data quality and crm

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WHITE PAPER / High Data Quality in the CRM(Customer Relationship Management) System: the proverbial icing on the cake

The goal of introducing a CRM (Customer Relationship Manage-ment) system is to optimize and stabilize the relationships with exist-ing and future customers in the long-term. The key to a satisfactory relationship for both sides is not only an intelligent CRM system but also the high quality of the data it contains.

There are indications of sub-optimum customer data quality if the return rate of mailshots is relatively high as a result of incorrect or incomplete addresses, or customers complain about multiple deliver-ies of the same advertising mail. For good measure, it is embarrass-ing if discriminatory comments can be read in the address line of a customer, because importance was not attached to the „hygiene“ of the name and address components. Even if the in-house staff have no confidence in the database and manually check each entry be-fore the customer is contacted, this should also be considered as an indication of poor data quality. On the basis of points stated here, it becomes evident that data quality in the CRM system is just as important as the system itself. If this is not the case, the hoped-for effect of long-term customer bonding combined with an increasing efficiency of the work carried out with customer data will not arise.

Various use scenarios of a CRM system are considered in the fol-lowing. The areas of focus are the relationship of the data quality and the consequences of poor data quality. Furthermore, a practical solution approach for providing a newly created or existing CRM system with high-quality data and maintaining this status quo is pre-sented.

—All company and product names and logos used in this document are trade names and/or registered trademarks of the

respective companies.

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Contents

In touch with your customers

Important components for the success of a CRM system

The perfect couple: CRM and Data quality

Data quality in the CRM: how to

Initial data cleansing

« first time right » - the Data Quality Firewall

Data Maintenance: Automated measures for maintai-ning the data quality standard

It’s time to get on board: The Data Quality Audit

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In touch with your customersThe awareness that the introduction of a CRM (Customer Relationship Management) system is a key factor for the long-term success of the company has rapidly developed in the managerial levels of many companies in the past few years.

Irrespective of the supplier and the components of the CRM system which are used, the focus is always on customer orientation and the underlying service concept.If the introduction of a CRM system is considered from an economic perspective, it quickly becomes clear that relationship management is associated with con-cepts such as the desire for long-term business rela-tionships and the economic security connected with this. Furthermore, a CRM system should contribute to the stabilization of the business contact.

Well-maintained business relationships as well as relationships with customers, i.e. a stable network of relationships, have a variety of very positive effects on the individual company. .

HERE ARE A FEW EXAMPLES:

– A satisfied customer is prepared to recom-mend the supplier and his products through simple word-of-mouth propaganda.

– In a long-standing, satisfactory relationship between the customer and the supplier, the customer may make suggestions for improving products, in order to call attention to changing demands in the market.

– A satisfied customer is more tolerant towards price increases than potential customers who are still comparing similar products and serv-ices of different suppliers.

– If there is an existing business relationship with a customer, the customer will contact the sup-plier if he is dissatisfied with a product or a service, in order to indicate the deficiencies. As a result, the supplier has the opportunity to opti-mize the product and performance. In the neg-ative case, the customer would simply change supplier without informing the supplier about the perceived deficiencies of the product.

WELL-MAINTAINED BUSINESS RELATION-SHIPS AS WELL AS RELATIONSHIPS WITH CUSTOMERS, I.E. A STABLE NETWORK OF RELATIONSHIPS, HAVE A VARIETY OF VERY POSITIVE EFFECTS ON THE INDIVIDUAL COMPANY.

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Important components for the success of a CRM systemThe above examples make clear that the customer is always the centre of the interest in a CRM system, since direct and indirect gains can be achieved in the long-term through a satisfactory relationship.

In this respect, there are various areas in a CRM system which are designed to help satisfy a wide variety of customer needs in the expected form and in an appropriate manner. Any information obtained can therefore be evaluated, in order to use it in marketing campaigns or other Business Intelligence-based analyses.A difference is made between an operative CRM and an analytical CRM.

ANALYTICAL CUSTOMER RELATIONSHIP MANAGEMENT

Analytical CRM is used to consider all the possible customer data for evaluations within the sphere of Business Intelligence. The term Customer Data Warehouse is also used to some extent. This shows that analytical CRM concerns a «snapshot» of the CRM data for the analysis and not the data of the actual live system. The data is stored in a specially designed system, as is the data of a Data Warehouse. It can be evaluated via a large number of different dimensions. The keyword here is Online Analytical Processing (OLAP), which is also used in the Data Warehouse.

