Turning Customer Data into Profits 
 
 
 
 
 

Making the best use of your customer data using sophisticated technology applications and processes 
 

July 2007 
 

 

Contents 
 
Introduction

Gathering, storing and maintaining customer data

Customer Relationship Management (CRM) Systems
Importance of CRM to customer retention
Key Applications of CRM
1. Operational CRM
2. Collaborative CRM
3. Analytical CRM
CRM and Technology

Turning Customer Data into Profits

Customer Analytics
Anticipating Consumer Behaviour using Analytics
The future of Customer Retention and Analytics
Analytics and customer retention in practice
Analysing Customer Interactions
Analytical Products and Processes
1. Statistical Analysis
2. Online Analytical Processing (OLAP)
3. Data Mining
4. Text Mining
Predictive Nature of Customer Analytics
Customer Analytics: Key profitability metrics for your business

Customer Intelligence
Customer Intelligence Processes
1. Personal demographic
2. Geographic demographic data
3. Attitudinal data
Customer Intelligence and Customer Retention

Business Intelligence
Four reasons why companies need Business Intelligence
1. Increase Profitability
2. Decrease Costs
3. Improve Customer Relationship Management (CRM)
4. Decrease Risk

Data Quality
Data Maintenance
Data Cleansing
Automated Data Cleansing
Manual Cleansing Process
Combined Cleansing Process
Outsourcing the CRM Process
Benefits to Outsourcing CRM

In Conclusion
 

 

Introduction

 

 

Part One

 

Bill Gates wrote, “How you gather, manage and use information to serve the needs of your customer will determine whether you win or lose in your business.” 

It can be said that many businesses spend large amounts of budget resources, marketing to consumers they know very little about.  Although necessary to ensure brand awareness there is a strong argument that the majority of resources should be spent on those customers you already have a relationship with and have a better understanding of. 

Therefore it is safe to say that it is worth focusing on your top customers in your customer retention strategy, but not to the exclusion of other customers. The key issue lies in the identification of who your top customers are. Most businesses do not have the metrics in place to allow them to identify which are their top 4% of customers, and which customers have the potential to become more profitable. 

This highlights the importance of customer databases and the use of technology to manage customer data electronically.  By doing so, businesses gain the ability to generate and analyse relevant and profitable information about their customers. Information that can be used to out-think and out-execute the competition!! 

It is true that most companies today have an abundance of information about their customers. It is not the scarcity of information that is posing a problem, but the over abundance. And many businesses struggle just to keep this information up-to-date and accurate. Once this hurdle is overcome, a universe of competitive advantage awaits! 

In this global economy that we operate, it is no longer a matter of throwing out a hook and waiting for a bite. It is about taking the time and using the right tools to truly understand customers, satisfy their needs and wants, and anticipate what they may want tomorrow. 

In response to spam marketing and new privacy legislations, a more scientific approach to the art of marketing and customer retention has become vital. With the massive amounts of customer data being generated every moment of every day, and the absolute necessity to carefully managing the customer relationship; CRM systems, customer intelligence and customer analytics technologies are no longer a novelty.  They are becoming essential to competitive differentiation in a global and borderless economy.

Gathering, storing and maintaining customer data

Customer Relationship Management (CRM) Systems

 

Part Two

 

As highlighted in our RCS Customer Retention Tools Report, a major trend in the world of customer retention and relationship marketing is the shift away from the shotgun approach to mass advertising.  There is a need for a more narrowly defined, rifle approach in targeting businesses’ most profitable existing customers and prospective new customers, who are most likely to purchase a product or service in the near future. The advancement of using databases to implement this rifle approach has become one of the most effective and strategic choices businesses are making towards increasing their customer retention efforts (Schoenbachler, Gordon, Foley and Spellman 1997).

Importance of CRM to customer retention 

A CRM database system describes the collection of customer data that businesses collect, such as customers’ names, addresses and purchases.  These databases provide marketers, advertisers and promotional managers with information that enables them to make better decisions in working toward accomplishing company objectives, increasing ROI and ultimately in increasing customer retention.  

Schoenbachler et al (1997) define database marketing as “gathering, saving and using the maximum amount of useful knowledge about your customers and prospects … to their benefit and your profit.”

 

The importance of a well designed and managed customer database lies in the evolution of relationship marketing and the realization that in order to be competitive, companies need to build a relationship with their customers, which is based on more than just price. Marketers have learned that it is easier and less expensive to get an existing customer to buy again than it is to acquire a new customer.  

The objective of database marketing is to build a profitable individual relationship with each customer. The relationship should make the customer feel that he or she is recognized and receives personal service and attention (Schoenbachler et al 1997).  Together with sophisticated CRM Analytics, this can be achieved!  Allowing businesses to slice and dice CRM data to answer important business decision such as, which customers are highly profitable and which customers are most loyal as well as which sales representatives have the best lead conversion rate (Morochove 2006).  
 

