1. Enhancing Data Quality to Improve Customer Data The issues facing you & What you can do about it Phil Corcoran – NS&I

2. POOR DATA WILL DAMAGE YOUR WEALTH !
WARNING
POOR DATA WILL DAMAGE YOUR WEALTH !

3. Scale: Very important = +2, Not at all important = -2
Q608 How important are the following factors to you when contacting a savings and investments provider?
Replies to application letter quickly
Letters and information that is clear and easy to understand
Letters that answer all my questions
Figures on letters are accurate
Details such as name and address are accurate
Acknowledges receipt of correspondence and cheques
Letters that show understanding of issues raised
Scale: Very important = +2, Not at all important = -2 3

4. The Eight Truths of Marketing
1. You will never have perfect customer data.
2. You will never analyse all your customer data.
3. You will never control every customer interaction.
4. You will never have enough in-house marketing expertise.
5. You will never be content with your in-house IS expertise.
6. You will never achieve the vision of one-to-one marketing.
7. You will never have a centralised marketing dashboard.
8. You will never be immune from legislation.
Source - Gartner Group

5. What Does the Problem Look Like?
P Corcoran
Sarck
Crowborough Hill
Crowborough
East Sussex
TN6 2HH
P Cochrane
Sarck
Crowborough Hill
Crowborough
East Sussex
TN6 2HH
P. Cockram
Sarawak
Crowborough Hill
Crowborough
East Sussex
TN6 2HH
P Corcoran
Sarck
Crowborough Hill
E. Sussex
TN6 2HH
Approximately 13% of the UK population move house each year - Office of Population Censuses & Surveys (OPCS)
Approximately 11% of addresses are mailed incorrectly each year - Direct Mail Information Service (DMIS)
45% of the UK population believe that a mis-spelt name or address is an indication of ‘junk mail’ – DMIS

6. So Many Problems with Data

7. The Perceived Problems
“If you can keep your head when all around you are losing theirs……..”
Kipling
Don’t know enough about customer
Different
Customer I.D’s
Waiting for
the data
Mis-matches
Duplications
Multiple Tables
A Segmented
Not
Customer
Missing
Values
Different Formats
in data
Low levels
of match to
Geodemographics
1 Month Delivery
of Data
8 Hr.
Data
Runs
Suppressions
Different
Network
Can’t do our
own file merges

8. Effect of Poor Quality Data
Duplicated records
Multiple mailings to same person / household
Increased mails costs
Increased customer annoyance
Inability to apply stops to all accounts in one go
Poor customer intelligence – poor targeting –low ROI
Restricted ability to overlay external data
Restricted ability to dedupe cold lists from your own data
Potential breach of Data Protection Act (4th principle -clean data)

9. What’s Caused the Problems?
Legacy Data
Data from a bygone age when a database was just information collected along the way.
Typical comments :-
“We never thought we’d need the full name”
“We didn’t like to ask people how old they are”
“We’ve always showed returned mails as opt-outs”
Etc.
Poor Data Entry procedures
Different standards / versions on different systems
No validation on Data Capture
No measures (metrics) kept on data quality
No attempt to establish a Single View of the Customer

10. Why Fix It? Financial Benefits:
No wasted mailings to undeliverable addresses
More Efficient Mailings – no duplicates, only one per household
Maximised mailsort discounts
Better targeting by being able to add insight to more records
Improved ROI
Non- Financial:
Faster processing of your data warehouse
More confidence in analysis
Ability to apply opt-outs / opt-ins to a single record
Better matching of external data
Improved customer experience

11. Financial Benefit Example
On a mailing size of 100,000
Pack cost of 50p
Duplication level of 5%
Undeliverable addresses at 5%
Assuming 4 mailings per year
4 x 50p x 10% x 100k = £20,000 wasted

12. But …..even better…. At a pack cost of 50p gives 40,000 extra mails
….. if we use that £20k more effectively in the mailing
At a pack cost of 50p gives 40,000 extra mails
At a response rate of 0.5%
40,000 x 0.5% = extra 200 responses p.a.

13. Where do you Start ? An External Data Audit Will look at:
Duplication Levels
Propagation of the data within fields
Erroneous data in fields (e.g. numerics in name field)
Address formats – (do they match PAF ?)
Validation of occupancy
Salacious names
What does it cost – often it costs nothing !

