how business can unlock value from its customer data
DESCRIPTION
How business can unlock value from its customer data.Provides examples of customer acquisition, customer retention, using behavioural analysis, customer segmentation and mapping technologieTRANSCRIPT
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Data2dollars
How business can unlock value from it’s data
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• The problems facing Sales and Marketing in today’s organisation• The Situation• The Solution, how it works and what it changes• Case Studies• Next Steps
Agenda
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Have you ever felt like this?
50% of my ad budget is wasted, I just don’t know which 50%
I can’t get unique customer profiles
My business is a mess of silos of unconnected data
I have to use assumptions to drive my analysis
If I need access to data for a marketing decision, I have to
wait in line for an IT report
External Data is never taken into consideration
I send the same direct mail to all of my customers
I need to know that the right customers are available in the new market
My factory sells products to major retailers, but I have no
idea what they do with it
I don’t understand what type of people my previous buyers were
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• How can business unlock value from its customer data?– IT is seen as a cost but data represents untapped business value.– Typically, databases are only used to measure $ historically – sales,
revenue and profit.
• Databases that work can also help business to:– Attract new customers– Retain existing customers– Identify opportunities for them to spend more– Develop existing customer relationships– …… and thus generate dollars
What we need to do: Convert Data to Dollars
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Dollars (Attract– Retain – Grow Customers)
Customer Communications
Knowledge
Data
The Data to Dollars Model
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Solution: The IT Solution
Financial System
Databases
Sales Tracking
ProductServicing
Warranty System
AnalysisWeb Site
Specific Business Applications
Desktop Applications (All Desktops anywhere)
Office Document Management
Single Customer
View
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Finance System Sales Leads
Do
llars
Cu
sto
mer
To
uch
esK
no
wle
dg
eD
ata External
Industry Data
Market
KnowledgeCustomer Segment&
Profiles
Customer
Behaviour
Customer
Relationship
History
Marketing -- Sales – Service to Repurchase – Upgrade -- Depart
Call Centre Sales Force
Service Centre Website
Retain
Customers
Acquire
Customers
Develop
Customers
Attract
Retain
Grow
The Typical Business Environment
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Social Net
Finance System Sales Leads
Do
llars
Cu
sto
me
r To
uc
he
sK
no
wle
dg
eD
ata External
Industry Data
Market KnowledgeCustomer
Segment& Profiles
Customer
Behaviour
Customer
Relationship History
Marketing -- Sales – Service to Repurchase – Upgrade -- Depart
Call Centre Sales Force Service Centre Web Site
Retain
Customers
Acquire
Customers
Develop
Customers
Attract
Retain
Grow
The Goal
Transform your existing data into a single view of the customer
Ensure the RELEVANT KNOWLEDGE is given to the RELEVANT PEOPLE in the organisation
Give the customers that YOU want the experience that THEY demand
Higher - Leads, Conversion Rate, Average Sale Value, Margin, Transactions, Lifetime Value, Referrals
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Finance
System Sales Leads
Do
llars
Cu
sto
mer
To
uch
esK
no
wle
dg
eD
ata External
Industry Data
Market KnowledgeCustomer
Segment& Profiles
Customer
Behaviour
Customer
Relationship History
Marketing -- Sales – Service to Repurchase – Upgrade -- Depart
Call Centre Sales Force Service Centre Web Site
Retain
Customers
Acquire
Customers
Develop
Customers
Attract
Retain
Grow
How it Works
Customer
Database
Delivery of customer insights in a Simple Desktop Viewer
Analysis of unified database including external data
Customer insights provided on desktop viewer power superior decisions & effective customer communication
Strategic sales programmes designed and built around preferred customer buying process – time, place, opportunity!
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The Issues• Fernwood franchisee applicant looking for funding to open a franchise in a particular franchise area
• Government funding agency wanted compelling market data on the viability of the franchise in
that area
The Solution• The ideal profile of Fernwood members was known
• Demographic information was analysed to identify the number and location of ideal customers in
the franchise area
• This was compared with the number and location of ideal customers in other franchise areas
• Analysis of this information provided compelling evidence as to the likely turnover of a franchise in
the chosen franchise area
• Further analysis of the data, using models such as 8 and 10 minute drive time analysis, identified
the most appropriate location for the franchise in the franchise area
The Result • The funding agency minimised its lending risk, the franchisee received the funding and opened the
franchise. The franchisee chose the most attractive store location based on the findings of the
report and was able to maximise its marketing spend by limiting mail drops to households who
fitted the ideal customer profile
Case Study: Fernwood Fitness
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“My business is a mess of silos of unconnected data”
“I regularly communicate with the same person several times, even when they are deceased”
Case Study: Health Services Provider
The Organisation
• Provides services to 30,000 mostly elderly customers across Melbourne• Fundraising a major revenue stream• Operational information in 10 different databases across three software
systems, managed by different departments.
The Issues
• Same person occurs in different roles in different databases and the organisation cannot tell
• The relationship between donors and service recipients or their contacts can’t be established
• Change of detail in one system cannot be updated in the other systems• The information cannot be used to understand the customer base better
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The Solution
• Set-up a simple software routine to take names and addresses from existing systems on a weekly basis, convert them to new format and add them to a new database. This found about 80,000 people in various databases.
• Provided a simple search screen for the new database to find where the same or related clients and their contacts exist in the various existing databases.
