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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
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
0%
1%
1%
2%
2%
3%
3%
4%
1 11 21 31 41 51 61 71 81Drop Volumes by Day
0
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
0.2
0.2
0.3
1
Calls by Day
0
50
100
150
200
250
300
1 11 21 31 41 51 61 71 81
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
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
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 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
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
We use an algorithm to test all possible memory processes on advertising between 0 and 100%
40% Memory
29
We use an algorithm to test all possible memory processes on advertising between 0 and 100%
60% Memory
30
When the correct memory process is applied to the model, there is no longer a consistent over-prediction
No pattern in residuals
80% Memory
31
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
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?
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
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