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Marketing tech: Deep learning and content tactics to boost customer engagement and conversion

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Presentation from NRF 2019 Retail's Big Show

David Bessis, Founder and CEO, Tinyclues
Shirley Chen, Founder and CEO, Narrativ
Richard Kestenbaum, Co-Founder and Partner, Triangle Capital
Karen Moon, Co-Founder and CEO, Trendalytics

Published in: Retail

Marketing tech: Deep learning and content tactics to boost customer engagement and conversion

  1. 1. Marketing tech: Deep learning and content tactics to boost customer engagement and conversion Monday, January 14 2:00 pm – 2:30 pm Richard Kestenbaum Co-Founder and Partner Triangle Capital David Bessis Founder and CEO Tinyclues Shirley Chen Founder and CEO Narrativ Karen Moon Co-Founder and CEO Trendalytics
  2. 2. 89% OF MARKETERS SAY THEY ARE PERSONALIZING EXPERIENCES AND MESSAGES ONLY 5% OF CONSUMERS SAY MESSAGE OFFERS ARE USUALLY WELL-TIMED WITH THEIR NEEDS Source: Forrester report Evolve Now To Personalization 2.0: Individualization; Forrester Data Consumer Technographics Global Online Benchmark Survey (Part 2), 2018
  3. 3. CUSTOMER MARKETING IS INCREDIBLY HARD YOU HAVE MILLIONS/BILLIONS OF DATA POINTS RELEVANCY PERSONALIZATION CUSTOMER EXPERIENCE DATA-ACTION GAP
  4. 4. IT’S HARD TO BUILD INTELLIGENT CAMPAIGNS WHEN YOUR MARTECH STACK REQUIRES YOU TO MICRO-MANAGE SEGMENTS & RULES
  5. 5. USING AI TO SOLVE CAMPAIGN PLANNING & TARGETING BUILD THE BEST CUSTOMER MARKETING PLAN IN MINUTES DIRECTLY FROM YOUR CAMPAIGN IDEAS AND BUSINESS GOALS And many many more…..
  6. 6. DEEP LEARNING WORKS -80% AVERAGE TIME TO CREATE CAMPAIGNS -19% +51% AVERAGE DECREASE IN UNSUBS AVERAGE INCREASE IN ENGAGEMENT +79% AVERAGE CAMPAIGN REVENUE UPLIFT
  7. 7. Xbox Fifa 19’ $59.99 HOLIDAY GIFTS PURCHASE HISTORY CUSTOMER SEGMENTS LIFETIME SPENT SIMILAR PRODUCT CATEGORY BROWSING HISTORY PRODUCT PAGE VISITORS TARGETING STILL HAS ROOM FOR IMPROVEMENT
  8. 8. SEGMENTING FOR FIFA ‘19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 MALE 3.2% 2.9% 2.0% 1.5% 1.6% 2.0% 1.7% 1.0% 0.6% 0.4% FEMALE 1.7% 1.2% 0.7% 0.8% 1.4% 1.9% 1.4% 0.8% 0.4% 0.3% SWEET SPOT
  9. 9. SEGMENTING FOR FIFA ‘19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 MALE 3.2% 2.9% 2.0% 1.5% 1.6% 2.0% 1.7% 1.0% 0.6% 0.4% FEMALE 1.7% 1.2% 0.7% 0.8% 1.4% 1.9% 1.4% 0.8% 0.4% 0.3% $934,502 IN REVENUE MISSED
  10. 10. HOW DATA SCIENCE SEES PAT WILL PAT BUY “FIFA 19” IN NOVEMBER? Gender = "F" Lives in the city RFM Segment = "High- value customer" Never purchased in this category -0.5% -4% +1% +3% -2% Email Address = "Patricia.Jones@gmail.com" Deep learning finds in your data that people with the email structure “firstname.lastname@gmail.com” are more likely to buy high-tech or gaming items now. Video games are not very popular in Hamilton, Montana right now. A Hoverboard. Not the same category, but activates "kid stuff" latent feature in the Deep Learning. The theme was “Holidays shopping guide”. Deep Learning detects that this link was about something involving the "Christmas" and "gaming" latent feature. Latent sociographics of high-end laptop users show they’re slightly more likely to buy video games for their kids or grandkids All attributes are calibrated and interpreted using patterns unique to your own business Zipcode = "59835" Bought SKU “TS9085” 5 months ago Clicked link #9 in your newsletter 2 weeks ago Browsed via Safari on latest MacBook Air + HUNDREDS of other "tiny clues" +0.3% -0.5% +3.2% +0.8% +1.2% +2.4% Conclusion: VERY LIKELY TO BUY. INCLUDE IN THE CAMPAIGN Age = "61" ATTRIBUTE IMPACT ATTRIBUTE IMPACTINTERPRETATION Conclusion: UNLIKELY TO BUY. DO NOT INCLUDE IN THE CAMPAIGN HOW DEEP LEARNING SEES PAT All attributes are calibrated and interpreted using patterns unique to your own business All attributes are calibrated and interpreted using patterns unique to your own business All attributes are calibrated and interpreted using patterns unique to your own business All attributes are calibrated and interpreted using patterns unique to your own business All attributes are calibrated and interpreted using patterns unique to your own business All attributes are calibrated and interpreted using patterns unique to your own business A chess game. Irrelevant. Bought SKU “MC8790” 2 months ago 0%
  11. 11. THANK YOU Visit us at Booth #643 David Bessis New York | Paris | London | Copenhagen | Munich
  12. 12. Top 10 Machine Learning Competition | Startup of the Year Karen Moon
  13. 13. Who’s driving your decisions on product naming, copy and attributes?
  14. 14. Who’s driving your decisions on product naming, copy and attributes?
  15. 15. soda pop
  16. 16. ripped jeans distressed denim
  17. 17. mom jeans high waisted jeans
  18. 18. high waisted frayed hem faded ripped cropped button fly inseam
  19. 19. • Excel spread
  20. 20. Top 10 Machine Learning Competition | Startup of the Year Karen Moon
  21. 21. What if you could unlock $25 Billion? Just look below SEM…
  22. 22. 10X Trusted by the industry’s best brands Grow 10X, not 10% New Traffic + New Data =
  23. 23. shirley@narrativ.com

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