6 Steps to CRM Transformation

In general, professionals and customers involved in unsuccessful CRM transformations commonly point to certain causes of failure. These vary from lack of customer-centric strategy to absence of measurable goals, poor change management to poor implementation.

In our personal experience, most CRM implementations have been beset with similar challenges, owing to lack of appreciation of functional strengths of COTS (commercial off-the-shelf) products in attempting IT-enabled CRM transformations. However, many of the causes are heavily accentuated by poor planning and phasing of CRM transformation programs.

Plan Your Journey and Know What to Expect

CRM transformation needs to be linked to overall IT strategy of the organization.

While the business demand is the key value driver in the phasing of CRM transformation, the interdependencies between CRM and other operational systems are a major cost and risk consideration. Therefore, the business demand alone cannot dictate the CRM transformation phasing. Otherwise, a huge investment may be required in the product customization and developing temporary interfaces.

Release Planning Framework

The release planning framework for CRM transformation is based on business drivers as well as footprint and data coupling analysis. This framework helps the program managers identify the paths of least resistance in transformation planning while accommodating business needs.

Following is a six-step journey in planning the CRM transformation.

  • Step 1: Establish Motives and Drivers. The first step in the transformation is to select the business drivers from the business case for the CRM transformation.
  • Step 2: Identify Process Enhancements Supporting Business Drivers and Their Expected Business Benefit. The selected drivers can then be broken down to business processes that need to be enhanced to deliver the business benefit. Expected business benefit metric against each process improvement should be clearly identified at this stage.

    For instance, in a logistics company the business driver is the need to increase first-call resolution (FCR). The expected benefits may come from FCR of various types of enquiries, e.g. track and trace enquiries, billing enquiries. We can assign a score (relative or absolute) of potential improvement that can be expected by the CRM transformation for each of the enquiry types. In the illustration in Fig. 1, FCR for billing queries from CRM transformation is expected to improve only by 3 points as compared to 7 for track and trace.

    Figure 1: Business Driver Decomposition
    (click image to enlarge)

  • Step 3: Identify the Transformation Candidates for a Release Milestone.Transformation candidates are the applications (COTS and bespoke) that will undergo change as part of automating/enhancing the business process to achieve the business process improvement objectives. It is important to identify all the transformation candidates that are rolled out in a release. We will refer to all the applications that are modified for the release as “Transformation Candidates for a Release Milestone”.

    For instance, in a communication service provider, the transformation candidates can be Siebel CRM, Kennan billing and a weblogic self service portal, etc.

  • Step 4: Analyze Footprint and Data Coupling for Identified Processes.At this stage, we have identified the business processes that need to improve and have also identified the transformation candidates that have to deliver that capability. We now need to analyse the extent of native capability in the transformation candidates to deliver on expectations and the extent of interfacing required to make them work together.

    Based on our experience in COTS implementation, we have formulated two key drivers of technical complexity in the solution.

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    1. Footprint Index: This index provides a measure of the native support for the enhanced process within the transformation candidates. The higher the value of Footprint Index, lower the expected customization on a COTS product.

      This index can be calculated as a ratio of the footprint of the transformation candidate’s native functionality in the execution of business process to the total number of supporting applications in the business process domain. The index should be assessed a value between 0 and 10.

      Figure 2: Footprint Index
      (click image to enlarge)

    2. Data Coupling Index: This index provides a measure of inter-dependence of the transformation candidates to the rest of the applications. The lower the Data Coupling Index, the lesser the dependence on the interfacing system and risk on the functionality.

      It is evaluated as a factor of number, frequency and volume of business entities exchanged multiplied by a complexity factor (real time integration). The index should be assessed a value between 0 and 10.

      Figure 3: Datacoupling Index
      (click image to enlarge)

      Usually to reap quick wins, taking a first step in the transformation journey requires more data containment within the system to reduce dependency.

  • Step 5: Maximise the Business Benefits While Minimising Cost of Ownership. At this stage, we have an assessed of benefits expected and costs associated with delivering each process. As a next step, we need to identify the processes that can give the maximum benefit while reducing the complexity, cost and risk of technical implementation.

    A benefit complexity matrix is prepared for each business processes that maps out the Expected Business Benefit, Footprint Ratio and Data Coupling Index as shown in Fig. 4.

    Figure 4: Benefit Complexity Matrix
    (click image to enlarge)

    (click image to enlarge)

    The business processes falling in the quadrant with high application footprint index and low data coupling index are the processes that have lower cost of ownership in implementation, support and upgrades. These business processes should be selected for transformation.

  • Step 6: Prepare Road Map. Determine the cumulative expected gains for the selected process enhancements. These processes should form a part of the initial release. The rest of the processes should be picked up in the next tranche of application uplift for the transformation when newer transformation candidates are identified.

    In case the business benefit expectation is lower, go to Step 3 and evaluate another set of transformation candidates.

In Conclusion

The first step in COTS-based CRM transformation is the most critical, and failure can mean loss of sponsorship. This CRM transformation risk can be substantially reduced with proper planning and phasing.

The release planning framework proposed here can help in planning the roadmap to a successful transformation. The high footprint index and low data coupling index are the key parameters to assess in release planning. It balances the business needs against implementation challenges effectively, and the framework makes decision-making easier, leading to better phasing of CRM transformations. Such a properly planned CRM transformation shows more resilience when faced with challenges of change management, technical implementation and stakeholder management.


Manish Sarswat and Himanshu Sharma are principal consultants with Infosys Technologies.

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