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Visitor Intent: Smart clues for understanding customer journeys
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Visitor Intent: Smart clues for understanding customer journeys

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  • 1. Visitor Intent Smart clues for understanding customer journeys Carmen Mardiros @carmenmardiros
  • 2. Visitor Intent = Customer’s Agenda Revenue = Your Agenda @carmenmardiros
  • 3. Your Agenda is irrelevant unless it matches the Customer’s Agenda… Visitor Intent = Customer’s Agenda Revenue = Your Agenda @carmenmardiros
  • 4. Website experience must match Visitor Intent Different jobs for different customer intentions Happy customers tick stuff off their agenda Greater overlap Customer’s Agenda = Your Agenda @carmenmardiros
  • 5. Website experience must match Visitor Intent Different jobs for different customer intentions Happy customers tick stuff off their agenda No conversion to do attribution for Conversion attribution is meaningless unless the visitor comes back. @carmenmardiros
  • 6. What decisions would you make if....? Sizeable discountseeker segment Measure profitability and break-even point of customer segment. Optimise campaigns to attract other, more profitable customer segments. Many researchers not-yet-ready to buy Introduce features to facilitate comparison and shortlisting. Nudge visitors to self-select based on drivers of choice. Committed buyers are Fix hurdles and in the process, improve conversion rate struggling with for less committed buyers. checkout @carmenmardiros
  • 7. Visitor Intent muddles Conversion Rate Segment size Conversion Rate Success measure Unqualified % of traffic Not shopping Task completion rate Researching Upgrade to Comparing offering & merchants Comparing Upgrade to Committed to Purchase Committed shopper Abandonment rate TOTAL Why do we still report in aggregate?
  • 8. How to Infer Visitor Intent using Advanced Segmentation @carmenmardiros
  • 9. What analytics folk can learn from Google @carmenmardiros
  • 10. What do these interactions tell me about Market segment Families vs couples, amateur vs pro photographers Existing relationship Customer, prospect, partners, internal staff? Decision stage Researching, comparing, close to decision point Drivers of choice Urgency of need, price sensitivity, service over price, existence of other decision makers Last minute shopper vs advance planner Shopping style Potential value Price range considered, deal & voucher seekers, long term value @carmenmardiros
  • 11. What do these interactions tell me about Market segment Families vs couples, amateur vs pro photographers Existing relationship Customer, prospect, partners, internal staff? Decision stage Researching, comparing, close to decision point Drivers of choice Urgency of need, price sensitivity, service over price, existence of other decision makers Last minute shopper vs advance planner Shopping style Potential value Price range considered, deal & voucher seekers, long term value @carmenmardiros
  • 12. Intent Building Block #1 Segment Overriding Behaviours First @carmenmardiros
  • 13. Fringe audience segments Explicit: Careers, Investors, Media Implicit: Not consumers Conversion likelihood: Low @carmenmardiros
  • 14. Post-purchase behaviour Explicit: Live Arrivals and Departures Implicit: Already flying, waiting for someone Conversion likelihood: Low @carmenmardiros
  • 15. Absence of certain behaviours Explicit: Login Implicit: Possibly customer IF logs in without registration Conversion likelihood: Uncertain @carmenmardiros
  • 16. High value market segments Explicit: Business section Implicit: Not consumer Potential value: High @carmenmardiros
  • 17. Persistent shopper attributes Explicit: Fills form Implicit: Planning, long distance move, owns lots of stuff Conversion likelihood: Low Potential value: High @carmenmardiros
  • 18. Keywords as Buckets of Intent Forget keywords. Align buckets of keywords to customer journey stage. @carmenmardiros
  • 19. Why Classify Overriding Behaviours First Quick and easy Small segments but remove noise from your convertible pie Fringe audiences Helps identify valuable but overlooked audience segments. Better measures of success? Attributes for customer First building blocks for understanding customer profiling journeys and mix of market segments @carmenmardiros
  • 20. Intent Building Block #2 Segment by First and Early Actions @carmenmardiros
  • 21. Purchase actions taken immediately Explicit: Order Now Implicit: Already researched, ready to buy Conversion likelihood: Very high @carmenmardiros
  • 22. Immediate deal-seeking behaviour Explicit:  Enter  voucher Implicit:  Deal  seeker,  price   sensi5ve,  commi7ed  to   buy Conversion  likelihood:   Very  high Poten7al  value:  Low @carmenmardiros
  • 23. First choice = Self-selection into segment Explicit:  More  informa5on Implicit:  High  end  market   segment Poten7al  value:  High @carmenmardiros
  • 24. Drivers of choice – Price, brand Explicit:  Under  £350 Implicit:  Price  sensi5ve,   more  flexible  about   brand Poten7al  value:  Lower Explicit:  Bosch Implicit:  Less  flexible   about  brand  &  less  price   sensi5ve Poten7al  value:  Higher @carmenmardiros
  • 25. Drivers of choice - Service Explicit: Delivery, recycling, returns Implicit: Close to decision point, mustknow before buying OR already purchased @carmenmardiros
  • 26. Researching and offline intent Explicit:  Brochures Implicit:  Researching,  may   buy  offline Conversion  likelihood:  Low @carmenmardiros
  • 27. Landing Page + First Action for Not Provided Explicit:  Naxos Explicit:  Things  to  do,  Regions Implicit:  Decided  resort,   checking  offering Implicit:  Undecided  on  resort Conversion  likelihood:  Medium Placebo  search  term: “naxos  holiday  flight  2  adults” Conversion  likelihood:  Low Placebo  search  term: “regions  in  greece” @carmenmardiros
  • 28. Why Segment by First and Early Actions Expression of visitor self- Users tell you their market segment, shopping selection attitude, context, existing relationship. Helps with “Not Provided” Segment Organic traffic by Landing Page (Fridge) + First Action taken (American). Good indicator for commitment to buy Segment immediate entry into conversion. Excellent baseline to test checkout usability against. Makes up for multidevice and cookie deletion Existing users or customers leave behavioural footprints. Improves segmentation by relationship. @carmenmardiros
  • 29. Intent Building Block #3 Segment by Variety and Amount of Certain Behaviours @carmenmardiros
  • 30. Category crossover – High potential value Explicit:  Washing  machine   AND  Dishwashers Implicit:  Planning  a  big   purchase,  bundle  savings   would  help. Poten7al  value:  High @carmenmardiros
  • 31. Amount of activity before Add to Basket } Ready for order? => Abandonment or success Number of Products considered Brands considered Reassurance and Convincer pages seen (TIP: Use Custom Metrics in Universal Analytics) @carmenmardiros
  • 32. Behavioural segmentation principles First step: Make sensible assumptions. • Segment overriding behaviours first • Classify what people do first and most • Ensure your segments are mutually exclusive • Refine segments based on multiple conditions @carmenmardiros
  • 33. How does Visitor Intent affect execution of your business model? Thank You Carmen Mardiros @carmenmardiros
  • 34. Thank You Carmen Mardiros @carmenmardiros