data & insight: 3 pillars - flaws and fixes
TRANSCRIPT
Data & Insight 3 Pillars – Flaws and Fixes
Nicola TravisCustomer Marketing Director – The Fragrance Shop
Data & Insight3 Pillars – Flaws and Fixes
Data ProcessingInput
OutputLoyalty
ChannelsChallengesCustomers
Data Processing – Input and Output
Data In Answers out
Who are the customer targets
How should we contact them
What behaviours do they have
What message and When
What activities are working
What does the lifecycle look like
Action Data
Demographic Data
Grouping / Linking
Attribution / Tracking
Data Processing – Input and OutputPRINCIPLES
GA
CRM
£TX
FB
STORES
AND
Lead platform for Customer AnalysisAlign or Accept but do acknowledge
Leverage data across systemsSupermetrics, connectors, helpful IT
Increments of TrackingGlobal, final or complex attributionMatch by Email, Payment, Code, ID
Data is for Decision not Definition.
GA Assisted versus Direct conversions – internal understanding.
Call of Duty Model, Global versus Last influence, Longitudinal measures, Channel v Channel,
Assumed Influence – customer led attributionSecond screening swerve, Store attribution, Caution on accidental or intentional view..
Stats with Caveats.Customer before algorithm. Econometric Modelling. Analytical
Test and Learn - consistency and transparencyApples with apples. Longitudinal testing,
Data Processing – Input and OutputEXAMPLES
Data & Insight3 Pillars – Flaws and Fixes
Data ProcessingInput
OutputLoyalty
ChannelsChallengesCustomers
Loyalty
Loyalty
Re funnel
Cross Channel
behaviour
data
raok / pr / personality
competitive adv
Hyper Info
Cross Device
Personality trade
LoyaltyPRINCIPLES
AIM – What does the business want
CUSTOMER – What will they respond to
SHAPE – What is the resource, system,
journey
Tailor loyalty marketing to the brandPoints or personality : Participation.
Focus on the AimsAnd align with the commercials
Measure and evolveAdapt and react – threat of disinterest
Keep the contextCelebrate milestones – with customers
Selecting customers to increase OF with tiersRe assessment of cost of discount and incremental sales – repositioning of club.
Covert loyalty – ad hoc communication targetingSuccessful in low loyalty environment, targeting OF where it CAN be increased
Reward with Kindness, Kudos and Competition elementsBeyond discount – consider RAOK, Social proof, entries and winners.
Loyalty as hygiene not driver – changing customer expectation
But if installing – ensure using.
Loyalty EXAMPLES
Data & Insight3 Pillars – Flaws and Fixes
Data ProcessingInput
OutputLoyalty
ChannelsChallengesCustomers
DIGITAL
Choose & Challenge Partners Overlay reality on tech Easy Measure – effort
understand No coincidence Avoid the blip storm
Data wasn’t invented in the digital age – it just
transformed it, increased it
exponentially, made it easier to get, created a
dashboard for everyone, brought
measurement to the masses, gave us greater platforms and greater
headaches … and greater potential.
Traditional Principles Tech Solutions - Measurable Data Paralysis Tech Solutions – Summaries Behaviour versus Intent Lots of What and not Why
Get everything on the same pageData connectors, unique identifiers, take the customer route.
Walk the customer journeyOnline browse in store buy focus – stock check log in gate, data collection, UX review.
Marketplace adaptationCustomer expectations – retail edge with camera driven solutions
ObserveContent square, hotjar, session cam – store visit, customer services conversation.
Channels, Challenges and Customers IN PRACTICE