ATTRIBUTION MODELS

Attribution models for mobile advertising

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A well-built media plan involves the very difficult challenge of trying to balance the different advertising channels used in acquiring and retaining a customer, and attributing the right merit to each of those channels to achieve a sustainable and scalable customer strategy. Therein lies the challenge: knowing the real contribution made by each channel, the value from the advertising spends invested and what attribution models to use.

The promise of “measurability” of digital marketing has led many to believe that the problem could be greatly simplified. This is partly true. The incredible power to measure digital channels has made it possible to find better solutions to the media planning process. However, over-simplistic approaches (where numbers replace reasoning) may achieve the opposite result of what was intended.

A simplistic (and in our view, wrong) approach to solving this complex challenge, is to try different channels simultaneously, measure them all with the same last-click attribution model, and keep eliminating those with the worse last-click as measured by CPA results.

The benefit of this method is its extraordinary simplicity. The problem is that it may achieve the opposite of what we want.

Measuring the sales funnel with different attribution models

Going back to marketing basics (the AIDA [Awareness, Interest, Desire, Action] framework is probably a good common reference to use): acquiring a user always follows a sales-funnel approach where we prospect potential leads at the top of the funnel (Awareness), find those that are interested in our product (Interest), follow them through the decision process (Desire), and finally convert leads into buying customers (Action),at the bottom of our funnel.

Each of these steps in the sales funnel has different objectives, and hence, can’t be measured equally. The objective of the prospecting phase is to make people aware of a product (top of the funnel), so that eventually, the lead will become interested enough progress through the sales funnel and end up buying. We can’t compare the cost of acquiring a new user to the cost of acquiring a user that is familiar with a product already (bottom of the funnel) and is ready to purchase.

If we do make the mistake of comparing these costs equally, we will tend to believe that the tool or channel that was helping us convert a lead at the bottom of the sales funnel is always better that the tool that helped build awareness to a new lead at the top of the funnel.

The catch is that if we follow the simple strategy defined above, and end up only with bottom of the funnel channels and tools, we will get very good CPA metrics, but very low scale. Indeed, once everyone that knows our products (those leads at the bottom of the sales funnel) are acquired, there’s no one else to convert.

This is typical of poorly designed media plans. It’s easier to quickly eliminate those channels that help us drive product awareness and end up with bottom-of-the-funnel tools such as search and retargeting channels that have great CPAs. The problem arises when we try to get scale. It’s then that we start pouring money into these channels and when CPAs become worse

The real key to the very complex problem of designing the perfect media plan, is not only which channels to use, but also how to attribute merit to each channel to measure the effectiveness of each with respect to the distinct goal that should be attributed to each acquisition stage.

That’s where programmatic buying can play a significant role, smartly utilizing its data processing capabilities to efficiently achieve properly measured results at each stage of the funnel, in conjunction with other marketing channels.

Ideally, last-click attribution should only be used to measure effectiveness of bottom of the funnel channels (search, retargeting). View or algorithmic attribution should be used to attribute merit and measure effectiveness of top-middle funnel channels, like programmatic.

The idea is to be able to measure “awareness” in a way that allows marketers to choose the right platforms without compromising scale and efficiency of a campaign. This measurement is possible. We should look into measuring which of the leads that arrived to the bottom of the funnel were impacted by top of the funnel channels.

For example, If I impact one million people with my awareness campaign, I should make sure that the 5,000 people that end up converting were among that million impacted a priori.

Properly combining channels, with the right measurements and attribution logics, one can achieve the holy grail of a scalable and effective marketing strategy.


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