SlideShare a Scribd company logo
1 of 3
Download to read offline
Cast Study – FastStats Marketing Database Solution for mypetstop




FastStats Marketing Database Solution
Delivered by



In Two Weeks!
The Problem                                    The Approach
mypetstop had three identical Access           Gratterpalm brought in specialists
databases in three locations generated by      Database Marketing Ltd to design and
their Pet Admin software. Each database        build a new marketing database solution
had the same customer ids in common            that would merge all three databases
and no easy way of creating reports or         together, create a unique range of
analysis for individual locations or across    customer ids, clean up any duplicates and
all three. The analysis was needed to          deliver back to mypetstop a solution that
derive the Marketing Strategy with their       the marketing department would have on
newly-appointed agency Gratterpalm and         their desktop. Non-technical users would
to drive the business forward. There was       be able to create their own counts,
also some duplication of records identified    selections, reports and analysis.
on the system.

The Solution
Database Marketing Ltd first created a Sql Server database to manipulate the data to
create a unique range of customer ids for each location, delete duplicate customer records
but maintain all transactions for the deleted customer ids thus creating a Single Customer
View. All three cleaned databases were merged into one with flags created to enable
analysis overall or by location. This was all done on systems at DBM with the clean
database loaded back to mypetstop. No internal IT resource was needed at mypetstop
other than to setup an automated ftp process to upload the Access databases to a secure
ftp site and then to download the final processed database.
FastStats was chosen as the Marketing Database software and a FastStats database
designed and built. The solution included weekly updates. The FastStats software was
installed and configured at mypetstop Head Office in Leeds and 4 staff members trained in
the use of FastStats.
The whole system, including Sql programming, FastStats design & build, software
installation and configuration, testing and training was completed within two weeks!

“I've been using Faststats for a month now and I'm very impressed with its user-friendly
interfaces, broad range of reports and interactive graphical views. Through interrogating
our previously dormant database on FastStats, we quickly and efficiently gained valuable
knowledge on our customer demographics, spending habits, average length of stay and
most popular service. This information will help us to deliver a more effective, targeted
marketing communication campaign in 2010.“ James Kundi - mypetstop
Cast Study – FastStats Marketing Database Solution for mypetstop

Introduction
mypetstop are part of the Mars group of companies. They provide a unique range of pet
services under one roof. From purpose built kennels and cattery which provide world
class boarding for pets to training clubs, grooming, retail and hydrotherapy facilities and
an on-site veterinary practice. There are currently three sites in Leeds, Manchester and
Newcastle.

mypetstop appointed Gratterpalm, a Leeds based specialist retail agency, as their
agency in November 2009 following a four-way pitch. Gratterpalm have been tasked
with developing campaigns to improve usage and loyalty with existing customers,
re-educate lapsed customers and drive awareness with potential new customers.

The agency will drive direct marketing and e-campaigns, oversee creative work for the
mypetstop website and drive all online activity.

During an initial consultation process between mypetstop and Gratterpalm to derive the
high-level strategy it became clear that the reporting and analysis was just not available
on the current systems to provide the information to develop the strategy. There was
no central database but rather three separate Access databases. To get the information
required using the operational system bespoke queries and reports would have to be
written and run in each location. mypetstop do not have in-house programmers or
analysts and use a third-party IT provider so this approach would prove both time-
consuming and expensive. It was also becoming clear that this process would have to be
repeated in the future for any new reports and analysis so it would not give them any
long-term benefit.

It was decided that the best way forward would be to commission the development of a
centralised Marketing Database solution that would enable the marketing department
to develop their own reports and analysis and generally analyse the data themselves.
This would solve the short-term problem and enable them to develop the strategy with
Gratterpalm but also give them ownership of the data within the company going
forward.
Cast Study – FastStats Marketing Database Solution for mypetstop


Solution

FastStats is award-winning marketing database analysis software designed to enable
non-technical users to do their own counts, selections and analysis. It is very user-
friendly and allows visualisation of the data. It is also cost-effective and scaleable.

Pet Admin is the operational database used by mypetstop. Separate databases exist at
the three locations in Leeds, Manchester and Newcastle. The three databases have an
identical format and all the key fields in the three databases have the same range of ids
– so there will be a cust_no 1 in each location with 3 different customers. This meant
that there was no single view of all customers and transactions across the three
locations. There was also a certain amount of duplication with the data.

