The DataWeave Blog

Tag: ecommerce content marketing

  • Bridging the Gap: How Digital Shelf Analytics Empowers Marketing Mix Modelling for Smarter Brand Decisions

    Bridging the Gap: How Digital Shelf Analytics Empowers Marketing Mix Modelling for Smarter Brand Decisions

    Marketing Mix Modeling (MMM) has been a cornerstone of marketing analytics for decades: first as a service offered by large consultancies like Nielsen and IRI, and later as software solutions from NielsenIQ and Ekimetrics. By 2024, some 64% of senior marketing leaders had already adopted and used MMM solutions.

    However, despite this widespread adoption, MMM faces significant limitations in our fast-moving digital marketplace. According to Gartner, opaque pricing models and siloed data integration remain substantial barriers to actionable insights from these tools. Most critically, traditional MMM often misses vital variables influencing consumer behavior, such as:

    • Competitor price drops and promotions
    • Product availability issues and stockouts
    • Negative review trends and sentiment shifts
    • Search ranking fluctuations

    These blind spots must be addressed to unlock the full value of MMM investments and make truly informed marketing decisions.

    The Critical Data Gap In Traditional MMM

    Traditional MMM solutions expose brands to considerable risk, especially in the CPG and retail space. The fundamental challenge lies in MMM’s reliance on lagging indicators for essential metrics like historical sales and ad spend. Data inputs may be months or quarters old before they’re used for scenario analysis.

    That’s like making million-dollar marketing decisions while only looking in the rearview mirror when you need to watch the road ahead simultaneously.

    MMM tools also typically overlook external market factors that can dramatically impact performance. In today’s retail landscape, where market conditions change rapidly, being blind to real-time competitive dynamics creates significant vulnerability. Key external factors that traditional MMMs fail to capture include:

    • Competitor moves: Price changes, promotions, content updates
    • Consumer sentiment: Review trends, ratings, social engagement
    • Market dynamics: Stockouts, search ranking shifts, category growth

    How Digital Shelf Analytics Completes The Picture

    This is where Digital Shelf Analytics (DSA) plays a crucial complementary role. Brands and retailers leveraging DSA gain insights into real-time market dynamics that MMM alone cannot provide. However, brands using DSA in isolation often struggle to quantify how digital shelf improvements directly impact revenue. Answering questions like “Did better product content drive sales, or was it the influencer campaign?” remains challenging.

    Bridging these disconnected platforms requires intentional integration and a DSA platform that can feed intensively cleaned and organized data into existing MMM platforms. With the right data inputs, companies establish a powerful feedback loop for agile, data-driven decisions.

    A comprehensive DSA solution like DataWeave provides granular, actionable data on critical external variables such as:

    • Daily or weekly competitor pricing movements and promotional activity
    • Product content standardization and optimization across retailers
    • Review sentiment trends and potential reputation issues
    • Share of search/shelf performance relative to competitors

    When merged with established MMM capabilities, DSA creates a complete picture that fills the blind spots holding marketing teams back from maximizing ROI.

    The DSA + MMM Advantage in Retail Media

    The popularity of retail media networks has further amplified the need for integrated DSA and MMM approaches. These advertising platforms, operated by retailers, allow brands to display targeted ads to shoppers across digital properties based on first-party customer data and purchase insights.

    The retail media revolution has transformed e-commerce pages into sophisticated search engines for product discovery. This evolution has been so impactful that retail media ad revenue surged 16.3% in 2023, reaching $43.7B in the U.S., with continued growth projected.

    Major platforms like Walmart have expanded their retail media networks to capitalize on closed-loop attribution. Since retailers own the entire customer journey, they can track everything from ad impression to purchase on their e-commerce sites. This creates a significant advantage through accurate ROI measurement, unlike traditional advertising where attribution remains challenging.

    How DSA Enhances Retail Media Optimization

    With retail media emerging as a top-performing sales channel, brands need sophisticated optimization strategies. Every brand wants to maximize visibility and performance across individual eCommerce sites, just as they optimize for Google or emerging AI platforms.

