All Customers Are Not Equal

80% of sales or profit will come from 20% ofcustomers

‘‘’’

What We Do

We identify Your Best Customers

We Build Detailed Pictures of Your Best Customers

Profitability Attitudes

LocationUsage

A Marriage of all the Elements

Demographics Lifestyle

Your Best Customers Online

Combining online and offline data

How We Do It

data

Data Interpretation• What Is Your Data Telling You?• We will audit your current data and create interpretation

from it– this often starts with the basics of quality and quantity– turning your data into timely, relevant and meaningful

information – turning that information into marketing advantage– Helping you ‘see the wood for the trees’

data

Data Analytics• What could your data be telling you?• We will undertake analysis on your data to build a fuller

picture. For example:– Basket analysis - identifies products likely to be purchased together,

usually for cross-selling– Propensity models - help maximise Return on Investment (ROI) by

targeting the most suitable audience– Churn modelling - predicting the likelihood to lapse– Lifetime value - quantifies the overall value

of each customer at a revenue, gross or net profit level

Predictive modelling, scoring, CHAID…

Venn diagramsused to detect

selection overlap

Drill-down analysis

Tracking data/customer movement

Geographic segmentation forsales or franchise territories…

We Identify Profit Opportunities

Data Strategy• What will your data allow you to do?• We develop data led business and marketing strategies to

maximise business growth– CRM, Acquisition & Retention Strategies– Cross-sell & Up-sell Strategies– Data Collection & Data Partnerships Strategies– Creative Testing & Message Hierarchies

Data Planning Process

define the problem

business, marketing and communications objectives

short. medium and long term needs

audit

data quality

datasystems

data usage

datacollection

data analysis

hygiene

analysisdatabase

strategy

evaluation

data needs

admin &reports

buildstrategy

futureproofing

predictivemodels

data:profile,cluster,

segment

accuracy

roi

retentionlapse

acquisitiontrial

crmdata

enhancing

single customer

view

define the objectives

What We Manage• Through a network of third party partners we will source

and manage – Data Enhancement– Data Cleaning– Database Design & Build– List Purchase– Data Collection– Processing Data– Data Monetisation – Web Analytics

Who We Are• A data planning & analytics consultancy• Based in Yorkshire• 5 core team members with a network of associate

consultants & partners• Working in the private and public sectors• Part of the Journey Group

Peter Rivett-Jones - Director

• 20 years of data and marketing experience

• Senior client services and planning positions in top DM agencies including Joshua, GGT Direct & EWA

• Founded DM agency Made With Love (MWL) in 1999 which was later sold to Chemistry in 2003

• Joined Poulters as Director & Shareholder in 2005 heading up all data and direct marketing accounts

• Co-founded The Data People in 2009

Steve Raper - Director

• A statistician with 25 years of data analysis and marketing experience

• Started career with British Gas in various sales and marketing positions

• Went agency side in 1994 as Data Manager for Bedrock Communications

• independent consultant since 1996 providing data strategy & data analysis for agencies and clients

• Co-founded The Data People in 2009

What Makes Us Different?• We are marketeers first and data planners second• We turn numbers into words and pictures. • We answer the "so what?" of data and statistics• We have vast experience in data and all its touch points• We are independent consultants with nothing to sell apart

from our time • We turn the complexity of data into strategies that make

sense• We champion simplicity

Sector Experience

• Retail • Leisure• Office Equipment• Telecoms• Financial Services• Mail Order• Utilities• Drinks

• NHS & Health• FMCG• Automotive• Industrial• B2B• Travel & Tourism• Airlines• Government

Alliance & Leicester

Case Study 1

The Brief• Alliance & Leicester had been using cold contact lists to

direct potential customers to their web site, with limited success

• Registered users of the site were segmented by answers to basic financial questions only upon registration

• Communications to registered users had minimal tailoring• With results from nearly 2 years’ activity now available, our

brief was to optimise results – – Increase visits to the site from dm activity– Maximise the potential value of visitors to the site

