Attribution & The Customer Lifecycle: What Search Marketers Need To Know
Too often, PPC campaigns are managed as though clicks and conversions take place within a vacuum, disconnected and unrelated to the customer engagement that occurs through other mediums and at other times. But PPC campaigns are tightly interwoven with broader marketing programs that reach customers over the entire customer lifecycle—across the web, in store, via email, by phone and every other way a company or brand interacts with its customers. In fact, viewing PPC transactions within the broader context of lifetime customer engagement, and attributing this information to bid rates and ROI calculations, is crucial to a company’s bottom line. In many cases a PPC conversion is the first of a string of interactions that together comprise a long and profitable customer relationship. And in other cases, PPC just happens to be the conduit through which a loyal, existing customer comes back to you for repeat business.
While this is not a difficult concept to grasp, marketers today struggle to break down the problem into actionable tactics that can be readily applied to their PPC campaign. Which specific metrics should be attributed and how? What tools are currently available to marketers to achieve this? And which metrics can be measured with enough precision to increase real program profitability?
The right metrics
The customer lifecycle metrics most readily actionable to a paid search campaign are:
1) The value of a new-customer conversion. For companies where repeat purchases are relevant, this is the expected value of both the up-front purchase and any subsequent revenue events. Also known as life-time value (LTV).
2) The percentage of campaign-converted customers that are new vs. returning.
Both of these metrics are powerful tools for more accurately valuing paid search conversions by taking into account the customer lifecycle (either measured or forecasted) that surrounds it. While overlapping in some areas conceptually, the mechanisms to track these metrics and the ways in which the data sets need to be applied are quite different.
With the first metric, the marketer seeks to find areas within his or her PPC campaign that produce a high degree of retained business after the up-front purchase (or click). Take a site selling baby clothing. As a group, users who converted on the term “newborn clothing” may have more LTV potential than those acquired on “toddler shoes.” The key is to measure the combined lifetime value of all conversions for as many individual adgroups and keywords within the campaign as possible. Once enough data has been collected, these values should be attributed back to the upfront click when calculating ROI, and factored into the bid rates being set. Clearly, running manual LTV analyses for the myriad components of a PPC campaign would require significant effort. Happily, search marketing application providers have developed robust capabilities in this area, enabling marketers to achieve these analyses quickly and efficiently.
With the second metric, marketers apply retention data to determine how much of the transaction “credit” to award various areas of the campaign for the conversions they generate. Keywords and ad groups converting a higher percentage of repeat users might be awarded a discounted portion of the additional revenue because some of those sales would likely occur anyway. This discounting is particularly relevant when assigning monetary value to actions that induce future sales (read more about this in our previous article, Attribution Alchemy: Mining Your Sales Funnel). If a sales-inducing action is completed by a new user, that action is more likely to directly affect future behavior and that click (in the case of paid search) is more valuable. In contrast, for the case of a repeat user, the user’s prior experience is a powerful factor. Furthermore, because the first purchase was being overcredited, the subsequent purchases need to be undercredited to make the total come out to be zero (whereby your allocated value will match your actual value).
Again, once the expected full lifetime value (metric #1 above) has been factored into ROI measurement and bidding, attribution should be adjusted based on whether a transaction is completed by a new or repeat customer. As you can see, this is where the two metrics discussed in this article overlap directly. Conversions by new users are much more likely to deserve full attribution for the multiple conversions expected to occur during the customer lifetime. Conversions by repeat users, however, are typically awarded less credit, because a higher portion of future sales would likely occur anyway.
Timing is everything
A major point of distinction between these metrics is the time period over which data must be measured.With the first metric, retention sales of users driven through the PPC campaign must be analyzed over weeks and months to get the necessary data. The need to conduct this analysis at the unique user (vs. visit) level also requires tools with powerful backend processing. In the second, the feedback is more instantaneous, as shopping carts and analytics packages will know upon close of the up-front sale or conversion, whether the customer was new or repeat. While tools exist to measure this and automatically adjust bid rates accordingly, marketers can get started today with top level analysis of the percentages of new/repeat customers driven by their major ad groups. Once completed, this can be used to make educated decisions as to which ad groups deserve more or less credit for the conversions they produce.
Clearly, factoring the full customer lifecycle, including past and future activity when determining how much to pay for a conversion today, can greatly improve campaign scale and efficiency. Luckily, the tools needed to measure, forecast and attribute pre- and post-click activity into ongoing management are increasingly available to marketers these days.
Some opinions expressed in this article may be those of a guest author and not necessarily Search Engine Land. Staff authors are listed here.
(Some images used under license from Shutterstock.com.)
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