    Integrating digital shelf analytics into marketing mix models enables brands to:

    • Allocate ad spend more intelligently using real-time competitive insights
    • Identify timely campaign activation opportunities in response to market changes
    • Monitor organic ranking trends to strategically time paid promotional activities
    • Measure true campaign impact on digital shelf performance metrics

    For example, when a competitor launches an aggressive price drop in your category, DSA provides visibility into this change. This intelligence can trigger recommended campaign adjustments, such as increased sponsored ad bidding in affected categories. Traditional MMM alone cannot deliver this level of responsive optimization.

    How to Integrate DSA into MMM: A 3-Step Framework

    Digital Shelf Analytics for Marketing Mix Modeling - 3 Step Framework

    Here’s how to integrate your Digital Shelf Analytics into your Marketing Mix Models to start making better data-driven decisions for your brand.

    Step 1: Map DSA Variables to MMM Inputs

    Begin by mapping specific DSA variables to your static MMM inputs. Ensure that competitors are properly configured for monitoring in your DSA platform and that metrics like price changes and search ranking positions are linked with your MMM’s models.

    This integration is crucial because traditional MMM models rely exclusively on historical data for forecasting. Adding real-time inputs delivers several benefits:

    • More accurate elasticity curves reflecting current market conditions
    • Better understanding of root causes behind demand shifts
    • Prevention of misattributing sales changes to your marketing activities when external factors may be responsible

    At DataWeave, our comprehensive coverage spans 500+ billion data points, 400,000 brands, and 1,500+ websites, ensuring brands never miss a competitor move and maintain complete visibility across the connected e-commerce landscape.

    Step 2: Feed High-Quality DSA Data into MMM Platforms

    Next, integrate critical digital shelf metrics into your MMM framework:

    • Review and sentiment scores and trends
    • Content quality measurements
    • Competitive positioning data
    • Price gap analytics
    • Search ranking performance

    DataWeave employs a rigorous data accuracy validation process to ensure teams work with the cleanest, most reliable data possible. Our sophisticated processing pipeline removes anomalies and standardizes information across retailers, providing the consistent, high-integrity data foundation that robust marketing mix modeling demands.

    Step 3: Validate and Iterate

    A powerful DSA solution helps measure whether your marketing efforts achieved their intended impact on the digital shelf. Use your DSA platform to assess campaigns’ actual effect on key performance indicators:

    • Do promo-driven sales lifts correlate with improved search rankings?
    • How do content improvements impact conversion rates?
    • What is the relationship between paid media and organic visibility?

    DataWeave enables users to correlate metrics across the entire consumer journey, from awareness through post-purchase. Rather than focusing solely on short-term spikes, brands can measure lasting impacts on digital shelf health. This end-to-end visibility empowers teams to make increasingly informed decisions with each campaign cycle.

    Executive Decision Support in Uncertain Times

    It is no surprise to anyone that we are living through volatile times. Executives may be uncomfortable if they cannot provide their teams with strategic direction based on data or the tools they need to accelerate their workdays.

    By integrating DSA with MMM, companies gain early warning signals about market shifts, enabling smarter resource allocation during budget constraints. This integration helps organizations move from tactical execution to strategic direction by:

    • Providing cross-channel impact analysis to understand the full marketing ecosystem
    • Equipping category managers with tactical optimization tools that support broader strategic objectives
    • Identifying competitive threats before they impact sales
    • Forecasting potential ROI impacts across various spending scenarios

    These capabilities help prevent wasted ad spend, missed opportunities, and lost sales.

    Future-Proofing with DSA-Driven MMM

    Several emerging trends highlight the growing importance of DSA-enhanced marketing mix modeling:

    • Trend 1: Navigating Economic Volatility – Brands can use DSA to track how competitors adjust pricing in response to cost shocks like tariffs and inflation. This real-time intelligence directly improves MMM’s inflation modeling accuracy.
    • Trend 2: AI-Powered Predictive Insights – Combining DSA trend detection (such as viral product reviews or sudden inventory fluctuations) with MMM helps forecast demand spikes from otherwise unforeseen events.
    • Trend 3: Automated Optimization – Smart campaign activations and adjustments based on real-time DSA triggers drive efficiency. DataWeave’s vision includes an automated retail media intelligence layer that optimizes spend across channels based on integrated insights.

