How business can unlock value from its customer data.

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Data2dollars How business can unlock value from it’s data www.symphony3.com www.symphony3.com 1

description

How business can unlock value from its customer data.Provides examples of customer acquisition, customer retention, using behavioural analysis, customer segmentation and mapping technologie

Transcript of How business can unlock value from its customer data.

Page 1: How business can unlock value from its customer data.

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Data2dollars

How business can unlock value from it’s data

www.symphony3.com

www.symphony3.com

Page 2: How business can unlock value from its customer data.

• 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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Page 3: How business can unlock value from its customer data.

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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Page 4: How business can unlock value from its customer data.

• 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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Page 6: How business can unlock value from its customer data.

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

Email

Single Customer

View

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Finance System Sales Leads

Do

llars

Cu

sto

mer

To

uch

esK

no

wle

dg

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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

Page 8: How business can unlock value from its customer data.

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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Page 9: How business can unlock value from its customer data.

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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WHICH HOUSES ARE PROFITABLE TO COMMUNICATE WITH ??

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Sales per CCD (200 hundred houses)

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WHERE DO PEOPLE SHOP ??

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Survey respondents by segment

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Segments by location

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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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Page 26: How business can unlock value from its customer data.

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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