Mimbi platform

Product Catalog Mapping: why and how to analyze Retail Media performance by product, category, or brand

Frederic CLEMENT
January 23, 2024
min read
mimbi product catalog mapping

In today’s digital-first retail landscape, brands are investing heavily in retail media to drive awareness, consideration, and sales on ecommerce sites. With this surge in investment comes a critical need to closely measure performance and ROI across retail media activities. Going beyond evaluating campaigns, tactics, and channels in aggregate, savvy advertisers are taking a more granular approach by analyzing retail media data at the product level.

Let’s explore why product-level retail media analysis is so vital for brands and how you can get started mapping your product catalog to unlock greater visibility into performance.

The benefits of product catalog analytics mapping

Catalog mapping allows you to break down retail media metrics by specific products, brands, categories, price points, and other attributes. This uncovers performance insights you’d completely miss at the campaign or account level. Key benefits include:

Identify high and low ROAS products

The most obvious advantage of catalog mapping is the ability to calculate the return on ad spend for each product you advertise on retail media. With ROAS metrics by product, you can quickly identify your top revenue-driving products with the highest returns. This allows you to double down on proven winners by increasing bids, budgets, and promotions to maximize sales.

On the flip side, analyzing product-level ROAS enables you to spot low-performing products that are dragging down overall performance and ROI. You can then reduce investment in these poor performers to improve your overall retail media efficiency.

Look for category outliers

Catalog mapping allows you to break down performance by product category to identify outliers over or underperforming against their peers. For example, you may find that products in your beverage category have significantly higher ROAS compared to your snack category.

This analysis enables you to dive into the factors causing certain categories to excel while others miss expectations. You can then optimize specific merchandising, content, bidding strategies, and creative by top or bottom category to improve results.

Identify outliers

Break down brand performance

For companies managing multi-brand portfolios, catalog mapping provides the unique ability to evaluate retail media effectiveness for each brand. You can analyze metrics like ROAS, impressions, clicks, and sales by brand to uncover performance gaps.

Oftentimes, company executives assume all brands receive equal visibility and investment. Catalog mapping reveals clear performance differences between brands, arming you with data to justify increased retail media investment for high-performing yet underfunded brands.

Understand price point differences

Catalog mapping allows you to break down retail media metrics by product price points. You can analyze ROAS, CPC, CTR, and other KPIs segmented by price tier. This tells you if lower, medium, or higher-priced products are more efficient in retail media.

These insights allow you to adjust bidding and budgets to better match the performance curve across price segments. For example, it is worthwhile to aggressively bid on lower-priced products driving outsized ROAS compared to expensive products with lower demonstrated efficiency.

Performance analysis

Continuous iteration drives results

Product catalog mapping is not a one-time effort. To maximize benefits, you must make an ongoing category, brand, and product analysis part of your regular retail media performance reviews. Look at trends over time, test new segments, and be ready to adapt strategies to evolving performance dynamics. You can break down performance by product, flavor, and pack size… the limit is your imagination.

Maintaining this rigor enables agile optimization of retail media precisely where you identify pockets of value. Small but consistent tweaks to focus on what works compound over time into significant performance improvements.

How to map product catalogs to Retail Media data with mimbi

Now that the value is clear, let’s explore some best practices for executing catalog mapping to enable product-level retail media analysis.

1/ Import Retail Media campaign data

First, you need access to detailed performance data across your retail media ad accounts and campaigns. Most major retail media platforms like Amazon DSP and Criteo provide robust analytics dashboards and reporting to extract raw performance data.

You’ll want to pull weekly or monthly campaign statistics segmented by important dimensions like impressions, clicks, spend, sales, revenue, ROAS, product targetings, etc. For automated ongoing analysis, leverage APIs from retail media platforms or work with an analytics integration platform like mimbi.

Campaign report

2/ Import your product catalog

The next step is aggregating a complete catalog file containing all products mapped to their attributes — category, brand, price point, etc. Pull your product catalog data in mimbi from sources like:

  • Your product information management (PIM) system (Salsify, Akeneo…)
  • E-commerce feed management platforms (Channeladvisor, Lengow…)
  • Your database, ERP, or CRM system
  • Manual product catalogs maintained in files or spreadsheets

Work internally to consolidate SKU-level product information into a single catalog reference file. Identify any gaps in product data that need to be addressed cross-departmentally to enable robust catalog analysis.

Product catalog

3/ Map catalog to Retail Media data

With retail media performance stats and a product catalog file ready, it’s time for the crucial mapping step, which is automated in mimbi. The goal is to tag each retail media impression, click, and sale event with the associated product attributes from your catalog.

For example, attaching product categories to each product targeted in a sponsored product ad on Amazon. This enables counts, spend, revenue, and ROAS calculations for each category to uncover top-performing ones.

As an eCommerce Manager, using product-level analysis will allow you to identify hero SKUs with the best ROAS to amplify promotions and creative for those star products.

Product view

Some platforms like Amazon DSP directly integrate product catalogs for this mapping. For others, you’ll need developer help to accurately match product IDs between data sources. Avoid manual mapping which is time-intensive, inaccurate, and unable to scale.

4 / Filter to relevant segments (optional)

Once catalog mapping is implemented, you can easily filter retail media data on product attributes to analyze the segments you care about. Look at performance for categories, brands, prices, product types, inventory levels, or any other catalog dimension that makes sense for your business.

Brand view

Pull weekly or monthly reports to enable quick analysis of product segment performance. Maintain dashboards highlighting key product metrics to inform retail media optimization decisions.

Continuously iterate and maximize ROI

Catalog mapping is not a “set it and forget it” process. Your products and retail media strategies evolve constantly. Revisit mapping periodically to fill catalog gaps, refine segments, and keep analysis tailored to your most pressing questions.

As you dig into results, let data-driven insights guide experiments to improve underperforming segments. Confirm winners continue delivering strong ROI then double down on what works. With continuous catalog iteration, you’ll compound results over time. As a Digital Marketing Director, you will review trends by category, brand, price, and product type with your team to optimize investment.

In today’s crowded digital shelves, winning brands use granular data to optimize retail media outperformance. Catalog mapping provides the missing level of visibility to answer questions impossible to discern at the campaign or account level. The insights uncovered through this process can ultimately make the difference between mediocre and outstanding ROI.

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