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Beyond Segmentation

The Challenge of Contact Optimisation

Mike Talbot

CTO/Founder

Alterian

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Agenda

• Challenges for marketers

• Traditional segmentation solutions

• The multi-channel explosion

• An introduction to contact optimization

• Strategies for implementing Alterian Contact Optimizer

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Our Aim as Digital Marketers

Increase Profitability by Matching People

to Appropriate Products

By either selecting the appropriate people or by

designing more appropriate products

This presentation focuses on

selecting recipients but your data is

vital for both tasks

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Traditional Marketing Problem…

“Customers for our store are women in their 20’s, single, they drive a golf enjoy beauty products and read HEAT”

“Actually I shop there too, it’s right by my tube station”

We miss significant minorities

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Traditional Marketing Problem…

“Vistors to our web site are women in their 20’s, single, they drive a golf enjoy beauty products and read HEAT. They mostly arrive at the home page from Google.”

“Actually I visit too, it’s got a great series of links and the News page is on myfavorites list”

We are still missing

significant minorities

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Our Challenge as Digital Marketers

We have a picture of each customer

But our customers are absolute individuals

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Traditional Approaches to Segmentation

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Simple Segmentation

Geo Demographic Codes

PersonalDetails

Knowing our product, select

the right individuals

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Bringing in Unique Data

Gold/Silver/Bronze Good/Bad Customer

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Available Data

Web Clicks

Call Centre

Complaints

ItemsTransactions

Invoices

Deliveries

Stock

Products

Outbound Marketing

Response

Demographics

Customers

Households

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Dark Data

Web Clicks

Call Centre

Complaints

ItemsTransactions

Invoices

Deliveries

Stock

Products

Outbound Marketing

Response

Demographics

Customers

Households

Summary

Or

Sample

Most unique corporate

data assets are not used

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The facts are thrown away!

– What happened just before a customer defected?• Did they complain?

• Did we deliver an order late?

• Did our product/service fail to work?

• Did we over market them?

• Did we price them out of the game?

– Were they a valuable individual in the first place?• Did they cost more to service than they made in revenue?

• What was their potential value to our company?

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The Data Challenge

• There are 60 million people in Britain• Your company has 200 products/variations on sale• You capture Age/Gender/Marital Status/Occupation• Customers buy using cash or credit cards

• 2 (Gender) x 4 (Marital Status) x 10 (Age Banded) x 25 (Occupation cleaned up) x 200 (Products) x 10 (Recency Bands) x 2 (Payment Types) x 20 (Total order value bands)

= 160m possibilities*

* (if they only buy one product)

Manual segmentation

rapidly becomes irrelevant if we use all the data

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Build Models to Utilise More Data

= 0.1342452121Propensity to purchase product X

Personal Details

Transaction Details

Location Details

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The Multi Channel Explosion

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The Multi Channel Explosion

• Lots of contact points

• Lots of data

• Need for more creative

• Some offers only available by some channels

• Lots of contact rules

• Lots of confusionSquandered

opportunities, no planning & silos

are frequently the result

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• All of the new channels still demand quality segmentation and offer selection

• Email and Internet are cheap to deploy but consider:– Customer fatigue– Damage to the brand– Missed opportunities

• We have limited opportunities to connect with the customer, we must still select the appropriate product(s) when a customer visits and the appropriate customers when we market a product

There’s no such thing as a free lunch

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Lots and Lots of Channels, Products, Rules, Segments, Data…

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Contact Optimization

+ +

Product Propensities

Business Rules &

Constraints

Available Campaigns

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What is ‘contact optimisation’

Understanding Contact Optimisation Technology, Suresh Vittal, 20 Sept 2006

Contact optimisation applications work by processing inputs — including customer data, global business rules, contact policies, predictive model scores, business constraints, and objectives — to identify optimal solutions. Vendor approach varies, but, at a conceptual level, the technology incorporates five key activities:

1. All possible contacts are calculated based on model scores, business rules, and campaign data.

2. Contacts that meet set exclusions or suppression rules are eliminated.

3. The impact of each contact on business objectives and constraints is calculated. This is expressed as a measure like profit or revenue.

4. Mathematical techniques are used to identify the best or closest solution in order to assign each customer to the right contact(s).

5. The optimized lists of customers per identified contact are produced — and often handed off to a campaign management system — for execution.

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What problems does Contact Optimizer solve?

Getting the best contribution from a direct marketing budget while:

• Meeting minimum sales targets

• Applying contact density rules over time

• Avoiding sending customers conflicting messages

• Respecting minimum ROI rules

• Living within channel capacities

• Resolving which channel is best for whom

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Scientific Marketing

• Phase One:– create or use models that predict the

likelihood that an individual will respond positively to a particular communication

– use predictors of both the product and the channel of communication

– Now you are able to choose the best individuals for a particular marketing campaign

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Traditional Model Driven Campaign Selections

Campaign oriented selections pick the ‘best’ customers for each campaign, in order of execution

First Selection

Second Selection

Third Selection

Customer Cheques Retail Loans ATI HouseSelected Customer

Score15 0.99 0.85 1.00 0.68 0.14 0.9920 0.99 0.42 0.98 0.01 0.11 0.995 0.99 0.60 0.54 0.98 0.74 0.993 0.98 0.20 0.09 0.25 0.46 0.98

