All Things Data

Increase Efficiency with Customer Data Integration

In previous postings, it was discussed how developing a single customer view by utilizing customer data integration (“CDI”) can help an organization increase effectiveness and decrease costs.  Today’s posting will look at a couple of ways that a CDI approach can help increase an organization’s efficiency with a single view of your customer.

With a single customer view, employees can view data from other data systems and/or multiple data entries within a single data system without having to switch to another data system, search their current data system, or ask someone to provide this information.  For example, a sales person can see all the authorized information about a customer in a single view without having to go into the accounting system, customer service system, the external marketing files, or other data sources.  Similarly, customer service, accounting, and other representatives can make more informed decisions based information that has been correlated from other systems without having to go into those systems.

Another major efficiency benefit of using customer data integration is that technical resources can spend less time on data management issues.  Records from the same organization from multiple data sources or a single data source can automatically be identified, correlated, and viewed as being part of the same organization without IT having to develop this process.  Duplicate records can be identified.  External lists can be matched against a database and duplicates can be identified in advance so only “Net New” records from the lists are imported.  Updates to data sources can be matched and linked by a Common ID from the CDI process on an automated basis.

Decreasing Costs Utilizing Customer Data Integration

While it has been our experience that the primary driver for most customer data integration projects and a desire for a single customer view is a need to increase market share, it should also be noted that developing Common IDs for organization and contact records that are the same or related can provide additional benefits such as decreasing costs.

The following are examples of how a single view of your customer that is matched and linked by a numbering schema can decrease costs:

a.  Multiple records from the same or related organization can be identified as one “opportunity” when these records are linked together with a Common ID.  Consequently, expenses are saved by only having to market to one “opportunity”.  For example, only one direct mail piece needs to be sent per desired contact and sales people only have to follow up with one “opportunity”.

b.  Data integration and data consolidation projects are easier and more complete since records from the same organization have a Common ID.  For example, with a Common ID between data sources, information from one or more data sources can be imported into another system on the appropriate records that is correlated by the Common IDs.

c.  Sales territories can be more appropriately assigned.  For example, by counting multiple records for the same organization as one “opportunity” based on a Common ID, an organization may be able to decrease the number of customers and prospects in a territory than previously realized by not over counting the multiple representations of the same organization in the database and therefore less sales reps are needed for that territory based on current activity levels or future growth.

These are just a couple examples of how matching and linking same or related organization and contact records by a Common ID as part of a customer data integration project can save money while additionally utilizing a single customer view to grow your business.

Business Intelligence, Customer Data Integration, and Excel Pivot Tables

In my last post regarding customer data integration and analysis, I mentioned that there are a lot of great business intelligence tools.  Also, these business intelligence applications can vary greatly in terms of cost, learning curve, and functionality.  Today, I would like to further elaborate on one of the most practical options, Excel Pivot Tables.

Excel Pivot Tables are an excellent option for anyone who is looking for a powerful, yet affordable reporting capability.  According to Microsoft, PivotTables allow you to create multidimensional data views by dragging and dropping column headings to move data around.  The Microsoft link also provides tips on how to use Pivot Tables.

We like Excel Pivot Tables as an entry level business intelligence tool because everyone already has Excel.  Therefore, an organization can roll-out their business intelligence application without purchasing any additional software.  Additionally, since most users already are familiar with Excel, the learning curve to understand how to develop and use the Pivot Tables is low compared to other alternatives.  Also, since Excel is universal, external books, courses, and professionals are available to help you learn and utilize Pivot Tables.

Even though Pivot Tables are comparably affordable, that does not mean they are not powerful.  As you can see from the above Microsoft link, not only can you create spreadsheets, but with the Pivot Chart capability, you can also create graphics.  Further, like other business intelligence tools, you can create reports that contain certain variables and then you can drill down further to view the details behind the grand totals.

Now, many of our clients are using the more sophisticated, higher-end applications.  Although I am not an expert on business intelligence tools, I am sure these higher-end systems provide much more capability than the Excel Pivot Tables.  However, if you are just starting out, we would suggest that the Pivot Tables are a great place to start.  Once you start using the Pivot Tables and are gaining value, you may at some point find the Pivot Tables limiting and want to take your reporting capabilities to another level.  At this point, you will be an educated buyer who will understand what features are important to your goals when evaluating your business intelligence options.

