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Success in Omnichannel Marketing Depends on Analytics

It's not enough to have a single customer view, even one compiled from numerous customer channels. You must also expand marketing via advanced analytics.

Marketing is currently one of the most successful business functions within the modern digital enterprise.

Much of the success comes from significant advances -- in data management, customer analytics, and software automation at unprecedented scale -- that enable a single view of the customer. Success also comes from new, sophisticated practices in omnichannel marketing, which relies on data consolidated in the single customer view and analytics that fully leverage that view, enabling enterprises to market to customers and prospects in a holistic and coordinated fashion.

The Latin prefix "omni" means "all," and omnichannel marketing pulls together all customers, all channels, and all sales/marketing methods.

For the customer, omnichannel marketing provides a seamless shopping experience, whether the customer is shopping via a computer, mobile device, telephone, brick-and-mortar store, or some combination of these. For the marketer, omnichannel marketing increases conversion rates by melding all aspects of marketing communication, including advertising, sales promotion, direct marketing, customer service, and social media.

Modern Omnichannel Marketing is Digital and Comprehensive

This type of marketing runs on data and the data must be complete. The data is collected from all channels about all customers and consolidated into the so-called single customer view. The clearer the picture created by the data in the single customer view, the better the digital marketing. For this reason, successful firms apply advanced analytics to expand the single customer view for maximum business value.

Advanced analytics comes in many forms and is based on techniques for data mining, text mining, clustering, graph, statistics, machine learning, and natural language processing (NLP). Most of these techniques facilitate correlations among far-flung facts from numerous sources, contexts, and vintages. This is why advanced analytics is a good fit for working with the multichannel data collected and integrated into a single customer view.

Analytics Draws Pan-Customer Insights from Single Customer Views

For example, data mining, clustering, and graph analytics can associate customers with similar characteristics (as recorded in standardized single views) for classic marketing analytics, such as customer profiling and customer-base segmentation.

Statistical methods can quantify probable customer churn while there is still time to retain the customer with discounts, fee waivers, and other incentives. Text analytics and NLP can discover facts in human language and other text sources then correlate these with other data to quantify elusive qualities such as sentiment and mood.

In fact, many of the characteristics assembled in a single customer view may actually be the output of analytics. For example, many marketing organizations are familiar with performance management techniques, so they apply performance metrics and key performance indicators (KPIs) to customer attributes. Metrics (not hard dollars) regularly represent customer spend, shipping costs, and service time burned up; these may roll up into a KPI for customer profitability.

Similarly, statistical programs may calculate probabilities for churn, fraud, and propensity to buy certain product types, then store these precalculated in views, ready for marketers to use when exploring customers or designing campaigns. Views regularly include codes that link individual customers to demographic profiles, customer segments, and other clusters as determined by analytics.

Expand the Customer View for Richer Omnichannel Marketing

There is clearly business value in a data-driven understanding of individual customers based on the fairly simple data of the average single customer view. However, additional value comes from the analytics-driven understanding of many customers, based on calculated metrics and the outcome of advanced analytics. Many organizations have become really good with the former, but they need the latter as well if they are to fully leverage their efforts in omnichannel marketing and other digital marketing practices.

In other words, it's not enough to have a single customer view, compiled from numerous customer channels. You must also expand and leverage the view via advanced analytics.

For more details about analytics and data requirements for omnichannel marketing, read TDWI Checklist Report: New Data Practices for a Single Customer View and Omnichannel Marketing.

About the Author

Philip Russom is director of TDWI Research for data management and oversees many of TDWI’s research-oriented publications, services, and events. He is a well-known figure in data warehousing and business intelligence, having published over 500 research reports, magazine articles, opinion columns, speeches, Webinars, and more. Before joining TDWI in 2005, Russom was an industry analyst covering BI at Forrester Research and Giga Information Group. He also ran his own business as an independent industry analyst and BI consultant and was a contributing editor with leading IT magazines. Before that, Russom worked in technical and marketing positions for various database vendors. You can reach him at prussom@tdwi.org, @prussom on Twitter, and on LinkedIn at linkedin.com/in/philiprussom.


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