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Transcript of Channels, operations and marketing M@ Kuperholz Brisbane, 10 May 2012 Connecting to drive customer...
Channels, operations and marketing
M@ Kuperholz Brisbane, 10 May 2012
Connecting to drive customer experience
In God we trust…
In God we trust……everyone else bring data
The sexiest job in the next decade will be…
The sexiest job in the next decade will be…… statistician
© 2012 Deloitte Touche Tohmatsu6
The Customer Experience
Disruptive Forces Impacting Customer
Needs and Power
Channel / access device proliferation
Blurring of global boundaries
Higher customer product feature
knowledge
Increased sales & service
expectations
Ubiquitous access to
information
Social network influence
Price transparency
Data ExplosionTechnologyInnovation
EnvironmentalChange
Regulatory Reform
Reduced switching costs
Between the dawn of civilisation and 2003, we only created five exabytes of information…
Between the dawn of civilisation and 2003, we only created five exabytes of information……now we create that amount in less than two days
Between the dawn of civilisation and 2003, we only created five exabytes of information……now we create that amount in less than two days
…by 2020 every hour
Data is an asset
BIG DATA
big data
Data is an asset
Huge amounts ofData is an asset
Data is an asset
Win : Win
© 2012 Deloitte Touche Tohmatsu16
Data is an asset which can improve customer experience
Acquire
Store and access
Prepare and structure
Analyse
Validate and interpret
Report and implement
What?
When?
Where?
How?
Who?
Why?
What next?
© 2012 Deloitte Touche Tohmatsu17
Channels
Physical presence
Live events
Telephone
Web
Social
Online chat
Mobile
© 2012 Deloitte Touche Tohmatsu18
Operations
Human resources
Customer care / contact centre
Production
Supply chain / logistics
Store placement / layout
Delivery
© 2012 Deloitte Touche Tohmatsu19
Marketing
Word of mouth
In store promotions
Sponsorship
Billboards / print
Cinema
Radio
Television
Web
Web 2.0
Social
Mobile
© 2012 Deloitte Touche Tohmatsu20
Enriching with external data
Census / ABS
Property details
Household expenditure / net worth
Geodemographics / Segmentation
Loyalty Programs
Data aggregators
Market research
2012 : GaSoLoMo
© 2012 Deloitte Touche Tohmatsu22
Socialytics
“The Big Shift” and increasing social capital
Social network analytics
Unstructured and structured data
© 2012 Deloitte Touche Tohmatsu23
Data is an asset which can improve customer experience
Acquire
Store and access
Prepare and structure
Analyse
Validate and interpret
Report and implement
What?
When?
Where?
How?
Who?
Why?
What next?
© 2012 Deloitte Touche Tohmatsu24
Text analytics
© 2012 Deloitte Touche Tohmatsu25
Social Network Analysis
© 2012 Deloitte Touche Tohmatsu26
Reporting, statistical analysis and geospacial
© 2012 Deloitte Touche Tohmatsu27
Artificial Intelligence and other advanced techniques
11 Suncorp © 2011 Deloitte Touche Tohmatsu
Indicative insights –S6, 7 Highest penetration: seek to “own”S5 Underpenetrated; immediate focus
S1 Emerging value; next best offerS4 Savings to Term; up-sell “lock-in”
S2, 3 Optimise margin: selective growth
© 2012 Deloitte Touche Tohmatsu28
MAKES PURCHASE
© 2012 Deloitte Touche Tohmatsu29
MAKES PURCHASE ACTIVE CUSTOMER
© 2012 Deloitte Touche Tohmatsu30
MAKES PURCHASE ACTIVE CUSTOMER IS MALE
© 2012 Deloitte Touche Tohmatsu31
CATEGORY SPEND BRAND PERCEPTION HOURS USED
© 2012 Deloitte Touche Tohmatsu32
CATEGORY SPEND BRAND PERCEPTION HOURS USED
MAKES PURCHASE ACTIVE CUSTOMER IS MALE
© 2012 Deloitte Touche Tohmatsu33
CATEGORY SPEND BRAND PERCEPTION HOURS USED
MAKES PURCHASE ACTIVE CUSTOMER IS MALE
© 2012 Deloitte Touche Tohmatsu34
Segmentation
Holistic and simultaneous consideration of all attributes
Assumption free
Hierarchical clustering : GRANULARITY aligned with capacity to act
© 2012 Deloitte Touche Tohmatsu35
Self Organising Map (Artificial Intelligence) videos
Technical
http://bit.ly/saxaHK
Business
http://bit.ly/Kla4Z5
© 2012 Deloitte Touche Tohmatsu36
Data is an asset which can improve customer experience
Acquire
Store and access
Prepare and structure
Analyse
Validate and interpret
Report and implement
What?
