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Visitor Intent
Smart clues for understanding
customer journeys

Carmen Mardiros
@carmenmardiros
Visitor Intent
=
Customer’s
Agenda

Revenue
=
Your Agenda

@carmenmardiros
Your Agenda is irrelevant unless it matches
the Customer’s Agenda…

Visitor Intent
=
Customer’s
Agenda

Revenue
=
Your Agenda

@carmenmardiros
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
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
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
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?
How to Infer Visitor Intent using
Advanced Segmentation

@carmenmardiros
What analytics folk can learn from Google

@carmenmardiros
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
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
Intent Building Block #1
Segment Overriding Behaviours First

@carmenmardiros
Fringe audience segments
Explicit: Careers,
Investors, Media
Implicit: Not consumers
Conversion likelihood:
Low

@carmenmardiros
Post-purchase behaviour
Explicit: Live Arrivals
and Departures
Implicit: Already flying,
waiting for someone
Conversion likelihood:
Low

@carmenmardiros
Absence of certain behaviours

Explicit: Login
Implicit: Possibly
customer IF logs in
without registration
Conversion likelihood:
Uncertain
@carmenmardiros
High value market segments
Explicit: Business
section
Implicit: Not consumer
Potential value: High

@carmenmardiros
Persistent shopper attributes

Explicit: Fills form
Implicit: Planning, long
distance move, owns lots of
stuff
Conversion likelihood: Low
Potential value: High
@carmenmardiros
Keywords as Buckets of Intent

Forget keywords.
Align buckets of keywords
to customer journey stage.

@carmenmardiros
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
Intent Building Block #2
Segment by First and Early Actions

@carmenmardiros
Purchase actions taken immediately
Explicit: Order Now
Implicit: Already
researched, ready to buy
Conversion likelihood:
Very high

@carmenmardiros
Immediate deal-seeking behaviour
Explicit:	
  Enter	
  voucher
Implicit:	
  Deal	
  seeker,	
  price	
  
sensi5ve,	
  commi7ed	
  to	
  
buy
Conversion	
  likelihood:	
  
Very	
  high
Poten7al	
  value:	
  Low
@carmenmardiros
First choice = Self-selection into segment
Explicit:	
  More	
  informa5on
Implicit:	
  High	
  end	
  market	
  
segment
Poten7al	
  value:	
  High

@carmenmardiros
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
Drivers of choice - Service

Explicit: Delivery,
recycling, returns
Implicit: Close to
decision point, mustknow before buying OR
already purchased

@carmenmardiros
Researching and offline intent
Explicit:	
  Brochures
Implicit:	
  Researching,	
  may	
  
buy	
  offline
Conversion	
  likelihood:	
  Low

@carmenmardiros
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
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
Intent Building Block #3
Segment by Variety and Amount
of Certain Behaviours

@carmenmardiros
Category crossover – High potential value
Explicit:	
  Washing	
  machine	
  
AND	
  Dishwashers
Implicit:	
  Planning	
  a	
  big	
  
purchase,	
  bundle	
  savings	
  
would	
  help.
Poten7al	
  value:	
  High

@carmenmardiros
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
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
How does Visitor Intent
affect execution of your business model?

Thank You
Carmen Mardiros
@carmenmardiros
Thank You
Carmen Mardiros
@carmenmardiros

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Visitor Intent: Smart clues for understanding customer journeys

  • 1. Visitor Intent Smart clues for understanding customer journeys Carmen Mardiros @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