Marketing Optimisation Application

Bonnie Law

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Overview

• How modelling and analytics can help to keep a wide range of stakeholders happy – all at the same time

• Advocates for your most valuable customers – a new skill for analysts involved in campaign targeting

• New way to build a contact policy for your top customers, one that get into action rather than locking you in meetings all day

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Business Problem

Multiple Priorities

• Different stakeholders

• Planned vs tactical reaction to market

Most Valuable Customers

• Everyone wants to target them

• Trading cards or treasure?

Over Contact

• Myths or opportunity?

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Priorities

• Many different goals at the same time, often conflicting requirements

• How often do you receive requests for contact delivery with all these stated as objectives?

Profit (short/long)Risk /

Delinquency

Retention

Market Share

Balance Sheet

Product SellsNPS

Reputation

Share of Wallet

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Prioritisation Process

• Different stakeholders want to send out marketing campaign at the same time to achieve their plan

• Customers are the limited resource (beside cost and capacity etc.)

• Round table discussion to prioritise

Campaign 1

Objective 1Objective 4Objective 6

Campaign 2

Objective 2Objective 1Objective 5

Campaign 3

Objective 3Objective 6

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Common Solution

• Rule based prioritisation – marketers decide most important objective and therefore which campaign should get the most customers

Issue:

• Lots of theory and assumptions and myths

• Stereotype use for rules, likely the most valuable customers

• Linear solution

• Many meetings and emotional conflicts

• No room for testing and experiments

Campaign 1

Objective 1Objective 4Objective 6

Campaign 2

Objective 2Objective 1Objective 5

Campaign 3

Objective 3Objective 6

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Solution using optimisation concept

• Better understanding of the drivers for each objective will help identify additional prospects

• Most valued customers are good target but not necessary the best

• Results – Each campaign get enough customers in their target pool without the fights

Objective 1 Objective 2 Objective 3

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Example

• Beginning of the year with both debt consolidation and deposit/savings as focus

• Savings score has a lift of 1.6 and Debt Consolidation score has an average lift of 5

• After redistributing targets (same number of contacts), savings campaign improve by 13% and debt consolidation campaign improve by 23%

Savings (not eligible)

Savings (Low)

Savings (High)

Debt (not eligible)

Debt (Low)

Debt (High)

Savings (not eligible)

Savings (Low)

Savings (High)

Debt (not eligible)

Debt (Low)

Debt (High)

Rule Based Model Based

Savings Campaign Debt Consolidation Campaign

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Simple and Easy to start

Understand different objectives and drivers – simple segmentation

Modelling framework – keep it high level and focus on targets that can compare different initiatives

PIR measurement framework – same set of measurement for all campaigns

• Stakeholders engagement can take time

• Continuous education is required to break stereotype

• Optimisation (and linear programming) are big words for marketers

• Establish common language between different stakeholders

• Clear on objectives for each initiatives

• Use same measurement framework to allow comparison of results

• Continuous testing of alternatives

• Pushing the boundary and breaking the “rules”

• Construct experiments while minimizing risks

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Pushing the Boundary

• Prioritisation focus on the objectives

- the function/measure that we attempt to maximise / minimise

• Pushing the Boundary focus on the constraints

- Some campaign exclusions are also self impose constraints

- To get the most out of modelling, we need to expand pool of eligible customers

- A step change in targeting approach towards fact based criteria

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Data Briefs

• Data Briefs usually consist of lots of tick boxes of exclusions

• When something went wrong with a campaign, learning are usually captured by introducing another exclusion to the list

Black and White documentation – but still have gaps between business rules and data rules

Not enough targets left to consider propensity to take up offer

Cannot distinguish between different types of exclusions or describe target audience

Problems :

