Analysing Customer Journeys to Predict

Behaviour

Adrian Carr

A Customer Journey Example

January February March

Outbound

Branch

Inbound

Account log in

Competitor Browsing

Outcome

All companies try and predict outcomes – e.g. sales or churn etc, or even sub outcome events leading up to the

target outcome. These events can be across multiple channels, inbound and outbound, and they can also be trigger

events just captured from the data.

It’s complicated

Terminology

Path = Journey = SequenceAll of the above mean ‘what happened

between two points in time’

Event = Step = PointAll of the above are the ‘what’ in ‘what

happened between two points in time’

Even though this is just an example, it is already very complicated.

• Just one customer

• 17 events

• Which events are relevant?

• Is the order of events important?

• Did some people have the same sequence of events, but not the same outcome?

• What about demographics and account data – is that relevant?

Sankey Diagrams

Who is / was Sankey?

A. A Clever Person in SAS R&D?

B. A Russian Mathematician?

C. A Railway Engineer?

D. A Mild Mannered Janitor?

Sankey diagrams are lovely…..but

…but they aren’t easy to draw in .ppt, and they aren’t simple, and they

only ever cover a fraction of the universe so let’s start simply with an

arrow to represent time and events on this arrow form the customer

journey

This path contains multiple events, across multiple

channels (e.g. web, phone, social, etc)

Each of the circles represents an event, that could have come from something similar to the diagram below

Some of which are significant objectives or goals of an organisation,

which one would want to predict and dis/encourage (e.g. churn, or

product sale or conversion, or sub conversion)

Examples –

• Putting something in basket

• Downloading a white paper

• Completing purchase

• Posting an application form

• Calling a call center

• Responding to an offer

• Accepting an offer

• Visiting a store

And some of which are irrelevant when predicting the

objective

Dropping the irrelevant events makes a

problem simpler

Back to our (now relevant event containing) journey…. cutting the time

frame (or length of sequence) of analysis to a more manageable length

also makes life more manageable

The ‘word on the street’ / ‘grapevine’ is

that the length of a journey is best

measured in number of events, and is 3-

5 events long.

Focussing on the decision that an organisation can make

to influence the objective then becomes an easier task

These can be

considered as

‘intervention points’

These can be

considered to be batch

or real time too.

An example

Customer starts to

download a new film

that is 3Gb large

Customer has less than

2Gb remaining of their

inclusive download

allowance

Offer of a data snack of

2Gb

Customer responds to

offer.

…and this then easily extends to multiple intervention

points across multiple paths

And sometimes the goal is not achieved, but again, this

can form an input to the next decisioning path

…similarly, ‘sub conversions’ can be the objective of an

activity, or form the entry to the next path (though of

course the customer is just on one journey)

In summary

Our inputs are a distilled set of

paths that are relevant to

driving a decision that can

drive a positive outcome

our decisioning is now

referenced at the point of

potential intervention, i.e. the

different times where we can

take action, with our desire

being to influence towards a

positive outcome goal

And we are driving the goals

(or sub goals) that we want a

customer journey to lead to

And these customer paths sit as a foundation source of

insight into the SAS Customer Decision Hub….which can

be optimised

Digging a bit deeper……

So we said before ‘Dropping the irrelevant events makes a

problem simpler’, let’s dig deeper into that ‘irrelevant’

definition…

An example of relevant vs irrelevant

This is some data that records events happening

(e.g. bill shock, or dropped call), and a positive

outcomes (e.g. churn, or ‘called call centre’)

On first inspection, both events seem predictive –

there are ten events of each type occurring and

there are ten outcomes that are also

occurring…..but when you look deeper…..

Customer Event 1 Event 2

Positive

Outcome

1 1 1 1

2 1 0 1

3 1 0 1

4 0 0 0

5 0 1 0

6 0 1 0

7 1 0 1

8 1 1 1

9 0 0 0

10 1 0 1

11 1 0 0

12 0 1 0

13 1 0 1

14 0 1 0

15 0 1 0

16 0 0 0

17 0 1 1

18 1 1 1

19 1 1 1

20 0 0 0

Total 10 10 10

Question: - which are relevant?