OPERATIVE CUSTOMER RELATIONSHIP MANAGEMENT

In contrast to analytical CRM, operative CRM covers the areas of sales, marketing and service. In other words: All the employees who are in direct or indirect contact with the customer use operative CRM.

In marketing, this actually means that e.g. there are possibilities in campaign management to fil-ter out the right target groups for the respective campaigns. (The evaluations of analytical CRMs normally provide indications of the correct filters.) In this respect, the right customers, the appropriate information and service offer, the selection of the optimum communication channel, etc. are the main concerns. The goal is that the presented information reaches the right customer target group. The solic-ited customers should be motivated to examine the respective contents of the campaigns and to identify the added value which the information (or the prod-uct) creates for themselves or their company.

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Sales uses the operative CRM for various tasks. Personal contact with the customer figures large, in order to develop and maintain a strong customer relationship. Functions such as the integration of e-mail clients, calendars or similar features are indispensable here. However, information from the CRM is also used e.g. to update sales opportuni-ties. It can also be evaluated why the customer rejected the offer (lost order analysis) or why the business relationship ended. A CRM system is also used as a “logbook”, in which all the activities with a customer are recorded. As a result, colleagues can very quickly gain an overview of all the cor-respondence with the customer.

The CRM system is also used to dynamically gener-ate reliable forecast analyses. These are extremely important for defining further business strategies.

The third area which makes intensive use of the CRM system is the service area.The individual customer requirements are consid-ered to a particularly large degree in this environ-ment, the customer is accompanied through the different stages of the relationship. Complaint man-agement and customer support are also important issues here.

Finally, a CRM system can be used interdepartmen-tally and across areas as a control instrument for business processes or can offer valuable assistance for compliance with business rules.

In the context of the importance of a profession-al Customer Relationship Management and the introduction of an appropriate CRM system, it is essential to keep one central aspect in mind: the employee in direct customer contact is the calling card of the company, because he or she is synony-mous with the quality of the product and service for the outside world. It is precisely here that enor-mous, usually inactive potentials can be activated for the benefit of satisfied customers on the basis of an efficient CRM system.

THE EMPLOYEE IN DIRECT CUSTOMER CONTACT IS THE CALLING CARD OF THE COMPANY, BECAUSE HE OR SHE IS SYN-ONYMOUS WITH THE QUALITY OF THE PRODUCT AND SERVICE FOR THE OUTSIDE WORLD.

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The perfect couple: CRM and Data qualityRegardless of whether an analytical or operative CRM is implemented, the above described areas of application indicate the importance of correct data, i.e. data quality.

– Correct address data, also in the inter-national environment, so that written cor-respondence reaches the recipient. In this regard

» Address data must be updated if places or streets are renamed

» Relocations must be recorded and the addresses updated

» No customer relationships are main-tained with deceased persons and

» Company changes (mergers, reloca-tions, etc.) must be recorded

– A duplicate-free customer data stock, i.e. there is really only a single instance of the customer in the database, in order:

» not to send the information several times in mailshots and save on postage costs

» not to unnecessarily annoy repeatedly solicited customers in marketing cam-paigns and therefore provoke customer losses and lost sales

» to be able to make reliable statements about sales opportunities and forecast analyses

» to design service more efficiently by hav-ing all the relevant information available for direct customer contact

In an analytical CRM, a high data quality is indispen-n an analytical CRM, a high data quality is indispen-sable, in order to be able to carry out appropriate analyses in the first place, not to falsify them and - building on this - to make the right strategic decisions in the long-term.

In an operative CRM, it is important that the contact data of the customer is correct, so that appropriate marketing campaigns and the service offer reach their target, i.e. the customer.

IN CONCRETE TERMS, DATA QUALITY MEANS:

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Against this background, it becomes evident that data quality, i.e. correct and duplicate-free data, is an important prerequisite for the so-called «Single View of Customer» or «Single Point of Truth», because only optimum data really allows all the data relating to a customer to be com-pressed into one data record, thereby enabling a comprehensive view of a customer.

This view must also have been authorized for the employees of the various departments within a company.

If all these aspects are not considered in a CRM system, the defective quality of the data can quickly the tip the scales. Analyses in Business Intelligence produce incorrect conclusions, cus-tomers are dissatisfied with the service and mar-keting campaigns and also terminate business relationships in the worst case.

Poor data quality can also have a direct effect on the motivation of the company’s employees. They may not satisfy the needs of the customers to the expected extent, since the information in the CRM system is not consistent.