• Used the results to clean/correct the existing poor data entry practices. (i.e. addresses of “As above”)
• Mapped the household locations of the whole community against external data like Census to segment and understand the client base and growth potential
• Used this to more effectively communicate with existing and potential clients/donors.
Case Study: Health Services Provider (cont’d)
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“I send the same direct mail to all of my customers”
“I know that my customers have various needs but I can’t segment them correctly”
Case Study: Major Retailer
The Organisation
• Major Australian Retailer• Single club database of over 500,000 customers receiving direct mail
regularly• Provide a range of different products that suit different stages of life• Main customer base is young families
The Issues
• A young single, expecting mother and retiree sent the same type of communication/offer
• Specifically households with babies vs. teenagers not segmented• The database not used to help the organisation better understand their
customers
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The Solution• Customers segmented using their previous spending patterns (RFM
analysis)• Outcomes used to predict who would respond to a direct marketing piece• The potential annual savings of not writing to those who wouldn’t respond
was $1 per club member• Segmentation tested against the complete mail out and found to have
high accuracy• By comparing RFM analysis outcomes and running test communication
pieces it is possible to make communication highly effective and relevant to the customer by both understanding what type of person they are and where they are in their spending cycle
• People with changing needs identified and communication adjusted accordingly
• The results used to analyse the profitability of each of 200 houses across Victoria
• The spend compared to total sector spend per 200 houses to allow competitor analysis and better understand market penetration
• The profit per communication piece per 200 houses was calculated to fine tune general mail drops and growth areas for NEW customers
Case Study: Major Retailer
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“My factory sells products to major retailers, but I have no idea what they do with it.”
“I cannot support the sales to the customer, keep my stock levels low or find new channels to market”
Case Study: Hardware Manufacturer
The Organisation
• Organisation’s product distributed all over Australia and offered by all the major hardware retailers
• Many of the larger stores hold no stock and make few sales, but smaller stores of the same brand have high stock/high sales levels
• The only information the company has is through the receipts in their financial system
• The sales are made with no co-ordinated sales strategy or information system
The Issues• No understanding of the split between sales to new houses and the
renovation industry• No understanding of the market penetration in different geographical areas
and thus the potential to grow the market• No way of knowing how many potential smaller retailers might sell the
product• No sales forecasts to give new potential market channels
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The Solution
• Sales data extracted from financial system and restructured by geographical area
• External data obtained to show the total spend on renovations and new houses in each area
• A market share was calculated for each major part of Australia• The top 25% of sales regions were identified• Modeling showed that if the top 25% market penetration was repeated for
the rest of Australia then sales would triple.• Lists of all potential retailers were obtained and put against existing sales• Information used to work out different distribution strategies for different
parts of Australia
Case Study: Hardware Manufacturer
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“I don’t understand what type of people my previous buyers were.”
“I don’t know how or where to find more of them”
Case Study: Residential Developer
The Organisation
• Organisation sells residential housing estates in suburban and holiday areas• Each display house has its own database and sales function• All of the existing advertising above the line
The Issues• No measured understanding of the household make up of buyers• Potential buyers who visit two display homes are in two parallel sales processes• No strategy to directly target potential buyers• No way of separating dreamers from buyers• No understanding of the sales potential in different geographical areas
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The Solution
• Sales data extracted from the various systems and formats and combined into a single database
• The buyers and visitors database was mapped to understand where they come from
• The database was segmented into different household profiles and compared to the general
population. One of the identified segments was the “Dreamers”
• Sophisticated modeling software was used to rank each group of 200 houses across Sydney from 1
to 10 by their likelihood to visit a display home
• The likelihood of each type of visitor to buy was checked
• The different types of buyers for different estates was modeled
• The results were tested against actual buyers and visitors for the following 3 months
• The reliability of the model was:
– 38% of the visitors came from the top 10% of the houses
– 58% came from the top 20% of ranked houses
– 83% came from the top 50% of the houses
• This virtually eliminated half the population as not being potential buyers and identified a group
four times more likely to buy
• This information is being used to both better understand the potential buyer and develop direct
marketing strategies
Case Study: Residential Developer
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• We know that to deliver a Memorable Business Outcome you need to build five pillars:
1. IT Infrastructure• Most businesses have this in place
2. Meaningful Data Requirements• Understanding what we need to know
– For decision support
– For performance metrics
– For strategy
3. Standards, Processes and Business Rules• So that there is consistent interpretation – One Version of the Truth
4. People and Organisation• Who can access the data and why
5. The Customer Environment• External factors data (including web data)
• The last four are in addition to most “IT” projects.
The D2D Proposition
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• We build a system that meets these criteria in any organisation, along with our recommendations.
• We undertake all phases of the system build in parallel.– No silos
• Outcomes are demonstrated and tangible benefits delivered very early in the project.
• No threat to existing systems– co-ordinatation of systems to deliver the outcomes. – minimises organisational change.
• Rapid prototyping checks all data, standards and organisational issues that may affect the project, as well as the very early demonstration of results
• The system can subsequently operate as a bureau service or as an in-house system
The D2D Proposition (cont’d)
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First Step: Proof of Concept
1. Customer Interviews to provide information on the existing environment, systems, data, knowledge etc
2. Review the Existing Environment and write a Short Report outlining:– The benefits of a Single Customer View to the business– The steps required to obtain the benefits– Time and Cost
3. Build a prototype that uses real data and delivers some real answers:– Take a copy of each database– Put into a temporary analysis database– Run some basic analysis to demonstrate potential outcomes
4. Evaluate business value and agree to proceed to full project.
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