Regardless of the reporting tool that you are using, customer data integration plays a key role in matching and linking data from disparate data systems in order to develop a single customer view.  The Common IDs that a CDI application can apply to same or related organization and contact records are critical in enabling data analysis across multiple data silos.  Common IDs are what enable business intelligence reports to accurately count the number of organizations and contacts for the criteria that you are analyzing.  Whether you are using Microsoft Excel Pivot Tables or the most sophisticated and expensive business intelligence application, Common IDs are necessary to provide accurate and meaningful reports.

Customer Data Integration and Analysis

There are lots of great applications of different levels of sophistication such as Cognos, Crystal Reports, Excel Pivot Tables, Qliktech, and many others that help analyze internal business intelligence.  However, one of the challenges in data mining this business intelligence is properly reporting on the multiple representations of the same company or contact that are in an organization’s disparate database systems.  There could be duplicate records, records that are part of the same company, but have different contacts, different locations for the same organization, and other combinations.  If these multiple representations of the same company or contact are counted as being more than “one”, counts will be erroneous due to over stating the number of locations and contacts rendering the business intelligence reports inaccurate. 

One of the very first benefits of any customer data integration project is the ability to analyze data from disparate data systems.  Typically, a company will want to perform analysis based on information in their CRM system, which contains customers and prospects, and their financial systems.  For example, a common analysis question is how much revenue is received from a certain organization.  This seemingly simple question can become quite complicated due to multiple records in your database systems with the same organization, but different locations, as well as subsidiaries and divisions that have different names.  Customer data integration can solve this issue by developing a numbering system that places Common IDs on all the location and contact records that are part of the same organization across multiple databases.  Once you have a Common ID, reports can be run that accurately count the multiple representations of the same organization and contact as “one”.  Once data is matched and linked with a numbering system of Common IDs, an organization can perform analysis immediately even if there has not been any data clean-up.

Multiple Representations of the Same Organization or Contact Within a Single Data Source

When we initially start a data integration project, most of our clients realize that they have data “all over the place” and are focused on the issues that are caused by the lack of integration of separate data silos.  However, one of the areas that are usually overlooked is the issues that are within the each separate independent data silo.

Typically, a client will have multiple representations of the same organization and same contact within a single data source.  These multiple data representations can be duplicate records, the same organization, but a different location, the same organization, same location, but a different contact, similar, but incomplete information in one or more of the records, and many other possibilities and combinations.

These multiple representations can cause an entity many issues.  A Gartner study says that poor quality customer data costs U.S. business an estimated $611 billion dollars a year in postage, printing, and staff overhead.  When you factor in the many other negatives of poor data quality besides mailing costs, then the costs are much higher.  For example, if I am a sales rep, am I looking at the complete history of communication with the customer or prospect when I am on a specific record in my CRM system?  If I am a marketing person, how do you properly analyze how many accounts and contacts that you have for a certain criteria if you are counting the same organization or contact more than once?  How do you avoid not just the expense but the potential alienation of the contact by not sending out the same communication multiple times to the same contact that is listed multiple times in your database?

The answer is a numbering system that can coordinate the multiple representations of the same organization and same contact so that you know that these multiple records are actually the same organization or the same contact.  Once you can coordinate the data, a single customer view can be set-up so that a representative can see the records that have been matched and linked by the numbering system.  Marketing can count the multiple representations of the same organization or the same contact as being only one organization or one contact for analysis purposes.  Further, marketing can now send just one communication to the one contact.

Customer Data Integration and M&A

According to the consulting firm Accenture, “as increased competition drives acquirers to pay closer to the full market value for these companies, the relentless pursuit of accelerated value creation is critical....The clock is ticking.  Any delay in capturing the value [of the merger] results in significant losses for the company and an uneasiness among analysts and investors who are waiting for that value to show up in the financials.  Companies must therefore up their entire game and become much better and much faster at exploiting synergies and capturing value."

