Analysing Customer Journeys to Predict Behaviour Adrian Carr A - - PowerPoint PPT Presentation

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 = Sequence

All of the above mean ‘what happened between two points in time’

Event = Step = Point

All 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

• nly 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

• bjective

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

• r 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

• ffer.

…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

• ur 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

• utcomes (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

• ccurring…..but when you look deeper…..

Customer Event 1 Event 2 Positive Outcome 1 1 1 1 2 1 1 3 1 1 4 5 1 6 1 7 1 1 8 1 1 1 9 10 1 1 11 1 12 1 13 1 1 14 1 15 1 16 17 1 1 18 1 1 1 19 1 1 1 20 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

• ccurs 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 1 3 1 1 4 5 1 6 1 7 1 1 8 1 1 1 9 10 1 1 11 1 12 1 13 1 1 14 1 15 1 16 17 1 1 18 1 1 1 19 1 1 1 20 Total 10 10 10

Hit Rate 1 9 1 10% 1 1 9 90% Total 10 10 50% Positive Outcome Event 1 Hit Rate 1 5 5 50% 1 5 5 50% Total 10 10 50% Positive Outcome Event 2

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

• ccurring. 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 /

• ptions 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

• f the things that need to be counted’

It is a potentially simple extension to traditional modelling methods

1 2 4 3 1 2 3 1 2 3 4

cust 1 2 3 4 Goal A 1 1 1 1 1 B 1 1 1 1 C 1 1 1 1

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 4 3 1 2 3 1 2 3 4

cust 1 2 3 4 Goal A 1 1 1 1 1 B 1 1 1 1 C 1 1 1 1

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 4 3 1 2 3 1 2 3 4

cust 1 2 3 4 2,3 3,2 Goal A 1 1 1 1 1 1 B 1 1 1 1 1 C 1 1 1 1 1 D 1 1 1 1 1 1

Cust A Cust B Cust C 1 3 2 4 Cust 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 4 3 Cust A 2 4 3 Cust B

On line browsing for credit card Email 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

• n a score based

selection criteria Some Paths will be purely event driven

Questions?