Target your Customers, Not Clicks: A Roadmap to Higher Return on Ad Spend

Nearly a century after his death, legendary retailer John Wannamaker’s famous phrase, “Half the money I spend on advertising is wasted — the trouble is, I don't know which half,” still resonates for today’s brands, marketing teams, and agencies. 

In order to see inside the “black box” of advertising spend, it’s time to flip the marketing funnel upside down. In the past, only online brands could capture actual customer sales data. With the emergence of new data analytics solutions, restaurant brands can now identify audiences at the sales conversion level for remarketing efforts. What’s more, they can even find look-a-like segments, which means they can stop depending on clicks, likes, views, and impressions to define their target audiences. 

Even with the application of machine learning to digital advertising, ad spend is still based on engagement and not on customer sales data.

Even with the application of machine learning to digital advertising, ad spend is still based on engagement and not on customer sales data. Though social media engagement is important for brand-building, it doesn’t necessarily tell us anything about who is buying, what they are buying, why they are buying and how we can get them to buy more. For that we need a reliable metric for accurately predicting conversion, and for businesses with real-world locations, that means data gathered from actual customer visits. 

Only by gathering and analyzing actual customer sales data and gaining insight into those who have already converted can brands build true customer segments for targeting high, medium, and low-frequency guests. This data can directly integrate with email/SMS loyalty program, social media and display-ad networks, as well as inform traditional advertising. 

In addition to identifying actual customer segments, utilizing guest sales data enables identification of A/B test locations that share common variables. Sample variables include guest frequency patterns, peak times, or average transaction amounts, so you can conduct normalized test scenarios identifying the true impact of your marketing efforts and the costs of generating these sales. 

With the emergence of new data analytics solutions, restaurant brands can now identify audiences at the sales conversion level for remarketing efforts.

Once comparable test market locations have been identified, historic sales trends can be utilized to measure the actual incremental lift generated from advertising and promotional efforts. Sales patterns in test vs control locations should be monitored for at least 90 days post campaign. This last point is especially important since a high percentage of sales lift often occurs after a campaign has ended in test locations. Despite the fact that campaign spending has ceased, the impact of your media and messaging have often gained momentum and achieved the highest reach and frequency levels.  

Last, leverage data to account for external variables that can impact sales trends, such as unusual weather patterns, holidays, school breaks, major sporting events, and more. By integrating market conditions and customer sales data, restaurant brands can finally see inside the “black box” of your advertising to identify the true incremental lift of your ad spend.

At the end of the day, customers vote with their dollars. Maximizing your advertising with data-driven sales insights based on actual guest visits will allow you to target customers rather than clicks and provide a roadmap to higher returns on your marketing dollars.