There are many different things that can ensure that your CRM implementation is successful, a clear set of objectives, senior management support, an effective training and communication programme. However if you have all of these things in place, one other thing is certainly true, bad data can kill your CRM.
In this, the first in a series of blogs on data, I will outline the golden rules for keeping data simple. At the end of the day having a clear data management plan and the right resources and tools in place to manage your data can really have a dramatically beneficial effect on the quality of your data.
Rule 1 – Implement a focused data management strategy
Most firms have far too much data. As a result it’s very difficult to maintain quality as resources are spread too thinly. There is a need therefore to have a much more focused approach to the way in which data is managed.
When Stanton Allen run audits of CRM systems we always find that the activities, relationships and opportunities are focused typically on less than 25% of the data. Having a process in place to identify the companies and contacts that you consider important is a crucial first step in an effective data management plan. After all, why waste effort on trying to maintain the 75% of the data when it’s not of any significant value to the firm?
In the past the strategy for many firms was to get their users to “share all of their business contacts”. Although not everyone did, on the whole most firms were fairly successful in this goal. However the end result was that the system was over-populated with contacts that didn’t have a great deal of value. They weren’t added to marketing lists, they were not associated with opportunities and no business development activities were recorded against them.
We very often find that one of the largest groups on any CRM system are those contacts where the only thing about them of any real value is that they have a relationship. In many cases (sometimes as much as 50% of the whole database) these contacts only have one relationship with one person at the firm.
Firms really need to ask themselves the question are these contacts of any value? Should we in fact be changing our approach to CRM and not including every single contact that the users have in their address books? Would it make more sense to focus our efforts only on those contacts that are of real value, or at the very least if we do include all contacts from users’ address books, not waste any effort in trying to manage that data.
Our approach is to implement a simple scoring mechanism. One point for each importance criterion. For example, are they a client? Do they have more than one relationship? What’s the strength of the relationship? Have they engaged with you in some way, for example responding to events? Are there business development activities recorded? By implementing a system like this you are able to identify those contacts that score on multiple criteria, those that only score for one and those that score for nothing. You could then focus your effort on those that are more important. Take email bounce backs for example, if a contact with no points bounces, should you waste any time trying to update the information or simply archive or inactivate the contact? What about marketing lists, if most of the contacts on a list don’t score any points then by removing them from the list you are probably going to increase your % response rates as you’ve taken off all the non-responsive people from the list.
On the basis that most firms have limited resources when it comes to data management it really does make much more sense to focus on those contacts and companies that are of the most value to you. You need to identify the triggers that make a contact suddenly become important e.g. when an opportunity is created and they are associated to it, so that you can then start to focus on them. Most users are more worried about the most important and the most current contacts being accurate rather than every single contact in the system so by having a simple scoring mechanism in place you can focus on these contacts much more easily.
Watch out for the next in the series when we look at what measures you can put in place to measure data quality and to incentivise users to follow your data management rules.