    DataWeave’s Unique Advantage

    At DataWeave, we’ve seen our digital shelf analytics customers significantly improve their organic search rankings because of better-sponsored ad campaigns. What makes our approach to DSA-MMM integration uniquely powerful? Our platform is specifically designed to address the challenges of modern marketing mix modeling:

    • Superior data refresh rates ensure timely insights when they matter most
    • Unmatched marketplace coverage across more than 1,500 eCommerce sites globally
    • Advanced data normalization that standardizes metrics across disparate categories and retailers
    • API-first architecture enabling flexible data access and utilization

    Conclusion – From Hindsight to Foresight

    In the past, companies relied primarily on historical data for their marketing mix models. Today’s market leaders are incorporating digital shelf analytics to unlock superior insights, improve decision accuracy, and drive measurable ROI.

    DataWeave serves as the essential bridge between MMM systems and real-time, comprehensive market intelligence. When DSA and MMM work together, brands gain a complete picture: MMM shows precisely what happened, while DSA explains why it happened—and together, they reveal what’s coming next.

    Ready to transform your marketing mix modeling from hindsight to foresight? Contact us today to discover how our Digital Shelf Analytics can enhance your existing MMM investments and drive measurable business results.

  • How AI-Powered Visual Highlighting Helps Brands Achieve Product Consistency Across eCommerce

    How AI-Powered Visual Highlighting Helps Brands Achieve Product Consistency Across eCommerce

    As eCommerce increasingly becomes a prolific channel of sales for consumer brands, they find that maintaining a consistent and trustworthy brand image is a constant struggle. In an ecosystem filled with dozens of marketplaces and hundreds of third-party merchants, ensuring that customers see what aligns with a brand’s intended image is quite tricky. With many fakes and counterfeit products doing the rounds, brands may further struggle to get the right representation.

    One way brands can track and identify inconsistencies in their brand representation across marketplaces is to use Digital Shelf Analytics solutions like DataWeave’s – specifically the Content Audit module.

    This solution uses advanced AI models to identify image similarities and dissimilarities compared with the original brand image. Brands could then use their PIM platform or work with the retailer to replace inaccurate images.

    But here’s the catch – AI can’t always accurately predict all the differences. Relying solely on scores given by these models poses a challenge in tracking the subtle differences between images. Often, image pairs with seemingly high match scores fail to catch important distinctions. Fake or counterfeit products and variations that slip past the AI’s scrutiny can lead to significant inaccuracies. Ultimately, it puts the reliability of the insights that brands depend on for crucial decisions at risk, impacting both top and bottom lines.

    Dealing with this challenge means finding a balance between the number-based assessments of AI models and the human touch needed for accurate decision-making. However, giving auditors the ability to pinpoint variations precisely goes beyond simply sharing numerical values of the match scores with them. Visualizing model-generated scores is important as it provides human auditors with a tangible and intuitive understanding of the differences between two images. While numerical scores are comparable in the relative sense, they lack specificity. Visual interpretation empowers auditors to identify precisely where variations occur, aiding in efficient decision-making.

    How AI-Powered Image Scoring Works

    At DataWeave, our approach involves employing sophisticated computer vision models to conduct extensive image comparisons. Convolutional Neural Network (CNN) models such as Resnet-50 or YOLO, in conjunction with feature extraction models, analyze images quantitatively. This AI-powered image scoring process yields scores that indicate the level of similarity between images.

    However, interpreting these scores and understanding the specific areas of difference can be challenging for human auditors. While computer vision models excel at processing vast amounts of data quickly, translating their output into actionable insights can be a stumbling block. A numerical score may not immediately convey the nature or extent of the differences between images

    In the assessment of these images, all fall within the 70 to 80 range of scores (out of a maximum of 100). However, discerning the nature of differences—whether they are apparent or subtle—poses a challenge for the AI models and human auditors. For example, there are differences in the placement or type of images in the packaging, as well as packing text that are often in an extremely small font size. It is, of course, possible for human auditors to identify the differences in these images, but it’s a slow, error-prone, and tiring process, especially when auditors often have to check hundreds of image pairs each day.

