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The Evolution of Attribution

Date: June, 2013 --

Technology is beginning to generate tools to enable the marketing world to more effectively and accurately answer the age-old question. It’s the question every merchant since the first trade of a stone arrowhead for a stone knife over a wood fire has wanted to know. What made this person engage with me? What caused them to trade? Or, more contemporaneously, what medium, promotion, and /or ad placement drove, either wholly or fractionally, the purchasing decision of any given customer? 

            The great copywriter Denny Hatch ‘attributes’ action by a prospect reading an offer to one of the basic human instincts: Fear, Greed, Guilt, Anger, Exclusivity, Salvation or Flattery.  What influence has caused a person to do something?   If it’s one week to Mother’s Day, good copy in a letter addressed to men with an offer of flowers and candy might be premised on fear, attributing fear of domestic punishment as a motivator of action. 

And while this is undoubtedly true for selling copy, the current subject of “attribution” is many times more complex and starts with the problem of channel choice, not the advertiser’s choice, but the customer’s.  

In a nutshell, the goal of any attribution strategy is the discovery of how to turn prospects into customers as economically as possible in an “omni-channel-media” world. In a direct mail-only world, this was relatively simple. What do your customers look like and what do they respond to at what time of year? In a media-over-loaded marketing environment, it is many times more complex. The goal is to generate insights into customer behavior and reaction to impressions and, when said customer makes a purchase, attributing credit to one or more of the sources of these impressions. Seasoned marketers have learned to fully appreciate that the “last-click” metric is quite possibly very misleading.

What are the impact of paid ads, search, email, mobile, social media and database-founded efforts? What does each contribute on the path of the buyer from unknown to prospect to customer? How much effort/money should we allocate to each medium?  At what point in the person’s trip to us should we use each medium? And how much should we spend on each step? Knowing the total picture will prevent cutting programs that were the true driver of sales, despite not being the last impression logged or medium/outlet used in the purchase. We’ve all heard stories of retailers who saw huge spikes in their eCommerce sales, decided to cut back on print catalogs to move those dollars online, which then led to a significant drop in online purchases because, as the retailer came to realize, it was the direct mail catalog that was driving website visits and purchases.

At the beginning of our experience of the Internet as a commercial space, as demonstrated in the aforementioned example, the retail and catalog companies who ventured online quickly discovered the complexity of attributing media to purchase behavior. Many readers will recall this type of fervent discussion about what amount of catalog buying was driven by the website, how much by the catalog and vice versa. And recall the fights between the catalog department and the new “website” / eCommerce department on who had “really” sold the goods! You couldn’t return a product you’d bought on the website at the local branch of that store, and you couldn’t pick up the product you had purchased online at the local branch, either!

These seem like elementary questions from the dark ages. There were few if any tools to do the measurement. 

Now the data exists, or can be captured, manipulated, and analyzed with quantative analysis tools as well as direct and fractional attribution models. These will help in understanding the path to purchase and thus in allocating budgets to relevant media. 

These tools can disclose the time from first contact to conversion and help shorten it. Perhaps presenting a different ad on the second appearance of the prospect will do that. If we know their path to their first click to the website, their similarity to existing customer populations helps inform what we show them on arrival. The optimal amount of time we present a banner or sidebar ad to a particular prospect can be predicted from analyzing behavior of similar prospects.

A frequent question is the significance of the channel through which the prospect first discovers you. The media entry point may be critical to a particular population, less so for another. The more data we have, the more accurate the prediction of behavior can be, the higher the ROI.

Implementing these programs is potentially disruptive. Having selected a vendor, you will have to assess your goals, assets, infrastructure and resources, both human and machine.  Decisions must be made as to what data must be collected, where it will come from and how it will be imported. Data must then be aggregated and normalized. Personnel may need retraining, and marketing and product silos may have to be collapsed. There will follow a testing phase which begins the process of providing customer behavior insights which lead to improved ROI. With each data-input cycle these insights, and the range of topics addressable, should broaden.   

In short, the machine will “learn” and make more and more accurate predictions on marketing strategy and spend.

Whether you’re a season veteran with tried and true processes or still finding your way, talk to the direct marketing experts at Data Services, Inc. about setting up the right processes and platforms that will allow you to better manage your direct marketing attribution strategy.