It was decided that rather than trying to immediately clean up the data at source
processes would be set up at DBM to clean up the data, apply unique ids and create a
Single Customer View that would be used to build the FastStats database that would be
delivered back to the marketing department. mypetstop could then assess as a separate
project what would be involved in implementing these processes back into the live
systems.

A Sql Server database was built from the three Access databases. Unique customer ids
were derived by adding a prefix of ‘L’, ‘M’ or ‘N’ to the existing customer id. Duplicate
customers were recognised and the earliest occurrence of customer id was kept, any
other ids were deleted in the customer table and the customer id changed in any
transaction tables. A location flag was added so that the database could be analysed
across all locations or individually.

On a weekly basis the three Access databases are uploaded to a secure ftp server. They
are automatically processed in Sql Server and the FastStats database is built. This is then
zipped up and loaded back to the secure ftp server. This process takes approximately 30
minutes. The zipfile is then automatically loaded back to the mypetstop system in Leeds
and the new data available for the marketing department.

More Related Content

What's hot

142230 633685297550892500
142230 633685297550892500142230 633685297550892500
142230 633685297550892500sumit621
 
Datawarehousing
DatawarehousingDatawarehousing
Datawarehousingsumit621
 
Engineering Machine Learning Data Pipelines Series: Tracking Data Lineage fro...
Engineering Machine Learning Data Pipelines Series: Tracking Data Lineage fro...Engineering Machine Learning Data Pipelines Series: Tracking Data Lineage fro...
Engineering Machine Learning Data Pipelines Series: Tracking Data Lineage fro...Precisely
 
Cssu dw dm
Cssu dw dmCssu dw dm
Cssu dw dmsumit621
 
Unit i big data introduction
Unit  i big data introductionUnit  i big data introduction
Unit i big data introductionSujaMaryD
 
Ehr challenges [bigdata]
Ehr challenges [bigdata]Ehr challenges [bigdata]
Ehr challenges [bigdata]Nesma Almoazamy
 
Datamining
DataminingDatamining
Dataminingsumit621
 
Data warehouse pricing & cost: what you'll really spend
Data warehouse pricing & cost: what you'll really spendData warehouse pricing & cost: what you'll really spend
Data warehouse pricing & cost: what you'll really spendnoviari sugianto
 
Importance of Data Mining
Importance of Data MiningImportance of Data Mining
Importance of Data MiningScottperrone
 
Predicting Customer Behavior With Big Data
Predicting Customer Behavior With Big Data Predicting Customer Behavior With Big Data
Predicting Customer Behavior With Big Data Pactera_US
 
Big DataParadigm, Challenges, Analysis, and Application
Big DataParadigm, Challenges, Analysis, and ApplicationBig DataParadigm, Challenges, Analysis, and Application
Big DataParadigm, Challenges, Analysis, and ApplicationUyoyo Edosio
 
Data Warehouse to Data Science
Data Warehouse to Data ScienceData Warehouse to Data Science
Data Warehouse to Data ScienceChandan Rajah
 
BigData Analytics_1.7
BigData Analytics_1.7BigData Analytics_1.7
BigData Analytics_1.7Rohit Mittal
 
Data Warehousing AWS 12345
Data Warehousing AWS 12345Data Warehousing AWS 12345
Data Warehousing AWS 12345AkhilSinghal21
 

What's hot (19)

Big data landscape
Big data landscapeBig data landscape
Big data landscape
 
142230 633685297550892500
142230 633685297550892500142230 633685297550892500
142230 633685297550892500
 
Datawarehousing
DatawarehousingDatawarehousing
Datawarehousing
 
Engineering Machine Learning Data Pipelines Series: Tracking Data Lineage fro...
Engineering Machine Learning Data Pipelines Series: Tracking Data Lineage fro...Engineering Machine Learning Data Pipelines Series: Tracking Data Lineage fro...
Engineering Machine Learning Data Pipelines Series: Tracking Data Lineage fro...
 
