The good old days when marketers more or less had complete control over what consumers experienced via traditional offline and online push media and channels are long gone. That world has been replaced by one where these traditional channels now coexist with other, newer influences on the customer experience, some of which are outside the control and even the direct visibility of the marketer (e.g., user generated content and other earned media).
In light of this, some would argue that it’s next to impossible to determine a customer’s true path to purchase. But if marketers don’t attribute sales accurately, their understanding of the relative contribution of various marketing expenditures is lacking—with suboptimal investment decisions sure to follow.
Now more than ever, marketers must undertake efforts to gain a comprehensive understanding of the various influences on customers’ buying behavior. Armed with this intelligence, marketers will be in a much better position to positively affect the customer journey, and do so in a cost-effective manner.
Building a path
So how does one go about constructing the path to purchase? A logical starting point is to simply take stock of information you should already have. Begin by reviewing campaign briefs for applicable marketing programs: objectives, audience selection, messaging, offers, channels, touch frequency, and cadence.
Next, review the current set of post-campaign reports that are produced for the applicable programs. How well do these reports paint a comprehensive picture of the customers’ path to purchase? Chances are the reports do not go far enough in this regard, since most campaign reporting focuses on discrete campaigns and lack the more complete multichannel views. If this is the case, take steps to create more holistic reporting of the customer journey. Examples of expanded reporting include:
When running these reports, it’s useful to compare customers who purchase during the period examined versus those who do not and the resulting index values as a way to determine what touch dynamics lead to more positive outcomes in the customer journey.
It’s not enough to construct these reporting views for the customer base at large. Generating segment-level views will likely reveal that not only does the journey vary for different types of customers, but that the effectiveness of those journeys also varies. When the above reporting views are added to the mix, what emerges is a more complete understanding of the journey that various customers experience on the way to making, or not making, a purchase (see chart below).
Of course, even these expanded views will not tell the whole story. There are “blind spots” in typical reporting data that result from customer interactions that occur in the pathway that are not directly visible to the marketer. Social media is probably the biggest arena for these interactions. The good news is that it’s possible to capture some of this data. Matching customers to Facebook, Twitter, and other prevalent social media websites—where conversations occur that could influence the customer journey—is possible. This makes it feasible to integrate social media data with traditional campaign reporting data to augment the view of the customer journey.
Finally, to account for remaining blind spots it’s useful to periodically conduct surveys that query customers on other aspects of the purchasing journey. This can cover factors including customer exposure to mass media such as TV, radio, and print, as well as brand interactions occurring around the retail point of sale that are in the path to purchase.
The complexity of the customer experience makes it more difficult, as well as more vital, than ever before to understand the true path to purchase and to leverage that insight to inform your marketing strategy. With the right approach, marketers can find their path to success.
## ## ##
About the Author:
John Young is Epsilon’s go-to analytics expert. As an SVP, he oversees Epsilon’s 110-plus member analytic consulting team, consulting on various database marketing engagements, such as predictive modeling, segmentation, measurement, and profi ling. He’s also an active member of the marketing community and speaks frequently at Direct Marketing Association events, writes articles, and is a steering committee member of the DMA Analytics Council. When not working or thinking about work, Young plays competitive soccer—“After spending time with my family, of course.” Despite his expertise in analytics, Young’s career began as an economist, following which he spent some time doing market research. However, when he noticed years ago that analytics was supporting direct marketing, he knew where he wanted his future path to take him.
This article first appeared in Direct Marketing News.