Duplicate data records of customers which con-tain information required for customer satisfaction are an example here. It is the employee who has to listen to the troubles of frustrated customers. And becomes dissatisfied at the same time.

The direct connection between reliable data from the CRM system and employee motivation is therefore proven.

IT BECOMES EVIDENT THAT DATA QUALITY, I.E. CORRECT AND DUPLICATE-FREE DATA, IS AN IMPORTANT PREREQUISITE FOR THE SO-CALLED «SINGLE VIEW OF CUSTOMER» OR «SINGLE POINT OF TRUTH», BECAUSE ONLY OPTIMUM DATA REALLY ALLOWS ALL THE DATA RELATING TO A CUSTOMER TO BE COMPRESSED INTO ONE DATA RECORD, THEREBY ENABLING A COMPRE-HENSIVE VIEW OF A CUSTOMER.

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Data quality in the CRM: how toREGARDLESS OF WHETHER

– a completely new CRM system is to be put in place,

– the data quality of an existing CRM system is to be optimized,

– or two or more independent systems are to be combined into a single CRM system,

the requisite high data quality can be achieved in three sub-processes:

1. Initial data cleansing2. «first time right» and mechanisms which inter-

cept poor data quality when the data is cre-ated or edited (Data Quality Firewall)

3. Use of data maintaining as a measure to preserve a high data quality standard

CLOSED DATA QUALITY CYCLE

Profiling

Cleansing

Real-Time CheckMaintaining

Monitoring

Implementation of Data Profiling and investigation of the data

Analysis of the data quality and cleans-ing of customer, transaction,order, financial, statistical data ...

Securing the data quality directly at input

Continuous monitoring of the data quality and compliance with thebusiness rules for transaction, order, financial, statistical data ...

Application of change reports from third-party companies. (anti-ageing)

Integration of external data.Provision of data for external systems.

Initial clean-up

THE DATA QUALITY PROCESS OF UNISERV SHOWS HOW THE ABOVE THREE STEPS ARE CONNECTED.

1.

3.

2.

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The data quality process of Uniserv shows how the above three steps are connected.

It is advisable to obtain an overview of the qual-ity of the data in a first step, so that an initial cleansing oriented towards results is possible.

In this respect, it not only concerns correctly writ-ten addresses or duplicate data records but also learning about the structure of the data to be migrated and checking the existing business rules. This step is typically implemented in a data quality audit.

Downstream monitoring is advisable for con-stant determination and verification of the status quo of the data quality. Compliance with the business rules can be automatically checked here and critical threshold values specified, in order to be able to carry out optimization measures in real-time. Such threshold values could also be key performance indicators (KPI), which provide information about the status quo of defined company goals.

IT NOT ONLY CONCERNS CORRECTLY WRITTEN ADDRESSES OR DUPLICATE DATA RECORDS BUT ALSO LEARNING ABOUT THE STRUCTURE OF THE DATA TO BE MI-GRATED AND CHECKING THE EXISTING BUSINESS RULES. THIS STEP IS TYPICALLY IMPLEMENTED IN A DATA QUALITY AUDIT.

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– The name components are analyzed. Very complex name lines which either consist

of several individuals or include the company name with the department and contact are analyzed. The analysis establishes whether the data concerns consumer data or company data. All the elements of the name line are also written to specially assigned fields, so that e.g. analyses of academic titles or legal forms of the company can be carried out.

Example: UNISERV GmbH

THE TYPICAL PROCEDURE FOR THIS INITIAL CLEANSING IS

AS FOLLOWS:

– The data is converted to a standardized format. Example: Standardized format for telephone numbers.

– The field contents of different data sources are assigned to standard fields.

Example: The name of the contact person is in fields with different names in each data source.

1. Initial data cleansingFirst of all, the initial cleansing of the data is of prime importance. In this respect, the entire database is checked and cleansed in a batch run. The number of different data sources or the countries which the data originates from are irrel-evant here.

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Company name:

Legal form:

Pfeiffer, RolandName:

Data source A :

Data source B :

Data source C :

Roland PfeifferContact :

Pfeiffer

RolandFirst name:

Last name:

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– A validation of the addresses is carried out. A postal validation is carried out irrespective

of whether national or international addresses are concerned. In this respect, the postcode, place, street and house number are checked for correctness. If possible, missing address components are corrected and / or added. Officially renamed streets and places are automatically updated. PO box validation and bulk customer postcode validation are also available for certain countries.

Example:

– Addresses of movers are updated. Around 8 million people change their place of residence in Germany each year. Only a very few of them actively advise of their new address. The data records of the individuals concerned can be updated initially or subse-quently periodically (see point 3) by means of a relocation check over the entire database.

