Journeys for Change: Social Entrepreneurship Journeys 2011-12
Visitor Intent: Smart clues for understanding customer journeys
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Transcript of Visitor Intent: Smart clues for understanding customer journeys
Visitor Intent Smart clues for understanding
customer journeys
Carmen Mardiros@carmenmardiros
Revenue=
Your Agenda
Visitor Intent=
Customer’s Agenda
@carmenmardiros
Revenue=
Your Agenda
Visitor Intent=
Customer’s Agenda
Your Agenda is irrelevant unless it matches the Customer’s Agenda…
@carmenmardiros
Different jobs for different customer
intentions
Happy customers tick stuff off their agenda
Greater overlapCustomer’s Agenda =
Your Agenda
Website experience must match Visitor Intent
@carmenmardiros
Different jobs for different customer
intentions
Happy customers tick stuff off their agenda
Conversion attribution is meaningless unless the visitor comes back.
No conversion to do attribution for
Website experience must match Visitor Intent
@carmenmardiros
Sizeable discount-seeker 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 struggling with checkout
Fix hurdles and in the process, improve conversion rate for less committed buyers.
What decisions would you make if....?
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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
Visitor Intent muddles Conversion Rate
Why do we still report in aggregate?
How to Infer Visitor Intent usingAdvanced Segmentation
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What analytics folk can learn from Google
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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
Shopping style Last minute shopper vs advance planner
Potential value Price range considered, deal & voucher seekers, long term value
What do these interactions tell me about
@carmenmardiros
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
Shopping style Last minute shopper vs advance planner
Potential value Price range considered, deal & voucher seekers, long term value
What do these interactions tell me about
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Intent Building Block #1
Segment Overriding Behaviours First
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Fringe audience segments
Explicit: Careers, Investors, Media
Implicit: Not consumers
Conversion likelihood: Low
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Post-purchase behaviour
Explicit: Live Arrivals and Departures
Implicit: Already flying, waiting for someone
Conversion likelihood: Low
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Absence of certain behaviours
Explicit: Login
Implicit: Possibly customer IF logs in without registration
Conversion likelihood: Uncertain
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High value market segments
Explicit: Business section
Implicit: Not consumer
Potential value: High
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Explicit: Fills form
Implicit: Planning, long distance move, owns lots of stuff
Conversion likelihood: Low
Potential value: High
Persistent shopper attributes
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Keywords as Buckets of Intent
Forget keywords.
Align buckets of keywords to customer journey stage.
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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 profiling
First building blocks for understanding customer journeys and mix of market segments
Why Classify Overriding Behaviours First
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Intent Building Block #2
Segment by First and Early Actions
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Purchase actions taken immediately
Explicit: Order Now
Implicit: Already researched, ready to buy
Conversion likelihood: Very high
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Immediate deal-seeking behaviour
Explicit: Enter voucher
Implicit: Deal seeker, price sensi5ve, commi7ed to buy
Conversion likelihood: Very high
Poten7al value: Low
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First choice = Self-selection into segment
Explicit: More informa5on
Implicit: High end market segment
Poten7al value: High
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Drivers of choice – Price, brand
Explicit: Bosch
Implicit: Less flexible about brand & less price sensi5ve
Poten7al value: Higher
Explicit: Under £350
Implicit: Price sensi5ve, more flexible about brand
Poten7al value: Lower
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Drivers of choice - Service
Explicit: Delivery, recycling, returns
Implicit: Close to decision point, must-know before buying OR already purchased
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Researching and offline intent
Explicit: Brochures
Implicit: Researching, may buy offline
Conversion likelihood: Low
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Landing Page + First Action for Not Provided
Explicit: Things to do, Regions
Implicit: Undecided on resort
Conversion likelihood: Low
Placebo search term:“regions in greece”
Explicit: Naxos
Implicit: Decided resort, checking offering
Conversion likelihood: Medium
Placebo search term:“naxos holiday flight 2 adults”
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Expression of visitor self-selection
Users tell you their market segment, shopping 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 multi-device and cookie deletion
Existing users or customers leave behavioural footprints. Improves segmentation by relationship.
Why Segment by First and Early Actions
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Intent Building Block #3
Segment by Variety and Amount of Certain Behaviours
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Category crossover – High potential value
Explicit: Washing machine AND Dishwashers
Implicit: Planning a big purchase, bundle savings would help.
Poten7al value: High
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Amount of activity before Add to Basket}Number of Products considered Brands considered Reassurance and Convincer pages seen
(TIP: Use Custom Metrics in Universal Analytics)
Ready for order? => Abandonment or success
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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
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How does Visitor Intent affect execution of your business model?
Thank YouCarmen Mardiros@carmenmardiros
Thank You
Carmen Mardiros@carmenmardiros