See the Whole Customer to Guide the Decision Journey

While “the journey” is a better metaphor than many previous ones used by marketers, especially the militaristic “campaign” or industrial “funnel,” it tends to belie the complexity of the process of turning interest into purchase.

The Blind Men and the Elephant

Content driven customer decision journeys require that the seller know an awful lot about the customer. For example, vendors need to know what product is the customer looking at and what is their attitude about it? How is that attitude changing over time and where will it be in the near future? Marketing professionals even need to know about important life events affecting the customer at this very moment. Are they buying a new home or starting a new job? In a nutshell, the vendor needs to know where the customer is in the journey right here and now.

Retail data, gathered on the website or cash register, is one type of information at the seller’s disposal. Customer and technical support systems hold amazing amounts of information about interactions with the company that can help predict whether they will buy from the company again. Website analytics exposes all types of information about a customer’s interests such as what products they may have been looking at recently. Social and digital media monitoring and analytics contains buying signals about individuals. All of this information is valuable in presenting relevant content that helps the customer move along the journey to purchase.

Or at least it should. The problem is that most of the data about a particular customer sits in different data silos. As in the fable of the “Blind Men and the Elephant” each pool of data only shows one aspect of the customer decision journey. Seeing the whole elephant is not easy given the amount and complexity of the data.

Mind the Gaps

The problem is not easily solved even with modern analytics software. Underneath all analysis is a model that determines which information is important and how each pool of data relates to other pools. Developing these models is devilishly difficult.

Part of the problem is that it requires lots of different skills, especially domain expertise about customers and how they buy. Software can automate some of the data integration tasks but the model needs to be infused with real business knowledge. A model is useless if it doesn’t capture the experience of the company in helping present content to customers that guides the customer's decision journey.

Another issue with personalizing content for the customer decision journey is stale data. Much of the information in corporate data stores is old and doesn’t say much about where a customer is in their journey. Survey data doesn’t fill this gap -- by the time it’s gathered and analyzed, it’s too old to provide insights that help with the customer decision journey.

This is true of a lot of data: it’s retrospective and doesn’t address the wants and desires of a specific customer as she decides on a purchase today. Social and digital media data is an important way to fill in the blanks since it can be obtained nearly real time. Quick mobile surveys that can ask an individual specific questions and get an immediate response is also a new tool in the marketing professional’s toolbox. These tools allow vendors to obtain relevant, up-to-date data quickly in order to fine tune content offerings.

Using content, alongside other offers, to drive a customer decision journey is interesting to marketing professionals. However, many companies will be disappointed with the results if they don’t first figure out how to get relevant information to guide their content decisions.

Title image by Rajesh_India (Flickr) via a CC BY-NC-ND 2.0 license