    So how do we ensure that we identify differences in images accurately? The answer lies in the process of visual highlighting.

    How Visual Highlighting Works

    Visual highlighting is a method that enhances our ability to comprehend differences in images by combining sophisticated algorithms with human understanding. Instead of relying solely on numerical scores, this approach introduces a visual layer, resembling a heatmap, guiding human auditors to specific areas where discrepancies are present.

    Consider the scenario depicted in the images above: a computer vision model assigns a score of 70-85 for these images. While this score suggests relatively high similarity, it fails to uncover major differences between the images. Visual highlighting comes into play to overcome this limitation, precisely indicating regions where even subtle differences are seen.

    Visual highlighting entails overlaying compared images and emphasizing areas of difference, achieved through techniques like color coding, outlining, or shading specific regions. The significance of the difference in a particular area determines the intensity of the visual highlight.

    For instance, if there’s a change in the product’s color or a discrepancy in the packaging, these variations will be visually emphasized. This not only streamlines the auditing process but also enables human evaluators to make well-informed decisions quickly.

    Benefits of Visual Highlighting

    • Intuitive Understanding: Visual highlighting offers an intuitive method for interpreting and acting upon the outcomes of computer vision models. Instead of delving into numerical scores, auditors can concentrate on the highlighted areas, enhancing the efficiency and accuracy of the decision-making process.
    • Accelerated Auditing: By bringing attention to specific regions of concern, visual highlighting speeds up the auditing process. Human evaluators can swiftly identify and address discrepancies without the need for exhaustive image analysis.
    • Seamless Communication: Visual highlighting promotes clearer communication between automated systems and human auditors. Serving as a visual guide, it enhances collaboration, ensuring that the subtleties captured by computer vision models are effectively conveyed.

    The Way Forward

    As technology continues to evolve, the integration of visual highlighting methodologies is likely to become more sophisticated. Artificial intelligence and machine learning algorithms may play an even more prominent role in not only detecting differences but also in refining the visual highlighting process.

    The collaboration between human auditors and AI ensures a comprehensive approach to maintaining brand integrity in the ever-expanding digital marketplace. By visually highlighting differences in images, brands can safeguard their visual identity, foster consumer trust, and deliver a consistent and reliable online shopping experience. In the intricate dance between technology and human intuition, visual highlighting emerges as a powerful tool, paving the way for brands to uphold their image with precision and efficiency.

    To learn more, reach out to us today!


    (This article was co-authored by Apurva Naik)

  • Top 4 ways to optimize content to drive e-commerce sales

    Top 4 ways to optimize content to drive e-commerce sales

    Content is the reigning king for e-commerce & plays a big role in driving sales and conversions. And, consumer-centric content that drives traffic is vital for e-commerce sales. Unlike offline retail where the sales staff on the ground is always available to answer customer queries, online that is not the case. When shopping online, customers rely on audio & visual product content to give them the information they need in order to make purchase decisions. Understanding that your product speaks to your customers directly on online channels is critical – so optimizing your product content to represent your brand in the best light is very important.

    Here are the Top 4 ways to optimize content & drive ROI.

    1. Focus on your customer & set a brand tone

    Who is your customer? And what type of product is your brand selling? The golden rule to getting the right content for your brand is to answer these two questions right. For instance, if you’re selling furniture and focusing on a family audience then using flowery language will not help your cause. You need to share factual, product-specific content, calling out furniture specs from color, fabric, size, and so on.

    Flower Glossary Categories

    Take for instance ProFlowers – a US-based flower retailer who created an entire Florapedia® – an in-depth flower guide. This content helped their customers learn more about the various flowers & discover new flowers they never knew of when making purchase decisions. To drive e-commerce sales, ProFlowers set the brand tone using educational content.
    On the other hand, if you are selling clothing or lingerie, you need to be extremely specific about the details of each product. Let’s look at reputed outdoor clothing brand Jack Wolfskin – they use high-quality images for content optimization and showcase real instances and moods in which the clothing can be worn or what they can be paired with. This is a good way to allow customers to picture themselves owning the item, as well as research their unique qualities.