Consider this scenario: the importance of CRM systems to customer retention   THE PROBLEM Traditionally, the main communication channels used by businesses have been contact centres, direct mail, and in-person contact. However, Jay McKeever Director of Worldwide Marketing for Cincom Systems, points out that these channels have now become just a small fraction of today’s communications scene. He notes that in fact, in some sectors, businesses use over thirty different communications methods to present new offerings to their customers.  Additionally, McKeever notes that quite often, businesses store data from each of these channels in separate information systems or NOT AT ALL!! This effectively means that they are strangling their own cross-marketing and direct marketing potential in gaining repeat customers.  THE SOLUTIONS  1. Consolidate customer relationship information By storing all customer data and customer interactions in a central location, businesses can begin to create relevant cross-selling and up-selling scenarios.  2. Create more effective customer profiles Consolidated access to customer relationship information will allow powerful and relevant customer profiles to be created, such as:  •    Customer purchase history  •    All customer communications with a business  •    Billing and transactional information  •    Channel or production preferences  •    Responsiveness to sales and marketing efforts  Do you have this information at your finger-tips in your business?  3. Refocus the message Approach individual customers using the channels they are most likely to respond to, and do it with a message that is targeted to that customer.  Jay McKeever published on ww.repeatcs.com.au

 

Part Three

 

Key Applications of CRM

There are three aspects of CRM database systems which can each be implemented in isolation from each other, while remaining important to the ultimate goal of increased sales and profitability.  These are: 

1. Operational CRM which involves the automation or support of customer processes that include a company’s sales or service representatives.
2. Collaborative CRM which involves direct communication with customers that does not include a company’s sales or service representative (i.e. self service).
3. Analytical CRM which is the analysis of customer data for a broad range of profit creating purposes.

 

META Group developed this conceptual architecture in the late 1990s, and dubbed it the “CRM Ecosystem” (wikipedia). These key aspects of CRM serve to highlight the benefits that information and data stored in CRM systems offer businesses.

1. Operational CRM

Operational CRM provides support to front office business processes, including sales, marketing and service. Each interaction with a customer is added to a customer's contact history, from which all staff can retrieve information on customers from the database, as necessary. 

One of the main benefits of this contact history is that customers can interact with different people or different contact channels in a company over time without having to repeat the history of their interaction each time.

2. Collaborative CRM

Collaborative CRM covers the direct interaction with customers. These include a variety of channels, such as internet, email and automated phone services, where the customer is directly communicating with a business and can be equated with self service. 

The objectives of Collaborative CRM can be broad, including cost reduction and service improvements, essential to ensuring customers return.

3. Analytical CRM

Analytical CRM analyses customer data for a variety of purposes including: 

• Design and execution of targeted marketing campaigns to optimize marketing effectiveness.
• Design and execution of specific customer campaigns, including customer acquisition, cross-selling, up-selling and retention.
• Analysis of customer behavior to aid product and service decision making (For example, pricing and new product development).
• Management decisions, such as financial forecasting and customer profitability analysis.
• Prediction of the probability of customer defection (churn).

 

Analytical CRM makes use of predictive analytics by using the data stored in CRM databases to predict future reactions and outcomes. 

Strategy It is important to remember that CRM is not just a technology, but rather a holistic approach to an organisation's philosophy to dealing with its customers and building long term relationships.  This holistic approach should encompass policies and processes, front-of-house customer service, employee training, marketing, systems and information management. All of which, work to create a memorable customer experience with your business. Therefore, it is important that any CRM implementation considers not only technology, but also broader organisational requirements. The objectives of a CRM strategy must consider a company’s specific situation and its customer’s needs and expectations.

 

CRM and Technology

The technology requirements of a CRM strategy can be complex and far reaching; however there are some basic requirements:  

• A database to store all customer information. This can be a CRM specific database or a data warehouse.
• Operational CRM requires customer support software, which is accessible by all employees.
• Collaborative CRM requires customer interaction systems, such as an interactive website and/or automated phone systems.
• Analytical CRM requires statistical analysis software as well as software that manages any specific marketing campaigns.

 

Each of these can be implemented at a basic level or in a high end complex installation (wikipedia). 
 

Checklist One  Does your business have a central CRM database to capture critical information about your customers (name, address, purchase history)? Has your business created meaningful profiles of all customers? Is your business able to identify the top 4% of profitable customers? Has your business made use of information stored in customer databases to implement a targeted communication messages or to assist in promotional campaigns? Does your customer database allow access to all relevant employees across all corporate sectors?

 

References: 

Jay McKeever Director of Worldwide Marketing for Cincom Systems Time to change your channels 

Denise D. Schoenbachler, Geoffrey L. Gordon, Dawn Foley and Linda Spellman Understanding consumer database marketing Journal of Consumer Marketing VOL. 14 NO. 1 1997 pp. 5-19 © MCB University Press, 0736-3761 

[n.d] [Online Available: http://en.wikipedia.org/wiki/Customer_relationship_management [Last Accessed 3 July 2007] 
 

[Online Available: http://www.allbusiness.com/technology/computer-software-customer-relation/3777364-1.html AllBusiness.com Essential CRM Features for Small Businesses By Richard Morochove December 2006 [Last Accessed 3 July 2007] 

[n.d] [Online Available: Repeat Customer Solutions www.repeatcs.com.au [Last Accessed 3 July 2007]

Turning Customer Data into Profits

Customer Analytics

 

Part Four

 

Anticipating Consumer Behaviour using Analytics

“Know your customers and give them what they want” - Colin Shearer, Vice-President, Customer Analytics SPSS. This statement highlights the fundamental principle of all business marketing. A principle that is simple in theory, but becoming increasingly challenging to put into practice. Short of being a mind reader or having a crystal ball, it is difficult for marketers to know what is on a customer’s mind today, or anticipate what the customer may need or want tomorrow.

