O.R. within Consumer Marketing - from simulation to optimisation

Chris Doel – Head of Marketing Analytics, Virgin Media

What will be covered…

The Operational Research Society is currently debating how far to include Analytics within its remit

Provide some examples of scientific approaches I have encountered within what is branded analytics

Mathematical Modelling

Simulation

Clustering

Regression analysis

Optimisation

Virgin Media – The UK’s leading entertainment & communications company

The first company in the UK to offer TV, Broadband, Phone and Mobile - all from one place

Formed in February 2007 from a merger of ntl, Telewest & Virgin Mobile

UK’s largest fibre optic cable network

Around 8 million customers across Cable, National and Mobile offerings

Clear leadership in Broadband

Market-leading multi-product take-up

1

Forecasting Call Centre Demand using Mathematical Modelling

Call centre forecasting

Providing call number forecasts to aid daily roster planning in the call centres

VM undertakes hundreds of different campaigns each month

We could forecast from the top down using time series techniques or from the bottom up using mathematical modelling

The call numbers are affected by factors such as:

The number of marketing contacts made

What channel the contact is made through

The effectiveness of the contact (e.g. what incentives are being offered, the size of the letter…).

The timing of the contacts with seasonality and day of week

Modelling the time delay of response

Response curves

Calls received follow a skewed bell curve from the date of contact

We build up separate response curves for direct mail, door drop, text and email.

When building the response curves we have to account for the fact that not all contacts happen on the same day

% Of Calls by Day

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1%

1%

2%

2%

3%

3%

4%

1 11 21 31 41 51 61 71 81Drop Volumes by Day

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50

100

150

200

250

1 11 21 31 41 51 61 71 81

000'

s

Modelling the day of week response

Dealing with Multiple Effects

We have to deconstruct the response curves using a least squares method

We also have to account for the fact that customers call in at different rates by day of week

Day of Week Distribution

0.0

0.1

0.1

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Calls by Day

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50

100

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The model

All campaigns have to be aggregated in their effects

For each day the number of calls expected is the sum of the expected responses from each campaign for that day

Factors affecting overall response rate such as incentives and letter format estimated in their effect

Legacy calls from previous months are also accounted for

0 5 10 15 20 25 30 35 40

Performance

Forecast accuracy is acceptable…

Daily view

01 03 05 07 09 11 13 15 17 19 21 23 25 27 29 31

ESTIMATE

ACTUAL

2

Simulation of Call Centre Performance

EmployeeSatisfaction

Issues simulation can address

Call centres have targets to meet on call delay times and the percentage of abandoned calls whilst meeting budget constraints and ensuring the staff are motivated

Cost Service Quality

Call centre model

Example simplified call centre structure - stochastic system with multiple queues

Sales

Service

Disconnect

CallsAutomatic

Call Distribution

Queues Agents

Random arrival

Random call

duration

Leakage

Leakage

Issues simulation can address

Business issues have to investigated within these remits

What would be the effect on customer service if we amalgamate two call centres into one?

Can we meet the target on call delay if the number of lines is reduced by X?

What will be the effect on call delays and abandoned calls when a new offer is introduced that lengthens the time each call requires?

Can we optimise shift patterns to improve response times?

Can we prioritise high value customers in the queues without large adverse impacts on the remainder?

Analysts role

The analyst has an instrumental role in this process:

Consult with the business on what issues should be investigated

Create an appropriate design for the simulation

Agent skill definitions

Queuing logic

Agent shifts and activities

Parameterise the model

Estimate call volumes and determine stochastic distributions and parameter values

Validate the model

Perform what-if analysis to address the issues

Communicate the results to influence decisions

3

Segmenting the Customer and Prospect Bases using Clustering

What might a segmentation look like?

What data may be available for such a clustering?

Bought in demographic data (mostly derived from the census) Household composition, age, household income, etc..

Customer Usage data Internet

BB uploads and downloads amounts by time of day TV

Relative likelihood of having Pay TV Relative likelihood of having PVR Relative likelihood of having HD Relative likelihood of having premium TV services (e.g. Sport & Movies)

Phone Fixed line usage and spend Mobile voice, SMS and data usage and spend Main reasons for use of these services Time spent on those services (focusing on on-line social network behaviour) Usage by time of day and day of week split by voice, SMS, MMS and data Relatively likelihood of owning different mobile phone types

How is the clustering structured?

N dimensional, centroid based least distance approach

Aim to have 6-10 segments

Make sure no segment is less than 5% of the base.

