In today’s environment we must often pull data from a variety of marketing vehicles from an email campaign, physical event, and an online advertising campaign to better understand purchasing behavior and correlate marketing programs with purchases. As organizations leverage both digital and traditional vehicles in their media mix, there has been an increased interest on multi-channel analysis to help measure the interaction of various channels such as web sites, customer service, phone support and print media to understand how these channels relate to each other and impact customer behavior. Organizations deploying using cross-channel analytics to understand the impact of each channel on customer behavior are trying to understand what’s really working and how to enrich customers’ experiences. What does it take to do cross channel analytics? We’re going to need skills, tools and models that go beyond web-analytics. While web analytics focus on “visitors” behavior, in cross-channel analytics we want to be able to track individual behavior across channels to fully understand our customers, their experiences and their actions.
Therefore, cross-channel analytics requires path to conversion analysis and cross-channel synergy analysis. Path to conversion analysis requires that we assess all the “events” in the customer pathway that the customer has been exposed to that contributed in some way to the customer’s conversion at the end of the path. Identifying cross-channel synergies requires looking at how different elements of the communication mix work together to move a customer through the buying process. Assuming that each element contributes something to the conversion path, the questions we want to be able to answer at a minimum from our cross-channel analytics are:
– What percent did each element contribute to the path to conversion?
– What is the ratio between these elements?
While cross-channel analytics takes some investment and effort, they bring value to many of the business intelligence tools we’ve already invested in. Here are just a few ways cross-channel analytics add value to our forecasting, predictive analytics and modeling capabilities.
– By creating and using forecasting algorithms from both digital and traditional channels we will be able to estimate sales based on changing customer behavior. The statistical models we will be able to build will enable us to determine a level of confidence of behaviors by various buying
– And as our analytical and cross channel analytics capabilities improve we will be able to develop models that can predict traffic and revenue impact associated with changes in the communication mix, which can be used for example to make real-time adjustments to key words used in pay-perclick marketing efforts.
– Cross-channel analytics can make our modeling capabilities and marketing mix models more effective because we will have a better understanding of the relationship between the different marketing channels and improve our use of decision optimization tools to determine the
combination or sequence of messages to maximize engagement and sales.
What it means to you? Cross-channel analytics is going to require every company to invest more in acquiring and analyzing data in order to produce true insights and recommendations that are valuable to the business. And marketers are going to need to embrace statistics, modeling and predictive analytics. As this need for whole business analysis increases, marketers are going to increasingly need tools and models that bridge online and offline data.
Because customers today can move across several channels in the process of making a single buying decision, organizations will need to improve their cross-channel marketing capabilities and deploy solutions that enable analyzing customer behavior, making customer-centric decisions, and responding quickly to marketing opportunities. This ability to overlay many complex layers of data in order to generate a holistic view of customer behavior and campaign effectiveness has numerous implications for all of us in marketing, starting with improving analytical skills, addressing data quality, and investing in better tools.