 
In line with the appropriate handling of consumer do-not-call and do-not-e-mail requests part of standard operating procedures, marketers need to do better in getting to the root causes of why people are dissatisfied and improve relationships with customers and prospects. That means starting from the consumer data and working backwards to the products and services offered. 

As Shearer points out, and it is becoming increasing obvious, the challenges in meeting customer expectations does not stem from a lack of customer data. The fact is, customers and prospects are giving us information about themselves all the time. Through every response, customer contact, event, transaction, website hit or email enquiry, they reveal something about themselves.  Businesses’ databases are full of useful information, and call centres and other customer management systems are overflowing with details about customers and contacts.  

The challenge is that raw data does not have value per se; it needs to be turned into useful information.

 
Relationship marketers are at a stage where they must avail themselves of all the tools available to maximize value to customers and minimize the effect on consumers who do not find value in what is being offered. New technologies to work with and manipulate consumer data are making that objective much easier to attain (Slaker 2003). Using analytical technologies, businesses are able to effectively manage the large amounts of data accumulated during customer interactions. 

A philosopher once wrote that finding the patterns in the randomness of life is the way we create beauty and make art. A similar statement could be made about analytics, which find patterns in the randomness of data so that you can discover valuable information and gain insight about your customers (Slaker 2003)

The future of Customer Retention and Analytics

Research carried out by Accenture Institute for High Performance Businesses suggests that top performers in business today have a secret weapon…. analytics. They are using analytics to out-think and out-execute their competition, by making extensive strategic use of data, quantitative techniques, predictive models, and fact-based management to drive decisions. 

Customer Analytics represent a new era in customer retention.  Although valuable, CRM systems only deal with today’s data, and can perhaps provide some historical information. But CRM systems are essentially static. In comparison, Analytics relies on looking into customers’ past behaviours in order to predict the future, and acting on that intelligence. 

Jean Harris, Director of Research at the Accenture Institute for High Performance Business notes, the war for customer retention will be fought on data. It certainly is all about people, but at a business planning and decision-making level, it is all about the numbers. What is the real value of a customer? What is the real ROI? Is segmenting your customer base the way to go? And on what are you going to base your decisions. 

Accenture’s research shows that high-performance businesses; those that significantly out-perform their competitors over the long-term, are five times as likely to use analytics than the low performers, and twice as likely to use analytics than the average performers.

 

In her recently published book “Competing on Analytics: The New Science of Winning,” Harris discusses four factors apart from technology which makes a company into a competitive analytical player: 

1. Leaders who get it;
2. People who love numbers;
3. Processes that revolve around facts; and
4. Technology to capture, sort, and make sense of the data.

 

These four fundamentals represent a quick checklist for any business looking at starting a strategically planned CRM Analytical campaign.

Analytics and customer retention in practice

According to Davenport and Harris (2007), the importance of customer analytics to business owners lies in the fact that we are doing business in a borderless global economy, so geographic advantages are no longer there. Protective regulation is now scarce, proprietary technologies are copied quickly and most companies offer similar products and use comparable technology.  

This leaves high-performance business processes among the last points of differentiation, to execute business with maximum efficiency and effectiveness. The key is to identify the distinctive capability of a business, the attribute at which it is better than anyone else in the industry. And then use analytics to make sure that you continue to be the best at it. 

Using analytics and data produced as a result, to replace intuition, ensures distinctive business capabilities can be strategically optimized to deliver a real source of competitive advantage.

Part Five

 

Analysing Customer Interactions

Gaining a better understanding of customers and their expectations, according to Lenzen (2004) requires gathering and analysing all customer interactions with the business. Customer interactions include browsing, purchasing, paying and communicating with customer service or sales staff. It is these interactions that may be used to develop customer profiles and eventually predict future actions. It is equally important to understand which of these interactions are marketing-driven and which were due to chance.  

Understanding the impact of marketing, risk and customer service decisions and how those decisions influence customer behaviour must also be tracked. The knowledge that a customer purchased due to a marketing campaign may be used to optimize future campaigns. Customer behavioural analysis combined with increased marketing efficiency will enhance future customer interactions.

Analytical Products and Processes

Customer Analytics comprises all the programming technologies that analyse data about a business’ customers and presents it so that better and quicker business decisions can be made. SearchCRM.com.  

Consider this scenario:  Not all 45- to 55-year-olds with a household income between $50,000 and $75,000 have the same purchase interests and spending habits. For this reason, static demographic data should not be used as the building blocks of a well-defined customer segmentation system. Demographic data may be used to describe customer segments (profiling), but it is much less effective in distinguishing interests and spending habits than customer behavioral data.

 

According to Lenzen (2003) customer analytics exploit customer behavioural data to identify unique and actionable segments of the customer base. These segments may be used to increase targeting methods. Ultimately, customer analytics enable effective and efficient customer relationship management. The analytical techniques vary based on objective, industry and application.   

An array of analytical products are available for desktop and enterprise systems and for pros and novices alike. These include:  

1. Statistical analysis
2. On-line analytical processing (OLAP)
3. Data mining
4. Text mining

1. Statistical Analysis

Refers to a collection of methods used to process large amounts of data to uncover key facts, patterns and trends. Numerous statistical analysis procedures can be applied to customer databases, but the most commonly used are classification and segmentation. Classification uses predictor fields to predict a categorical target field, such as which groups of people will respond to a mail out. Segmentation divides subjects, objects or variables into various relatively homogeneous groups (such as segmenting customers into usage-pattern groups).  

