There has been more discussion about cross-channel analytics as more organizations leverage both digital and traditional vehicles in their communication mix.
The emergence of Yahoo Web Analytics, Adobe’s acquisition of Omniture (recall that Omniture acquired Visual Sciences, WebSideStory, Offermatica, Instadia, and TouchClarity), and Google’s continued push into the enterprise all signal an increased emphasis on multichannel data analysis.
Before exploring what cross-channel analytics is and how marketers can use it to analyze customer behavior, it makes sense to define analytics and how analytics is being used by marketers.
Basically, analytics is about deriving insights from data. Analysis involves breaking data into smaller parts to increase insight.
For example, chemical analysis entails breaking down chemical processes to understand the chemical reactions between elements of matter. In their books Competing on Analytics and Super Crunchers, Thomas Davenport and Ian Ayres, respectively, make compelling cases for the value of a structured analytics practice in business as a way to create a competitive advantage.
Davenport says that the creation of strategic data assets and business processes is one of the “last remaining points of differentiation” in an increasingly competitive world. More specifically, marketing analytics is a methodical examination of customer and market data to increase understanding of the customer and the market to make and take action to improve your competitive advantage.
What Is Cross-Channel Analytics?
Organizations that pull together data from various marketing programs across different channels use cross-channel analytics to understand the impact of each channel on customer behavior.
For example, in today’s environment, we may need to gather data from an email campaign, a physical event, and an online advertising campaign to better understand purchasing behavior and correlate marketing programs with purchases.
Cross-channel analytics enables you to measure the interaction of various channels, such as websites, customer service, phone support, and print media, to understand how those channels relate to one another and affect customer behavior.
If you believe that customers and prospects are the same regardless of the channel they choose to use, then you must believe that it is time to leverage the data flowing in from every channel to better understand the customer.
For example, if a retail marketer implements a multichannel campaign, the marketer will want to analyze the relationship of the different marketing channels on shopping behavior.
The complexity occurs when a retail marketer implements a multichannel campaign designed to affect both online and offline shopping, and then wants to analyze the relationship between online and shopping behavior across all the channels.
That example can apply to numerous industries, such as financial services, hospitality, communications, etc. The message for every marketer is that such an analysis requires more skills and better tools than the traditional marketing analytics that we’ve previously used to delve into the impact of an integrated campaign on a specific behavior.
What It Takes to Do Cross-Channel Analytics
If you are using multiple channels and you want to both understand what’s really working and enrich your customers’ experiences with your organization, then you’ll have to step into the world of cross-channel analytics.
However, with data spread out across so many applications and locations, doing cross-channel analytics can be tricky. You’ll need skills, tools, and models that go beyond Web analytics. Web analytics focuses on visitors’ behavior, whereas cross-channel analytics tracks individual behavior across channels to understand your customers and their experiences and actions.
Cross-channel analytics requires path-to-conversion analysis and cross-channel synergy analysis. Path-to-conversion analysis requires that you 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.
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 you want your cross-channel analytics to answer—at a minimum—are these:
- What percent did each element contribute to the path to conversion?
- What is the ratio between those elements?
Cross-channel analytics brings value to many of the business intelligence (BI) tools you’ve already invested in. Here are a few ways that cross-channel analytics adds value to your forecasting, predictive analytics, and modeling capabilities:
- By creating and using forecasting algorithms from both digital and traditional channels, you will be able to estimate sales based on changing customer behavior. The statistical models you will build will allow you to determine a level of confidence regarding various buying segments’ behaviors.
- And as your analytical and cross-channel analytics capabilities improve, you 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 keywords used in pay-per-click marketing efforts.
- Cross-channel analytics can make your modeling capabilities and marketing-mix models more effective because you will have a better understanding of the relationship between the different marketing channels and improve your use of decision optimization tools to determine the combination or sequence of messages to maximize engagement and sales.
Gartner reports that the global market for analytics applications, performance management, and BI solutions was $8.7 billion in 2008—roughly 20 times the global investment in Web analytics.
Cross-channel analytics is going to require every company to invest more in acquiring and analyzing data to produce true insights and recommendations that are valuable to the business. As the need for whole-business analysis increases, marketers will 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, marketers are deploying cross-channel marketing and leveraging every communication channel.
Successful cross-channel marketing requires a sophisticated marketing solution that can help marketers analyze customer behavior, make customer-centric decisions, and respond quickly to marketing opportunities.
The ability to overlay many complex layers of data to generate a holistic view of customer behavior and campaign effectiveness has numerous implications for marketing professionals, from improving analytical skills to improving data quality to investing in better tools.
Marketers are going to need to embrace statistics, modeling, and predictive analytics.