“Post-merger activities should be prioritized according to the value they create.  For example, if the greatest value in a merger is cross selling to the new base of common customers, then integration needs to enable and ensure the rapid transfer of customer information and the development of integrated account plans.  Lower-value activities can simply be postponed.”

Accenture even recommends bringing the two organizations together before the merger is closed so that the acquirer can gain their return on investment as soon as possible.  In order to do so and be in compliance with government requirements, a third-party is needed to analyze the data.  This is a process that Accenture calls a “clean room”.

Even if it is not possible to bring the organizations together prior to closing for legal reasons or it is just not practical, if one of the main reasons for the acquisition is to increase market share, then one of the very first things that the newly created organization should do is integrate the customer bases of the two organizations.  With this integrated customer view, you can plan and execute on the cross-selling and up-selling opportunities created by the merger.  Using internal data as well as appending external marketing data, you can enhance this integrated customer view even further in order to find the multiple locations for organizations that are related to your customers.  The sooner that you can integrate your customer data and understand the uniqueness and overlap within the customer base, the sooner you can achieve the return on investment that was anticipated when the acquisition was made.

Enhancing Your Single Customer View by Appending External Data

When an organization integrates multiple internal database systems and creates a single customer view that includes such sources as accounting, sales, marketing, customer service, and other systems, they begin to gain insights based on their internal business intelligence.  Additionally, once an organization starts to benefit from the analysis of their single customer view, this typically leads to a desire to get answers to questions that are not available by only looking at internal database systems.  For example, once its been identified which clients have purchased the most products, produced the most revenue, and are the most profitable by integrating internal systems, the next step is to identify the characteristics of these organizations so that other organizations with similar characteristics can be found.  What are the revenues, employee sizes, and industries of these best customers?  What other organizations have these similar characteristics?  Are there other locations or subsidiaries within your existing best customers that can be penetrated?

Appending external marketing data to your single customer view can help find the answers to these follow up questions.  General business external marketing data can provide information such as company revenue, number of employees, and industries.  The external files can also provide the contact information for follow up and other information such as is the location a headquarter, subsidiary, or branch location of a larger organization or does the organization only have this one location.  External files that are specific to an industry may also provide other valuable information such as when a contract is expiring. 

Once you have identified the external files that you are going to append to your internal database systems, a data integration software application can help match the external files with internal sources.  As an added benefit, external files may also include native intelligence, a numbering schema indicating that two or more records in the external marketing file are part of the same organization.  If the data integration application is able to utilize the external file’s native intelligence, then not only can the appended data provide valuable information for the internal records, but it can also help improve the match of the internal data sources.  For example, if internal record “A” matches external record “B” and external record “B” matches internal record “C”, then the data integration application can infer that internal record “A” and internal record “C” are also a match based on the use of the external data’s native intelligence.   

A Data Filtration System

I can’t tell you how many times when our company starts talking with a client about a data integration or other data management project that before we get started with the main purpose of the project the client first thinks that they have to “clean-up” the data.

If your organization has multiple data silos and has a significant amount of data records, a data clean-up effort could be a massive project.  Most likely, your organization will never get around to cleaning up the data, and if does, its going to be a one-time event that only provides clean data for a moment in time.  Therefore, if you are waiting for the day that you have perfectly clean data before solving your other data issues, that day will never come.

Instead, we suggest that your organization develops a Data Filtration System.  A Data Filtration system is a process where you can clean-up data on an on-going and systematic basis.  The system should match and link same and related organization and contact information from disparate data sources as well as within single data sources so that you can have a single view of your customers, prospects, and other organizations. 

With this single customer view, the data should be sufficient so that a person can make a determination on how to handle a group of records that have been matched and linked by your numbering schema.  Sometimes, a best record can be made from the group and the remaining duplicates can be purged.  Other times, however, records that at first appear to be duplicates are in fact records from the same organization but are separate sites.  Instead of merging and purging these records, these records should be kept separate but correlated by your numbering system.  There are other times when some records in your group need to be merged and purged while other records needs to be correlated.  Whatever the scenario, the system should be set-up so that these “Solomon” like decisions can be made.