When?
Where?
How?
Who?
Why?
What next?
Test and Learn
© 2012 Deloitte Touche Tohmatsu40
Data is an asset which can improve customer experience
Acquire
Store and access
Prepare and structure
Analyse
Validate and interpret
Report and implement
What?
When?
Where?
How?
Who?
Why?
What next?
© 2012 Deloitte Touche Tohmatsu41
Large Financial Institution : target marketing customer case study
Profile, Segment, Target : 10,000,000 customers based on 17,000 metrics derived from data assets relating to Channels, Operations and Marketing
• Deliver 17 targeted lists and models for Bank
• Retention
• Upsell
• Cross-sell
• Each model consisted of a marketing objective, primarily a targeted cross-sell product or service
Offer Group Segment
E TDCT customers with shallow money-in product portfolio with us
4 Offers
D TDCT chequing customers who are likely to attrite
4 Offers
4 Offers
B TDCT customers who currently have a chequing account
1 Offer
CTDCT customers who have short tenure 9-24 months and have a
chequing account
Sub-Segments / Offers
A TDCT customers who currently have an unsecured credit product
4 Offers
© 2012 Deloitte Touche Tohmatsu42
Granular segmentation assigned customers into 40 segments
C22
C4
C40
C37
C1
C33
C27
C31
C32
C26
C3
C39
C9
C2C8
C24
C11
C34C25
C6
C30
C35C10
C20
C13
C5 C14 C19
C16
C17
C15
C18
C12
C28
C38
C21
C29
C7
C36
C23
© 2012 Deloitte Touche Tohmatsu43
Tenure, gender, age
• Two main typologies of TD WaterHouse customers
• New and young customers are predominantly found in the bottom right hand side of the SOM model.
• Less affluent customers tend to be found along the bottom part of the SOM model.
© 2012 Deloitte Touche Tohmatsu44
Product holdings
44
Mortgages Line of Credit Visa
• Generally the customers with investments are high value, older and have a longer tenure – but there are pockets of younger/newer customers who also have investment products.
• Visa product held by both high and low profit customers as well customers with both deep and very shallow banking relationships.
Investments Demand Loans
© 2012 Deloitte Touche Tohmatsu45
Risk and value
45
Other FI Utilization
Total average chequing balance- 6 month average
NSF FeesBeacon Score
Total Money In Total Money Out
• Highly utilized, high NSF fees, high risk customers
• Older and long tenure customers tend to have higher than average chequing account balances
• Investment customers with high total money-in balances
© 2012 Deloitte Touche Tohmatsu46
Changes over time
46
Future log increase in product holdings Future log increase in investment balance Ezyweb transactions as a percentage oftotal channel usage (Historical 6 months)
Future log increase in chequing balance Future log increase in Visa balanceEzyweb transactions as a percentage oftotal channel usage (Future 6 months)
• These are a cluster of older customers who are taking money out of their investment accounts probably to spend during retirement.
• There are strong clusters of customers whose visa balance has increased over the year.
• New customers tend to increase their chequing balance the most, the customers who reduce their balance tend to attrite
• This cluster of customers have the number of product holdings increase over the one year period. They are also younger customers who have some investments.
• • The usage of Ezyweb as a percentage of total channel usage increase over the year for this cluster. This top cluster is majority TDWH customers. There are several smaller clusters as well.
