CMOs Need A New Model For Retail Advertising Success

There is always dissonance between the capabilities that are discussed here in trade business coverage and the day-to-day realities that exist on the ground for marketers, behind their own four company walls. Nowhere is this more evident than in the routine way that large corporate retail giants choose to measure retail advertising effectiveness, and how that routine falls short. The way CMOs measure retail advertising success today is better suited to demonstrating efficiency than it is to actually realizing it. Over-reliance on outdated KPIs leaves a lot of opportunity unrealized.  

Old Habits Die Hard

Many long-standing brand organizations operate with KPIs that date back 10 years, and which hardly reflect the granular detail and transparent efficiency that is becoming a reality within the advertising toolset. For example, even though different products sell at entirely different margins, the routine (read: legacy) model for return on advertising spend simply factors in revenue and outright ignores those margins. It fails to distinguish whether a purchase was made by a new customer or as a repeat purchase by an existing customer. It doesn’t respect return rates and presumes that, for example, shoes and laptops are returned equally often. And it ignores whether or not the product needed advertising in the first place. A clear, useful picture of advertising effectiveness is impossible without understanding product margin, local stock levels, and the identity (i.e., incremental growth) of unique customers.



The types KPIs and metrics I discuss here are hardly new ideas — they have been proposed, critiqued and debated for nearly a decade. But in speaking with retail marketers, one quickly realizes that the reality lags so far behind the discourse. In fact, the larger the retailer, the more likely it is that they are motivated by pressure to demonstrate return on spend to the satisfaction of the rest of the C-suite — the CFO or CEO, for example — and less by the need to improve real marketing effectiveness and efficiency. That creates a powerful incentive toward the status quo. 

To illustrate how the current “state of the technology” marketing capabilities have not made their way into let alone broken through the routine of current retailer practices, it’s helpful to plot the evolution of KPIs, so that you can understanding the emerging possibilities to impact your business more powerfully.

The focus on these progressively useful particular KPIs, in ascending order from the least sophisticated to most mature and instrumental, maps from low to high data quality. In short, the richer and more multi-dimensional your retail performance data the more valuable the associated KPI is to guiding your business.

Focus: orders (CPO) In this volume-based scenario, every order is equal — regardless of order dollar value. Revenues, margins and new customer levels are merely estimated based on averages. Look no further than this hypothetical: We can spend $100,000 in advertising to sell 1,000 Gucci handbags or 1,000 cheap socks, and the CPO will be $100 in both cases. This means that a bidding system will over-bid on the socks and under-bid on the Gucci bags. 

Focus: revenue (ROAS) This typical KPI focuses on revenue and ignores dimensional margin variation across different products, based on any number of production, marketing, fulfillment factors, and other costs of doing business.

Focus: profit (ROI) Oriented around relative high per-product margins, the team promotes those products more aggressively, but yet repeat purchases are ignored. You are working with slight richer data but it’s still not the full picture.

Focus: lifetime value (LTV ROI) Armed with all data factors — optimization to this KPI takes into account exact margins and repeat purchases from new customers.

So, as your team evaluates the state of business and progress on retail marketing and sales -- it’s worth checking in with yourself to determine whether you’ve been defaulting to more outdated KPIs, on the lower end of data quality and flatter by nature. CMOs have a tough enough job staying on top of the rapidly evolving customer experience as it migrates across devices and platforms; it becomes even harder still to persuade the rest of the C-Suite that their marketing activities need to be understood in a completely different light.

The good news is that the data science and retail technology are ready to support such an  elevation in your approach, provided that the rest of the C-Suite is open to it.

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