The Solution• The first step was to take the client’s database of registered

users, plus a sample file of non-respondents, and append lifestyle and demographic overlays to the data

• CHAID modelling based on each set of overlays was carried out and gains charts compared to improve targeting

• The client’s registered user base was segmented in terms of their long-term behaviour in relation to the site

• The resulting 6 clusters were profiled in terms of their likely financial requirements and long-term value potential

• The rules for optimum allocation to segments were modelled using discriminant analysis

The Solution• A series of new questions at registration were identified to

give the client data to allocate the new user immediately to the appropriate segment

Drill-down Charts

& Tables

2, 3, 4 & 5 wayVenn

Charts

Transaction, basket & tree

analysis

Data Categories

The Results• There was an immediate increase of over 100% in site visits

generated from direct mail through the improved targeting• Value models within the segmentation allowed the client to

estimate long-term potential value• Thus determining the products advertised and marketing

investment for each segment• In addition, extra information about customers’ potential

value are being added to the model as experience gives us more accurate information about the web-site’s longer term usage patterns and sales values

Holmes Place

Case Study 2

The Brief• Like many of its competitors, Holmes Place concentrated on

acquisition during the unprecedented growth phase of the industry

• Customer retention and improved targeting for acquisition were recognised as important business drivers as:– competition increased – cost of acquisition increased– attrition rates exceeded 50% per annum

• Little was known about the customer, and no estimates of customer value and what drives it had been evaluated

• The brief was to understand the customer better to allow for smarter and more efficient marketing activity

The Solution• The first step was to take the client’s membership and

transaction databases and combine them• Append demographic and lifestyle information• Identify valuable customers through data modelling –

including length of membership and additional spend (e.g. personal training)

• Profiles for each club by value band were compiled• Key variables – transactional and lifestyle - for predicting

closure of membership were identified• The resulting churn model was applied to the customer base

to predict the likelihood of attrition

The Solution• Although there are many factors affecting renewal of

membership (such as moving away from the area), many members do not renew because of their lack of usage of the facilities available

• The models allowed us to identify the probability of each member renewing, and allows communication strategies to be put into practice for valuable but potentially disloyal customers

The Results• Targeting for new customers has been revitalised • After years of reducing returns from marketing targeted by

demographics only, the new models coupled with data cleaning processes have resulted in a five-fold increase in response rates

• Costs per new member have been reduced• Average value of each new member acquired was increased• Early indications are that the modelling of likely defectors,

coupled with communications designed to retain them, is starting to reduce churn rates

Nescafe

Case Study 3

The Brief

• A major development in the Nescafe Ultra Premium brand strategy was to narrow the target audience that for marketing communications

• Extensive work by the brand team had re-defined the audience that Nescafe UP would target

• Two target audiences called Roast & Ground Dippers and Instant Dippers had been identified – c1.7m HH’s

• The brief was how, from a data perspective, do we find this audience to allow a major dm sampling campaign to take place

The Solution• Nescafe did not have marketing data of their own• There was not sufficient volumes of external data to

purchase that identified ‘dipping’• In order to get the quantity and quality of data needed we

proposed data modelling• In simple terms, this meant creating a profile of the people

we wanted and then finding lookalikes• The secret lay in having the most accurate profile at the start

The Solution

• We recommended using Tesco Clubcard data to create the profile that the data model would be built around

• The model were built using CHAID and then applied to external lifestyle data sources

The Results

• The data model used in the direct marketing campaign proved to be highly successful

• The mailing delivered £280k uplift in the first three months alone

• The mailing had an impact on customers behaviour resulting in sustained change over a year – once customers had tried it they remained loyal

• Customers moved from the targeted product areas of Freeze Dried and R&G proving the model’s accuracy

• At a brand level customers were most likely to have moved from Kenco Ultra Premium and other Premium freeze dried coffees

The Data People turn customer data into greater profits

Thank You