19 0.97 0.01 0.47 0.48 0.60 0.9712 0.84 0.05 0.90 0.90 0.45 0.844 0.75 0.23 0.84 0.53 0.51 0.75

14 0.69 0.55 0.50 0.92 0.20 0.696 0.64 0.34 0.39 0.08 0.52 0.64

17 0.59 0.07 0.71 0.23 0.07 0.599 0.48 0.21 0.22 0.59 0.77 0.48

18 0.46 0.94 0.82 0.32 0.33 0.4610 0.26 0.05 0.91 0.57 0.33 0.9113 0.39 0.57 0.91 0.60 0.33 0.9111 0.10 0.10 0.69 0.21 0.88 0.6916 0.33 0.83 0.57 0.57 0.93 0.572 0.20 0.81 0.49 0.55 0.82 0.491 0.12 0.43 0.34 0.15 0.92 0.927 0.13 0.37 0.15 0.50 0.79 0.798 0.14 0.31 0.35 0.84 0.20 0.20

Expected sales 9.37 3.57 1.91 14.85

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Traditional Model Driven Campaign Selections

• Traditionally the best recipients are picked for the campaigns you run – however, these are not necessarily the best communications for the recipient!

• Contact Optimization helps you choose the recipients for whom your Campaign is the most appropriate

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How Contact Optimization Works

Contact optimization picks the ‘best’ campaign for each customer

It next applies the constraintsyou set it

It then translates that result into individual selections or a campaign plan

In this case the optimization improvement is 24%

Selections based on optimum contribution

Customer Cheques Retail Loans ATI HouseSelected Customer

Score15 0.99 0.85 1.00 0.68 0.14 1.0020 0.99 0.42 0.98 0.01 0.11 0.995 0.99 0.60 0.54 0.98 0.74 0.993 0.98 0.20 0.09 0.25 0.46 0.98

19 0.97 0.01 0.47 0.48 0.60 0.9712 0.84 0.05 0.90 0.90 0.45 0.904 0.75 0.23 0.84 0.53 0.51 0.84

14 0.69 0.55 0.50 0.92 0.20 0.926 0.64 0.34 0.39 0.08 0.52 0.64

17 0.59 0.07 0.71 0.23 0.07 0.719 0.48 0.21 0.22 0.59 0.77 0.59

18 0.46 0.94 0.82 0.32 0.33 0.9410 0.26 0.05 0.91 0.57 0.33 0.9113 0.39 0.57 0.91 0.60 0.33 0.9111 0.10 0.10 0.69 0.21 0.88 0.8816 0.33 0.83 0.57 0.57 0.93 0.932 0.20 0.81 0.49 0.55 0.82 0.821 0.12 0.43 0.34 0.15 0.92 0.927 0.13 0.37 0.15 0.50 0.79 0.798 0.14 0.31 0.35 0.84 0.20 0.84

Expected sales

18.37

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What is the optimization based on?

P V Cx -

For each customer or prospect on your database, we calculate foreach of the propositions or offers you could make them, a single score

Propensity Value Cost

Using our Contact Optimizer software we can then pick the best contributing treatment, or we can just pick the one with the highest response propensity

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Contact Optimizer• Only when you base your campaign planning on the

knowledge of individual customer and prospect propensities can you optimize the result

• Yet you cannot simply give every customer your best proposition for them all the time because:– There are specific product sales targets

– Customers prefer a mixed diet

– Outbound channels do not have infinite capacity

– A need to live within a budget

• The role of Contact Optimizer is to optimize return while satisfying the business constraints

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Contact Optimizer

• Allows you to simulate your business in multiple scenarios, each of which can define– How often you can contact customers about particular

products or using particular channels– How many products you want to sell– What capacity your communication channels have– Mandatory communications

• Compare multiple scenarios at a point in time• Project the results of strategies over time

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Case Study – Automotive Services Company

This client has a contactable base of customers and enquirers of 10m, makes 40mselections each year for direct mail, telesales, and e-mail, for 30 core campaigns

Scenario View Contribution 000s

% increase incontribution

Business as usual 15,117

Optimized 1 By individual customer

20,302 33.4

Optimized 2 By plan 19,591 28.7

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Scenarios, Selections, and Plans

CustomerDataInput

Contact OptimizerContact

Optimization

Plans

Selections

Scenarios

There are three distinctly different types of output from contact optimization

Selections can be driven directly into automated

marketing campaigns

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Strategy for Implementation

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Implementation Challenges

Models are required• You can start with very crude models and refine them

over time, but you do need to be able to build models and be prepared to drive segmentation using them.

The Organization must change• Once you are driving your marketing processes using

optimization it may change the way in which marketing functions interact

• Changes to product management• Changes to budget allocation

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Start Simple

• Rather than trying to boil the ocean use Contact Optimization to:– Choose the products to be included on a daily email

newsletter– Choose between two offers being made to a customer

• Contact Optimization allows you to model Business As Usual to see how big improvements are

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Add Complexity

• As you add complexity you must be aware that time will be needed to model BAU and verify new models

• Start by managing more products within a single business unit

• Add multiple channels of communication

• Implementing CO is a process and has a methodology delivered alongside the software

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Conclusion

• Contact Optimization allows data rich businesses to maximize the use of their data assets

• Contact Optimization allows marketers to model and assess the financial impact of different strategies

• Contact Optimization is a practical, comprehensible solution that drives directly at profitability and customer satisfaction.