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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.
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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
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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
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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?
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