A. Neither

B. Both

C. Event 1 Only

D. Event 2 Only

….looking deeper….

When Event 1 occurs (e.g. ‘Bill

Shock’, the positive outcome

occurs 90% of the time

But when Event 2 occurs (e.g. dropped call),

the positive outcome only occurs 50% of the

time…..the same frequency as when Event 2

doesn’t happen.

Customer Event 1 Event 2

Positive

Outcome

1 1 1 1

2 1 0 1

3 1 0 1

4 0 0 0

5 0 1 0

6 0 1 0

7 1 0 1

8 1 1 1

9 0 0 0

10 1 0 1

11 1 0 0

12 0 1 0

13 1 0 1

14 0 1 0

15 0 1 0

16 0 0 0

17 0 1 1

18 1 1 1

19 1 1 1

20 0 0 0

Total 10 10 10

Hit Rate

0 1

0 9 1 10%

1 1 9 90%

Total 10 10 50%

Positive Outcome

Even

t 1

Hit Rate

0 1

0 5 5 50%

1 5 5 50%

Total 10 10 50%

Positive Outcome

Even

t 2

We can ‘attribute’ the positive outcome occurring to the Event 1

occurring. We can also say that Event 2 is ‘irrelevant’, and therefore

we can ignore it from any path analysis.N.B. In reality, the combination may also need analysing, this is for example

purposes only

Many of you will be aware of the attribution techniques /

options that exist when considering digital spend…..

The successful

goals (e.g.

completed

purchase) are found

The lead up events are

known (e.g. customer

searched for ‘lovely

wine’ in Google)

One of the traditional

methods are used to

‘attribute’ the success to

the action

None of these are analytical – these are ‘rules based counting, whilst ignoring most

of the things that need to be counted’

It is a potentially simple extension to traditional modelling

methods

1 2 43

1 2 3

1 2 3 4

cust 1 2 3 4 Goal

A 1 1 1 1 1

B 1 1 1 0 1

C 1 1 1 1 0

The paths can easily be

represented as data, and

easily considered in a

predictive model

The goal is used as the

variable to be predicted,

and the events are the

predictive input variables.

This is then a potentially

smart way to identify if 4 is

truly predictive or not

Cust A

Cust B

Cust C

Caveat – traditional logistic regression usually only picks out 10-15 variables per goal,

so additional intelligence or other methods should be considered

Why is this different to normal analytics?

1 2 43

1 2 3

1 2 3 4

cust 1 2 3 4 Goal

A 1 1 1 1 1

B 1 1 1 0 1

C 1 1 1 1 0

The only thing we are

missing here compared to

path analytics is….

The order of events

Cust A

Cust B

Cust C

Why is this different to normal analytics?

1 2 43

1 2 3

1 2 3 4

cust 1 2 3 4 2,3 3,2 Goal

A 1 1 1 1 1 0 1

B 1 1 1 0 1 0 1

C 1 1 1 1 1 0 0

D 1 1 1 1 0 1 1

Cust A

Cust B

Cust C

1 3 2 4Cust D

Sequence style variables

can easily be created to be

represented in a normal

model.

One could argue that there is no

point in doing path analytics, unless

these ‘ordered combination

variables’ add more discriminative

power over and above existing data

More pictorially….

Only if you build two models – and

compare them, will you identify how

much the order of the events is

actually incrementally predictive.

Credit Card Sales Journeys…

1 43Cust A

2 43Cust B

On line browsing

for credit cardEmail Sent

Credit Card

Response Score

>200

Customer

Applies

Email Sent

Successful

Application

Customer

Applies

Successful

Application

‘New School’ / ‘Digital Marketing’

‘Old School’ Marketing

Q. Why not simply consider

model scores as events

within the path, i.e.

dummy events?

So now our decision hub is driven by both relevant paths

and relevant scores

2

2

Others may benefit

from the inclusion of

scores (perhaps even

make a trigger

campaign work better)

And other paths are

just what we used to

call campaigns, based

on a score based

selection criteria

Some Paths will

be purely event

driven

Questions?