Cssu dw dm
Cssu dw dmCssu dw dm
Cssu dw dm
 
Unit i big data introduction
Unit  i big data introductionUnit  i big data introduction
Unit i big data introduction
 
Ehr challenges [bigdata]
Ehr challenges [bigdata]Ehr challenges [bigdata]
Ehr challenges [bigdata]
 
Datamining
DataminingDatamining
Datamining
 
Data warehouse pricing & cost: what you'll really spend
Data warehouse pricing & cost: what you'll really spendData warehouse pricing & cost: what you'll really spend
Data warehouse pricing & cost: what you'll really spend
 
Importance of Data Mining
Importance of Data MiningImportance of Data Mining
Importance of Data Mining
 
Big data in action
Big data in actionBig data in action
Big data in action
 
Predicting Customer Behavior With Big Data
Predicting Customer Behavior With Big Data Predicting Customer Behavior With Big Data
Predicting Customer Behavior With Big Data
 
Part1
Part1Part1
Part1
 
Big DataParadigm, Challenges, Analysis, and Application
Big DataParadigm, Challenges, Analysis, and ApplicationBig DataParadigm, Challenges, Analysis, and Application
Big DataParadigm, Challenges, Analysis, and Application
 
Data Warehouse to Data Science
Data Warehouse to Data ScienceData Warehouse to Data Science
Data Warehouse to Data Science
 
BigData Analytics_1.7
BigData Analytics_1.7BigData Analytics_1.7
BigData Analytics_1.7
 
Data Warehousing AWS 12345
Data Warehousing AWS 12345Data Warehousing AWS 12345
Data Warehousing AWS 12345
 
Taniya a64
Taniya a64Taniya a64
Taniya a64
 
Data Warehouse
Data WarehouseData Warehouse
Data Warehouse
 

Viewers also liked

Cd obregon
Cd obregonCd obregon
Cd obregonRaquelNA
 
CCRR Istituto Comprensivo di Pegognaga Mantova
CCRR Istituto Comprensivo di Pegognaga MantovaCCRR Istituto Comprensivo di Pegognaga Mantova
CCRR Istituto Comprensivo di Pegognaga Mantovaicpego
 
Sergio becerra,leonardo marin,jonathan vargas 902 nuevo
Sergio becerra,leonardo marin,jonathan vargas 902 nuevoSergio becerra,leonardo marin,jonathan vargas 902 nuevo
Sergio becerra,leonardo marin,jonathan vargas 902 nuevogioandr
 
Mito_e_Bellezza
Mito_e_BellezzaMito_e_Bellezza
Mito_e_Bellezzamauvet52
 
Case study nay b2 bgroup_google
Case study nay b2 bgroup_googleCase study nay b2 bgroup_google
Case study nay b2 bgroup_googleTomáš Hanáček
 
Código Splash Screen
Código Splash ScreenCódigo Splash Screen
Código Splash Screencymbron
 
CASE-ASAP District III 2012
CASE-ASAP District III 2012CASE-ASAP District III 2012
CASE-ASAP District III 2012dekimmel
 
Serdelitos informaticos
Serdelitos informaticosSerdelitos informaticos
Serdelitos informaticosSergio Llerena
 
case study template for emerge online conference
case study template for emerge online conferencecase study template for emerge online conference
case study template for emerge online conferenceYishay Mor
 
C.C.T.E.C
C.C.T.E.CC.C.T.E.C
C.C.T.E.Cauxilc
 
Case study superbrands polska 2010 ecco
Case study superbrands polska 2010 eccoCase study superbrands polska 2010 ecco
Case study superbrands polska 2010 eccoSuperbrands Polska
 
Les coulisses de la retouche photo
Les coulisses de la retouche photoLes coulisses de la retouche photo
Les coulisses de la retouche photosublim
 
Cdteca folha
Cdteca folhaCdteca folha
Cdteca folhasodajovem
 
Casemovies presentation 092409
Casemovies presentation 092409Casemovies presentation 092409
Casemovies presentation 092409theaelisabet
 
Seri Beauty Secret - Azli
Seri Beauty Secret - AzliSeri Beauty Secret - Azli
Seri Beauty Secret - AzliMHidzir Ismail
 
Case study module5_final (1)
Case study module5_final (1)Case study module5_final (1)
Case study module5_final (1)sparoad
 

Viewers also liked (18)

Cd obregon
Cd obregonCd obregon
Cd obregon
 
CCRR Istituto Comprensivo di Pegognaga Mantova
CCRR Istituto Comprensivo di Pegognaga MantovaCCRR Istituto Comprensivo di Pegognaga Mantova
CCRR Istituto Comprensivo di Pegognaga Mantova
 
