How to use personalised communications to improve customer engagement

1. Using personalized communications
to improve customer engagement
Steve Burnstone
Eighty20
#SAFactADay

2. Agenda
2
DATA
INTRODUCTION
SEGMENTATION AND PERSONALISATION
WRAPPING UP

3. Introduction
3
What is
good for
the
business?
What is
good for
the
customer?

4. Introduction
4

5. Customer centricity is recognising that your customers are
central to your business’s success
5
PRODUCT
CENTRIC
CUSTOMER
CENTRIC

6. Customer centricity is recognising that your customers are
central to your business’s success
6
PRODUCT
CENTRIC
CUSTOMER
CENTRIC
Optimise
the value
created by
each
product
Optimize
the value
created by
existing
customers

7. The theory of loyalty
A business must first understand their customers needs
7
PERSONALISATION
(targeted offers and
communications ….)
PREFERENTIAL TREATMENT
(tiering …)
BASIC PROPOSITION
(price, product, sevice …)
RELEVANCE
(self actualisation)
RECOGNITION
(social belonging)
REWARDS / VALUE
(economics)
CUSTOMER NEED ENGAGEMENT STRATEGY

8. Targeted communications have been proven to be beneficial
to the business. Relevant communications have the most
impact on behaviour
8
Relevance: 6x
Richer offers: 3x
Timing: 2x
Creative: 1.35x
Channel: 1.25x

9. Millennials are driving the need for businesses to send
personalised communications
9

10. Implementing an analytics strategy can start anywhere on
the analytics continuum
10
Complexity
BusinessValue
Reports
Query
Drilldown
Statistical
Analysis
Forecasting
Predictive
Modelling
Optimization
What happened?
Where exactly is the problem?
What if these trends continue?
What will happen next?
What’s the best that can happen?
Why is this happening?

11. Agenda
11
DATA
INTRODUCTION
SEGMENTATION AND PERSONALISATION
WRAPPING UP

12. Companies have access to a wealth of data, but they do not
always leverage this to their advantage
12
EXTERNAL DATA
AGGREGATEDCUSTOMERSPECIFIC
Survey data
Loyalty programmesSocial media
INTERNAL DATA
Purpose:
High level strategic view
Penetration
Purpose:
Augmentation
Profitability
Purpose:
Understanding customers
Improving value offering
Purpose:
Augmentation
Financial data
Partner dataStatistical
augmentation
Transactional Data
(from all BUs)

13. 13
Discovery is able to view data across their business units,
which allows them to consider unusual metrics to manage
their risks

14. Using a Walled Garden…
14
Shopping
frequency
and time
Have
children
Online late
at night
Pay TV
Gym
membership
Large car /
many miles
Pet owner
DATE TIME DESCRIPTION AMOUNT
2015-04-01 15:24 Grocery Retailer X -139.90
2015-04-03 08:23 School fees -4200.00
2015-04-04 10:44 Petrol Station A -759.99
2015-04-05 11:00 Coffee on the Go -45.00
2015-04-06 09:34 Beauty Clinic -480.00
2015-04-07 14:53 Grocery Retailer Y -739.30
2015-04-08 09:34 Toys for Kids Shop -189.99
2015-04-09 12:42 Rental Income G Fox 7800.00
2015-04-10 09:33 Internet Payment Roof fixed -3200.00
2015-04-11 08:53 Bank fees -120.00
2015-04-13 14:23 Grocery Retailer Y -348.80
2015-04-17 17:33 Veterinary Clinic -650.00
2015-04-18 14:22 Grocery Retailer X -49.60
2015-04-19 09:34 Petrol Station C -698.87
2015-04-20 10:24 Coffee Shop 25.00
2015-04-20 11:00 Grocery Retailer X -134.55
2015-04-25 09:22 Monthly Salary Deposit 24846.98
2015-04-25 00:00 Insurance Debit Order -1988.00
2015-04-25 15:00 Kids Clothing Retailer -340.90
2015-04-28 16:33 Grocery Retailer Y -156.22
2015-04-29 19:00 Medical Aid Debit Order -1789.00
2015-04-29 20:24 Water and Electricity -650.00
2015-04-30 15:03 Pay TV Debit Order -650.00
2015-04-30 15:15 Grocery Retailer X -984.22
2015-04-30 16:05 Active Gym Fees -590.00
2015-04-30 23:24 Amazon.com -870.00

