricing for the same SKUs from Pre-Black Friday November 20-26th 2021 and Black Friday November 26th, 2020, versus average pricing for the same SKUs from Pre-Black Friday November 19th-25th 2020

At an account level, we noticed a few interesting differences happening on Black Friday 2021 versus 2020 regarding category price changes. On Target.com, almost ninety percent of the bed and bath SKUs analyzed had a price change on Black Friday in 2021 versus 2020 with eighty-two percent presenting a higher price year-over-year versus only around seven percent showing a decrease, where on Amazon nearly forty-four percent of bed and bath SKUs showed an increase in price and around thirty-eight percent showed a decrease. Except for the health and beauty category on Target.com, more than half of the SKUs in each category saw a price increase on Black Friday versus a price decrease.

Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus average pricing for the same SKUs on Black Friday November 26th, 2020.

The magnitude of year-over-year price changes seen on Black Friday 2021 was significant across all categories, but the magnitude of price increases found on Amazon.com within the health and beauty category outpaced the rest by far. We reviewed three hundred and sixty-five SKUs on Amazon.com within the health & beauty category and saw almost eighty-three percent of them had a price change with around thirty-one percent decreasing prices and around fifty-two percent increasing prices. This means that within the health & beauty category on Amazon.com, more than fifty percent of the SKUs tracked were sold at a one hundred and seventy-six percent higher price on average during Black Friday 2021 versus 2020.

Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus average pricing for the same SKUs on Black Friday November 26th, 2020.

The subcategories offering the greatest number of SKUs with price increases on Black Friday 2021 were cameras, followed by men’s fragrances, laptops, and desktops & accessories, but the magnitude of discounts offered were all under ten percent.

Source: Commerce Intelligence – Pricing Insights for Bed & Bath, Electronics, Furniture, Healthy & Beauty, and Fashion categories on Amazon.com and Target.com on Black Friday November 27th, 2021, versus pricing for the same SKUs from Pre-Black Friday November 20-26th 2021 and Black Friday November 26th, 2020, versus average pricing for the same SKUs from Pre-Black Friday November 19th-25th 2020

The Aftermath Post-2021 Cyber Weekend

Extending this analysis beyond the holiday weekend, we analyzed price change activity from December third through the ninth across the top US retailers (chart below) and found that price decreases have been very minimal, comparatively speaking. Though there was a spike in number of price decreases from December 8^th to the 9^th, the percentage of SKUs with price decreases was still very low (less than three percent). We anticipate this trend will continue into 2022.

Source: Commerce Intelligence – Pricing insights for Home & Garden, Jewelry & Watches, Clothing & Shoes, Bed N Bath, Lighting & Ceiling Fans categories

A Sign of Things to Come

A confluence of inflationary trends, product shortages and consumer liquidity have driven many marketplace changes to occur simultaneously. Government programs in the form of stimulus checks, have put extra money in consumers’ hands, and so they’ve been more willing to spend. That, coupled with the shock in the supply chain, has motivated people to buy far ahead of the 2021 holiday season. Hence, retailers have needed to rely much less on across-the-board discounts. Promotions have been more strategic – we’ve seen deeper discounts over fewer products, likely used to draw consumers in to buy certain items, and once they’re there, customers are buying everything else at a non-discount level. When these factors once again normalize, we could see a return to the “race to the bottom” that has occurred since the financial crisis of 2008-2009, but for once, retailers may be able to maintain some pricing power as the 2021 holiday shopping season played out.

Even though performance was not as anticipated and holiday sales did not grow as rapidly as they did in 2020, Cyber Monday was still the greatest online shopping day in 2021. Through it all, retailers managed to keep their digital shelves stocked and orders filled in time for the holidays for the most part, running the risk of housing aged inventory if goods didn’t arrive in time. Despite predictions for steep promotions in January 2022, with supply chains still challenged and inflationary pressures still full steam ahead, we don’t anticipate much in the way of enhanced discounts to continue beyond the holidays.