For example:  Rural Cellular Corporation (RCC), which provides wireless service to subscribers in 14 overseas states covering a population of 5.9 million, uses statistical analysis for market research. This research includes customer satisfaction and branding studies to determine positioning for its products and service features. Before investing money in any new feature, RCC surveys its customers to determine exactly what features they want, what they want each of the features to do and how much they are willing to pay for them. Example from: marketingprofs.com (www.crm2day.com/library/EEplpkZllyASiURqyN.php)

2. Online Analytical Processing (OLAP)

OLAP enables users to easily and selectively extract data and then view it from different perspectives. For example, a user can request that data be analyzed and presented in a format that shows all of a company’s products sold in a state in a particular month, compares revenue figures with those for the same products in a previous month, and then compares other product sales in state for the same time period.

 
To facilitate this kind of analysis, OLAP data is stored in a multidimensional database, which considers each data attribute (such as product, geographic sales region and time period) as a separate dimension. This management tool allows marketers to quickly review history and trends to take advantage of emerging opportunities, and take corrective action on developing problems.  

For example  Johnsonville Sausage Inc., a manufacturer and marketer of fresh, smoked and cooked sausage products, uses OLAP to access operational and financial data. Johnsonville can compare sales by customer, region and brand. With this information, it develops more accurate sales forecasts for production and manufacturing scheduling. Example from: marketingprofs.com (www.crm2day.com/library/EEplpkZllyASiURqyN.php

 

3. Data Mining

Discovers the meaningful patterns and relationships in data by separating signals from noise, providing decision-making information about the future. Data mining procedures include:  

• Association: looking for patterns where one event is connected to another event.
• Sequence or path analysis: looking for patterns where one event leads to a later event.
• Classification: looking for new patterns.
• Clustering: finding and visually documenting groups of facts not previously known.
• Forecasting: discovering patterns in data that can lead to reasonable predictions about the future.

 
Data mining provides a clear picture of what is going to happen, in time to change it.  Such as which customers might be most valuable, which customers are likely to defect, or, if the right data is gathered, which carry the risk of adverse reactions to marketing offers. 
 

For example  Standard Life, a global mutual financial services company, needed to expand its share of the increasingly competitive mortgage market. A major part of its efforts was to develop models that could identify customer characteristics relevant to any mortgage product. Data mining enabled Standard Life to better understand the characteristics of its mortgage customers so that it could more accurately search for potential new clients. As a result, the company achieved a nine-times greater response to offers and has secured approximately $50 million worth of mortgage application revenue. Example from: marketingprofs.com (www.crm2day.com/library/EEplpkZllyASiURqyN.php

 

4. Text Mining

This analyzes unstructured textual data by finding and discovering the patterns and relationships within thousands of documents, such as emails, call reports, websites and other information sources.

 
Text mining extracts terms and phrases and then classifies the terms into related groups, such as products, organizations or people, using the meaning and context of the text. This distilled information can be combined with other data sources and used with traditional data mining techniques such as clustering, classification and predictive modelling.

 
Questions to explore in text mining include: 

1. Which concepts occur together?
2. What else are they linked to?
3. What do they predict?

 

With answers to such questions marketers are better able to identify potential customer defection, head it off and then maximize consumer satisfaction.  

For example  A major online retailer combines data mining with text mining to analyse customer calls, emails, web surveys and other customer communications to better understand what offers and recommendations are appropriate for each customer. As a result, the retailer has tripled its profits from the previous year. Example from: marketingprofs.com (www.crm2day.com/library/EEplpkZllyASiURqyN.php

 
 

Part Six

 

Predictive Nature of Customer Analytics

Analytical technologies rely heavily on predictive models, which use previous customer interactions to predict future events. Aligned with segmentation techniques, which are used to place customers with similar behaviours and attributes into distinct groups (clusters), similar groups and predicted events, allow marketers to optimize their campaign management and customer retention objectives. 

Predictive models predict profitability or likelihood and timing of various events based on typical customer behaviour and deviations from that behaviour. 

Segmentation techniques segment groups of the customer base that have similar spending and purchasing behaviour. Such groups are used to enhance the predictive models as well as improve offers and channel targeting.  

Rising interest in predictive business  There is a transition going on all around in the business world, from “Real-time Business” to “Predictive Business”.  Real-time business as we know it is about doing things faster. Predictive Business is not about doing things faster. It is about doing things you could not do before. It is all about being able to predict business problems and opportunities and acting pre-emptively. For example predicting and preventing the loss of a customer to a competing supplier or accurately anticipating rising demand for a product.   Imagine how this could change the way you do business?  Predictive Business goes hand in hand with customer data, sophisticated analytics, a combination of Business Intelligence technologies and synchronised processes to leverage corporate assets. Repeat Customer Solution www.repeatcs.com.au

 

Customer Analytics: Key profitability metrics for your business

Posted in the Newtechforo blog, is a useful article identifying 6 key profitability justifications for evaluating business-related customer issues using customer analytics.  These are: 

1. Customer Profitability

• What value are customers bringing to the company?
• Is this value acceptable or not?
• Improving or declining?

 

2. Customer Attrition

• The percentage of customers leaving over time.

 

Merely knowing that customers are leaving, or even how many, is often not enough to develop an effective strategy to keep them. The question of “why?” also needs to be answered. 