14. What do we have ?
A boat with a hole in it – full of water and more water coming in.

15. to benchmark your start point
Step 1
A Data Audit
to benchmark your start point

16. Sort Internal Problems First
Roger Rabbit, Michael Road, The Boat Ashore, D00 DAH
Woger Rabbit, Michael Road, The Boat Ashore, D00 DAH
Wodger Rabbit, Michael Road, The Boat Ashore, D00 DAH
Rodger Rabbit, Micheal Road, The Boat Ashore, D00 DAH
By correcting data errors before external referencing you will reduce matching problems

17. Step 2 address validation on entry e.g. QAS
Plug the hole
Ensure only clean data gets onto your database - use
address validation on entry e.g. QAS
Ensure front-end staff aren’t allowed free-format fields
for key data
Ensure application records can’t be completed without
all data being valid.
Regular MI checks on data quality – consider
establishing Quality Gates to control data entry

18. Step 3 Empty the water Evaluate using a scoring method for subjects:-
Invite tenders in order to acquire the services of a good Data Bureau to clean your database
Evaluate using a scoring method for subjects:-
e.g. i) can they cope with your size of database, will they sub-contract
ii) did they spot all the problems with your data (compare to the others)
iii) do they appear knowledgeable on the subject and take the time to explain their
thoughts
iv) do they feel comfortable to work with
v) did they field a good team to answer questions (remember this is their best crew)
vi) Can they validate occupancy or otherwise add value to your data (how many
sources have they access to?)
vii) Did they come up with a one-off solution or an ongoing proposal
viii) What’s the cost / timescales – second / third year costs?
ix) Are they a member of the DMA and therefore bound by its Code of Practice?

19. In-House or External ? In-House solution
Has greatest knowledge of your own data
May (but not necessarily) be cheaper solution
May be able to notice some knock-on effects (snags or benefits) which an external supplier may not
External solution
Will have access to external validation sources (ER / PAF / Suppression files etc.)
Will have wider reaching expertise
May have an off-the-shelf solution which can save development costs
Suffer less from scope-creep
Are usually more accountable for delivery dates and solution

20. Equifax Euro Direct Experian
Electoral Roll
Insight
(360m credit accounts, 130m live)
PAF
(standard postal address file)
Search Enquiry Database
(>100m searches conducted in last yr)
SHARE
(closed user group – a/c behavr)
Credit Actives
CCJs
(6m records)
Alias File
(name changes)
Investors Database
Directors Database
Locate Database
(home movers over time)
BT OSIS
Equifax Alias file
(previous name information)
BAIs
(Bankruptcies, Administrations & Insolvencies)
Equifax Biographic Details Database
UK Investors
Credit A/C Previous Searches
Halo Deceased Database
Directors at Home
Gone Away Files
Sanctions File
Absolute Movers
Persona File
Experian Mortality File
Gone Away Suppression File
Forwarding Address file
Deceased Indicators
200m records unique to experian

21. Issues to Tackle early
Consider an internal ‘cleanup’ of the data before trying to carry out close matching e.g. remove spurious characters
Standardise the quality of addresses you hold – benchmark against PAF
If you wish to hold ‘cherished addresses’ consider holding them in a separate field so you can use the primary one in the matching process.
Depending upon use of data (operational or marketing) establish tight matching rules (operational matching will be much tighter than marketing)
Consider customer validation/verification of any address you ‘recover’ e.g. from NCOA – i.e. write to them to verify.

22. An Example of an External Exercise
2. Enhancement /Align
4. Negative
Verification
3. Positive
1. Data Audit
6. Marketing
Data Append
5. Single Mktng.
Customer View
7. Segmentation
8. Final Data
Integration
9. Customer
Tracing for unverified
Output enhanced data
Customer Data

23. Then you can start to use your data
By now you’re holding a more accurate and verified view of your customer with rules around a single view of that customer you can start to understand your customer better :-
Understand their potential value to you, the share of their wallet you hold and how you can develop the relationship with them.
Enhance records with geodemographics overlays e.g. Mosaic, FSS, Acorn or Cameo to profile them and help discrimination
Consider holding your own suppression licenses (e.g. TBR, Mortascreen) in-house
Lifestyle Data to understand customer lifestages and drivers
Develop a strategy to apply to each customer whereby you know if you want to cross-sell, upsell, retain or develop the relationship with them.
Understand which channel or media your customer responds best to.
The world is your lobster……

24. Manage Expectations
All database projects take time – the bigger the database the longer the time
Define what your objectives are at the outset for any database exercise – to avoid scope creep
Benchmark the data as part of the initial audit and again at the end
Establish regular MI reporting on the quality of the data compared to benchmark – e.g. level of ‘returned-undelivered’
Involve ‘user areas’ e.g. Marcomms in the project, even if only in an ‘informed’ capacity. Ensure they understand the ‘critical path’ of what will be delivered and when.
Understand the difference in matching criteria between the marketing database and an operational system. You may have different levels of your SCV.

25. Starting To Build SALES TARGETING MODEL BUILDING SEGMENTATION
ATTRIBUTE PROPAGATION
DATA QUALITY
Phil Corcoran NS&I