The all-seeing crystal ball—once relegated to the realms of magic and myth—has entered the real world. In our switched-on, hyperconnected society, where digital interactions permeate virtually every moment of our lives, we now have tools to peer into the future. The conduit is data: admittedly not a glowing orb, but close. It enables us to see where our business is going.
Specifically, I mean predictive analytics: using new and historical data to foresee the results, activity, behavior and trends of our consumer base. It illuminates the architecture of successful businesses and enterprises primed for growth in today’s ultra-competitive marketplace build on it from the foundations. Through predictive analytics, they gain a deep understanding of customers to maximize revenue, wring efficacy from marketing budgets and of course, increase profits.
So how can you unlock the benefits of predictive analytics? Let’s look at some key predictive tools and how to deploy them for healthy business growth.
1. Predictive modeling of customer behavior
Data points from previous campaigns (particularly, those that help us see what worked and what didn’t), plus all customer base demographic information, can help build predictive models. These draw correlations that link past behavior and demographics.
Financial institutions have used predictive analytics for years to protect consumers. Have you ever wondered why you get a call the minute your teenager or a pickpocket makes a mega purchase with your credit card? Or get a text when a purchase you never made gets charged to your card miles from your hometown? Financial institutions pioneered the use of collective customer behavior data to identify breaks in the spending pattern that could indicate identity theft. This illustrates how this model works in execution.
2. Qualification and prioritization of leads
Chasing a lead unlikely to convert can be ex:pensive. Predictive analytics in lead modeling can yield more bang for your lead investment buck. It uses an algorithm to score leads based on known interest, authority to buy, need, urgency and available funds. Combining public and proprietary information, the algorithm analyzes, compares and contrasts customers who converted with those who did not—and then finds “alikes” among the incoming leads.
Thus the higher the score, the more qualified the lead. The highest-scoring prospects should get directed to sales or offered immediate incentives to convert; medium scores deserve a drip campaign; and low scores? Forget it.
3. Customer targeting and segmentation:
Among the most common uses of predictive analytics, customer targeting and segmentation takes three basic forms:
a) Affinity analysis refers to the process of clustering/segmenting the customer base according to common attributes, facilitating “fine tune” targeting.
b) Response modeling looks at past stimuli presented to customers, as well as the response generated (converted or not) to predict the likelihood of a certain approach to get positive response.
c) Attrition rate (or churn analysis) looks at the percentage of customers lost during a certain time period, as well as the opportunity cost/potential revenue lost with their departure.
With the deliberate use of these predictive analytics tools, a business can then predict the Customer Lifetime Value (CLV). This measurement looks at several aspects of historical behavior to identify the most profitable customers over time; acquisition spending trends based on which activities generate the best ROI; and types of loyal customers (retention traits).
This model then adds an estimate of expected retention to the equation in order to estimate future value. Once you understand the CLV, you can right-size the cost of acquisition and marketing budget to reach the desired ROI.
When applying predictive analytics, it’s critical to apply an A/B test approach to inform your output. Known as causal inference, A/B testing of the same target audience allows us to infer the WHY behind the WHAT customers do.
A/B testing of headlines and taglines in marketing messages to consumers led JPMorgan Chase to partner with Persado, a software company that applies artificial intelligence to the messaging task. Two messages to generate home equity loan applications were tested against each other: one from legacy writers at JPMorgan and the other from Persado that used learnings from consumers’ reactions to messages and calls to action. T
The result: Persado’s headline and call to action generated 80 percent more applications. Knowing the WHY behind the WHAT pays off.
Remember, it all starts with the clear articulation of the business strategy. When all parties align, the chips fall into place:
a) Predictive modeling of customer behavior helps educate campaigns to drive loyalty or generate leads.
b) Lead qualification modeling helps the sales team focus on the most probable customers to buy/close the deals.
c) Together these help finance understand the CLV and educate the whole organization on the acceptable customer acquisition cost to drive the targeted ROI.
In sum: If you don’t predict, you will lose ground. It’s a prediction more reliable than any a crystal ball can produce.
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Adriana Lynch is the CMO of Chief Outsiders, the nation’s leading fractional CMO firm focused on mid-size company growth. She works with companies to differentiate, drive customer loyalty, and unlock profitable growth.