3. Lifetime Value Recognition

• What is the current value of a customer over the lifetime of transactions? What is the optimal value?

 

4. Products per Customer

• An indicator of cross-selling.

 

5. Customer Satisfaction

• Customer satisfaction “can help predict customer loyalty.”
• Can be used to highlight areas for improvement.

 

6. Customer Needs and Preference

• Gain an understanding of what products or services customers are interested in, and what features, functions and options they prefer.

 

Checklist Two  Do you make use of analytical technologies, either in-house or outsourced, to make predictions about your customers’ spending habits? Have your analytics identified:       Actionable segments of your customer base      More effective and profitable marketing strategies      Most effective and efficient sales staff  Have you been able to identify those customers that are leaving or finding alternative products?  And more importantly WHY they are leaving? Does your business know the lifetime value of your customers and what an optimal value is (based on past behaviors and spending patterns)? Does your business foster the following attitudes in relation to analytics:     Leaders who get it     People who love numbers     Processes that revolve around facts    Technologies to capture, sort and make sense of customer data

 
 

References: 

[Online Available: http://www.spss.com/articles/analytics_satisfy_customers.htm, SPSS News Room, Use Analytics to Satisfy Consumers Sept. 23, 2003 By: Robert Slaker, SPSS Inc. [Last Accessed 3 July 2007] 

[n.d] [Online Available: marketingprofs.com: http://www.crm2day.com/library/EEplpkZllyASiURqyN.php

Colin Shearer, Vice-President, Customer Analytics SPSS [Last Accessed 3 July 2007] 

[n.d] [Online Available: www.cognos.com.au Roman Lenzen Originally published by DM Review Magazine, June 2004 [Last Accessed 3 July 2007] 

[n.d] [Online Available: Repeat Customer Solutions www.repeatcs.com.au Davenport, D.H. and Harris, J.G., 2007, “Competing on Analytics: The New Science of Winning,” p. 8-10, Harvard Business School Publishing, U.S.A. au [Last Accessed 3 July 2007] 

[n.d] [Online Available: http://www.crm2day.com/library/EpAVkVpFyZoxUYKRLg.php] [Last Accessed 25 May 2007]  

[n.d] [Online Available: Repeat Customer Solutions www.repeatcs.com.au [Last Accessed 3 July 2007] 

[n.d] [Online Available: http://www.newtechforo.blogspot.com/2007/06/start-understanding-customer-issues.html [Last Accessed 19 July 2007]

 

Customer Intelligence

Part Seven

Closely aligned to customer analytics, customer intelligence is described by Britton Manasco as the process of gathering, analyzing and exploiting information of a company's customer base. Information is typically obtained about customers existing and future needs, customer decision making processes, customer behaviour and trends as well as using data about the competition, conditions in the industry, and general economic, technological, and cultural trends.

Customer Intelligence Processes

Customer Intelligence is a supplementary methodology, which when combined with information obtained from analytical processes and CRM systems can deliver businesses the competitive edge in a global operating environment.  

Thus far we have discussed the importance of information obtained about customers' existing and future needs, how they reach decisions and about their behaviour.  But, what about the impact of variables such as competition, conditions in the industry, and general trends.  

When businesses think about populating their customer information files, they look internally at their customer databases. These sources, typically management systems, call centre systems and sales systems, contain information the organization has about each customer and each customer's touch points with the company. Manasco notes that while these sources of information are critical, they do have a major deficiency in that they only contain information that the company has collected about its customers.  

Businesses could be much more effective in their marketing efforts if they could augment or enhance their customer data using relevant and reliable external sources of data (Monasco).

There are a variety of external data sources that can be used to do this including:  

1. Personal demographic data including age, household (and personal) income, marital status, number of children, credit card debt, home ownership and net worth. Critical business questions that could be answered using this data include: 

• What are the buying patterns of people in a specific income bracket?
• How do sales patterns change as people migrate from one age group to another?
• Which products sell better to homeowners and which sell better to renters?
• What are the characteristics of customers who buy certain products?
• Who are the other customers who share these characteristics and do not buy these products?

 

Each of these questions yields a group of prospects for which a directed sales campaign can be targeted. Companies that use such information in developing their direct mail and other campaigns are more likely to have a higher success rate than those that do not. The higher success rate occurs because the people who receive the mailing are those who are most likely to buy the promoted product. Additionally, by screening out people who do not fit specified profiles, the company is not wasting time and money and is not bothering customers with offers they are not interested in.  

According to Lisa Loftis, Jonathan Geiger and Claudia Imhoff external data sources, in addition to businesses’ internal databases, can significantly enhance marketing capabilities. Armed with more complete data about customers and prospects, companies can perform more comprehensive customer analytics and can be much more effective in their customer retention efforts.  

2. Geographic demographic data provides similar information, but instead of providing it on individuals and households, the information is provided for geographic areas such as census tracts, postal codes and municipalities. By knowing where the existing customers live, businesses can extrapolate this data to obtain likely characteristics about its customers. Some caution is needed in this case, since individuals within the geographic area may be exceptions. For example, although the geographic demographics for an area indicate that it is primarily populated by young professional adults, some of the people living in that area may not fit that profile.  

