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Kde jsou limity zákaznické 360°?

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Jakub Štěch na konferenci Data Restart 2020: Demonstrujeme propojení online a offline dat na případové studii České spořitelny. Jak vytvořit zákaznickou 360° a kde jsou možná omezení? Jak s cílením souvisí NLP a neuronové sítě? Proč je důležité mít celé datové prostředí u sebe jako klient?

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Kde jsou limity zákaznické 360°?

  1. 1. Jakub Stech Data Science architect in DataSentics Responsibilities: • Find & Translate business problems across projects for data science teams (Ceska Sporitelna) • Personalizing user experience using data and machine learning approaches • Help building data science products (CV, NLP, …)
  2. 2. © 2019 Adpicker, a product developed by European Data Science Center of Excellence DataSentics in a nutshell How We Work Machine learning 30+ Data engineering Cloud 25+ ML Center of Excellence in Prague & Brno Partners of Key References "Make data science and machine learning have a real impact on organizations across the world... ...bring to life transparent production-level data science. Quality inspection Computer-vision-based AI solution for automatically analyzing the quality of placement of my products in a store shelf Customer Engagement 360° Combining digital and offline behavioral data and machine learning to personalize the customer experience Fraud / AML / Risk Using advanced methods such as NLP and graph analytics to identify anomalies, etc. Contact center Using NLP for automatic routing of emails/request, topic detection, etc.
  3. 3. AGENDA • 360 in CSAS • Case study I: personalised CRM campaigns • Case study II: contextual online targeting
  4. 4. Leading every client to prosperity = Data-driven advisory based on clients needs and real-time situations 4
  5. 5. … is not easy Customer- centricity…. Low frequency of measured interactions between a client in offline channels
  6. 6. 100 Things, We touch our phones 2,617 times a day, says studyAround 100 sessions every day…
  7. 7. Typical internal/CRM data Age/sex/address Contracts Sales channels …
  8. 8. Digital „footprints“ Ad interactions Web interaction Apps …
  9. 9. Typical CRM data Digital „footprints“ • Static, mostly long-term behaviour • Facts and transactions • Structured • Easy to process with traditional tech • Dynamically changing, reflecting needs • Uncertainty, fragments of interests, lifestyle • Enormous data • Messy, unstructured, changing interfaces
  10. 10. Emailing / SMS / Push Branches & sales networks Transactional data Callcenter data / call logs Classic CRM / data processes Classic campaign management tools ONPREM Client profile (CRM)
  11. 11. Client profile (CRM) Classic campaign management tools Emailing / SMS / Push Branches & sales networks Transactional data Callcenter data / call logs Classic CRM / data processes Digital channels behaviour INTERNET BANKING CONNECT ONLINE AND OFFLINE DATA
  12. 12. Client profile (CRM) Classic campaign management tools Emailing / SMS / Push Branches & sales networks Transactional data Callcenter data / call logs Classic CRM / data processes INTERNET BANKING Digital channels behaviour Digital campaign management tools Digital non-client & client behaviour profiles Machine learning CREATE DATAPLATFORM IN CLOUD
  13. 13. Client profile (CRM) Classic campaign management tools Emailing / SMS / Push Branches & sales networks Transactional data Callcenter data / call logs Classic CRM / data processes Digital channels behaviour ONLINE ADVERTISING INTERNET BANKING ADDING ONLINE CHANNELS: CASE BY CASE, TRANSPARENT VENDOR Digital non-client & client behaviour profiles Machine learning Digital campaign management tools
  14. 14. Client profile (CRM) Classic campaign management tools Emailing / SMS / Push Branches & sales networks Transactional data Callcenter data / call logs Classic CRM / data processes Digital channels behaviour ONLINE ADVERTISING WEBSITE INTERNET BANKING ADDING ONLINE CHANNELS: TRACKING Digital non-client & client behaviour profiles Machine learning Digital campaign management tools
  15. 15. Omnichannel Banking Experience Case study #2 15
  16. 16. Client profile (CRM) Classic campaign management tools Emailing / SMS / Push Branches & sales networks Transactional data Callcenter data / call logs Classic CRM / data processes ONLINE ADVERTISING WEBSITE APP INTERNET BANKING Digital channels behaviour Digital campaign management tools Digital non-client & client behaviour profiles Machine learning AI-augmented Customer Engagement 360°
