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The 168-Year-Old DTC Startup

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How can the 168-year-old insurance brand MassMutual turnaround a 300-year tradition of broker-based consumer connections? It creates an in-house DTC that marries classic mortality and underwriting data with cutting edge psychographics and cross-screen behavioral targeting. Matt Myers illustrates how Haven Life uses new and old data types to model, activate and measure its DTC outreach. Meet your new programmatic broker.

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The 168-Year-Old DTC Startup

  1. 1. The 168-year old DTC startup
  2. 2. Our parent company: MassMutual ● 168-year-old company ● #84 in the Fortune 500 ● $39.2BN in 2018 Revenue ● Over 5,000,000 customers ● Over $744,000,000,000 of insurance coverage “in-force” ● Network of 7,000+ Financial Advisers ● A++ A.M. Best rating for financial strength and claims- paying ability.
  3. 3. Our industry is in flux ● Life insurance ownership is at record low levels ● Agent sales forces are retiring at record numbers ● Seismic shifts in consumer purchase behaviors towards DTC
  4. 4. An industry in flux ● Life insurance ownership is at record low levels ● Agent sales forces are retiring at record numbers ● Seismic shifts in consumer purchase behaviors towards DTC Enter Haven Life….
  5. 5. Our story is about a father, his family, and their future When Haven Life’s co-founder Yaron Ben-Zvi and his wife were expecting their first child, they decided that to help protect their growing family’s financial future, they needed to buy life insurance. Easier said than done. After going through the traditional application process and waiting weeks (yes, plural) to receive a decision on eligibility, Yaron felt compelled to build a better experience for today’s insurance shoppers and their loved ones. An experience that was actually simple.
  6. 6. What is Haven Life? MassMutual’s in-house start-up focused on selling life insurance in an online, agentless direct-to-consumer environment ● Simple to understand, DTC term life insurance product ● Founded in 2015 ● Launched in 2016 ● Available nationally in 2017 ● 200+ employees ● Nearly $9,000,000,000 in coverage in force ● New products coming in Q4
  7. 7. But no one’s ever done this before... Haven Life was the first to launch a 100% online direct-to-consumer purchase experience for buying term life insurance How do you launch a purchase experience no one has ever done before?
  8. 8. We started with a big media blitz
  9. 9. But that didn’t pan out
  10. 10. So we needed a pivot
  11. 11. So we needed a pivotSo we needed a pivot But more importantly, we evolved our targeting
  12. 12. Our targeting evolution 2015 - 2016 Persona Based Marketing ● Life Stage Based ● Limited Channels ● No data to work with ● Spray & Pray
  13. 13. Our targeting evolution 2015 - 2016 Persona Based Marketing ● Life Stage Based ● Limited Channels ● No data to work with ● Spray & Pray 2017 - 2018 Black Box Lookalikes ● Used our first 100 sales ● Built platform based LALs ● Pixel based model changes ● Targeted but poorly calibrated
  14. 14. Our targeting evolution 2015 - 2016 Persona Based Marketing ● Life Stage Based ● Limited Channels ● No data to work with ● Spray & Pray 2017 - 2018 Black Box Lookalikes ● Used our first 100 sales ● Built platform based LALs ● Pixel based model changes ● Targeted but poorly calibrated 2018 - 2019 1:1 Person Based Marketing ● 200+ Model Attributes ● Utilizes Underwriting Decisions ● Real-time feedback loop to ad partners ● Correctly targeted
  15. 15. Our targeting evolution 2015 - 2016 Persona Based Marketing ● Life Stage Based ● Limited Channels ● No data to work with ● Spray & Pray 2017 - 2018 Black Box Lookalikes ● Used our first 100 sales ● Built platform based LALs ● Pixel based model changes ● Targeted but poorly calibrated 47.5% LOWER CAC 2018 - 2019 1:1 Person Based Marketing ● 200+ Model Attributes ● Utilizes Underwriting Decisions ● Real-time feedback loop to ad partners ● Correctly targeted
  16. 16. Our targeting evolution 2015 - 2016 Persona Based Marketing ● Life Stage Based ● Limited Channels ● No data to work with ● Spray & Pray 2017 - 2018 Black Box Lookalikes ● Used our first 100 sales ● Built platform based LALs ● Pixel based model changes ● Targeted but poorly calibrated 47.5% LOWER CAC 2018 - 2019 1:1 Person Based Marketing ● 200+ Model Attributes ● Utilizes Underwriting Decisions ● Real-time feedback loop to ad partners ● Correctly targeted 57.2% LOWER CAC
  17. 17. Moving to 1:1 marketing resulted in... ● 78% improvement in customer acquisition costs ● 44% improvement in applicant quality (likelihood to purchase)
  18. 18. How our model works ● Define customer segment (i.e. lowest risk) ● Append 400+ data attributes to each customer record ● Select attributes w/ largest deviation from the general population ● Stack rank prospecting set using those attribute model scores ● Activate audience across platforms ● Champion vs. Challenger mentality
  19. 19. Our marketing stack Acxiom Live Ramp Epsilon Segment.io Trade Desk Simulmedia Amazon Facebook Google IndicativeAttribution App Data: DMP: Activation: Analytics:
  20. 20. Future state Looking to the future, we’re piloting a couple new ways to improve our models & targeting ● Location data -- understanding trends in where our customers are moving relative to the general population. Learning where they shop, where they travel, etc. ● CTV Lookalikes -- using IP addresses to model TV lookalike audiences. Pilot under way now.
  21. 21. Best practices If you’re ever in New York, please stop by our offices on Madison Square Park ● It starts with the correct seed audience ● Choose the right variables ● Religiously test new data sources ● Bring a champion vs challenger mentality to your models
  22. 22. Thank you!

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