Find out why measuring retail media incrementality is crucial in today’s omnichannel shopping world, why it’s causing brands and advertisers problems, and how next generation incrementality measurement models are solving these complex challenges.
Why is measuring retail media incrementality crucial for brands that sell online
Measuring retail media incrementality helps determine the actual value of your advertising efforts. According to the IAB and Media Rating Council’s definition, “Incrementality measures the true value created by any business strategy, determined by isolating and measuring related results, independent of other potential business factors. In other words, incrementality is the potential causal impact of marketing.” It allows you to see which sales are directly driven by your advertising, beyond what shoppers would have purchased without marketing influence. When they’re accurate, these insights are priceless when it comes to allocating advertising spend, making sure your budget is optimized on the channels and media mix that contribute to growth. If you have a high degree of confidence in the concepts, data and models you use for measuring incrementality in retail media, you will be able to:
- Ensure that advertising spend is driving new sales rather than simply shifting existing demand or cannibalizing other channels.
- Identify which campaigns, channels, or strategies genuinely drive additional sales, helping you focus on high-performing tactics and reduce spend on less effective ones.
- Make more informed decisions about product promotions, pricing strategies, and audience targeting.
- Reduce wasted resources, so that every dollar spent contributes to growth, rather than maintaining existing sales.
- Reveal which customer segments respond best to retail media campaigns so that you can refine your targeting.
So why is incrementality in retail media causing so much frustration for brands and advertisers?
The challenges of measuring incrementality on retail media networks
Measuring incrementality on retail media networks presents several challenges, while concepts and models are still developing beyond more traditional methods such as MMM (Media Mix Modeling), and lift testing. The sheer pace of change in retail media challenges these traditional methods.
Here are some of the limitations of legacy approaches and common challenges advertisers face when measuring retail media incrementally:
Attribution complexity
Assigning credit accurately to different marketing touch points is difficult. Customers often interact with multiple channels (for example, search ads, display ads and social media) before purchasing. Identifying which interactions genuinely drive incremental sales, beyond what would have occurred organically, is complex.
Data sources, data silos and integration
Retailers and brands often operate in silos with separate datasets. Integrating these datasets to get a comprehensive view of customer behavior and accurately measure incrementality is challenging. Different data standards, privacy concerns, and varying levels of data granularity complicate this further. If your confidence in your incrementality insights is low, it’s likely that there is little data integration.
Bias from existing customers
Existing loyal customers might interact with your ads, leading to misleading incrementality data if not accounted for properly. Measuring the actual incremental impact on new-to-brand versus existing customers is necessary but difficult.
Lack of customer journey data
Retail media networks may not offer full visibility into the entire customer journey, particularly if a customer purchases offline or through a different channel. This lack of comprehensive data can make it hard to measure true incrementality. Even when this data is available, via Amazon Marketing Cloud (AMC) for example, brands and agencies can find it difficult to utilize without the complex technical skills you need to write SQL requests.
ROAS vs iROAS in retail media
In retail media, ROAS (Return on Advertising Spend) and iROAS (Incremental Return on Advertising Spend) are two key metrics used to evaluate the effectiveness of advertising campaigns. ROAS measures the total revenue attributed to a campaign, while iROAS measures the sales (often from new-to-brand shoppers) that would not have occurred without the influence of your ad campaign.
iROAS is only possible when you can establish a confident baseline of sales from your existing customers. iROAS measurement models can use complex data mining techniques to give you insights on attribution, customer journey, media mix analysis, and much more.
Four advantages brands can gain when they achieve a high level of confidence in their incrementality measurement
Achieving a high level of confidence in your incrementality measurement will improve your marketing effectiveness, return on investment (ROI) and your competitive advantage, all because you will know where to cut your wasted spend and where to double down on advertising for growth including new-to-brand sales. High confidence iROAS concepts, data and tools will help you:
- Understand which campaigns, channels, and tactics truly drive additional sales. This understanding helps optimize marketing budgets by reallocating spend from less effective to more effective strategies, maximizing the returns.
- Gain a clearer understanding of what works and what doesn’t in the media mix, making strategic adjustments more confidently, reducing guesswork and enhancing your overall marketing strategy.
- Identify the customer segments most likely to respond to your ads and reduce ad dollars spent on audiences that would have converted anyway.
- Strengthen your case for further marketing investment, particularly in ‘experimental’ channels like Retail Media.
What next generation retail media incrementality models can offer brands and advertising agencies
Advancements in technology combined with new concepts and measurement models are radically changing how we measure incrementality. Incrementality is becoming increasingly more accurate due to integrated data sources, automation and more mature machine learning models to overcome some of the limitations we’ve discussed. The next generation of incrementality measurement tools and concepts will be able to integrate and blend multiple sources of data for more accurate insights while also offering significant advancements in these areas.
Get in touch if you’d like to talk about retail media budgeting, campaign measurement, or to find out more about how brands and agencies are getting better results without increasing their advertising spend.