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Type of Exclusions

Target Selection – define target audience that start the selection process

Marketing Contact exclusions – contact detail, opt out

Eligibility exclusions – offer specific but often include “relevant” filters

Risk exclusions – credit risk and delinquency

Contact Policy

Customers available for predictive model prioritisation

Candidates to expand target pool

New way to define Contact Policy

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Type of Exclusions

Target Selection – define target audience that start the selection process

Marketing Contact exclusions – contact detail, opt out

Eligibility exclusions

Risk exclusions

Contact Policy

Customers available for predictive model prioritisation

Significantly increase available customers

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Impact of Data Briefs

• Rule based exclusion rules removed high proportion of prospects from the availability pool

• Eligibility criteria often used to define “relevant” offers

- Common sense propensity criteria

- Often direct targeting to the most valuable customers

- Effectiveness of propensity modelling is lowered

• Rule based exclusion rules are linear – same rule apply to all segments

• Data environment is often complex and not easy to replicate business rules. Putting more rules in the list is not going to fix this gap

• Data Briefs written in this way turn targeting into order taking – limit innovation and improvement

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Solution using optimisation concept

• Trade between risk and reward follow by experiments little by little

• Clear measurements of both cost and benefits

• Keep repeating to expand pool of eligible customers

• Need patience: take time to change rules that have been around for years

Objective 2

Sensitivity Analysis

Calculate benefits of relaxing exclusion criteria by an extra unit

Extra risk and cost of relaxing exclusion criteria by an extra unit

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Example

• Personal Loan campaign that have very good response rate (and high profitability)

• Challenge – how to make a good campaign even better

Before

2 years of history & learning – data brief with close to 50 exclusion rules

Enough customers available for contact 3 times a year

After

Utilise Risk score instead of rules. Reduce to 38 exclusion rules (still too many)

3.5 times more available customers for contact

Benefits – response rate increase by 20% (probably little previous contacts)

Risks – delinquency rate 10% better than normal application (still room for improvement)

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Role of Analysts

• Ask questions and push to understand the true objectives

• Critically evaluate every exclusion rules in the brief – understand the reason/logic behind the rules

• Ask why not – and not let tradition and history limit imagination

• Look for alternatives and test (analytically)

• Balance Risks and Rewards and be courageous to take risk

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Contact Policy

Background

• Most valuable customers are targets of many different campaigns/initiatives

• Customers are at risk of being over contact

- Reduce effectiveness of contacts

- Conflicting message for customers

• Customer satisfaction and advocacy

We have seen some causes of this – multiple objectives and rule based stereotype targeting

How often should we contact customers?

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Over Contact – is there a problem?

Little or No proactive contacts

Don’t remember seeing mail

Don’t recall contents of last letter

Keep sending me irrelevant offers

Have not talked to anyone in the bank for 5 years

40% of customers receive 1 or no direct marketing contacts in 1 year

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Business and the Contact Policy• Marketers don’t like contact policy – yet another exclusion rule and reduce available

customers

• Multiple sources of data so data rules tend to be repeated and remove excess records

• Contact policy / frequency often manage by committee – another round table meetings

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Break the Data Briefs

• Contact frequency and Contact policy are just another set of exclusion/ constrains

• Build models to prioritise contacts rather than exclusion rules

Last Outbound Contacts & Channels

Last Inbound Contacts & Channels

Last Response & Channels

Objective 3

Let each customer decide how frequent and via what channels they want to be contacted

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Minimum Contact policy

• Construct and build minimum contact policy – every customers in the high value segments are to receive at least x proactive marketing communication each month/quarter

• Planning at the customer level and aim to fill every square above with a contact

• Rather than spending time debating the detail of contact frequency rule, invest in constructing new contacts for customers

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

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Example

• Quarterly program for top personal loan customers

• Before campaign, lots of fear and hypothesis that customers will be angry with repeated “debt” push

• Results:

- First wave: 30%

- Second wave 31%

- Third wave 28%

• No complaints directly relate to the campaign and two customers who made enquiry of when they will next get their offer.

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Conclusion

• How modelling and analytics can help to keep a wide range of stakeholders happy – all at the same time

• Advocates for your most valuable customers – a new skill for analysts involved in campaign targeting

• New way to build a contact policy for your top customers, one that get into action rather than locking you in meetings all day