– The addresses are converted to specific formats. In certain countries, e.g. France, the address must

be formatted according to the specifications of the national postal authorities, in order to be able to take advantage of postage rate optimization meas-ures for the cheapest possible delivery options.

Example of an address from France:

– The addresses are enhanced with additional information.

The addresses can be enhanced with rel-evant information as required. This could be geocoordinates, but sector codes or in-house, user-defined information can also be attached to the data records.

For example:

Rastaterstrasse 13

75197 Forzheim

Rastatter Str. 13

75179 Pforzheim

Rastatter Str. 13

75179 Pforzheim

Y coordinate +04889883

X coordinate +00866723

12 Route de Locmine

56150 BAUD

Immeuble de corbusier

Esc B

Stephane Petit STEPHANE PETIT

ESCALIER B

IMMEUBLE DE CORBUSIER

12 ROUTE DE LOCMINE

56150 BAUD

Input Formatted output

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– Duplicates are identified. Potential duplicates are identified according

to individually customized search algorithms. Suitable business rules can be applied in the search, so that subsequent elimination can take place automatically to some extent. The duplicates are also evaluated, so that state-ments about the “certainty”, i.e. probability of a duplicate can be made. It goes without saying that standardized matching schemes which can be applied to consumer or busi-ness data are available here. It is also pos-sible to incorporate further individual fields in additional free fields in the duplicate search.For example:

– The “Golden Record” is formed. The formation of a “Golden Record” is funda-

mental, particularly when data comes from a variety of sources which have further relevant contents attached in addition to the postal information. As a result, there is the possibility of transferring all the information from the next duplicate to the head duplicate, i.e. to one data record. Even if duplicate data records do not have to be eliminated, marking (flagging) is possible, so that the information contained in a duplicate can be displayed to the subsequent user of the CRM system.

Example: The second data record has an addi-tional field with coordinates which are to be attached to the first data record. If the second data record is deleted from the database, the first data record, which is now the “Golden Record”, also includes this information.

Roland Pfeiffer R. Pfeifer

75179 Pforzheim 75179 Forzheim

Y coordinate +04889883 Y coordinate +04889883

X coordinate +00866723 X coordinate +00866723

Rastatter Str. 13 Rastaterstrasse 31

Data record 1(head duplicate)

Data record 2 (next duplicate)

Roland Pfeiffer

75179 Pforzheim

Rastatter Str. 13

R. Pfeifer

75179 Forzheim

Rastaterstrasse 31

Data record 1 Data record 2

Comparison of the two data records pro-vides a high measure of similarity, since the name, street, house number and place differ. However, the difference in this example and with the selected matching algorithm is not great, so that the data records are identified as a single block.

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Since work is carried out under high pressure of time at peak periods, especially in call centres, address validation must take place very quickly.

A rapid entry client which completes the address components after input of the initial letters or num-bers can be used as an alternative.

No matter which technology is used, the possibility of a simple, quick and error-tolerant search for any existing customer data is fundamental. This func-tion is not used if matching takes too long or does not furnish the desired results. The Data Quality Firewall is by-passed.

Firstly, the user of the CRM system can ensure that certain input rules are complied with, e.g. street names should only be entered in the fields pro-vided. A syntax check is also possible for fields for telephone numbers or e-mail addresses. In addi-tion, there is the possibility of checking the stated address for correctness.

This could be important, e.g. if the address is only given to a call centre by telephone and errors quickly arise in the notation or because of different interpretations of what was heard or typing errors. If the information received over the telephone is incorrect or unambiguous, the employee can also immediately ask for missing additional information such as the town district, in order to be able to transfer a postally correct address to the system.

The performance of the underlying technology is the decisive factor for acceptance here.

2. « first time right » - the Data Quality FirewallIt is important to specify certain standards after the transfer of the initially cleansed data stock. Only in this way can the obtained high data quality be preserved. A variety of options from the online area present themselves here:

NO MATTER WHICH TECHNOLOGY IS USED, THE POSSIBILITY OF A SIMPLE, QUICK AND ERROR-TOLERANT SEARCH FOR ANY EXISTING CUSTOMER DATA IS FUNDAMENTAL.



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An everyday example demonstrates this: if the search for information in the most well-known Internet search engines, Google or Yahoo, took longer than the typical 0.3 seconds, these search engines would not be used on account of the slowness.

