Superweek 2014

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