How to Get Value from Big Data
Big data has become so pervasive in marketing that it has almost become meaningless in terms of its practicality. However, ignoring its applicability to your marketing strategy is incredibly risky. Think about it—every second, customer data is flowing into your organization, and if you're not using it correctly, revenue opportunities are being lost. Here are some practical steps to understanding how to get value from big data, while mapping it directly to your marketing strategy.
Step 1: Zero in on the Customer Life Cycle
In developing a multichannel marketing strategy and executing campaigns, most marketers profess to being very customer-centric. But often that customer centricity is very linear. It views the customer typically in the context of new customer acquisition only. This view discounts the value of data from your existing customers and lost revenue opportunity from account penetration strategies, i.e., up-sell and cross-sell.
According to Forrester Research (Customer Life Cycle Marketing Demands New Metrics, February 8, 2011), a customer life cycle approach forces the marketer to look at four dimensions of the customer buying process: discover, explore, engage, and buy.
These four dimensions represent different strategies, channels, and forms of customer interaction and buying opportunities, e.g., discovering and capturing interest, exploring and cultivating interest, engaging and enhancing interest, and buying (new customer or up-sell/cross-sell). The only way to create a practical road map so that you can get value out of big data is to build it from the outside in—through the customer life cycle, mapping the various stages to your customer buying behavior, channels, platforms, and data sources. The combination of buyer behavior, channels, platforms, and data sources around the customer life cycle contains the necessary information to assemble a customer profile that becomes the hub to drive strategy, campaigns, and results.
Step 2: Where to Start in the Customer Life Cycle
The most obvious place to take advantage of your big data and begin multichannel marketing is with the discover phase of the customer life cycle. This is especially true of marketing organizations that are totally driven by selling more products. At this juncture, these organizations often set up their outbound channels—email, events—and their inbound channels—SEO, pay-per-click, and social media—to drive the most leads into the top of the sales funnel or to create buying opportunities, in the case of B2C. However, an initial mistake they make in taking this approach is failing to review their customer interaction history. This history, with the idea that past behavior is the best predictor of future performance, will guide the marketing organization toward the right combination of channels to use in its multichannel marketing strategy, and is an integral part of the big data that you already have.
Step 3: Define the Use of Customer Intelligence
Marketers realize the need for customer intelligence to provide them with actionable insight to optimize revenue. But many view creating customer intelligence as costly and complex, especially in the big data era. The issue isn't whether customer intelligence is necessary; it's what kind of customer intelligence marketers need to effectively segment their markets and then apply it to the customer life cycle. Defining the issue that way enables marketers to treat customer intelligence as an evolution, rather than a revolution, by:
- Treating existing data as customer intelligence
- Learning from it, e.g., what size customer or title groups are your best prospects
- Improving upon it over time and filling in the gaps—even if it is basic firmographic or demographic information
Taking this approach enables marketers to learn and achieve results. Marketers will discover the benefits of database marketing and applying customer intelligence in more meaningful ways, and will begin the execution of multichannel marketing campaigns, at the right time, and in the right places, to optimize revenue.
Step 4: Apply Customer Intelligence to Big Data in the Customer Life Cycle
Most marketers would be surprised to learn they already have what they need to begin to create more sophisticated segmentation strategies that are customer life cycle–based. In fact, they need to unlock the power of customer intelligence in the form of their existing data. Whether all of that data is readily accessible, inaccurate, or complete is not the point, nor should it hinder anyone from beginning their customer intelligence journey. What they have is actionable today, right now. Even the simplest marketing campaigns produce customer interaction history that qualifies as customer intelligence. For example:
- What is the most popular message or offer to a particular type of contact, and what has worked at different life cycle stages?
- How many contacts, with what title and types of company, actually responded to the message or offer?
- Which channel was the most effective at which life cycle stage?
In this simple example, a marketer would form the beginning stages of segmenting, profiling, and interacting with customers in ways they want to be communicated with and at the most opportune time. Isn't that the objective of life cycle marketing?
Step 5: Explore Your Customer's Entire Journey Through the Big Data Lens
The customer life cycle should not be viewed solely from a lead generation or discover phase perspective. It is a continuum from the life cycle of a prospect to being a satisfied customer who makes repeat purchases. It's both acquisition and retention, and it fuels the big data engine.
Most marketers, especially in the B2B world, think primarily about their prospects as future customers, and then think about their existing customers as having limited up-sell and cross-sell opportunity. But if you think about customers holistically, your existing customers can provide insight into the behavior of your best prospects, while also significantly increasing the opportunity for up-sell and cross-sell. This enables you to leverage the customer life cycle fully and drives a holistic use of big data.
The Need for a Big Data Hub
Bringing all of this data together into a single marketing database provides a big data engine that leads to more effective segmentation, campaign strategy, and execution, and ultimately increased revenue. Marketers need to embrace big data, starting today, with the data they already have available. Using the customer life cycle as a framework for how to practically apply the use of big data will enable marketers to derive the most value.
Joe Cordo is CMO of Extraprise, a provider of database marketing and demand generation services for B2B and B2C enterprises. For more information, contact him at email@example.com or visit www.extraprise.com.
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