Geographic demographic data is also useful for supporting geographic analysis. With internal data, the company can identify distances. For example, it can discern that its customers travel to the retail outlets. Armed with this information, the companies can also perform an analysis to see if the travel distances differ based on the characteristics of the geographic areas. Retail companies analyzing sites for future stores cannot only estimate the cannibalism (existing customers migrating to their new stores), they can also estimate the compatibility of their offerings with people living in certain geographic areas.  

3. Attitudinal data refers to the opinions that customers have about the company's products and services. Often, these opinions go unnoticed except when the company receives a complimentary letter or a complaint. To better understand its customers, a company could conduct a customer survey. A properly structured and administered customer survey can provide a wealth of information that can be used to adjust the product, its delivery, the associated services and fees.

Customer Intelligence and Customer Retention

Customer Intelligence enables senior level managers and executives responsible for the customer experience to: 

• Define and measure the customer experience
• Understand the experience of their customers
• Identify the reasons why customers call
• Maximize loyalty and retention
• Gain market and competitive intelligence
• Increase sales effectiveness

 
 

Checklist Three  Does your business have an understanding of how customer sales patterns change as they age? Do you know which geographical locations have a greater need for your product than others? Do you know how far your customers are prepared to travel for certain goods and services? Do you know which market segments match your optimal customer profile, and which are not currently buying from you? Has your business integrated external data and information into existing customer databases to enhance the overall view of existing and potential market segments?

 
 
 

Resources: 

[n.d] [Online Available: http://www.crm2day.com/library/EpAVkVpFyZoxUYKRLg.php] [Last Accessed 25 May 2007]  

[n.d] [Online Available: http://www.crm2day.com/customer-intelligence by Britton Manasco [Last Accessed 25 May 2007]  

[n.d] [Online Available: http://searchcrm.techtarget.com/originalContent/0,289142,sid11_gci994359,00.html] [Last Accessed 25 May 2007]  Customer intelligence - the next generation   Lisa Loftis, Jonathan Geiger and Claudia Imhoff  Intelligent Solutions Inc [Last Accessed 3 July 2007] 

[n.d] [Online Available: www.cognos.com.au [Last Accessed 25 May 2007] 

[n.d] [Online Available: http://en.wikipedia.org/wiki/Customer_Intelligence [Last Accessed 3 July 2007] 

 

Business Intelligence

Part Eight

According to key commentators Williams and Williams (2007), business intelligence is “business information and business analyses within the context of key business processes that lead to decisions and actions and that result in improved business performance.” 

It is all about “leveraging information assets within key business processes to achieve improved business performance.” Quite simply, those businesses involved in Business Intelligence strategies have a focus on improving performance and increasing profits. Consequently one of the most critical information assets in a business is information and data about their customers. 

Business Intelligence and its reliance on CRM systems and analytics are becoming the heart of effective customer retention. Customer retention is about having hard data and hard strategy and being one up on the competition. It is all about having business intelligence (Williams and Williams 2007).

Four reasons why companies need Business Intelligence

It is surprising how many businesses are not able to answer some simple but critical questions about their customers and business. For example questions such as: 

• How many customers do they have?
• For each product, how many were sold over the last 12 months?
• Who are their 20 best customers?
• What is the value of a particular customer?
• Who are their 20 best suppliers?

 

It is evident that many businesses do not have the systems in place to provide them these relevant answers.  According to Loshin (2003) information gathered through well planned and maintained Business Intelligence systems can be used to:

1. Increase Profitability

Business Intelligence can help companies properly distinguish between profitable and non-profitable customers, and help drive strategies to optimise results from profitable customers.

2. Decrease Costs

Business Intelligence makes it easier to evaluate organisational costs and to cut out what is not required.

3. Improve Customer Relationship Management (CRM)

Business Intelligence can be used to analyse aggregated customer information to improve customer service responsiveness, to highlight cross-sell and up-sell opportunities, and to increase customer loyalty and customer retention.  That is, to manage all the processes and systems involved in managing customer relationships.

4. Decrease Risk

Business Intelligence methods can be used to analyse credit data to improve credit risk analysis. Analysing supplier and customer activity can also be used to streamline and minimise risk in the supply chain. 

Business Intelligence IS NOT:  A single product There will be many a salesperson who will try to tell you that they have a BI product that is just right for you. But the reality is that there is no one “out-of-the-box” product that can be purchased and installed, which solves all problems.  A technology Business Intelligence is not just one technology, although it uses technology such as data-warehousing tools, relational databases, ETL tools, BU user interface tools and servers.  A methodology You need a good methodology, but the methodology goes together with the right technology and the right organisational changes. Williams and Williams (2007) on www.repeatcs.com.au

Business Intelligence involves achieving a balance between CRM technologies and strategic business operations to ensure all customer and stakeholder information is effectively incorporated in business strategies to the end result of increased ROI and customer retention.  
 

Checklist Four  Does your Business Plan outline strategies to leverage information sources to achieve improved business results?

