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....?

@carmenmardiros

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

@carmenmardiros

What analytics folk can learn from Google

@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

@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

@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

Explicit: Fills form

Implicit: Planning, long distance move, owns lots of stuff

Conversion likelihood: Low

Potential value: High

Persistent shopper attributes

@carmenmardiros

Keywords as Buckets of Intent

Forget keywords.

Align buckets of keywords to customer journey stage.

@carmenmardiros

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

@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:  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

@carmenmardiros

Drivers of choice - Service

Explicit: Delivery, recycling, returns

Implicit: Close to decision point, must-know 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:  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”

@carmenmardiros

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

@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}Number of Products considered Brands considered Reassurance and Convincer pages seen

(TIP: Use Custom Metrics in Universal Analytics)

Ready for order? => Abandonment or success

@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 YouCarmen Mardiros@carmenmardiros

Thank You

Carmen Mardiros@carmenmardiros