Use profiling to understand the segments

Illustrative resultsD

igit

al E

ng

agem

ent

Motivation Quality TimeValue for Money

Lower

Higher

Illustrative resultsD

igit

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Motivation Quality TimeValue for Money

Lower

Higher Meet segment needs over time as motivation changes and customer lifetime value increases

Build products and services to retain and cross-sell into these segments

Other uses for segmentations

Use

• Offer a lens on consumers’ use of services

• Allow us to understand the appetite in the market for company services

• Allow us to identify the parts of the market where the company is successful and where it needs to raise its game

• Help identify the opportunity there is to grow the business through acquisition and customer management

• Allow us to find structured and measurable ways of managing customers

• Provide a framework to track market share of segments against competitors

• Enable a common currency across the business for both acquisition and customer management

• Provide an understanding of key consumer attitudes to quality or price

4

Understanding the Effectiveness of Marketing using Econometrics

The marketing feedback loop

23

If we know who we are contacting, we can set up a feedback loop to track the effectiveness of our campaigns

If we know the cost of our campaigns and the revenue/margin generated through linked sales we can work out return on investment

However, this loop breaks down for TV, radio, outdoor and press media

Marketing based econometrics

The application of statistical and mathematical methods to help quantify the effect that different types of internal business activities (e.g. spend on DM, product pricing) and external factors (e.g. competitor activity, consumer confidence) have on key company objectives.

With these relationships defined, a process of optimising marketing spend can be undertaken to more efficiently meet our targets.

Econometrics inputs

25

Sales

Public Relations/Events Availability and Delivery

Economic variables

VM Pricing & Offers

Competitor Products and Pricing

Direct Marketing

Advertising

The model

This can be formulated as a multiple linear regression

Independent variables

Spend/contact volume on direct mail

Price differentials with competitors

Market saturation

Views of TV advertising

Dependent variable

Sales

Calls

Customer satisfaction

Disconnections (churn)

Functional forms

Diminishing returns

Spend

Retu

rn /

Cos

t

Return Cost

Spend

Net

Ret

urn

Net Return

Returns rise at increasing rate as campaign builds towards critical mass

Returns start to diminish as reach of advertising is exhausted and potential to generate returns starts to diminish

Optimal Spend Range

Modelling persistent effects

0% Memory

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Ad stocks

We use an algorithm to test all possible memory processes on advertising between 0 and 100%

40% Memory

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We use an algorithm to test all possible memory processes on advertising between 0 and 100%

60% Memory

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When the correct memory process is applied to the model, there is no longer a consistent over-prediction

No pattern in residuals

80% Memory

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Note that if the memory process applied is too high the model will not fit correctly either

99% Memory

32

Building the model

33

~150 data points for the outcome measure (weekly measures over 3 years)

Over 1000 independent variables to be assessed

Interaction effects investigated

Variable statistical significance and r2 used to direct modelling

Treat conclusions with fair degree of caution and verify findings through testing

Illustrative results

34

Large unexplained element

Clear effects of seasonality

Large r2

Relative contributions of marketing apparent but unverified

5

Optimising Customer Contacts

The customer management dilemma

• How to maximise return across a universe of customer contact plans whilst managing day to day

business constraints?

Channel?(multiple)

Campaign?(hundreds)

Customer?(thousands/millions)

Timing?(any day/time)

25,000 volume

1,200 sales

£450,000 budget

10% ROI

Offer “A”

Offer “B”

Product “C”

Product “D”

Action “E”

Action “F”

Missed opportunity?

Wrong timing?

Saturation?

Preference?Transaction trigger

End of Term

Up-sell opportunity

Competitor product renewal

Recent contact

100,000 mail volume

Minimum 32,000 leads

Contact frequency?

Channel usage?

Channel preference?

Variety of Offers

Contact Optimisation

Max Value = P*V-c

The goal

Channel constraints

Budget constraints

Creative constraints

Frequency constraints

Sequence constraints

Relevance constraints

Business Needs

Optimum communication mix

(who, with what, when and how)

Solution

Hundreds of offers

Millions of customers

Multiple channels

Any time

Any sequence

Any combination

The problem

Analysts role

The analyst has an instrumental role in this process:

Consult with the business on what issues should be investigated

Create an appropriate inputs for the optimisation

Logistic regression models to estimate response probabilities

Incremental value models for the value of a response

Define contact rules that are in use in the business

Setup the model within our optimisation software MarketSwitch.

Validate the model

Perform what-if analysis to address the issues

Communicate the results to influence decisions

MarketSwitch

A powerful optimisation tool

Has the capability of working with millions of customers and dozens of potential offers

Will only return feasible solutions

Uses genetic algorithms to search through the solution space

Usually returns results within minutes but can run on samples of the customer set to speed up what-if analysis

Can run with mixed objectives to define maximum efficient frontiers - for example when comparing max. sales vs. max. profit

Final Remarks

Will Rogers

"Let advertisers spend the same amount of money improving their product that they do on advertising and they wouldn't have to advertise it."