The biggest problem child of CRM users is no longer usability, functionality or performance. It has a new name: Data quality. Nothing is more annoying and time-consuming than working with incomplete or even incorrect data in CRM. A recent customer survey by Vision11 on the reasons for poor data quality sheds light on the problem. In this article, we present possible solutions and concrete examples on the topic of "Data Quality".
Customer survey: usage problems of CRM solutions
In the third quarter of 2018, Vision11 conducted a targeted customer survey to identify current problems related to the use of CRM solutions. Survey participants were 300 CRM users from different business units. Not only Vision11's current and former customers were asked, but also numerous CRM users from the German midmarket and large enterprises. Most of the respondents use one of the leading CRM systems from SAP, Salesforce or Microsoft. The result of the survey: data maintenance, data quality and data analysis are the biggest concerns for CRM users. More than a third of respondents see low data quality in CRM as the most important cause for acceptance of the solution. The following graphic shows the results of the study.
If we take a closer look at the causes of this problem, three main reasons crystallize: The intensity of data collection in German companies is steadily increasing. In addition, the willingness to use professional data analysis tools is very low. Last but not least, the short-circuit reactions due to the new data protection regulation (DSGVO) have had a very negative impact on the quality of customer data in CRM. In the following, these three main causes are explained in detail.
1. intensity of data collection
The focus of CRM projects so far has been on connecting the customer touchpoints to the CRM systems and the associated generation of the data via the online and offline channels. This has resulted in a very large amount of the customer data that has been automatically transferred to the respective CRM solution. Very few CRM managers dealt with the questions "What is this data needed for?" or "What do you want to do with the data?". The answer was usually: "We want to know everything about our customers!" or "We want to have a 360° view of our customers!" and so on. In recent years, the numerous expert papers have raved about how data is the oil of the 21st century.
Data is critical to CRM success - without a doubt. Let's stick with the illustrative example of oil. Oil is just a raw product and offers little useful value without extensive processing and refinement. Moreover, the original oil quality is crucial for the future end product, such as gasoline or diesel. The same is true for data. It is not the quantity of data that matters, but primarily the quality. The more data is to be collected or recorded in the CRM, the greater the effort required for data maintenance. According to the motto: "Here and there a mandatory field, we absolutely need the telephone number and e-mail address, date of birth, of course, and don't forget a social media profile", the data entry mask for entering a new customer grows and grows in the CRM. The question arises as to how to maintain this information in the CRM. The users of the system are mostly called sales and service employees, key account managers, customer service agents, marketing employees, etc.
Collecting data en masse without pursuing a concrete concept, let alone having a clear strategy for extracting meaningful and useful information from it, is unfortunately becoming more and more widespread. Almost all of this data has been created in companies in the last 10 years (see figure "Data growth through digitalization"). The term "Big Data" is no longer big enough; today, people rather talk about "Data Lakes". Here, data lake refers to a very large data storage facility that holds data en masse in its raw format. Humans cannot handle the data in such a raw format; machines with excellently thought-out algorithms are needed for this.
2. professional data analysis tools
The most used data analysis tool on the screens of German CRM users is still Excel. There are numerous professional data analysis tools on the market which, in the age of digitalization and Big Data Lakes, undoubtedly represent a quantum leap compared to Excel. Such data analysis tools have evolved in parallel with the increase in data volume, while today's Excel 2018 still basically has the same functions as Excel 98 back then. Data analysis and visualization are now done with professional tools, such as Tableau, Qlik, Lumira or Power BI, but in no case with Excel.
Such professional data analysis tools are still not used enough by large German companies and medium-sized businesses. Most of them either limit themselves to the reporting options within the CRM system. Or the necessary data is extracted from the CRM and further analyzed in Excel. It is relatively easy to connect a CRM system as a data source to a BI solution. An extensive and, above all, high-performance analysis of the data should then take place in the BI tool. Possible analysis use cases include checking data quality, postal validation of addresses, analyzing data by input channels, determining duplicates, etc.
Leading business intelligence tools include Tableau and Qlik. Tableau provides deep insight into data and enables efficient visualization for complex decision-making processes. Tableau dashboards are particularly flexible and offer a wide range of features for customization. Whether the data resides in a spreadsheet, database, data warehouse, CRM system, or all of the above, Tableau provides the flexibility to efficiently connect to and consolidate the data you need.
Qlik provides an entire discovery platform to analyze the complex data relationships. With Qlik products, different data sources can be combined in one view, regardless of size, thus indexing all possible relationships. Unlike query-based tools, you are not limited to pre-aggregated data and pre-defined queries. This means that results can be immediately scrutinized and analyzed without having to create new queries.
With Uniserv, another professional tool for improving data quality is available. Uniserv looks at the data of a CRM system less from an analytical point of view, but much more from the point of view of data quality. The tool "Uniserv Data Analyzer" can be used primarily to check the condition of the data in order to take suitable measures for quality assurance on this basis. With the interactive data analysis or data profiling of Uniserv Data Analyzer, reliable statements can be made about the condition and quality of even large quantities of data. The Data Analyzer creates the basis for the targeted use of data cleansing and data protection. With the Data Quality Service Hub from Uniserv, it is possible to re-establish the required data consistency for processes and applications.
Conclusion and solution approach
The most effective way to address the issue of poor data quality in CRM is where it originates. First and foremost, all sources where data originates must be analyzed and defined according to Data Quality Standard. When entering or importing data, it is important to impose validation rules in the system that prevent incomplete or invalid data from being entered. Once the data capture guidelines have been defined and applied, a central clearing process should be defined to clean up the duplicates and especially the "wanted" duplicates. Almost all modern CRM systems offer integrated duplicate checking and cleansing as standard. However, this is not sufficient for the use cases in operational use and must be extended or even replaced. From our many years of project experience, we recommend the definition and establishment of so-called quality gates.
