Marketers have always used data points like where customers live, how old they are or whether they like pets and gardening. This is static data. It's captured once and rarely changes. And it works. However, the power of static data is limited.
The most predictive data—and traditionally the hardest to capture and organize—is how customers behave. This is dynamic data. It changes every week, day, hour and even minute. Behavioral data is the perfect fit for targeting. It also offers many more pieces to the puzzle than just online behavior.
Here's a roadmap for how to capture and use dynamic data:
1. Know Your Customers and Segment Them Religiously. This Is Your Baseline.
Everything starts with your current customers. You need all of them for this first step—the best, the worst and everything in between. The way to know and benchmark best behavior is to create a baseline for comparison. Some customers are loyal; they want your next product the minute you release it, pay their bills on time and refer you to friends. This is knowledge that only you hold about their behavior.
Most customers are in the middle. They bought your first product, called customer service to complain, you solved the issue and they went on to buy a minor product, but never interacted again. And some customers are thorns. They bought your product, returned it and complained loudly on Facebook and Twitter—and cost you some valuable new business. Segmentation algorithms are abundant; using a statistical software package and selecting a clustering algorithm is a great start. Grouping customers into segments is the first step in creating a customer contact strategy.
2. Know Where Customers Came From and Their Preferred Channel of Communication. (Did You Ask?)
A tremendous challenge today is the "opt out," and sadly it even comes from our existing customers. Email is cheap to send, but inboxes are full. Not all marketing touches require a response: however, increasing "unsubscribes" are likely a flag that the communication is too frequent or irrelevant.
As new customers are acquired, a wise strategy is to ask how they would like to be contacted: Direct mail? Email? Phone? Follow this up with uncovering how often they want to be contacted. Is daily too much? What about weekly or monthly? Let customers tell you what they want to hear about, how they would like you to communicate and how frequently. As each customer is acquired, keep track of the originating data source. This may be direct mail, email, a store walk-in or reference from a friend.
3. Organize Customer Data to Make It Easily Accessible for Analysis and Predictive Modeling.
In a world of Big Data, everything is captured including transactions, demographics, online cookies, retail store purchases, social interactions, inbound customer service calls and the list goes on. While data is valuable, without organization it can drown any marketing effort. For example, we may have thousands of pieces of information, but when we want to segment or describe our customers, we struggle. Many organizations delegate questions about customers to their business intelligence analysts, who access it with tools like SAS, SPSS, Tableau and Spotfire. However, many overlook another key role: identifying someone to organize the information to make it consumable.
Where to start?
A. Conduct a Data Audit. What data is currently available? Where is it stored? How far back does it go?
B. Organize the Information With a Singular Focus—to get a view of the individual customer not the campaign. For example, how long has she been a customer? How many and what type of products has she purchased? How frequently? How many times has she called customer service? How many marketing touches has she received—and did she respond?
C. Create a Marketing Universe. This task involves connecting contact data to all past marketing history. Summarize transactions using time-series information. For example, how many contacts were made during the past three months? The past year? How many times did marketing elicit a response?
By now, you've probably discovered this is a lot of work. While it may be overwhelming, this is the gold mine. Nobody said mining gold is easy or cheap; however, once the initial investment is made, the payoff is huge.
4. Create 'Model-friendly' Behavior Predictors.
Once the partner who helped organize the data into an accessible structure is finished, it's time to extract a story from the data. The objective is to identify the key pieces of information about individual customers or the household that will help predict behavior.
How do we get there?
A skilled marketing data scientist with experience connecting large datasets—and sifting for gold—should be engaged. This experience is needed to work with database developers to uncover key pieces of information and plug them into analytic tools to provide a solution.
Together they will work with marketing to:
- Map each marketing outcome for all individual customers. This can be an extensive task; especially if you've collected several years' worth of transaction and marketing data.
- Build an analytics layer to help summarize data, such as how long you've had a customer, whether the customer purchased one product or many, how many contacts you've made, which contacts elicited a response, and whether your customers influence others positively or negatively.
5. Always Test and Learn.
These recommendations will not be a fast fix to an ailing marketing program. Instead, expect to take a phased approach for implementing these ideas. While doing so, it's best to always keep a control group to measure the incremental effect of each marketing program.
As programs become more complex, questions will arise that only a well-designed test can answer. Many statistical software solutions provide tools for the construction of well-designed, structured tests that will maximize learning with minimum cost.
Knowing customers and how they behave ranks first in importance for marketers. Collect this data with abandon, buy storage to save it and mine it like gold. Once internal behavior is understood, finding new customers who fit a "best behavior" profile, either online or offline, becomes a planned acquisition strategy.