A 2015 study of 228 CMOs shows that companies expect their share of spending on marketing analytics to almost double in the next three years. But for the C-suite to refine big data and make it actionable will require a bit of pressure: that of advanced analytics, followed by marketing adoption of the learnings found within the unrefined “big” source.
The rise in big data comes from four main sources:
• Mobile: As mobile commerce and mobile payments increase, that anonymous and aggregated data can make an actionable contribution to a company’s data set.
• Social: Because big data is unstructured (meaning it does not contain a predefined data request), it is harder to make actionable. But by using it to map the customer experience and conduct sentiment analysis, “big” social data can be refined into actionable pieces.
• Internet of Things: The IoT will include wearables and smartphones, with both of those categories becoming payment platforms. The result: More data.
• Cloud-based data: The awesome scale of velocity and volume of data requires companies to master open-source, cloud-based technology, and to use it as well as secure it. The structure of data management and security has changed since the advent of Hadoop and its open-source approach. As a software platform, cloud-based data transcends storage constraints and enables the kind of advanced analytics that will produce actionable insights for social, mobile, and IoT data.
The complete data set that emerges from these four sources, as well as from Internet traffic analytics, has come very close to producing a 360-degree view of the customer. This will enable advanced segmentation analytics that drive businesses forward.
But a 360-degree view is not enough to make advanced segmentation decisions. In order to see that picture with clarity, transactional data is critical.
Let’s look at an example. Suppose a community-based bank has the mission of providing a full-service menu of credit, debit, mortgages, mobile banking, and affluent-level products. It has anonymized data sets to show credit history, outstanding debt, and current product usage. It has rich demographic information, social network analysis, customer journey information—but minimal transaction information based on banking transactions. That’s because some banking products are relatively inert. After all, most consumers only touch their mortgages once a month, when they pay the bill.
So the bank may have a 360-degree view of the customer, but the ability to make the data actionable only comes to life when a richer set of transactional data is used.
For the local bank, building a segmentation schema based on how consumers interact and use checking is critically important. By carefully examining a customer’s account activity by transaction type (direct deposits, withdrawals, check-writing behavior, ATM usage, debit card use, and the like) and evaluating meaningful breakpoints that tie to profitability metrics, a holistic picture of engagement and total value across all segments can be created. Then, primary and secondary account relationships can be better understood.
The additional insight can be applied to all kinds of marketing programs: upgrade strategies, product development, solution positioning, cardholder communication activities, and marketing channel tactics. Those are action items for the bank. They create advanced segments. They improve the customer experience.
Advanced Segmentation Practices
The combination of more data, cloud-based storage and better analytics will amplify current abilities to segment customers. I see three important new advanced segmentation practices already in the works:
• Affluent segmentation: The challenge with affluent marketing has been to define spending behaviors and parse them into nondiscretionary and discretionary spending. Big data analytics can easily define overall spending and overall income. That has a level of actionability, especially in marketing high-end product. However, apply the lens of transactional data, and the affluent customer spend patterns magnify. Companies can see how apparel purchases have a range of nondiscretionary, mixed, and discretionary spending. They can see how gasoline and groceries are completely nondiscretionary. And they can see how cruise lines and jewelry are completely discretionary. Companies can act on this advanced segmentation by pricing based on the level of discretion and marketing products based on shopping behavior rather than overall income.
• Purchase sequence analytics: Transactional data completes the shopper journey picture and allows companies to act on different points of that journey. For example, let’s take the center point of a purchase journey as a gasoline purchase. By analyzing that big data set, purchase behavior that happened before and after can be segmented. For example, 30% of all gasoline purchases over a period of time and similar geography were preceded by a convenience or pharmacy store purchase. Twenty-five percent were followed by a sports or athletic facility purchase. Transactional data can be aggregated for further analytics and can predict next behaviors. That’s advanced analytics, and it’s actionable to all merchants along the purchase journey.
• Spend density analytics: While digital marketers are familiar with click density, spend density matches shopper spend to ZIP codes and then to more specific locations. For example, a department store chain can make inventory, pricing, media, and even expansion decisions based on the total spend by ZIP code. Rather than judging a ZIP code based on real-estate values, actual spend data can define where purchases are truly happening.
The speed of data waits for no company. Several market factors have driven the size and speed of data over the past decade, but the time to wait has passed. Smart companies are embracing social, mobile, and IoT data to increase the volume of customer data, but, more importantly, to benefit from the positive pressure of advanced analytics. Cloud-based data will increase speed and access. The most effective marketers will focus on increasing the value of the process by producing customer insights, creating advanced segment analysis, and transforming the customer experience
The entire landscape will be one that is best addressed by transforming big data into actionable data.
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