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Insurance has long been closely tied to data in the form of actuarial tables. The more you know about categories of clients in aggregate, the more accurately you can make projections based on probabilities. That has helped insurers budget for the unknown and survive despite massive payouts.
The same principles apply to how insurers can use marketing data to insulate themselves from the volatility of market forces. The more accurately your marketing team can model the customer, the more precise you can be in crafting original services and messaging. There are three times when big data applications and software can deliver an enormous benefit to marketers:
If you're thinking that this covers nearly everything marketing does, you're right. The limits of uses for big data are bounded only by the expertise of your data analysts and the productive imagination of your marketing leaders.
A good example that bridged the gap from actuarial tables to experiential marketing was the Fitbit for Life campaign co-sponsored by John Hancock and Vitality.
Consumers who signed up for the program received a free Fitbit from John Hancock and then earned Vitality points for each activity they engaged in to improve their health. The more points each consumer earned -- for walking, running, etc. -- the more they saved on insurance premiums each year. Top-scoring clients earned up to 15 percent of their premiums, adding up to thousands of dollars. Partner agreements also allowed active clients to claim discounts at retailers like fitness-wear maker REI.
The creativity of the experiential marketing program captured the imagination of consumers for better sales on the front end, while the data collected from the Fitbits helped Hancock's actuaries fine-tune the ROI of premiums on the backend.
Not all applications of data in experiential marketing are that flashy, though. Insurance carriers can apply data analysis to plan more targeted messaging for activations at trade shows, for sponsorships, at advisor events, accompanying seminars and for many other types of live events.
These are the sorts of quiet applications of data that can end up having the biggest impact on marketing productivity and profitability. They don't result in flashy national campaigns but simply support better decisions based on data instead of intuition.
A study by McKinsey on driving growth with data reported how a property-and-casualty insurance company used data for decision support throughout the organization. As a result, the company as a whole was able to dial up marketing productivity by over 15 percent annually while holding spending flat. Meanwhile, their competitors watched underlying cost soar by 62 percent. In this study, the company's CMO commented simply, "Marketing analytics have allowed us to make every decision we made before, better."
Another way insurance firms can use data now is to measure and compare the performance of marketing spend across multiple channels. Marketing-mix modeling (MMM) depends on the power of statistics to correlate how much of revenue can be traced back to marketing versus the other drivers of sales.
The calculations can fold in the influence of external variables like the time of year and actions by competitors. You can then break down the results by longitudinal effects (such as changes for specific customers or segments over time) or interaction effects (which compare online to offline efforts). If you have access to a repository of sales data going back years, you can get a solid historical view of marketing's impact on revenues. However, deploying MMM accurately requires someone on your staff to have enough experience with interpreting data to make sense of modeling and scenario-planning tools.
For those who don't have access to that kind of historical data, analytics known as "reach, cost, quality" (RCQ) is useful because it sets equivalencies to help you more easily compare metrics like the number of target consumers reached, the cost per unique touch and quality of engagement. In practice, firms may use a combination of MMM for more easily measured investments like TV/radio/media buys and RCQ for experiential marketing efforts. A good case study for how this looks in the real world is the story of how BRIDGEi2i used RCQ to re-evaluate their overall marketing budget and re-allocate resources across a host of marketing channels.
One of the most useful but least used applications of data is in measuring the effects of experiential marketing on attendees. Unlike online marketing, where engagement is measured in clicks or eye movement heat maps, experiential marketing gives project leads the chance to see the faces of the audience. Scanning through video of crowd reactions at a trade show or member event can instantly provide a host of metrics on audience sentiment.
Deb Curtis, the VP of global partnerships and experiential marketing at American Express explained, "The data piece is becoming more central, and for experiential marketers, it's fantastic to have more and more data, but an experience is also about an emotional connection and there's an intangibility. Sometimes the best research of all is looking at people's faces and seeing how they're responding. It's a tried-and-true way to see what's working."
Marketing leaders are facing greater pressure than ever to prove their value to the bottom line in quantifiable terms. Big data and analytics can be a marketer's greatest allies in that effort. Right now, only them most progressive firms are effectively deploying data to boost performance, acquire new accounts, recommend more cross-sells/up-sells and grow the business.
Leaders in the insurance industry, including a Fortune 500 life insurance company profiled by PwC, have used data to ramp up revenue goals and enhance their customer experience. In the bigger picture, experiential marketing events backed by better decisions and metrics from big data are shifting the emphasis of marketing's initiatives from the tactical up to the strategic level.
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