Friday lunchtime lecture: Open data: bringing small businesses into the big leagues

8/11/2019 Friday lunchtime lecture: Open data: bringing small businesses into the big leagues

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Open data: bringing smallbusinesses into the big leagues

John MurrayFusion Data Science


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Customer Profiling

Customer profiling is not new.

Big companies have been doing it for years.

In early days, the preserve of the mainframe. Software tools and data products have

evolved and become easier to use.

External data sources such as Census andcommercial data sources commonly used to

augment existing data.

3 October 2014 CC BY-SA 2.0 UK 2


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Applications of Customer Profiling

Help to inform advertising purchase decisions.

Identify new retail location sites and

rationalise existing networks.

Target new prospects through direct

marketing.

Identify factors leading to customer churn toimprove customer loyalty and retention.

Reduce fraud and defaults.



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Analytical Software

Specialist commercial analytical software:

SAS

SPSS

MATLAB

Open source analytical software

R Project

Octave



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Commercial Data Products

Geo-demographic segmentation systems:

Acxiom Personicx

CACI Acorn

Callcredit Cameo Experian Mosaic

Lifestyle and transactional data providers.

Public registers: Shareholders

Court judgements



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What is Customer Profiling?

A description of a customer or set of customers

that includes demographic, geographic, and

psychographic characteristics, as well as buying

patterns, creditworthiness, and purchase history.(Business Dictionary)

Customer analytics is a process by which data

from customer behaviour is used to help makekey business decisions via market segmentation

and predictive analytics. (Wikipedia)



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Customer Profiling Basics

Typically customer profiling is presentedstatistically as a set of percentages orlikelihood scores against behavioural and

demographic attributes. In most cases the profile of a target group is

compared with the profile of a base group.

Key differences in these two profiles areidentified and used to inform businessdecisions.



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Target Group

This may be all customers, or a selected group

of customers identified by a characteristic.

For example

Respondents to direct marketing.

High value customers.

Lapsed customers.

Fraudsters.

Defaulters.



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Base Group

The base group of people to compare against.

For example

Non-respondents to direct marketing.

Low value customers.

Active customers.

Trustworthy customers.

Creditworthy customers.



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Case Study

Delicatessen in suburb of Chester.

Offered newsletter and loyalty incentive scheme

via in-store capture.

230 customer records in database.

Wanted to launch home delivery service.

3 mile radius from store, south of river area only.

10,000 households in base are, wanted to target

best 2,000.



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Delivery Zone



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Data Sources

Customer Database

ONS Postcode Directory (ONSPD)

2011 Census Datasets ONS Postcode Estimates (Headcounts)

OS Open Data mapping products



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Methodology

Delivery zone postcodes matched to ONSPD toappend Census Output Area identifiers.

Customer database postcodes matched to

above. Using ONSPD, customer profiles produced

from Census variables expressed as

percentage. Delivery zone profile weighted using ONS

headcounts at postcode level.



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Census Variables Used

Age

Household composition

Age of children in household

Tenure

Occupation Type

Social Grade

Deprivation (from Census)

Length of Residence



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Profile of Social Grade



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Profile of Occupation Type



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Result



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Optimise Call Centre Queues

Call centre resources are expensive.

Demographic data can be used to prioritise

queues for resource optimisation:

At peaks select priority customers

Utilise slack periods more effectively

Minimise no answer/unavailable calls



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Call Time Preference V Employment


0.00%

5.00%

10.00%

15.00%20.00%

25.00%

30.00%

35.00%

Employed

Self-emp

Retired

Not working


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Methodology

Enquiries from prospective customers receivedvia the website.

Census data used to estimate likelihood of

preferred call time based on postcode built into aset of models.

Call centre queues organised according tolikelihood and availability of call centre resources

(constrained optimisation) Feedback loop created from the dialler to

improve performance of predictive models.



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Result

Reduction in unsuccessful calls

39%Equates to productivity improvement of

14%



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Retail Location Planning

Measure demographic profile of your existing

stores.

Use this to find other areas with similar

profiles.

Cautionother factors are involved!

Footfall, e.g. how much passing trade?

Presence of competitor outlets



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Specialist Golf Outlet



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Methodology

Extract all customers acquired in preceding 3years within 45 minutes drive time of a shop.

Convert these to proportions of population in

each postcode sector (e.g. CH1 2). Build a predictive model using these as

dependent variable, and census proportions

as predictor. Apply the model to other areas where no

present store presence.



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Customer Loyalty

Target group, lapsed customers (havent

transacted in a period)

Base group, current active customers.

Append census variables.

Identify customers most likely to lapse.

Set up early warning system. Trigger reactivation event, such as offers.



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Direct Marketing

Target set: existing customers within 30 mins

drive time.

Base set: all adults in same area.

Profile comparison.

Strongest variables identified.

Logistic Regression model built in R. Postcode level model used to drive door to

door leaflet campaign.



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Gains Table

Incremental Analysis Cumulative Analysis

Rank Base Target Index Base Target Index

1 5.0% 24.0% 479.9 5.0% 24.0% 479.9

2 5.0% 18.3% 365.7 10.0% 42.3% 422.8

3 5.0% 11.2% 224.3 15.0% 53.5% 356.7

4 5.0% 10.0% 199.9 20.0% 63.5% 317.5

5 5.0% 5.7% 114.2 25.0% 69.2% 276.8

6 5.0% 4.3% 85.7 30.0% 73.5% 245.0

7 5.0% 4.1% 81.6 35.0% 77.6% 221.68 5.0% 3.1% 62.5 40.0% 80.7% 201.7

9 5.0% 4.6% 92.5 45.0% 85.3% 189.6

10 5.0% 2.7% 53.0 50.0% 88.0% 175.9

11 5.0% 2.3% 46.2 55.0% 90.3% 164.1

12 5.0% 2.9% 57.1 60.0% 93.1% 155.2

13 5.0% 1.8% 35.4 65.0% 94.9% 146.0

14 5.0% 1.1% 21.8 70.0% 96.0% 137.1

15 5.0% 0.9% 17.7 75.0% 96.9% 129.2

16 5.0% 1.6% 32.6 80.0% 98.5% 123.1

17 5.0% 1.1% 21.8 85.0% 99.6% 117.2

18 5.0% 0.2% 4.1 90.0% 99.8% 110.9

19 5.0% 0.1% 2.1 95.0% 99.9% 105.2

20 5.0% 0.1% 2.0 100.0% 100.0% 100.0



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Gains Chart


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Gains Chart

Base %

Target %


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Heat Map



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Advertising Purchase

Produce customer profile.

Compare with profiles provided by media

outlets.

With radio and TV consider time of day.

Use unique phone numbers/urls to track

responses by media.

Measure intelligence.



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Software Tools

Microsoft Office

Excel

Access

Mapping Software

ArcGIS

MapInfo

Microsoft MapPoint

Open source software e.g. Quantum GIS



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Conclusion

Open data can help you:

Improve business process efficiency.

Reduce fraud and default.

Retain your customers.

React to market changes.

Find new customers.

Plan retail branch networks.

Purchase advertising more effectively.



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Thank You

Questions?

3 October 2014 CC BY-SA 2 0 UK 33

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Friday lunchtime lecture: Open data: bringing small businesses into the big leagues