15. MILK 2L
MILK 2L
APPLES 1.5KG
PEARS 1KG
BUTTER 500g
CEREAL BRAND A
CEREAL BRAND B
YOGHURT BRAND C
YOGHURT FULL CREAM
LINDT 90g
LINDT 90g
CHICKEN 2-FOR-1
CHICKEN 2-FOR-1
POTATOES 2.5KG
HUGGIES NAPPIES
BABY FORMULA
BABY POWDER
VEGETABLE MIX 2-FOR-1
VEGETABLE MIX 2-FOR-1
BABY FOOD 90g
BABY FOOD 90g
BABY FOOD 90g
TOTAL 739.30
11.99
11.99
21.99
16.99
32.49
35.99
31.99
19.99
21.99
31.99
31.99
69.99
---
19.99
98.99
189.99
24.99
35.99
---
9.99
9.99
9.99
Using a Walled Garden…
15
HYPERMARKET
Big City
High spend in certain
product categories
Promotional
focus
Prefers
mainstream brands
Drives 3km
to store
Switches between
brands
Regular high
value customer

16. Agenda
16
DATA
INTRODUCTION
SEGMENTATION AND PERSONALISATION
WRAPPING UP

17. Targeted customer engagement should be very different to
spam
17

18. 18
6%
7%
8%
9%
10%
10%
11%
11%
22%
20%
17%
20%
18%
30%
17%
28%
26%
24%
39%
47%
41%
40%
32%
49%
36%
38%
31%
23%
19%
21%
22%
20%
14%
16%
15%
15%
12%
9%
8%
10%
7%
8%
8%
10%
8%
Sorbet Society
My School / My Village / My Planet
EDCON Thank U
Dischem Benefits
Shoprite Checkers EeziCoupons
TFG Rewards and More
Clicks ClubCard
WRewards
Pick n Pay Smart Shopper
Strongly agree Agree Neither agree or disagree Disagree Strongly disagree Not answered
AGREE OR DISAGREE: THIS PROGRAMME USES DATA EFFECTIVELY TO CUSTOMIZE
THEIR REWARD / BENEFIT OFFERINGS
The 2014 survey of loyalty programme owners in South Africa
indicated that over three quarters of these programme owners say
they are utilising data for targeted marketing campaigns. However
results from the latest survey indicate that customers think they
could be doing better at personalising rewards
With the launch of its “Thank U”
loyalty programme, Edcon now has
the largest retail customer
database in South Africa with
more than 12 million
customers, which enables it to
provide customers with relevant
offers across its suite of retail and
financial services products

19. Profiling allows us to understand and engage with our customers
better, but these methods have different uses. There is a trade off
between corporate costs and customer needs
CAMPAIGN
CENTRIC
CUSTOMER
CENTRIC
Filtering
Single segmentation
Behavioural propensity
Personalisation
TARGETED ENGAGEMENT CONTINUUM
INTELLIGENCE /
COST / ROI
CAMPAIGN
STRATEGY
Sequential
Parallel
PROS AND CONS
- Overly simplistic
+ Simpler to
implement
- May be perceived as
invasive
+ Able to run more
campaigns
+ Different campaign
objectives per
individual
Multiple segmentation
19

20. 20
Segmentations are better suited to macro-level strategic
interventions. They are not well suited to individualised
communications to a customer
SEGMENTATION TYPES
CONS
PROS  Suitable for macro-level decisions like product and branch design and large marketing
campaigns
 Different segmentations may be used for different purposes
 Not suitable for targeted communication or offers to customers
Demographic
Lifestyle & Psychographic
Lifestage
Vs
Value

21. Demographic segmentation
A demographic segmentation is the most basic. It is the
easiest to create and understand but it is generally not the
most useful
21
Age, Income
This assumes that demographics are related to behaviour, which is
not always appropriate
Segment Measurement
Age, Income
Age, Income
Age, Income
Young professional
Middle class
Wealthy pensioners
Student

22. Lifestage segmentation
A lifestage segmentation is similar to demographic
segmentations, but be more useful in product development
22
Young Couple Age, Relationship status
Segment Measurement
Young family Living together, children
under 5 years
Full Family Living together, children
over 5 years
Mature family
Living together, children
over 5 years, more than one
child, assets

23. Lifestyle and behavioural segmentation
A lifestyle segmentation considers spend preferences and
behaviour which can give insight into customer behaviour
23
Active Spend on gym membership,
equipment, supplements
Travellers
Spend on fuel, bus tickets,
airplane tickets
Spend mostly on weekends,
spend on alcohol,
recreational activities
Online shoppers Online shopping activity
Fun lovers
Segment Measurement