Sergio becerra,leonardo marin,jonathan vargas 902 nuevo
Sergio becerra,leonardo marin,jonathan vargas 902 nuevoSergio becerra,leonardo marin,jonathan vargas 902 nuevo
Sergio becerra,leonardo marin,jonathan vargas 902 nuevo
 
CDs Final Report.ppt
CDs Final Report.pptCDs Final Report.ppt
CDs Final Report.ppt
 
Mito_e_Bellezza
Mito_e_BellezzaMito_e_Bellezza
Mito_e_Bellezza
 
Case study nay b2 bgroup_google
Case study nay b2 bgroup_googleCase study nay b2 bgroup_google
Case study nay b2 bgroup_google
 
Código Splash Screen
Código Splash ScreenCódigo Splash Screen
Código Splash Screen
 
CASE-ASAP District III 2012
CASE-ASAP District III 2012CASE-ASAP District III 2012
CASE-ASAP District III 2012
 
Cdevass2
Cdevass2Cdevass2
Cdevass2
 
Serdelitos informaticos
Serdelitos informaticosSerdelitos informaticos
Serdelitos informaticos
 
case study template for emerge online conference
case study template for emerge online conferencecase study template for emerge online conference
case study template for emerge online conference
 
C.C.T.E.C
C.C.T.E.CC.C.T.E.C
C.C.T.E.C
 
Case study superbrands polska 2010 ecco
Case study superbrands polska 2010 eccoCase study superbrands polska 2010 ecco
Case study superbrands polska 2010 ecco
 
Les coulisses de la retouche photo
Les coulisses de la retouche photoLes coulisses de la retouche photo
Les coulisses de la retouche photo
 
Cdteca folha
Cdteca folhaCdteca folha
Cdteca folha
 
Casemovies presentation 092409
Casemovies presentation 092409Casemovies presentation 092409
Casemovies presentation 092409
 
Seri Beauty Secret - Azli
Seri Beauty Secret - AzliSeri Beauty Secret - Azli
Seri Beauty Secret - Azli
 
Case study module5_final (1)
Case study module5_final (1)Case study module5_final (1)
Case study module5_final (1)
 

Similar to FastStats Marketing Database Solution Delivered in Two Weeks

Become Data Driven With Hadoop as-a-Service
Become Data Driven With Hadoop as-a-ServiceBecome Data Driven With Hadoop as-a-Service
Become Data Driven With Hadoop as-a-ServiceMammoth Data
 
MAALBS Big Data agile framwork
MAALBS Big Data agile framwork MAALBS Big Data agile framwork
MAALBS Big Data agile framwork balvis_ms
 
A guide to preparing your data for tableau
A guide to preparing your data for tableauA guide to preparing your data for tableau
A guide to preparing your data for tableauPhillip Reinhart
 
Key Principles Of Data Mining
Key Principles Of Data MiningKey Principles Of Data Mining
Key Principles Of Data Miningtobiemuir
 
BIG DATA - The Killer App for Motor Dealers
BIG DATA - The Killer App for Motor DealersBIG DATA - The Killer App for Motor Dealers
BIG DATA - The Killer App for Motor DealersNewton Day Uploads
 
Innovaccer service capabilities with case studies
Innovaccer service capabilities with case studiesInnovaccer service capabilities with case studies
Innovaccer service capabilities with case studiesAbhinav Shashank
 
Data mining & data warehousing
Data mining & data warehousingData mining & data warehousing
Data mining & data warehousingShubha Brota Raha
 
Running head CS688 – Data Analytics with R1CS688 – Data Analyt.docx
Running head CS688 – Data Analytics with R1CS688 – Data Analyt.docxRunning head CS688 – Data Analytics with R1CS688 – Data Analyt.docx
Running head CS688 – Data Analytics with R1CS688 – Data Analyt.docxtodd271
 
Using a Semantic and Graph-based Data Catalog in a Modern Data Fabric
Using a Semantic and Graph-based Data Catalog in a Modern Data FabricUsing a Semantic and Graph-based Data Catalog in a Modern Data Fabric
Using a Semantic and Graph-based Data Catalog in a Modern Data FabricCambridge Semantics
 
How In-memory Computing Drives IT Simplification
How In-memory Computing Drives IT SimplificationHow In-memory Computing Drives IT Simplification
How In-memory Computing Drives IT SimplificationSAP Technology
 
5 Things You Should Know About Data Products
5 Things You Should Know About Data Products5 Things You Should Know About Data Products
5 Things You Should Know About Data ProductsScribble Data
 