    Clothing brand Jack Wolfskin
    Educational, visual-heavy, or fun & quirky – pick your content style based on your brand tone.

    2. Use videos as a powerful content optimization tool

    Videos empower content and hook your customers in. In a report, Cisco had earlier projected that by the year 2022, videos will be responsible for 82% of all consumer internet traffic. For e-commerce, video content can not only deliver a message but is easily shareable across all platforms. Videos not only possess the power to captivate people for extended amounts of time, but according to research, if a video is embedded on your website, you’re 53 times more likely to rank on the first page of Google. 

    Videos work as a descriptive medium to give more details about your product. Further, explanatory videos relieve consumer fears regarding the quality of the product by allowing viewers to visually experience its usages and benefits. MAC for instance uses a host of make-up tutorials and other video content on their website.

    videos as a powerful content optimization tool

    Videos bring brand storytelling to life and keep visitors informed. In the case of the videos created by MAC, they are not only informative and engaging, but they also help the brand answer common shopper questions with live examples. Customers who land upon the MAC website can watch videos relevant to the products they want to shop for, understand the product details and then decide if the product is for them. Thus, brands using videos can create a better customer experience by giving the visitors an immersive brand exposure online, just like they could have got offline. 

    3. Focus on making your product page consumer-centric

    A product page can be highly discoverable if it aligns with the best practices & standard e-commerce algorithms put in place by popular marketplaces and e-commerce channels. This is because the organic product ranking algorithms vary across channels and are composed of direct and indirect factors used to match a consumer’s popular search queries to products they are most likely to purchase. For better content optimization that ensures visibility, start by mapping platform-specific content standards. Then follow the SEO trends and tweak your product titles and description, to give your brand content a boost.

    LOVE Hair product
    Love Hair Product

    Take for instance the LOVE Hair product pages – the product titles are crafted using product features and benefits like revitalizing, nourishing, volumizing. Consumers searching for shampoos normally type in these attributes to look for shampoos that may suit their requirements – so using attributes as a hook in the product title is a great idea to make the product more discoverable against that keyword or attribute. 

    Next, keeping these standards as a backbone, fine-tune the product details you are putting out on the page. Your product features are the reasons why your consumer will buy your brand as compared to your competitors, so your descriptions should be crisp, easy to read, highlighting all the product features & facts that help them make that purchase decision. Let’s look at the Fitbit product page…

    Fitbit product page
    Fitbit Product Page

    4. Optimize content based on devices

    With mobile commerce reaching the tipping point in the e-commerce sales funnel, you cannot ignore attending to content optimization for hand-held devices. Many times, buyers on the go use mobile devices to conduct their research and your success lies in being able to entice them with a perfectly optimized e-commerce page even on their mobile device. Here are a few tips to keep in mind:

    • Keep their reading experience in mind. Use shorter titles, and bite-sized product information so key points are upfront and visible on a tinier screen 
    • Be concise with your content presentation
    • Video content should not autoplay on mobile. The less invasive your content, the better
    • Keep video & image file sizes small so that page load time is quick
    Optimize content based on devices
    E-Commerce Product Page

    A perfectly balanced e-commerce product page is even more vital in the new normal, given that COVID has accelerated e-commerce, globally. So, whether you are selling furniture, books, clothes, or health juices, with the right focus on product content, you can convert shoppers into customers more easily and increase your sales & revenue. Feel free to take inspiration from some of the examples above to apply some of these strategies to your online store.

    And if you need to get your brand discovered with content optimization, here’s how DataWeave can help! 

    Building the right product page with the right content is not enough. You will also need to keep rehashing your product pages with reviews, offers, and other such relevant nodes to deliver the right punch. After all, delivering the right customer experience starts with a product page done right. 

    Want to see first-hand how DataWeave can help brands with content optimization? Sign up for a demo with our Digital Shelf experts to know more.