Access to these types of real-time digital marketplace insights can enable retailers and brands to make strategic decisions like how and when to address inflationary pressures, while also supporting many other day-to-day operations and help drive profitable growth in an intensifying competitive environment. Continue to follow us in the coming weeks for a detailed 2021 year-end review across more retailers and categories. Be sure to reach out to our Retail Analytics experts for access to more details regarding the above analysis.

January 4, 2022
6 Promotional Strategies for the Holiday Season

For eCommerce companies, holidays are the busiest season of the year. Whether creating brand awareness with your marketing campaigns or freshening up your landing pages or finding new ways to segment & understand your customers, the list of tasks seems endless. It’s the time of the year when most people look forward to shopping for friends and family.

The holiday shopping season begins with Black Friday and Cyber Monday and leads to the December holidays, including Christmas and New Year. Consequently, proper planning and marketing are essential for a successful holiday season.

In fact, holiday sales during November and December are forecasted to be between $843.4B – $859B, up 10.5% over 2020, according to the National Retail Federation (NRF). For online stores specifically, sales are predicted to increase between 11% – 15% to a total of between $218.3B and $226.2B driven by online purchases.

This guide will share eight promotional strategies retailers can use during the holiday season. We will also discuss how data analytics can help retailers improve their promotional strategies.

Using data analytics to guide promotional strategies

Promotional Strategies

Data is the foundation of every successful marketing campaign. Data analysis helps companies understand which graphics worked well and campaigns that generated the most revenue. Gathering data and running analysis helps companies improve their next marketing campaign. Retailers can also get deeper insights into campaigns/channels with the highest conversion rate or average order value (AOV).

With data analytics, retailers can prioritize campaigns and channels that resonate the most with their customers this holiday season. But, it would be best to try more than one promotional strategy to ensure you double down on what works without placing all of your eggs in one Christmas-themed basket.

Here are four ways that data analytics can help guide promotional strategies:

a. Customized alerts for listing pages

Data analysis helps retailers determine if certain products are out of stock on their rival’s website and adjust their own pricing accordingly. It allows retailers to grab market share for trending items. For example, if you get an out-of-stock alert for a particular product at competitors’ stores, you can invest more in advertising that product on your online store. In addition, customized alerts keep retailers informed about their inventory status, allowing them to plan promotions and ads. They can see which products are becoming commoditized due to intense competition and which ones offer better revenue opportunities.

Learn how DataWeave can help retailers track their competitor’s stock and inventory status.

b. Maximize conversions by tracking product trends

Assortment Analytics

Customers are always looking for products that are currently trending. With assortment analytics, eCommerce companies can get insights into hot trends, allowing them to stock in-demand categories and products. Integrating assortment analytics with AI-powered image analytics can also provide insights into attributes that are popular among customers. By filling gaps in their current assortments, retailers can improve conversion rates and increase revenue.

Here’s a case study on how DataWeave helped Douglas, a luxury beauty retailer in Germany boost sales by building an in-demand product assortment

c. Monitor competitor promotions

Promotional Insights

With increased competition and consumer demand for deals, it has become important for retailers to monitor their competitor’s promotions. Monitoring promotions helps retailers to optimize their ad spend accordingly. AI-powered image analysis tools can capture important information from competitors’ ad banners and deliver insights into metrics that are working to deliver sales.

Here’s how DataWeave can help retailers make their marketing magnetic with competitive promotional insights!

d. Optimize margins with a data-driven pricing strategy

Pricing Intelligence

It has become challenging to price products in recent years since digital tools enable price transparency across channels. Although this trend is excellent for consumers, it makes competition fierce for retailers. A data-driven pricing strategy incorporates a variety of factors, including industry needs, competitor analysis, consumer demand, production costs, and profit margins.

With data-driven competitive pricing, retailers can keep pace with the changing eCommerce environment with real-time pricing updates. It also helps them optimize margins and quickly respond to changes in prices on rival stores.