24. Value segmentation
Segmenting customers by value allows you to provide
differentiated services and products to retain high value
customers and up-sell lower value customers
24
Top 1%
Segment Measurement
1 - 5%
6% - 50%
Bottom 50%
Platinum
Gold
Bronze
Silver

25. Using multiple segmentations at once to identify and target
customers can be more effective at targeting smaller groups
than traditional filtering or single segmentation driven
techniques
25
SEGMENTATIONS TARGETED OFFER LIST
A
B
C
1 2 3
4 5 6
7 8 9
Customer Seg A Seg B Seg C Channel
1 3 4 E-mail
5 4 2 Post
6 9 1 Mobile App
5 8 7 SMS
1 2 3
4 5 6
7 8 9
1 2 3
4 5 6
7 8 9

26. 26
Propensity modelling is the first step in a personalisation
strategy
0%
25%
50%
75%
100%
0% 25% 50% 75% 100%
PropensitytoincreasePOS
Propensity to increase take up a new product

27. 27
Personalisation is better suited to target and communicate with
individual customers
BEHAVIOURS TO MODEL
CONS
PROS  Suitable for targeted communication or offers to customers
 Does not have to be real-time
 Can be simple to implement
 More expensive
Churn
Frequency
Cross/Up-sell
Channel Preference
Vs
POS

28. Traditional communication approach
There are many processes and stakeholders involved in
traditional communications. The transition to a more
personalised approach will have to be managed carefully
28
TRADITIONAL COMMUNICATION
Campaign
brief
Look for relevant
customers
Define offer and
collateral
Wash leads
against charter
(consent)
Execute via
contact centreResults
?

29. Personalised communication approach
Personalisation aims to send the right message to the right
customer, at the right time and through the right channel
29
Message /
offer
Customer info
Customer
transaction
history
Analysis
RESPONSEOUTPUT
Algorithms
Reporting
Personalised
messages
INPUT
Customer
action
CHANNEL
SMS
eNewsletter
Outbound
Social Media
Mobile App
Website
Branch
Desired
behaviour
Constraints
Feedback
loop
ANALYSIS

30. RESULTS:
Personalisation example – Canadian Health & Beauty
Ongoing campaigns
30
DETAILS:
 Multichannel (email, mobile, web and more)
 3.5m customers
 Offers (from multiple sources) + TPR’s
 Targeted communication 4 per week
1
 Incremental visits = 160,000 per month
 Incremental sales = $10m per month

31. Personalisation example – US FMCG
Sprint campaigns
31
RESULTS:
DETAILS:
 Determine customer group based on
marketing objective
 Multichannel (Direct Mail, email, Entry
Kiosks, Catalina)
 Coupons (from multiple sources) + TPR’s
 25,000 incremental visits
 50% of those targeted redeemed (150k)
 $2m short term incremental sales and
$1m post campaign effect

32. Agenda
32
DATA
INTRODUCTION
SEGMENTATION AND PERSONALISATION
WRAPPING UP

33. Complex statistics and machine learning techniques can help
with an effective personalisation strategy, but these methods
need to be used with the customer experience in mind and
not (only) improving conversion rates
33

34. A feedback loop is needed between customers and the
business to understand the customer’s experience by channel
and the impact of personalisation initiatives
34
FEEDBACK LOOP
RANDOM SURVEYS
BEHAVIOURAL
ANALYSIS
SIMPLE QUESTIONS
Remember: It has to me more than an economic transaction,
recognise and be relevant

35. Data, technology, processes and corporate culture are key to
rolling out a personalisation strategy
35
 Where are you getting
your data? Are you using
all data available?
 Is it in a format where you
have identifiable and
contactable customers
 Is there scope to improve
it by augmenting it with
other data sources?
 Do not confuse
technology with a data
driven strategy
 Do you have the right
channel and offer
fulfilment technologies
to be able to execute on
a near / real time basis
 Determining objectives,
sourcing offers and
creative all need to
operate efficiently
 Do you have the buy in
from the c-suite?
 Is your business
customer centric and
focussing on the
customer experience
 Do business silos speak
to customers
individually?
 Change management –
marketing,
merchandising/product
and servicing
DATA & QUALITY TECHNOLOGY, PROCESS &
CAPABILITIES
CULTURE CHANGE
1 2 3
MAKING IT HAPPEN:

36. Using personalized communications
to improve customer engagement
Steve Burnstone
Eighty20
#SAFactADay

37. Questions?
Thank you