Customer survey: usage problems of CRM solutions
In the third quarter of 2018, Vision11 conducted a targeted customer survey to identify current problems related to the use of CRM solutions. Survey participants were 300 CRM users from different business units. Not only Vision11's current and former customers were asked, but also numerous CRM users from the German midmarket and large enterprises. Most of the respondents use one of the leading CRM systems from SAP, Salesforce or Microsoft. The result of the survey: data maintenance, data quality and data analysis are the biggest concerns for CRM users. More than a third of respondents see low data quality in CRM as the most important cause for acceptance of the solution. The following graphic shows the results of the study.
If we take a closer look at the causes of this problem, three main reasons crystallize: The intensity of data collection in German companies is steadily increasing. In addition, the willingness to use professional data analysis tools is very low. Last but not least, the short-circuit reactions due to the new data protection regulation (DSGVO) have had a very negative impact on the quality of customer data in CRM. In the following, these three main causes are explained in detail.
1. intensity of data collection
The focus of CRM projects so far has been on connecting the customer touchpoints to the CRM systems and the associated generation of the data via the online and offline channels. This has resulted in a very large amount of the customer data that has been automatically transferred to the respective CRM solution. Very few CRM managers dealt with the questions "What is this data needed for?" or "What do you want to do with the data?". The answer was usually: "We want to know everything about our customers!" or "We want to have a 360° view of our customers!" and so on. In recent years, the numerous expert papers have raved about how data is the oil of the 21st century.
Data is critical to CRM success - without a doubt. Let's stick with the illustrative example of oil. Oil is just a raw product and offers little useful value without extensive processing and refinement. Moreover, the original oil quality is crucial for the future end product, such as gasoline or diesel. The same is true for data. It is not the quantity of data that matters, but primarily the quality. The more data is to be collected or recorded in the CRM, the greater the effort required for data maintenance. According to the motto: "Here and there a mandatory field, we absolutely need the telephone number and e-mail address, date of birth, of course, and don't forget a social media profile", the data entry mask for entering a new customer grows and grows in the CRM. The question arises as to how to maintain this information in the CRM. The users of the system are mostly called sales and service employees, key account managers, customer service agents, marketing employees, etc.
Collecting data en masse without pursuing a concrete concept, let alone having a clear strategy for extracting meaningful and useful information from it, is unfortunately becoming more and more widespread. Almost all of this data has been created in companies in the last 10 years (see figure "Data growth through digitalization"). The term "Big Data" is no longer big enough; today, people rather talk about "Data Lakes". Here, data lake refers to a very large data storage facility that holds data en masse in its raw format. Humans cannot handle the data in such a raw format; machines with excellently thought-out algorithms are needed for this.
2. professional data analysis tools
The most used data analysis tool on the screens of German CRM users is still Excel. There are numerous professional data analysis tools on the market which, in the age of digitalization and Big Data Lakes, undoubtedly represent a quantum leap compared to Excel. Such data analysis tools have evolved in parallel with the increase in data volume, while today's Excel 2018 still basically has the same functions as Excel 98 back then. Data analysis and visualization are now done with professional tools, such as Tableau, Qlik, Lumira or Power BI, but in no case with Excel.
Such professional data analysis tools are still not used enough by large German companies and medium-sized businesses. Most of them either limit themselves to the reporting options within the CRM system. Or the necessary data is extracted from the CRM and further analyzed in Excel. It is relatively easy to connect a CRM system as a data source to a BI solution. An extensive and, above all, high-performance analysis of the data should then take place in the BI tool. Possible analysis use cases include checking data quality, postal validation of addresses, analyzing data by input channels, determining duplicates, etc.
Leading business intelligence tools include Tableau and Qlik. Tableau provides deep insight into data and enables efficient visualization for complex decision-making processes. Tableau dashboards are particularly flexible and offer a wide range of features for customization. Whether the data resides in a spreadsheet, database, data warehouse, CRM system, or all of the above, Tableau provides the flexibility to efficiently connect to and consolidate the data you need.
Qlik provides an entire discovery platform to analyze the complex data relationships. With Qlik products, different data sources can be combined in one view, regardless of size, thus indexing all possible relationships. Unlike query-based tools, you are not limited to pre-aggregated data and pre-defined queries. This means that results can be immediately scrutinized and analyzed without having to create new queries.
With Uniserv, another professional tool for improving data quality is available. Uniserv looks at the data of a CRM system less from an analytical point of view, but much more from the point of view of data quality. The tool "Uniserv Data Analyzer" can be used primarily to check the condition of the data in order to take suitable measures for quality assurance on this basis. With the interactive data analysis or data profiling of Uniserv Data Analyzer, reliable statements can be made about the condition and quality of even large quantities of data. The Data Analyzer creates the basis for the targeted use of data cleansing and data protection. With the Data Quality Service Hub from Uniserv, it is possible to re-establish the required data consistency for processes and applications.
Conclusion and solution approach
The most effective way to address the issue of poor data quality in CRM is where it originates. First and foremost, all sources where data originates must be analyzed and defined according to Data Quality Standard. When entering or importing data, it is important to impose validation rules in the system that prevent incomplete or invalid data from being entered. Once the data capture guidelines have been defined and applied, a central clearing process should be defined to clean up the duplicates and especially the "wanted" duplicates. Almost all modern CRM systems offer integrated duplicate checking and cleansing as standard. However, this is not sufficient for the use cases in operational use and must be extended or even replaced. From our many years of project experience, we recommend the definition and establishment of so-called quality gates.