  17. 17. ADSERVER DSP … MASTER DATA Automated download CLOUD PLATFORM STORAGE HOW OUR AI REALLY WORKS?
  18. 18. AUTOMATICALLY CREATING USER JOURNEYS
  19. 19. ALGORITHM (optimizing towards goal) … MASTER DATA CLOUD PLATFORM STORAGE (user journeys) HOW OUR AI REALLY WORKS?
  20. 20. Mortgage / Loan / Saving account / Investments … What product? DIGI DATA BASED SALES SIGNALS
  21. 21. MACHINE LEARNING WHAT DRIVES THE GOAL USER PATH TARGET 1. IMPRESSION 2. IMPRESSION 3. IMPRESSION LOAN finance sauto sauto 1 finance sreality - 1 seznam rohlik finance 0 lidl seznam seznam 0 idnes novinky - 0 idnes - - 0
  22. 22. ALGORITHM (optimizing towards goal) ADSERVER DSP … MASTER DATA Automated download YOUR AI IN CLOUD PLATFORM STORAGE (for free) USER1; BID 0.2 ; USER2; BID 0.6 ; USER3; BID 0.0 ; USER4; BID 0.9 ; … USER1; BID 0.2 ; USER2; BID 0.6 ; USER3; BID 0.0 ; USER4; BID 0.9 ; … USER1; BID 0.2 ; USER2; BID 0.6 ; USER3; BID 0.0 ; USER4; BID 0.9 ; … HOW OUR AI REALLY WORKS?
  23. 23. ALGORITHM (optimizing towards goal) ADSERVER DSP … MASTER DATA STORAGE (for free) USER1; BID 0.2 ; USER2; BID 0.6 ; USER3; BID 0.0 ; USER4; BID 0.9 ; … USER1; BID 0.2 ; USER2; BID 0.6 ; USER3; BID 0.0 ; USER4; BID 0.9 ; … USER1; BID 0.2 ; USER2; BID 0.6 ; USER3; BID 0.0 ; USER4; BID 0.9 ; … TARGETING PLATFORM HOW OUR AI REALLY WORKS? USER / DOMAIN LIST USER / DOMAIN LIST USER LIST AI CRM CLOUD PLATFORM
  24. 24. OK, Loan… but what message? DIGI DATA BASED SALES SIGNALS
  25. 25. DIGI DATA BASED SALES SIGNALS
  26. 26. DIGI DATA BASED SALES SIGNALS
  27. 27. Call script: “Client is interested in taking loan. He is planning wedding according to his activity on internet.” DIGI DATA BASED SALES SIGNALS
  28. 28. 50% Increased effectivity of call center calls in mortgages:
  29. 29. • What are the costs? • What is the operation risk? • Where are the limits? • What portfolio can be targeted? • …
  30. 30. • Cheap storage, computational power on demand • 30% clients training => 100% scoring • 1st party data (Web paths) => safe • 3rd party data (Adform paths)=> dying • …
  31. 31. CONTEXT
  32. 32. CONTEXT Case study #3 Similar arcticles/URLs Keyword based targeting Topic/Semantic based targeting
  33. 33. ENRICH URLS (KEYWORDS, TOPIC, …) https://www.sport.cz/hokej/extraliga/clanek/1186624-hokej-online-liberec-znovu-unika-sparta-je-na-pokraji-porazky.html
  34. 34. We are extending off-line insights by AI 5mil domains... 20 domains 20 keywords 500 domains 500 keywords All users Our solution Regular approach Uncovering domains/kws uknown to other bidders word2vec Vec(king) – Vec(man) + Vec(woman) = Vec(queen)
  35. 35. ENRICH URLS ('https://www.sport.cz/hokej/extraliga/clanek/1186624-hokej-online-liberec-znovu-unika-sparta-je-na-pokraji-porazky.html') https://www.tyden.cz/rubriky/sport/hokej/extraliga/sparta-padla-na-trun-usedl-liberec-zlin-potupil-kladno.html 2.19 https://isport.blesk.cz/clanek/hokej-tipsport-extraliga/373550/sestrihy-hradec-sesadil-spartu-z-trunu.html 1.96 https://www.hokej.cz/dvojnasobny-extraligovy-sampion-se-spartou-znovu-prerusil-karieru 1.61 https://www.ceskenoviny.cz/zpravy/1824498 1.55 OUTPUT
  36. 36. Outcome Regular approach 20 domains 20 keywords New uncovered 500 domains 500 keywords Everyone bids for this (High CPM) Way less buyers (Low CPM)
  37. 37. Outcome Regular approach 20 domains 20 keywords New uncovered 500 domains 500 keywords Everyone bids for this (High CPM) Way less buyers (Low CPM) 20% lower CPA!
  38. 38. ALGORITHM (optimizing towards goal) ADSERVER DSP … MASTER DATA STORAGE (for free) USER1; BID 0.2 ; USER2; BID 0.6 ; USER3; BID 0.0 ; USER4; BID 0.9 ; … USER1; BID 0.2 ; USER2; BID 0.6 ; USER3; BID 0.0 ; USER4; BID 0.9 ; … USER1; BID 0.2 ; USER2; BID 0.6 ; USER3; BID 0.0 ; USER4; BID 0.9 ; … IDNES.CZ; BID 0.4; ZOZNAM.SK; BID 0.7; SEZNAM.CZ; BID 0.6; … TARGETING PLATFORM HOW OUR AI REALLY WORKS? USER / DOMAIN LIST USER / DOMAIN LIST USER / DOMAIN LIST AI CRM CLOUD PLATFORM
  39. 39. • Raw user-level data • Connection of data sources • Dynamic environment Takeaways LIMITS
  40. 40. • Raw user-level data • Connection of data sources • Dynamic environment Takeaways LIMITS Takeaways SOLUTIONS • Transparent vendors • Building 360 • Platform, intern capability
  41. 41. Washingtonova 17/1599 Prague 1, 110 00 Czech Republic +420 775 556 122 jakub.stech@datasentics.com Thank you for your attention www.datasentics.com Jakub Stech, Data Science Architect

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