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While helping hundreds of companies deploy and manage their CRM systems, I have seen one common theme arise in every project: The process of getting the maximum value from your CRM investment is a process of continuous improvement. CRM is not a “set it and forget it” technology. Change will be a constant in the evolution of an effective CRM. But how do you make sure you are changing the right things rather than making changes that will hurt, rather than help, the value of the system? In this series of blog posts, we’ll look at three strategies for effective, continuous improvement of your CRM.
The foundation of every CRM system is the quality of the data it contains. More often than not, when a client wishes to answer a business question with the help of her CRM system, it is not the client’s analytical skills that get in the way. More typically, questions can’t be answered because the data needed to complete the analysis simply does not exist in the CRM or is of such poor quality as to be rendered useless.
Good planning and design during system implementation will help, but not eliminate, this issue. Companies change and with that change come inevitable changes to a company’s CRM system. By continuously asking if the quality and availability of data is sufficient to answer critical business questions, our client can determine if changes are needed to the structure and organization of her CRM system.
Suppose that our company, early in its CRM deployment, identifies “average time to close a sales opportunity” as a critical success metric. To measure this, the CRM is setup to track the date each new sales opportunity is added to the pipeline and to track the date each deal is closed. The system then calculates the difference between these two dates and uses this data to display the average time for opportunities to close. This is a pretty straightforward example and a common configuration request during CRM system set-up.
Now fast-forward 18 months. Our company is doing great and sales are growing. But we are also noticing that the ‘average time to close” metric is no longer predictive or meaningful. Digging into this issue, we notice that our product line has grown significantly and we are also selling larger deals into a wider array of industries. Average time to close, which used to be a simple and predictable metric when all opportunities were very similar, is now valueless number because of the complexity and diversity of deals in our pipeline.
The root cause of our issue is the growing complexity of our product offerings and the expansion of our target markets. To adjust to this change, we now need to track three other characteristics of each opportunity. First, we need to know what product line or product category we are selling; second, we need to know our prospect’s industry; and third, we need to know the size of our prospect’s company, (measured in annual revenue, employee headcount, or other metric that is meaningful to us). With these two additional data points, we can calculate average time to close by product line, industry vertical, or customer size. This simple enhancement in data quality and availability makes our average time to close metric meaningful again.
But before we dive into making this change. Let’s think carefully about the consequences. If we want to track product line, target industry, and customer size for each of our opportunities, where will that information come from and who will be responsible for accurately entering it into the CRM?
I will pause here to call special attention to this problem. All too often, well-intentioned managers, wishing to gain greater insight into business performance, add data elements to a CRM and then expect someone else (typically marketing staff or sales reps) to manually enter this information and ensure that it is accurate. Following this path is often a recipe for disaster. Humans make mistakes and forget to do things. And almost everyone can find better uses for their time than the mundane task of entering data into a CRM system.
A better solution is to find a way to automate the collection of this data. If an opportunity is associated with a product in our product catalog, can’t we simply categorize the opportunity automatically with this data? (Don’t have a product catalog in your CRM? Perhaps now is the time to set one up). Rather than asking sales or marketing staff to specify the industry classification of each opportunity, perhaps this information can be gathered automatically using the SIC or NAICS code of the target company.
My point here is that many sources of external data can be used to enhance data already in your CRM. There is a rich ecosystem of information providers set-up for this very purpose. Leveraging them to automate data enhancement is almost always a better strategy than relying on your staff to perform this task manually.
So now that we have our strategy for improvement, we need to complete three tasks to get it implemented.
Once you have these three tasks completed, you can move on to the final part of this improvement project, building the reports and dashboards needed to display your new, more refined metrics.
Let’s review what we have covered here. First, I have made a case that everyone using a CRM system needs to focus on continuous improvement, looking for new ways to get added value from an existing investment in CRM. Second, I argued that focusing on data availability and quality is a great place to find opportunities for improvement. Almost all CRM systems have data quality issues and most managers who rely on CRM for business decision-making would love to have additional data to perform more thorough analysis.
In the next two posts in this series, I will focus on other aspects of CRM improvement: creating more useable dashboards, and automating routine tasks.