The fully automatic prevention of new duplicates is also important. Here too, there is the possibility of checking whether the customer is already recorded in the system when the data is created.If this is the case, a new customer account does not have to be created. The existing information can even be enhanced by the current process. This search runs in the background without being spe-cifically triggered by the employee at each initial data creation or change of the address data.

If the company or individual already exists in the database, the employee receives a relevant indica-tion via the input mask.

An error-tolerant search is also appropriate here, so that the respective data record can be found in spite of hearing errors or synonyms or incomplete company names. It goes without saying that this so-called implicit search also has to take place very quickly and pre-cisely, so that the work-flow of the employee in the CRM system is not impeded.

These requirements for the Data Quality Firewall are implemented by means of the DQ Connectors. In conjunction with development partners, Uniserv has created DQ Connectors for the most impor-tant CRM systems, such as SAP CRM, Microsoft Dynamics CRM, Siebel as well as update.seven and salesforce.com, which enable the integration of data quality mechanisms at data acceptance and for record-by-record processing. As a result, nothing stands in the way of these functions, which are important for implementing high data quality in the CRM system.

UNISERV HAS CREATED DQ CONNECTORS FOR THE MOST IMPORTANT CRM SYS-TEMS, SUCH AS SAP CRM, MICROSOFT DYNAMICS CRM, SIEBEL AS WELL AS UP-DATE.SEVEN AND SALESFORCE.COM

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These periodic checks of the overall database should ideally be executed as a batch process. This guarantees that all the data complies with a common data quality standard at specified intervals.

A data quality high enough to accomplish the actu-al tasks of the CRM system cannot be assumed until the three process steps described here have been implemented and adopted as practice, thereby enabling the CRM system to reach its full potential and provide a return on investment.

The evaluations in the analytical CRM are now on a sound basis. The data in the operative CRM permits a customer-oriented approach in all areas. Finally, the customer relationships are strengthened in the long-term. The confidence of the company’s employees in the quality of the data increases at the same time. This means that an additional check of the data is no longer required. The direct results are an increase in efficiency and reduction in costs.

This is also necessary if the databases are to be consolidated e.g. after corporate takeovers. Another scenario is a periodic check for street and place renaming.

Relocations must be tracked and maintained, and the data records of deceased customers should be flagged at least. The requirement for enhancement of the existing data with addition-al information is also not excluded.

3. Data Maintenance: Automated measures for maintaining the data quality standardIn spite of the initial data cleansing and mechanisms to maintain the status quo of the data quality, it is good policy to carry out a periodic check of the overall database.

A DATA QUALITY HIGH ENOUGH TO AC-COMPLISH THE ACTUAL TASKS OF THE CRM SYSTEM CANNOT BE ASSUMED UN-TIL THE THREE PROCESS STEPS DESCRIBED HERE HAVE BEEN IMPLEMENTED AND ADOPTED AS PRACTICE, THEREBY ENA-BLING THE CRM SYSTEM TO REACH ITS FULL POTENTIAL AND PROVIDE A RETURN ON INVESTMENT.

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The audit is the first step with clear goals in mind for sound decision-making and marks your per-sonal introduction to the project «Data Quality in your CRM System».

During the audit, the quality of the addresses is primarily evaluated with the support of the data quality tools from Uniserv. In a second step, there is the possibility of getting to the root of the possi-ble causes of the deficient data quality in a proc-ess analysis. So the best thing to do is to Contact us right away!

It’s time to get on board: The Data Quality AuditThe Uniserv DQ Audit presents itself, in order to be able to make statements about the status quo of the in-house data in the CRM system.

For further informationplease visit our web page www.uniserv.com or contact us directly:

We are looking forward for advising and sup-porting you through your project.




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UniservUniserv is the largest specialised supplier of data quality solutions in Europe with an internationally usable software portfolio and services for the quality as-surance of data in business intelligence, CRM applications, data warehousing, eBusiness and direct and database marketing.

With several thousand installations worldwide, Uniserv supports hundreds of customers in their endeavours to map the Single View of Customer in their customer data-base. Uniserv employs more than 110 people at its head-quarters in Pforzheim and its subsidiary in Paris, France, and serves a large number of prestigious customers in all sectors of industry and commerce, such as ADAC, Al-lianz, BMW, Commerzbank, DBV Winterthur, Deutsche Bank, Deutsche Börse Group, France Telecom, Green-peace, GEZ, Heineken, Johnson & Johnson, Nestlé, Payback, PSA Peugeot Citroën as well as Time Life and Union Investment.

Further information is available at www.uniserv.com

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