Promotional Strategies for the Holiday Season

a. Virtual Webrooms

When customers want to see a product in-person, they go to a store showroom. It helps them make a purchase decision. However, with the Internet, eCommerce companies can bring this tactic online. The only difference between showrooming and webrooming is that the former takes in-person, whereas the latter happens digitally. Webrooming grew in popularity during the COVID-19 pandemic. Instead of spending weekends browsing stores, consumers took to the Internet for most of their product research.

A webroom allows customers to explore products from every angle, providing them with the complete in-person showroom experience online. Webrooming is a powerful holiday marketing strategy, especially regarding expensive purchases. Customers prefer to understand how the product will look. However, building a webroom is extensive and requires retailers to hire developers and professional photographers.

Webrooms allow retailers to share their collections, schedule virtual appointments, share 3D product images, set up virtual fitting rooms for clothing products, and accept purchase orders. For example, in 2015, Tommy Hilfiger launched its first digital showroom in Amsterdam to improve sustainability and minimize its carbon footprint. Through remote wholesale selling and digital product creation, a digital showroom helped Tommy Hilfiger transform the buying journey and retail value chain.

b. Loyalty-rewarding sales and perks

Customer Loyalty

Consider building customer loyalty during your holiday promotions. First, encourage your holiday shoppers to become loyal customers by offering bonus rewards, contests, or giveaways when they sign up for your loyalty program. You can encourage them to purchase right away by providing instant discount coupons or points for a reward to redeem on their next purchase. For maximum impact, you should run this promotion throughout the holiday season.

Second, you should attract your current loyalty program members with discount codes. Offer free shipping or provide a one-day-only discount code to ensure your customers choose you during their last-minute purchases. With these rewards, you’ll attract customers who are window shopping and simultaneously bring your loyal customers back throughout the holiday season.

c. Charitable Tie-Ins

AmazonSmile

Research shows that customers are four times more likely to purchase from brands with a strong sense of purpose. With the festive season being the time of giving, working with a charity and giving back to your community is a great way to reach out to customers.

After a tough one and half years because of the pandemic, people want to give back and help those in need this holiday season. You can partner with a non-profit and run campaigns that allow customers to give back. It’s also great for sharing your brand mission with your customers. For instance, Amazon allows customers to shop from AmazonSmile, which donates 0.5% of their eligible Charity List purchases to a selected charity, at no extra cost to the customers.

Consider partnering with an organization within your industry. For example, you can pair up with a non-profit that collects and gives clothes to the needy if you sell clothes. You can involve customers by asking them to exchange old dresses for coupons or cash discounts.

d. Omni-channel customer experience

Omni-channel marketing provides customers with a seamless, consistent, and cohesive experience over multiple marketing channels. Omnichannel marketing aims to provide a meaningful and cohesive experience that inspires your customers to make a purchase. Unlike multichannel marketing, this strategy puts the customer at the center of marketing campaigns and elevates the cross-channel customer experience.

Omnichannel shoppers spend 10% more money and purchase 15% more items than the original shoppers. eCommerce companies can use historical data to analyze successful channels and create a more transparent marketing strategy for the holiday season. Omnichannel analytics will provide a holistic picture of customer data that will help retailers to better meet the customer’s requirements and predict inventory.

e. Buy now, pay later (BNPL)

Buy Now Pay Later (BNPL)

Technically, buy now, pay later isn’t a promotional idea since your customers will still be paying the full price. However, BNPL allows them to delay their payments and not pay in full right away at checkout. Buy now, pay later needs to be on every eCommerce company’s holiday promotions plans. Various retailers, including Walmart, offer affordable monthly payments at the pace of 3 to 24 months with Affirm. Target also has a similar scheme with Sezzle and Affirm. Whereas Sephora and Macy’s offer 4 interest-free payments with Klarna.

