How to make us of data gathered from loyalty programs in order to target specific marketing actions towards specific audiences?

1. FROM DATA TO INSIGHTS
HOW TO LEVERAGE LOYALTY DATA TO GENERATE DEEP
CUSTOMER SEGMENTATION
Comarch Loyalty Breakfast 2017
Maximize profitability thanks to customer insights from Loyalty & Gamification programs

2. Anna.bazela@comarch.com
Anna Bazela
Comarch BI Consultant

3. TODAY’S QUESTION IS…
-More and more data
-Cheaper space to keep it
-Lack of data structure & understanding
OBJECTIVE
- How do we get from raw data to actionable insight?
- How do we use this huge amount of information to understand our customers better
and take our business to the next level?
- Understand what’s behind the data
- Make it meaningful
- More and more data
- Lack of data structure & understanding
CHALLENGEDATA MARKET TRENDS

4. AGENDA FOR TODAY
1. HOW DO WE USE LOYALTY DATA TO FIND
MEANINGFUL PATTERNS ABOUT OUR
CUSTOMERS
2. SAMPLE CUSTOMERS REPORTS
& DASHBOARDS - LIVE DEMO
3. DATA BASED DECISION MAKING SUPPORT
TO OPTIMIZE CUSTOMERS EXPERIENCE

5. AGENDA FOR TODAY
1. HOW DO WE USE LOYALTY DATA TO FIND
MEANINGFUL PATTERNS ABOUT OUR
CUSTOMERS
2. SAMPLE CUSTOMERS REPORTS
& DASHBOARDS - LIVE DEMO
3. DATA BASED DECISION MAKING SUPPORT
TO OPTIMIZE CUSTOMERS EXPERIENCE

6. CUSTOMERS DO NOT BECOME EASIER
CUSTOMER TODAY:
 Less loyal & less trusting
 Having more and more power
(choice, comparison, social media)
 Diverse customers types (lifestyle,
purchasing power, expectations)

7. HOW DO WE GET TO UNDERSTAND
OUR CUSTOMERS BETTER?

8. … IT’S EASIER THAN YOU THINK!

9. INFORMATION TECHNOLOGY, DATA GATHERING
AND ANALYTICS MAKE IT POSSIBLE
SELF SERVICE BI – USER
EXPERIENCE ADVANCED ANALYTICS MOBILE
SOCIAL INTELLIGENCEBIG DATAVISUAL ANALYTICS
ANALYTICAL SOLUTIONS
INTEGRATION WITH OTHER
SYSTEMS
IN-MEMORY ANALYTICS
(HIGHER PERFORMANCE) IOT DATA

10. ROLE OF BUSINESS INTELLIGENCE IN LOYALTY PROGRAMS
BETTER CONTROL
OVER THE
PROGRAM
 Costs
 Benefits
 ROI
UNDERSTANDING
CUSTOMER NEEDS
INCREASING
CUSTOMER
EXPERIENCE
ABILITY TO PERSONALIZE
CUSTOMER EXPERIENCE
IN CONTEXT OF:
 Communication Channel
 Offer
Monitoring,
control,
decision
support
Better
Customer
understan-
ding
Create proper
recommendations

11. AGENDA FOR TODAY
1. HOW DO WE USE LOYALTY DATA TO FIND
MEANINGFUL PATTERNS ABOUT OUR
CUSTOMERS
2. SAMPLE CUSTOMERS REPORTS
& DASHBOARDS - LIVE DEMO
3. DATA BASED DECISION MAKING SUPPORT
TO OPTIMIZE CUSTOMERS EXPERIENCE

12. LIVE DEMO

13. AGENDA FOR TODAY
1. HOW DO WE USE LOYALTY DATA TO FIND
MEANINGFUL PATTERNS ABOUT OUR
CUSTOMERS
2. SAMPLE CUSTOMERS REPORTS
& DASHBOARDS - LIVE DEMO
3. DATA BASED DECISION MAKING SUPPORT
TO OPTIMIZE CUSTOMERS EXPERIENCE

14. BEHIND
THE CURTAINS…

15. COMARCH BI
POINT
Dashboards &
Reports
COMARCH DATA
WAREHOUSE
MANAGER
ETL & Admin of
Data Warehouse
OUT OF THE BOX
ANALYTICAL
MODEL
Predefined
Reports and
Dashboards
ADVANCED
ANAYTICS
SERVICES
Analytical
Extensions
… BI PLATFORM TO OPTIMZE CUSTOMER EXPERIENCE

16. POWER OF DATA IN YOUR LOYALTY STRATEGY
Client Lifetime Value Customer Retention /
Churn Analysis
Points issuance/
redemptions
Shop / Site
performance
Next Best Offer Points/marketing
budget expenditures
Call center - contact
center analysis
Sales Staff Analysis

17. WITH ADVANCED ANALYTICS YOU CAN FIND OUT
 Which products are being sold together?
 Which products are being sold more often in
location A than in location B and why?
 Who is my typical client? How can I encourage
more of them?
 Which products is my bestseller now and why?
 To whom shall I direct my next campaign?

18. x
y
z
f2
f1
SEGMENTATION
LOCATION
RECOMMENDATION
Standard
Data
ERP
CRM
CLM
SOCIAL MEDIA
Other
New Data
What products
he was
interested in
How much time
did he spent
What was his
route
What bought
the customer in
the first place
What action did
he make
GOING BEYOND TRADITIONAL DATA SOURCES

19. LOCATION – INTEGRATION WITH COMARCH BEACON
 The most visited places
analyses,
 Heat maps,
 Effective exposure and
merchandising,
 Data mining techniques.

20.  Geography segmentation: e.g. continent, country,
city, district, distance
 Socio-demographic segments: e.g. gender, age,
family, job, income, car
 Behavioral segments: e.g. RFM, segment
migrations, churners
 Activity segments: within the loyalty program,
participation in promotion, cherry pickers, points
issuance/redemption/expiration, responsiveness to
campaigns, rewards ordering, call-center contacts,
mobile application activity, social media activity,
surveys answers
 Product segmentation: e.g. garden, food, animals
 Lifestyle segmentation: home birds, romantic,
grab&go
DEEP CUSTOMER SEGMENTATION

21. RECOMMENDATION ENGINE
 The most visited
places analyses,
 Heat maps,
 Effective
exposure and
merchandising,
 Data mining
techniques.
Base Parameters
InputData

22. DATA BASED DECISION MAKING SUPPORT TO OPTIMIZE CUSTOMERS EXPERIENCE
(example)
SALES &
TRANSACTIONAL
DATA
CUSTOMER DATA
ADVANCED ANALYTICS
 build segmentation where one of the
segments will contain all customers
liking product X
Plan new
promotions
CLM
BI ANALYSIS
 Find which customers bought
the most of product X
RECOMMENDATION
ENGINE
 send those customers personalized
offer
MONITORING
 Follow up on results of
promotion ; get new insights

23. IMPROVE CUSTOMER EXPERIENCE (CX)
SINGLE CUSTOMER VIEW
consumption logs &
devices
content metadata,
ratings, comments
transactions and
subscriptions
social media
activity
marketing campaign
response
demographic data
customer contacts
and service cases
personalized
content
targeted
offers and sales
next best
offer
customer
acquisition, retention
multi-channel
marketing
programmatic
advertising
content acquisition
product & price
strategy
data-driven products
 micro-segments
 scoring
 preferences
 behavior
 interests
 history

24. THANK YOU
Any questions?