Making smarter marketing decisions in a COVID world

The pandemic has thrown consumers and businesses for a loop.

While it is certainly the case that some consumers have fared well, large numbers of consumers have lost their jobs or seen their wages shrink. For marketers, that means the commonly used strategies to reach potential customers or conduct a segmentation analysis might not be as useful as they once were.

Despite the economic upheaval, marketing teams don’t have the luxury of standing still. For their businesses to thrive, they need to find new customers and retain existing ones.

To do that, they need to update their marketing data to reflect current conditions. At the same time, they need their strategies to be as efficient as possible, because frankly, they are facing the same pressures and uncertainties that everyone else is.

Powerlytics’ consumer data and retention models have a demonstrated track record of providing banks, insurance providers, and asset managers with powerful information they need to fine tune their marketing plans. We’ve helped lenders target pre-approved loan solicitations to customers that fit their risk profile and are likely to respond. We’ve also helped life insurance companies predict which customers are likely to let their policy lapse and target new customers who are likely to stay longer.

Powerlytics’ Consumer Research databases contain more than 200 financial variables, such as interest, dividends, and capital gains for over 150 million households and more than 200 million consumers. From this data, we have created over 3,000 data transformations that have proven to be highly predictive in marketing, targeting, and customer segmentation with a risk-based lens.

Here are a few examples of how businesses have used our data to find and retain clients:

We helped a direct-to-consumer, non-bank lender increase the efficiency of its multi-step marketing funnel, which started with a pool of approximately 2.6 million loan applicants. We were able to not only help them determine who to solicit with the best chance of responding, but also for those that did respond, determine who had the best chance of passing underwriting and ultimately take the funding.

By successfully identifying consumers that were more likely to complete the loan process and less likely to default this lender could save millions of dollars.

Another example comes from the insurance industry. We worked with a major insurance company to identify auto insurance customers that were more or less likely to switch providers. For perspective, improving retention rates by just 0.5 percent would save approximately 60,000 policies and $108 million in premiums paid.

We identified several factors that were associated with policy non-renewal. It turned out that getting married was a big factor in consumers switching insurance providers. That makes sense: when a couple combines finances, one of them will likely join the other’s policy. Another factor was the source of income. Consumers that derived a large portion of their incomes from less stable sources than a regular bi-monthly paycheck tended to be likely to switch. We theorize that because non-wage income is more variable, these consumers tend to be more price sensitive. Our income stability metrics helped to identify this trend.

All marketing programs – whether they sell a product, offer loans, identify potential long-term customers, or help to cross-sell or upsell a current customer – are only as good as the data that underpins the targeting model. Powerlytics proprietary consumer and business databases and predictive analytics solutions are based on source of truth data. Survey-based and heavily modeled data contribute to suboptimal results. Changing times require more robust data; don’t settle for yesterday’s data solutions to handle today’s complex challenges.

If you are interested in learning how Powerlytics can help sharpen your marketing program, contact us.

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Published 5th Mar 2021

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