 

References: 

[n.d] [Online Available:  Repeat Customer Solutions www.repeatcs.com.au Loshin, D., 2003, “Business Intelligence: The Savvy Manager’s Guide – Getting Onboard with Emerging IT,” pp.2,3, Morgan Kaufmann Publishers, San Francisco. [Last Accessed 3 July 2007] 

[n.d] [Online Available:  Repeat Customer Solutions www.repeatcs.com.au “Williams, S. and Williams, N. ,2007, The Profit Impact of Business Intelligence, p.2, Morgan Kaufman, California. [Last Accessed 3 July 2007] 

[n.d] [Online Available:  Repeat Customer Solutions: www.repeatcs.com.au [Last Accessed 3 July 2007] 
 
 
 
 
 

 

Data Quality

Part Nine

The expected benefits of a CRM strategy include a 360-degree view of a customer. However as Tony Fisher points out, all too often a variety of speed bumps and roadblocks seem to crop up, no matter how “foolproof” a system is. Some failure points can include: 

• Inability to identify the most valuable customers

• Misdirected marketing

• Wasted time, money and resources trying to identify a true representation of the customer 

Errors in data quality can occur for many reasons, for example, the sales force might spend hours calling unreachable phone numbers. Or marketing might send out a mailer where 10 percent of the addresses are undeliverable. Why does this happen? Because even though the CRM system might be best available, the data that serves the CRM system contains inconsistent, inaccurate or outdated information. At the end of the day, a CRM application is only as good as the data that runs it. 

Bad data can lead to unnecessary printing, postage, and staffing costs, as well as the slow but steady erosion of an organization’s credibility among customers and suppliers. And, the company would fight the constant inability to make sound decisions based on accurate information.  The problem with data is that its quality degenerates over time. Experts say 2 percent of records in a customer file become obsolete in one month, for one reason or another, because the customer dies, divorces, marries or moves. For a medium-sized database of 500,000 records, that adds up to 120,000 invalid addresses each year. In addition, data entry errors, systems migrations and changes to source systems generate large volumes of errors.          Tony Fisher

 

Achieving high quality data does not need to become a monumental task. The key is to treat data as a strategic resource. Companies must develop a program for managing data quality with a commitment from the top.  It may then become necessary to allow experienced data quality professionals to oversee and carry out the program. Once established, it is critical for organizations to sustain a commitment to managing data quality over time and adjust monitoring and cleansing processes as the business needs and underlying systems change. 

So where to start? Commercially available data quality technologies can automate the process of auditing, cleaning and even monitoring data quality. These resources can play a significant role in data quality efforts and help companies achieve immediate benefits. Many commercial tools are beginning to step up to the challenge of validating company-specific business rules, and augmenting addresses with geospatial and demographic data, among other things.

Data Maintenance

There are five key methodological steps that businesses should use as a foundation for a data quality strategy. According to Fisher, if followed, these will position the company to be most successful at not only eliminating bad data from CRM systems, but also building better information to support the entire enterprise. 

Step 1: Data Profiling

Data profiling helps discover what is wrong with data and what needs to be addressed to improve the quality of corporate information. With profiling, users can analyze data before adding it to a CRM system to uncover problems with the true content and structure of information. Through data profiling routines, companies can come to understand the strengths and weaknesses of data and can pinpoint areas that need improvement. 

Step 2: Data Quality

At the data quality phase, users can correct errors, standardize data and validate information that is inconsistent and inaccurate. This step is about correcting existing data and making it more useful for sales and marketing purposes. The data quality step also can verify that addresses or other contact information is accurate. Then, companies can use matching technology to identify logical “households” to refine marketing programs. 

Step 3: Data Integration

A common problem in many modern organizations is the spread of data. Over time, companies add new systems or databases to their operations and each of these new data sources often has its own unique values, nomenclatures and protocols. Proper data integration techniques can help companies avoid costly mistakes and embarrassment by reducing or eliminating duplicate messages to customers or prospects. By linking and joining information from a variety of sources, organizations can create a true 360-degree view of the customer to support sales and marketing efforts. 

Step 4: Data Augmentation

Data augmentation takes the available data and enriches it with additional information. For example, companies can populate records with missing phone numbers through third-party databases using a customer’s name and address. 

Similarly, organizations can add behavioral data to customer records to help understand the customer’s previous buying patterns and forecast potential purchases. With augmentation, companies add value to their existing data by strengthening customer records and gaining a more complete understanding of their customer base. 

Step 5: Data Monitoring

Keeping high-quality data takes constant vigilance. Data monitoring builds on previous data management initiatives by examining data over time – and alerting users when good data goes bad. Continuous data monitoring provides the insight to recognize immediately when quality falls below acceptable limits. Data monitoring can alert the appropriate data owner when information does not meet business requirements. 

Part Ten

 

Data Cleansing

Data cleansing involves detecting and correcting or removing, corrupt or inaccurate records from a database. After cleansing, a data set should be consistent with other similar data sets in the system.  The inconsistencies detected or removed may have been originally caused by different data directory definitions of similar entities in different stores, may have been caused by user entry errors, or may have been corrupted in transmission or storage. 

The process of data cleansing may involve removing typos or validating and correcting values against a known list of entities. The validation may be strict (such as rejecting any address that does not have a valid ZIP code) or fuzzy (such as correcting records that partially match existing, known records). Data conversion and migration analyst Kuldeep Dongre notes several variables which determine data quality and which are important in taking advantage of available data.  These are:  

• Accuracy
• Integrity
• Cleanliness
• Correctness
• Completeness
• Consistency

 

Inconsistencies and therefore bad data can arise as a result of: 

• Application errors: where errors occur because of the inability of an application to validate certain user inputs, and
• Human Errors: for example a user validly inputs a date, but that date in not within a valid perspective relevant to the business (ie. before the business’ inception).

