How To Waste Retail Ad Dollars: Ignore Product Stock Levels, Chase Clicks

andreas-reiffenData-Driven Thinking" is written by members of the media community and contains fresh ideas on the digital revolution in media.

Today’s column is written by Andreas Reiffen, founder and CEO at Crealytics.

If marketers are looking to waste their ad dollars, today’s digital ecosystem offers no shortage of options.

They can advertise to a user who blocks their ads. Marketers can place their ads where they can’t be seen or pay for ads that are seen only by bots. Marketers can advertise on sites that don’t fit their brands or pay extra to advertise on low-quality sites. They also can advertise to the wrong consumers on the wrong devices, at the wrong times and in the wrong places.

But the oldest and easiest way to waste ad money may be to advertise products that don’t need to be advertised at all. This is the silent killer of profitability.

This fundamental flaw is often overlooked amid the torrent of news and opinion about fraud and inefficiencies in our ecosystem. But make no mistake – the issue is widespread.

An ad that sells the last unit of a top-selling product may satisfy existing KPIs, but the value of such an ad is close to zero. Top-selling products tend to perform best in sales and revenues, but they also run out of stock. It leads to a situation where today a significant chunk of retailers’ budgets is squandered on products that would have sold within three weeks even without the ads.

Managing inventory and stock levels remains very challenging for online retailers. No web analytics system, advertising or attribution tool takes stock levels into account when measuring advertising performance. So even the largest retailers are making big mistakes without realizing it.

Retailers need to have stock-level forecasts and SKU levels available and connect the back-end systems with that data to make them actionable by the ad tech stack. Inventory management and pricing systems already do some version of this, but mostly in the realm of pricing management because when there’s too much stock, prices decrease. The same thinking should be applied to the amount of advertising a product gets, so that advertising is suspended for products that are likely to sell out. The true value of digital retail advertising can't be measured until those stock levels are taken into account.

That hurdle is only one factor driving marketers’ ongoing challenges with this inefficiency. Deeper and more fundamental still is a misalignment of incentives between advertising KPIs and a retailer’s true business objectives.

Ad management and bidding systems are designed to target revenues or margins, and this creates a de facto incentive to advertise the highest-performing products. Ads for products that everyone already wants will see more engagement and lead to higher margins, so performance-driven retail marketers are incentivized to push more ads for those products. The problem is, they aren’t the ones that need to be marketed as aggressively. Those dollars are as good as wasted.

Retailers need a better way to measure marketing effectiveness as a driver of business results. Breaking out of the click-based paradigm might require a new KPI metric altogether – maybe something along the lines of “yield.” Yield would always be between zero, if a product will sell automatically and can't be re-ordered, and the full net sales price, where it's clear that there is too much stock that will be hard to sell. The industry hasn't figured out an exact formula yet to calculate everything in between, but the incremental ROI of marketing dollars depends on it.

The back-end data integrations and “yield” measurement would allow retailers to focus on promoting inventory at better margins, regardless of the velocity of demand. Given all the technology that has been developed to prevent other forms of ad fraud and waste, these practical steps are both achievable and warranted.

Follow Andreas Reiffen (@AndreasReiffen), Crealytics (@crealytics) and AdExchanger (@adexchanger) on Twitter.

 

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