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CRM Implementation Best Practices Best Practices in CRM Software Implementation


Data Conversions

Quite often companies attempt to migrate data into CRM software without reviewing and cleaning it up first. This can create numerous problems and even result in moving your company backwards from where it started before CRM was implemented. How? By placing old data in the hands of personnel who previously had updated information stored elsewhere, maybe in a computer file, a rolodex, a PDA or on a napkin even.

Consultants consistently share that legacy data for conversion is never as clean as the company thinks it is, and failing to recognize this early will result in a setback near the beginning of the CRM implementation, and thereby possibly push back a number of successive steps as well.

A CRM implementation best practice is to test legacy data early in order to understand data quality and permit sufficient time for data cleansing. During the data testing process, it's extremely common to find bogus data, such as data records without required fields, records with no primary key fields, duplicate records or invalid records.

Failing to identify and remediate poor data quality can impose dire CRM implementation results. Old and useless data will show itself in glaring and inappropriate ways after implementation and will prove quite difficult to correct at that stage. And the effects of poor data extend beyond staff. Think of how confused and upset your customers will be if their orders are suddenly shipped to their previous address or their warranties are no longer stored for easy access. There are an infinite number of such scenarios where bad data can adversely affect your business. It is far wiser and cheaper to clean your legacy data before you implement CRM software.

Another CRM implementation best practice is to align new data fields with their desired reporting. Once you know the data is clean and up-to-date, align CRM data fields with the correct reporting functions. If a field aligns with a specific report, dashboard or business process, the data pulled will be the correct information for the request. Absence of alignment can result in isolated or out of context information that is difficult to put to use.

Moving data from legacy systems to a modern CRM application is often the most problematic migration. Map out all the steps necessary to move the data before you proceed with the migration. Back up everything multiple times before migration as well to ensure data is not lost or compromised in the process.

Expectation Setting

Overinflated expectations are a common contributor to CRM failure. It is vital to understand that CRM software will not replace a sales force, customer service people or any other staff member for that matter. A CRM application is a tool that adds efficiencies and effectiveness to those roles; it is not an automaton that can perform those roles itself.

In other words, one cannot install CRM software, walk away and expect it to magically cure business problems or instantly inflate the bottom line. If any of those expectations are present anywhere in your organization, CRM will fail to meet those expectations and thus will be deemed a failure itself.

Therefore, it's a best practice to involve all potential company users from the selection process forward. Seeking input from various business units, not only from the beginning but throughout the selection and implementation process, will ensure that everyone has realistic expectations of the software, priorities are properly aligned, and widespread adoption of the software and business practices is a high probability.

In summary, ensure that everyone in your organization understands what CRM cannot do as well as what it can do. Align expectations accordingly and early in the CRM implementation process. Really listen to business units and individual users to ensure you are setting up the system to maximize the functions and results they need.

Start Small and Build Progressively

The impulse is to launch the full CRM software battlements immediately in order to cut costs and improve sales instantly. Fight that impulse. In order to reap the full benefits of a powerful Customer Relationship Management program, each feature must be deployed in a meaningful way, consistently used and then reviewed for refinement. This will not happen if the implementation process is reduced to a down-and-dirty plug-and-play of the entire software suite.

This self-destruct impulse is more prevalent during slow economic times. It is quite natural to want to do something fast to improve company performance or to relieve pressure from higher management to "make something happen." However, the better and more successful approach is to implement slower, not faster.

Start small. This approach has several advantages. First, it enables the organization to fully benefit from each feature deployed and then add success on top of success with each new feature deployed thereafter. Second, this approach allows you to learn from your mistakes and make adjustments. Had you started bigger, there would likely be more chaos and confusion, neither of which is conducive to growing the bottom-line.

In a nutshell, it is more profitable to have two or three CRM features fully functional and fully adopted than to have dozens of features of which none are consistently used or particularly useful. It is also infinitely less costly to implement CRM software in stages with sufficient time and budget to ensure it is implemented correctly from the outset rather than to pay someone to go back and attempt to fix whatever is wrong with CRM software later.

When implementing CRM software, think in terms of the long view not the short-term. By progressively building each feature to fit your organization precisely you build value for that feature. Your organization can then extract that value every day for years to come. Conversely, if you fail to deploy and tweak each feature to maximize your organization’s efforts, there is no value to be extracted, ever.

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A CRM implementation best practice is to test legacy data early in order to understand data quality and permit sufficient time for data cleansing.


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