One Small Step For Marketers, One Giant Leap In Profit

Why does accuracy matter when measuring marketing effectiveness? Accuracy matters because in today’s world, marketing decisions are made on data. The best creative is not the one that makes us laugh the hardest, and it’s not the one that we remember for the longest period of time. No, it’s the one that produces the most profit. The more accurately we can measure our marketing effectiveness, the better decisions we make, which ultimately leads to more profit.

If you agree with these statements so far, you should also agree that any improvement we make to the accuracy of measuring our marketing effectiveness, the more profit we stand to garner.

Attribution management is about giving credit where credit is due, but herein lies the marketers dilemma, “How much credit is due?” Every marketer that has used or contemplated attribution has struggled with this question. Most marketers have struggled with this question so much that they’ve chosen to not implement an attribution strategy. Instead, they continue to use last click attribution.

If you’re still reading at this point, that means you have agreed that any step towards accuracy improves profit, but if you haven’t implemented an attribution strategy because you are unaware how much credit is due, you are essentially contradicting yourself.

Yes, attribution can be an incredibly complex mathematical exercise to develop a proper algorithm to accurately value all of your advertising. However, to be more accurate than the last click, it does not have to be complicated at all.

When we start working with a new client we have the ability to develop very complex mathematical models to more accurately assign credit to where credit is due. However, we cannot offer that on day one because we don’t have enough client data to build sound mathematical models. Does that mean that we keep a client on last click for months or maybe longer until we collect enough data to apply an accurate algorithm? Absolutely not.

The first thing we want to do is get them removed from the most inaccurate attribution model that exists today—the last click. The attribution model that we put in place on day one is an even attribution model, which takes the profit and revenue earned on a conversion and distributes the credit evenly across a team of ads that we tracked a specific lead to a conversion. We have found across our entire client base that this simple, yet more accurate attribution model, does increase their profitability, which is the ultimate evidence that we need to trust this “simple” attribution model.

We also, on day one, implement one more simple model that further increases the accuracy, which is to exclude giving credit to brand keywords when they are the last click of a purchase path. We have found, and other research indicates, when brand keywords are the last click in a path, that it is a navigational search, and when we give credit where credit is due, we want to give credit to the ads responsible for the sale, not an ad that was purely navigational.

When it comes to attribution, I like to quote one of our partners, Andrew Wheeler, Managing Director at iProspect Chicago: “Think evolution, not revolution.”

Evolution is implementing a model, like even attribution, whereas revolution is going from last click to algorithmic attribution. If you believe that increases in accuracy yield more profit, then you should believe that a simple switch from last click to even attribution will be more accurate and thus more profitable. This model has been successful for our clients, and I have no doubt that it will be successful for you too.

Opinions expressed in the article are those of the guest author and not necessarily Search Engine Land.

Related Topics: Channel: Other | Features: Analysis


About The Author: is the Co-Founder and Chief Innovation Officer at ClearSaleing, Inc., a technology provider of advertising analytics. Adam founded ClearSaleing after working at Google for 3 years, where he started their Inside Sales organization.

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