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

Loyalty

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

Channels, Challenges and Customers

Awareness

Desire

Interest

Action

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

Final takeaways Keep it simple, keep it clear. Walk the customer journey Test, Measure and Evolve Embrace the challenges!

Nicola TravisCustomer Marketing Director – The Fragrance Shop