 

Bad data therefore poses a serious threat to management and can affect decisions being made.  According to Dongre, data cleansing can be an elaborate process depending on the methods chosen, and must be planned carefully to achieve the objective of elimination of dirty data.  Some methods to accomplish the task of data cleansing include: 

• Automated data cleansing
• Manual data cleansing
• A combined cleansing process

Automated Data Cleansing

The first and foremost step in data cleansing is to identify and categorise the various errors in the CRM system. This is also called the data audit process. A data audit process using the CRM software will provide:

• The error types that need cleansing. These are called as critical errors types.
• The error types that can safely be ignored as they are not business critical. These can be classified as non-critical error types.
• Data volume of each of the critical error types.

 

An automated cleansing process is adopted when:

• Data to be cleansed is too huge to accomplish it manually.
• All or majority of the data errors can be fixed programmatically by applying logical rules.
• The cost involved in manual cleansing is high when compared to the time in which it can be done with an automated process in place.

Manual Cleansing Process

The need for manual data cleansing is a result of the fact that not all errors can be automatically cleansed. For example, missing or incomplete data.  These can be corrected by making contact with the relevant customers and asking to confirm these details or by cross checking with other databases or sources of information. This is best suited in the following circumstances: 

• Erroneous data cannot be fixed programmatically.
• Data volumes to be cleansed are very less making the automation process laborious in comparison to the manual process.

Combined Cleansing Process

The combined process can be employed in the following scenarios:

• Erroneous data are distributed equally between the ones that can be addressed automatically and the ones that are to be handled manually.
• Use of a single process does not produce noticeable data cleansing in the system.

Outsourcing the CRM Process

According to Anthony Plewes, in most developed industry sectors, competition enforces razor thin margins and encourages companies to look at a variety of ways of lowering costs to help the bottom line. Delivering customer care is often seen as too expensive or at the bottom of the budget list, tempting organisations to seek cheaper, specialist providers.  

Outsourcing the CRM process offers businesses the opportunity to draw on proven approaches, fresh perspectives and innovative thinking, delivered through a single management structure. That is, professionals skilled in CRM have the knowledge, technology, tools and human resources needed to improve the effectiveness and efficiency of customer marketing, sales and service functions.

Benefits to Outsourcing CRM

• Increased revenue and competitive advantage: businesses can increase revenue and competitive advantage by leveraging professional CRM strategist’s expertise in CRM, world-class consulting and technology resources, and proven methodologies to improve CRM processes.
• Enhanced customer satisfaction and loyalty: the hands on nature of skilled CRM consultants, their experience and industry insight can help to identify new ways to increase customer satisfaction and loyalty by improving responsiveness and ensuring a quality customer experience across multiple channels, touch-points and geographies.
• Dramatically reduced costs: due to their specialised nature, outsourced agencies are able to streamline the CRM processes and leverage state-of-the industry technology to cut costs dramatically.

   

Checklist Five  What is the current state of your customer data?  Have you taken the step towards updating and cleaning your customer database? Is management committed to the continual achievement of quality data? Is your data quality maintained an a daily basis, with checks in place to identify when good data becomes ‘dirty/bad data’ Have you undertaken a data audit (It could save you thousands of dollars otherwise spend on wasted promotions)? If you do not have the time, expertise or resources to maintain your data quality and make the best use of your customer databases – have you sought the assistance of a skilled analyst?

 

 

References:  

[n.d] [Online Available:

http://www.silicon.com/research/specialreports/crm/0,3800002402,39118921,00.htm Analysis: Outsourcing CRM operations...and staying in control By Anthony Plewes [Last Accessed 1 August 2007] 

Bad Data: The “Dirty Little Secret” of CRM By Tony Fisher Dataflux Corporation

 

In Conclusion

Part Eleven

 

Analytical processes and techniques deliver businesses hundreds of reports, metrics, and connections from standard data sources to help businesses understand sales performance, product sales, and the health of customer relationships.  Information can be used to answer key questions such as:

• Who are my top-10 revenue-generating customers? My most profitable customers?
• What products are customers purchasing?
• Which customers have the highest rates of return?
• Which customers present a potential risk of bad debt?

Armed with this level of information businesses are able to implement informed Customer Relationship Marketing initiatives aimed at customer retention. 

CRM Databases, Customer Analytics and Predictive Models   Analytical applications and predictive models are best completed using a tool outside the database environment. This process involves extracting data, transforming attributes, scoring models, updating clusters and storing the results back into the businesses CRM database. Placing the scores and clusters in the database allows marketing, customer service staff and risk analyzers to use this information for all customer relationship management (CRM) decisions. Lenzen 2003

 

Information produced allows businesses to monitor and report on performance using these key areas:

• Customer profiling and valuation - definition of best customers based on factors such as revenue, frequency of purchase, cost to service, allowing businesses to direct activities to retain high-value customers.
• Customer satisfaction - Analyze customer buying patterns, rates of return, time to pay, and other factors to detect customer satisfaction issues before they affect bottom line.
• Customer credit - Track the number and age of receivables to identify and deal with potential sources of bad debt.
• Product performance - Identify high-demand products and cross-sell opportunities. Align production and sales force to take advantage of these insights.
• Sales performance - Help sales staff to understand customer purchase patterns and trends in various market segments, to improve sales efforts. (www.cognos.com.au)

Resources: 

[n.d] [Online Available: www.cognos.com.au Roman Lenzen Originally published by DM Review Magazine, June 2004 [Last Accessed 3 July 2007]