The book of elephant tattoo
The book of elephant tattooThe book of elephant tattoo
The book of elephant tattooMohamed Magdy
 
Ab cs of big data
Ab cs of big dataAb cs of big data
Ab cs of big dataDigimark
 
Should You Integrate DataOps in Your Business Process?
Should You Integrate DataOps in Your Business Process?Should You Integrate DataOps in Your Business Process?
Should You Integrate DataOps in Your Business Process?Enov8
 

Similar to FastStats Marketing Database Solution Delivered in Two Weeks (20)

Become Data Driven With Hadoop as-a-Service
Become Data Driven With Hadoop as-a-ServiceBecome Data Driven With Hadoop as-a-Service
Become Data Driven With Hadoop as-a-Service
 
Unlocking big data
Unlocking big dataUnlocking big data
Unlocking big data
 
Spss
SpssSpss
Spss
 
MAALBS Big Data agile framwork
MAALBS Big Data agile framwork MAALBS Big Data agile framwork
MAALBS Big Data agile framwork
 
A guide to preparing your data for tableau
A guide to preparing your data for tableauA guide to preparing your data for tableau
A guide to preparing your data for tableau
 
Key Principles Of Data Mining
Key Principles Of Data MiningKey Principles Of Data Mining
Key Principles Of Data Mining
 
BIG DATA - The Killer App for Motor Dealers
BIG DATA - The Killer App for Motor DealersBIG DATA - The Killer App for Motor Dealers
BIG DATA - The Killer App for Motor Dealers
 
Innovaccer service capabilities with case studies
Innovaccer service capabilities with case studiesInnovaccer service capabilities with case studies
Innovaccer service capabilities with case studies
 
case study-pharma
case study-pharmacase study-pharma
case study-pharma
 
Data mining & data warehousing
Data mining & data warehousingData mining & data warehousing
Data mining & data warehousing
 
Improving Data Extraction Performance
Improving Data Extraction PerformanceImproving Data Extraction Performance
Improving Data Extraction Performance
 
Machine Data Analytics
Machine Data AnalyticsMachine Data Analytics
Machine Data Analytics
 
The ABCs of Big Data
The ABCs of Big DataThe ABCs of Big Data
The ABCs of Big Data
 
Running head CS688 – Data Analytics with R1CS688 – Data Analyt.docx
Running head CS688 – Data Analytics with R1CS688 – Data Analyt.docxRunning head CS688 – Data Analytics with R1CS688 – Data Analyt.docx
Running head CS688 – Data Analytics with R1CS688 – Data Analyt.docx
 
Using a Semantic and Graph-based Data Catalog in a Modern Data Fabric
Using a Semantic and Graph-based Data Catalog in a Modern Data FabricUsing a Semantic and Graph-based Data Catalog in a Modern Data Fabric
Using a Semantic and Graph-based Data Catalog in a Modern Data Fabric
 
How In-memory Computing Drives IT Simplification
How In-memory Computing Drives IT SimplificationHow In-memory Computing Drives IT Simplification
How In-memory Computing Drives IT Simplification
 
5 Things You Should Know About Data Products
5 Things You Should Know About Data Products5 Things You Should Know About Data Products
5 Things You Should Know About Data Products
 
The book of elephant tattoo
The book of elephant tattooThe book of elephant tattoo
The book of elephant tattoo
 
Ab cs of big data
Ab cs of big dataAb cs of big data
Ab cs of big data
 
Should You Integrate DataOps in Your Business Process?
Should You Integrate DataOps in Your Business Process?Should You Integrate DataOps in Your Business Process?
Should You Integrate DataOps in Your Business Process?
 

