Retail media has exploded in popularity over the past few years. As retailers look to diversify revenue streams beyond just product sales, offering advertising opportunities has become an extremely lucrative endeavor. The global retail media advertising market is expected to reach nearly $100 billion by 2027.
For brands, retail media represents a major opportunity to directly influence consumers during the shopping journey. Research shows that retail media ads can significantly improve brand metrics like awareness, consideration, and sales. However, measuring the impact of these ads requires robust analytics capabilities. In this post, we’ll explore the key requirements brands and agencies should look for when evaluating retail media analytics platforms.
The foundation of any analytics platform is its data. When it comes to retail media, brands need a complete view of campaign data across multiple touchpoints. This includes ad impressions, clicks, purchases, product views, add-to-carts, and any other relevant actions. Data should be captured both on the retailer’s owned properties (site, app, etc.) as well as offsite media bought through the retailer.
Ideally, retail media analytics platforms should unify data from every major retail media network into one location. This eliminates the need to log into multiple portals to access campaign insights. Unified data also enables cross-channel measurement. Brands can see how media budgets are interacting across retailers and channels to identify trends and optimization opportunities.
Once data is aggregated, brands need powerful analytics capabilities to measure performance. At a minimum, platforms should provide campaign reporting on impressions, clicks, click-through-rate (CTR), cost-per-click (CPC), and return-on-ad-spend (ROAS).
However, basic metrics only reveal so much. Advanced analytics enable deeper analysis by product, ad type, placement, creative, audience, and more. Brands should be able to break down metrics by various dimensions to understand what’s driving performance.
For example, detailed placement reporting can show which pages, products, or searches are generating the most conversions from ads. And audience analytics help you identify your best customer profiles for targeting.
The true value of analytics comes from identifying actionable insights. Beyond just reporting metrics, analytics platforms should provide data visualizations, benchmarks, and recommendations to help inform decisions.
Data visualizations (charts, graphs, etc.) allow brands to easily spot trends and patterns. Benchmarks compare performance against internal goals and external industry standards. This helps assess campaign effectiveness. Finally, platforms can take data analysis to the next level by offering concrete recommendations to improve campaign performance.
For example, an analytics platform may recommend lowering bids on underperforming products or increasing bids during high-converting times of day. These data-driven recommendations enable brands to continually optimize retail media programs.
A key advantage of retail media is the (theoretical?) ability to track sales both online and offline. However, attribution remains a major challenge. Most retailers still operate in silos, making omnichannel measurement difficult.
Advanced analytics platforms are starting to overcome these limitations using probabilistic models. For example, if a customer views a product ad on their mobile device, then purchases that product in-store shortly after, technology can attribute that sale back to the ad with high confidence (even without a direct match).
Omnichannel attribution provides a more complete view of retail media’s impact across online and offline sales. This helps brands allocate budgets more efficiently between digital ads and in-store marketing.
In today’s fast-paced digital landscape, waiting days or weeks to access campaign data is no longer sufficient. Leading analytics platforms offer real-time reporting to enable “always-on” optimization (and major retailers like Amazon make it possible, see https://advertising.amazon.com/blog/introducing-rapid-retail-analytics). Brands can monitor performance 24/7 and quickly adjust bids, budgets, targeting, and creative in response to insights.
Real-time analytics empower brands to capitalize on emerging trends and opportunities. On Prime Day or Black Friday, for example, brands can scale bids on top-selling products in real-time to drive incremental sales. Rapid insights and optimization help maximize returns from retail media.
Looking forward, advanced platforms are exploring how to apply predictive analytics and machine learning to retail media. This moves beyond just reporting on past performance to forecasting future outcomes.
Predictive analytics can estimate potential sales by product based on historical trends, seasonality, and other factors. Brands could then optimize ads and offers accordingly to achieve sales targets. Platforms are also leveraging AI and optimization algorithms to automatically manage bids across campaigns.
While still emerging in retail media, predictive analytics represent an exciting innovation for driving performance. Expect these capabilities to rapidly evolve in the coming years.
Flexible reporting and alerts
Every brand has unique KPIs and reporting needs. Analytics platforms should offer customizable dashboards, reporting templates, and scheduled reports to align with specific business goals. Flexible reporting options allow brands to focus on the metrics and insights that matter most.
Platforms can also provide real-time alerts and notifications to flag issues (like budget pace or policy violations) or opportunities. For example, an alert may notify a brand when a high-priority product goes on sale so they can reallocate budget. Customizable alerts keep brands on top of their retail media programs.
Integrations and APIs
Analytics platforms should integrate seamlessly into the retail media tech stack. This includes connecting directly to major retail media APIs, ad servers, marketing platforms, and other martech tools.
Open APIs also allow analytics to be embedded within other systems. For example, insights can be piped into a DSP to trigger automated optimization. Integrations and APIs enable the full-stack use of retail media analytics.
Auditing and fraud prevention
It’s vital for platforms to provide visibility into how campaigns are actually performing. Independent auditing verifies campaign delivery and sales attribution to ensure brands are getting what they paid for. Fraud detection identifies and prevents issues like invalid traffic and attribution manipulation.
Without proper auditing and fraud prevention, brands can’t trust the accuracy of analytics. Platforms should proactively monitor campaign delivery and attribution to give brands confidence in the data.
In today’s privacy-focused environment, analytics platforms must follow industry data standards and regulations (like GDPR in Europe). Personally identifiable information (PII) like names and emails should never be used. Data also needs to be aggregated or anonymized to avoid tracking specific individuals.
Choose a platform that collects only essential data, processes and secures data responsibly, and provides transparency into its privacy practices. Privacy and compliance are top priorities for protecting consumers and brands.
The bottom line
Sophisticated analytics are crucial for maximizing returns from retail media and measuring performance. The right platform empowers brands to optimize budgets across retailers, channels, products, audiences, and creative elements for stronger campaign results.
When vetting retail media analytics providers, look for unification of cross-channel data, granular reporting, actionable insights, omnichannel attribution, real-time capabilities, predictive analytics, customization, integrations, auditing, and privacy-first practices. The platform you choose should enable data-driven decision making to drive continuous improvement of retail media programs!