BNPL is especially popular with millennials and Gen Z shoppers and will factor into their 2021 holiday shopping plans. Research showed 62% growth in the use of buy now, pay later service in consumers aged 18 to 24. Giving customers a means to manage their budgets during holidays while still taking home their purchases will attract more customers. While the customer doesn’t pay the full price right away for their purchase, businesses still get the total worth of the item. In the eCommerce industry—nearly 50% of BNPL users say they use it while shopping online, and among them, 45% use the service frequently.

f. Buy One, Get One

The last promotional idea is a classic buy one, get one offer. Everyone likes a good BOGO promotional offer. In fact, 66% of shoppers from a survey preferred BOGO over other promotions. It’s a win-win promotional strategy for retailers and customers. With this offer, people shop and stock up on gifts for their friends and family, while retailers make a more significant profit than 50% off sales. People prefer to get 100% off on a product over 50% on two items.

BOGO sales are best to move inventory by giving shoppers a deal they can’t pass up. If you have stocked up extra items during Black Friday, you can move those last-minute gifts as end-of-year BOGO sales, making room for new merchandise in January.

Conclusion

In this post, you saw that there’s more to holiday marketing than a few social media posts. eCommerce companies can use these holiday promotional ideas to offer Loyalty-rewarding sales and perks, buy now pay later service, and an omnichannel customer experience. Regardless of which strategies you’re using, remember that historical data analytics and early planning will play a significant role in increasing your sales and revenue.

Proper planning backed by insights into key metrics will help your team develop a one-of-a-kind holiday marketing strategy to drive your holiday sales upward. From sharing gratitude to offering personalized experiences, retailers have various options for promoting business this holiday season.

Learn how DataWeave can help make sense of your and your competitor’s pricing, promotional, and assortment data this holiday season. Sign up for a demo with our team to know more.

December 15, 2021
AI-Driven Mapping of Retail Taxonomies- Part 2
Mapping product taxonomies using Deep Learning

In Part 1 we discussed the importance of Retail taxonomy and the applications of mapping retail taxonomies in Assortment Analytics, building Knowledge Graph, etc. Here, we will discuss how we approached the problem of mapping retail taxonomies across sources.

We solved this problem by classifying every retail product to a standard DataWeave defined taxonomy so that products from different websites could be brought at the same level. Once these products are at the same level, mapping taxonomies becomes straightforward.

We’ve built an AI-based solution that uses state-of-the-art algorithms to predict the correct DataWeave Taxonomy for a product from its textual information like Title, Taxonomy and Description. Our model predicts a standard 4 level (L1-L2-L3-L4) taxonomy for any given product. These Levels denote Category, Sub Category, Parent Product Type and Product Type respectively.

Approach

Conventional methods for taxonomy prediction are typically based on machine learning classification algorithms. Here, we need to provide textual data and the classifier will predict the entire taxonomy as a class.

We used the classification approach as a baseline, but found a few inherent flaws in this:
- A Classification model cannot understand the semantic relation between input text and output hierarchy. Which means, it cannot understand if there’s any relation between the textual input and the text present in the taxonomy. For a classifier, the output class is just a label encoded value
- Since the taxonomy is a tree and each leaf node uniquely defines a path from the root to leaf, the classification algorithms effectively output an existing root-to-leaf path. However, it cannot predict new relationships in the tree structure
- Let’s say, our training set has only the records for “Clothing, Shoes & Jewelry > Men > Clothing > Shorts” and “Clothing, Shoes & Jewelry > Baby > Shoes > Boots”, Example:
{‘title’: “Russell Athletic Men’s Cotton Baseline Short with Pockets – Black – XXX-Large”,

‘dw_taxonomy’: “ Clothing, Shoes & Jewelry > Men > Clothing > Shorts”},

{‘title’:” Surprise by Stride Rite Baby Boys Branly Faux-Leather Ankle Boots(Infant/Toddler) – Brown -”,

’dw_taxonomy:” Clothing, Shoes & Jewelry > Baby > Shoes > Boots”}

Now, if a product with Title “Burt’s Bees Baby Baby Boys’ Terry Short” comes for prediction, then the classifier will never be able to predict the correct taxonomy. Although, it would have seen the data points of Shorts and Baby.