FastStats Marketing Database Solution Delivered in Two Weeks

  • 1. Cast Study – FastStats Marketing Database Solution for mypetstop FastStats Marketing Database Solution Delivered by In Two Weeks! The Problem The Approach mypetstop had three identical Access Gratterpalm brought in specialists databases in three locations generated by Database Marketing Ltd to design and their Pet Admin software. Each database build a new marketing database solution had the same customer ids in common that would merge all three databases and no easy way of creating reports or together, create a unique range of analysis for individual locations or across customer ids, clean up any duplicates and all three. The analysis was needed to deliver back to mypetstop a solution that derive the Marketing Strategy with their the marketing department would have on newly-appointed agency Gratterpalm and their desktop. Non-technical users would to drive the business forward. There was be able to create their own counts, also some duplication of records identified selections, reports and analysis. on the system. The Solution Database Marketing Ltd first created a Sql Server database to manipulate the data to create a unique range of customer ids for each location, delete duplicate customer records but maintain all transactions for the deleted customer ids thus creating a Single Customer View. All three cleaned databases were merged into one with flags created to enable analysis overall or by location. This was all done on systems at DBM with the clean database loaded back to mypetstop. No internal IT resource was needed at mypetstop other than to setup an automated ftp process to upload the Access databases to a secure ftp site and then to download the final processed database. FastStats was chosen as the Marketing Database software and a FastStats database designed and built. The solution included weekly updates. The FastStats software was installed and configured at mypetstop Head Office in Leeds and 4 staff members trained in the use of FastStats. The whole system, including Sql programming, FastStats design & build, software installation and configuration, testing and training was completed within two weeks! “I've been using Faststats for a month now and I'm very impressed with its user-friendly interfaces, broad range of reports and interactive graphical views. Through interrogating our previously dormant database on FastStats, we quickly and efficiently gained valuable knowledge on our customer demographics, spending habits, average length of stay and most popular service. This information will help us to deliver a more effective, targeted marketing communication campaign in 2010.“ James Kundi - mypetstop
  • 2. Cast Study – FastStats Marketing Database Solution for mypetstop Introduction mypetstop are part of the Mars group of companies. They provide a unique range of pet services under one roof. From purpose built kennels and cattery which provide world class boarding for pets to training clubs, grooming, retail and hydrotherapy facilities and an on-site veterinary practice. There are currently three sites in Leeds, Manchester and Newcastle. mypetstop appointed Gratterpalm, a Leeds based specialist retail agency, as their agency in November 2009 following a four-way pitch. Gratterpalm have been tasked with developing campaigns to improve usage and loyalty with existing customers, re-educate lapsed customers and drive awareness with potential new customers. The agency will drive direct marketing and e-campaigns, oversee creative work for the mypetstop website and drive all online activity. During an initial consultation process between mypetstop and Gratterpalm to derive the high-level strategy it became clear that the reporting and analysis was just not available on the current systems to provide the information to develop the strategy. There was no central database but rather three separate Access databases. To get the information required using the operational system bespoke queries and reports would have to be written and run in each location. mypetstop do not have in-house programmers or analysts and use a third-party IT provider so this approach would prove both time- consuming and expensive. It was also becoming clear that this process would have to be repeated in the future for any new reports and analysis so it would not give them any long-term benefit. It was decided that the best way forward would be to commission the development of a centralised Marketing Database solution that would enable the marketing department to develop their own reports and analysis and generally analyse the data themselves. This would solve the short-term problem and enable them to develop the strategy with Gratterpalm but also give them ownership of the data within the company going forward.
  • 3. Cast Study – FastStats Marketing Database Solution for mypetstop Solution FastStats is award-winning marketing database analysis software designed to enable non-technical users to do their own counts, selections and analysis. It is very user- friendly and allows visualisation of the data. It is also cost-effective and scaleable. Pet Admin is the operational database used by mypetstop. Separate databases exist at the three locations in Leeds, Manchester and Newcastle. The three databases have an identical format and all the key fields in the three databases have the same range of ids – so there will be a cust_no 1 in each location with 3 different customers. This meant that there was no single view of all customers and transactions across the three locations. There was also a certain amount of duplication with the data. It was decided that rather than trying to immediately clean up the data at source processes would be set up at DBM to clean up the data, apply unique ids and create a Single Customer View that would be used to build the FastStats database that would be delivered back to the marketing department. mypetstop could then assess as a separate project what would be involved in implementing these processes back into the live systems. A Sql Server database was built from the three Access databases. Unique customer ids were derived by adding a prefix of ‘L’, ‘M’ or ‘N’ to the existing customer id. Duplicate customers were recognised and the earliest occurrence of customer id was kept, any other ids were deleted in the customer table and the customer id changed in any transaction tables. A location flag was added so that the database could be analysed across all locations or individually. On a weekly basis the three Access databases are uploaded to a secure ftp server. They are automatically processed in Sql Server and the FastStats database is built. This is then zipped up and loaded back to the secure ftp server. This process takes approximately 30 minutes. The zipfile is then automatically loaded back to the mypetstop system in Leeds and the new data available for the marketing department.