© 2012 Deloitte Touche Tohmatsu47
All Offers
A1, A3, E1, E3 – Cluster 1
A3, B1, E2 - Cluster 1
A1,A2,A4,Z3,E1,E2,E3,E4 –Cluster 1
A2, A3, E2, E3 – Cluster 1
A1, A4, Z3, E2 – Cluster 1 A1, A4, Z3 – Cluster 1
A1, E1, E2 Cluster 1 A1 – Cluster 1 A1, Z3 - Cluster 1
B1, Z1, Z2 – Cluster 2
A1, A2, Z3, E3 - Cluster 1
A1, A3, E1, E3 –- Cluster 2
A1, A4 – Cluster 1 E1 – Cluster 3
A1, A3 –- Cluster 2
A1, D1 – Cluster 1
A1 – Cluster 4
D1 – Cluster 1
Z1, Z2, E1, E3, E4 – Cluster 1 B1, D1 – Cluster 1
A1, A3 - Cluster 1
A2, Z3, E1, E3 –Cluster 1
A1, A3, E1 –
Cluster 1
B1 –Cluster 2
A1, E1, E3 –
Cluster 1
A1 –Cluster 2
A4 – Cluster 2
B1 – Cluster 1
B1, Z1, Z2 -
Cluster 1
Z1, Z2 – Cluster 1
Z1 – Cluster 2
D1 – Cluster 2
Z1, Z2 –Cluster 2
Z3, E1, E4 –
Cluster 1
Z3, E1, E4 –
Cluster 2
A2, Z3, E1, E4 - Cluster 1
E1 –Cluster1
E4, E1 –Cluster 1
E3 – Cluster 1 A1 –
Cluster 3
E4 –Cluster 2
A2, Z3, E2, E4 – - Cluster 1
A3, Z3, E1, E4 – Cluster 1
A1, Z3, E1, E2, E3 - Cluster 3
A1, D1, E1, E3 - Cluster 1
Z1, Z2, E1 –Cluster 1
B1, Z1, Z2, E1 -Cluster 1
B1, Z1, Z2 -
Cluster 2
3. We identified segments of opportunity
• Segments identified based on sales, response to marketing, product, pricing and brand performance
• We then characterised the segments defining their distinguishing attributes, example:
Large Global FMCG Organisation – Promotional, marketing and pricing
• Lower sales
• Year on year decline
• Categories 1, 2, 3 over represented
• Low volume per household
• High advertising activity (TV and radio) with lower incremental return
• Weak competitor brand positioning
• Higher income earners
• Above average membership of Segment Y
1. Problem Statement
Situation Pricing / marketing strategy to maximise return on promotion spend
Goal Make decisive marketing, promotion and pricing choices in order to grow
Challenge What drives customer purchases? Where are we over-investing? Where should we invest? How?
4. We helped embed the model into their marketing and sponsorship plan
We have embedded the model with the marketing and strategy teams who refresh the model on a regular basis to measure performance.
2. Granular customer insights
We modelled the purchase patterns of 10,000 stores with ~ 700 attributes simultaneously to identify 46 granular segments, with a focus on return on promotional spend.
We also overlayed promotions, marketing, pricing, TV, radio, online, social media, weather data and more to identify and predict effects of marketing
5. Interventions were Identified to reallocate marketing spend, channel mix and pricing adjustments
- Withdraw promotional spend from X segments with, low market share and no clear intervention strategy to rectify
- Pricing adjustments in areas with low price elasticity
- Channel and promotional brand and sponsorship mix tuned based on granular segment insights
Segment 4
• Reasonable sized sales
• Year on year growth in value and volume
• Generic split of categories
• High volume per household
• Low advertising activity on television with high return
• Competitor brand positioning growing
• Lower income earners
• Above average membership of Segment X
An unforeseen opportunity– how should we engage the distribution channel and end consumer to capitalise on the growth?
Segment 5
A market of no excuses – how do we negotiate and drive improved distribution? How best to improve Marketing ROI?
Analyse & Interpret Design Interventions Execute Campaign
© 2012 Deloitte Touche Tohmatsu49
Airline targeted marketing
Granular Insight
1. Problem Statement
Situation: Incumbent under threat
Goal: Increased share of wallet
Challenge: Who is the true customer? What is their travel behaviour? How can we improve loyalty?
3. Setting Growth Priorities
We aligned market growth priorities to the organisation’s overall strategic objectives
Prioritising which segments to target for maximum growth
Quantifying growth goals against segment priorities
4. We Defined the Intervention Program
We defined a program of personalised campaigns for customer acquisition over a 12 month program
2. Granular customer insights
2.7m customers, 30 months of history to trace customer behaviour:
Domestic, international, leisure business; corporate, SME, retail
Product (fare, route, Valet, QC) Value (revenue, price sensitivity)Booking (ltime, channel, checkin) Points usage Campaign response
5. We refresh, test & refineWe refresh the segmentation to measure
program effectiveness, respond to shifts in behaviour & re-align for sustained growth
Strategic Alignment Agile Execution & Measurement
© 2012 Deloitte Touche Tohmatsu
Granular Insight
1. Problem Statement
Situation: Increased competition
Goal: Improve service whilst lowering cost to serve
Challenge: How do we best meed the needs of customers to drive online adoption?
3. Defining adoption strategy
We prioritised key segments & defined relevant strategies for online adoption:
• We articulated the value proposition that best aligned with customer needs & behaviour;
• We tied this to quantified benefits for lowering cost to serve.
4. We designed the Interventions
We improved specific business processes and online user experience flows to support key market interventions for adoption
2. Granular customer insights
We developed a granular segmentation model to understand the long-tail of cross-channel customer behaviour: across all aspects – transaction, product, channel, attitudinal & demographic
5. We designed targeted campaigns for execution
We drove into the segmentation model to select customer lists and execute targeted cross-channel campaigns for adoption
Agile Strategy Targeted Execution & Measurement
Telcommunications company marketing strategy for digital adoption
Questions?
Channels, operations and marketing
M@ Kuperholz Brisbane, 10 May 2012
Connecting to drive customer experience