E-commerce product taxonomy has a very long tail, i.e. there’s a huge imbalance in counts of data per taxonomy. Classification algorithms do not perform well for very long tail problems.

Encoder-Decoder with Attention for Taxonomy Classification

What is Encoder-Decoder?

Encoder-Decoder is a classical Deep Learning architecture where there are two Deep Neural Nets, an Encoder and a Decoder linked with each other to generate desired outputs.

The objective of an Encoder is to encode the required information from the input data and store it in a feature vector. In case of text input, the encoder is mostly an RNN or Transformer based architecture and for image input, it is mostly a CNN-based architecture. Once the encoded feature vector is created, the Decoder uses it to produce the required output. The Encoder and Decoder can be interfaced by another layer which is called Attention. The Role of Attention mechanism is to train the model to selectively focus on useful parts of the input data and hence, learn the alignment between them. This helps the model to cope effectively with long input sentences (when dealing with text) or complex portions of images (when input is an image).

Instead of classification-based approaches, we use an Encoder-Decoder architecture and map the problem of taxonomy classification to the task of machine translation (MT) AKA, Seq2Seq. An MT system takes the text in one language as input and outputs its translation as a sequence of words in another language. In our case, the input maps to the textual description of a product, and the output maps to the sequence of categories and sub-categories in our taxonomy (e.g., Clothing, Shoes & Jewelry > Baby > Shoes > Boots). By framing taxonomy classification as an MT problem, we overcome a lot of limitations present in classical classification approaches.
- This architecture has the capability to predict a taxonomy that is not even present in the training data.
  - Talking about the example we discussed earlier where a traditional classification model was not able to predict the taxonomy for “Baby Boys knit terry shorts – cat & jack gray 12 m”, this Encoder-decoder model easily predicts the correct taxonomy as “ Clothing, Shoes & Jewelry > Baby > Clothing > Shorts”
- We achieved a much higher accuracy because the model understands the semantic relationship between the input and output text, as well as giving attention to the most relevant parts in the input, when generating the output
Fig. Attention visualization for product title “South of France lavender fields Bar Soap”. It can be seen from the image that the attention weights of “soap” word is very high when predicting the output at different time-steps.

We used pre-trained fasttext word embeddings to vectorize textual input, pass on to the GRU-RNN based encoder which processes the input sequentially, and generates the final encoded vector. The Decoder which is also a GRU-RNN takes this encoded input and generates the output sequentially. Along with the encoded vector, there is also an attention vector which is passed to the Decoder for the output at every time-step.

We trained both the Classification model (Baseline) and the Encoder-Decoder model for the Fashion category and the Beauty & Personal Care category.

For Fashion, we trained the model with 170,000 data points and validated it on a 30k set. For Beauty Category, we trained the model on 88k data points and validated it on a 20k set. We were able to achieve 92% Seq2Seq accuracy in 1,240 classes for the Fashion category and 96% Seq2Seq accuracy in 343 classes for the Beauty Category, using the Encoder-Decoder approach.

Summary and the Way Forward

Since we moved to this approach, we have seen drastic improvements in the accuracy of our Assortment Intelligence accounts. But the road doesn’t end here. There are several challenges to be tackled and worked upon. We’re planning on making this process language agnostic by using cross-lingual embeddings, merging models from different categories and also using product Image to complement the text-based model with visual input via a Multi-Modal approach.

References

Don’t Classify, Translate: Multi-Level E-Commerce Product Categorization Via Machine Translation by Maggie Yundi Li, Stanley Kok and Liling Tan

SIGIR eCom’18 Data Challenge is organized by Rakuten Institute of Technology Boston (RIT-Boston)

Massive Exploration of Neural Machine Translation Architectures by Denny Britz, Anna Goldie, Minh-Thang, and Luong Quoc Le
January 13, 2021