Types of Metrics

One of the overwhelming discoveries of new CRM users is that the tool gathers so much data that you could literally spend all day looking at data about any aspect of the process, including minute details. Before you go data-happy, it's important to determine what metrics make sense for you. Rather than starting with the data, start with your objectives. Consider both your business goals (for the business metrics) and your project goals (for the project metrics), although both sets can and do overlap for most implementations. Your metrics choices depend greatly on your industry, the types of functions being automated, and the specific business strategy you are following. Nevertheless, you can think of metrics as belonging to four categories: revenue, cost, efficiency, and customer satisfaction. Table 10.1 gives examples of metrics for each of the four categories, grouped by logical subcategories. The examples are simply meant to illustrate what the four categories mean. Certain examples may not fit your requirements and you may have ideas that are not listed in the table.

Table 10.1. Sample Metrics by Category




Sales volume, average sales size, revenue by customer, revenue per lead, revenue per campaign, number of new customers Forecast volume, sales versus quota Lifetime value of a customer, size and frequency of orders


Cost per lead Average cost of sales Customers per support staff, cost per service call Order error rate, profit by customer or by segment number of service requests


Campaign response rate, leads per source, prospects per source Daily calls per telemarketing rep, appointments per telesales rep, lead conversion rate, RFP response time, revenue per sales rep Number of prospects in the pipeline (funnel analysis), lead to sales time by source, length of sales cycle, % close rate Cases per support rep, case backlog, service call length, number of contacts to resolve a service issue, percentage of issues resolved through self-service, case by problem type CRM tool usage Cross-selling rate

Customer Satisfaction

Customer retention, customer churn, support renewals rates Ratings on satisfaction surveys, ratings of knowledge base documents Customer references

What Makes a Good Metric?

A good metric is easy to collect, easy to understand, and meaningful. Ease of collection means that it should not require excessive effort to gather the underlying data, which ideally should be collected as a normal part of the users doing their job. For instance, if you are looking to measure the length of the sales cycle and you can get the tool to automatically time-stamp leads as they are entered into the system, you've just set up an automatic data entry for the beginning of the sales cycle. Besides saving effort, an automatic record also means that the results will be unbiased, which may not be the case if the users are free to enter leads at their leisure (yes, users have been known to manipulate the system to meet stated goals). The second criterion of good metrics is that they should be easy to understand, which means that they should be "blindingly obvious." For instance, a pie graph that shows the percentage of leads coming from different sources is a blindingly obvious illustration of your most effective lead-generation efforts, whereas the very same information presented in a long list of detailed numbers for each and every campaign would not be half as effective. Blindingly obvious may also be blindingly misleading, which is where the third criterion, meaningfulness, comes into play. The example we just considered of the distribution of leads from various sources may be easy to understand, but it's not as meaningful as the average cost per lead for the various sources you used. For instance, cost per lead would tell you that, although webinars generated only 15% of your leads, their cost per lead is just 1% of the cost for trade-show leads and therefore you should expand your webinar program. The three criteria—easy to collect, easy to understand, and meaningful—often require tradeoffs. In particular, meaningful metrics are likely to be harder to collect and generate, so much so that you may need to give up some of the meaning in favor of easier collection. A common example is the desire to track effort time for various tasks (acquiring a customer, resolving a support issue, etc.). For most tools and in most environments the burden on users to keep detailed records of their time is very large, and you may decide to forego detailed time metrics for fear of alienating them from the system entirely. We revisit the issue of data collection in the next section.

Tangibles vs. Intangibles

Some metrics seem easy to quantify even without a CRM tool: how much revenue has been tutorialed this quarter? How much are you spending on support? Others become a cinch with a proper CRM system: What's the average sales cycle? What's the size of the support backlog? Some metrics are difficult to quantify, the most notorious being satisfaction for all the various users of the system. Are your customers happy? Are your employees happy? While it's very difficult to create an unbiased and meaningful measure of such soft benefits, it doesn't mean that you shouldn't try. Customer satisfaction is a critical component of business success, while employee satisfaction is an unrecognized but interesting objective to include in the CRM project goals. So what can you do with intangibles? The direct approach is to create a concrete, yet artificial measurement, typically a survey. To maximize the reliability of the survey, keep it short and focus it on a specific event such as a sales transaction or a support case. We'll come back to customer satisfaction surveys when we discuss data collection later in this chapter. Another useful approach is to rely on measurements of tangible events that are viewed as proxies for customer satisfaction. For instance, instead of asking employees how satisfied they are with the new tool you could track their usage of the tool, or go for a big-picture measurement such as employee turnover. Proxy measurements are valuable because they are easier to collect and they don't suffer from traditional survey biases (for instance, employees may fear retribution if they give negative ratings to the tool). On the other hand, proxies are just that and may be misleading. For instance, employees may be using the system but they are only entering stub information because it takes too much time to track everything. In this case, you would have a positive proxy measurement (usage) but in fact employees are not satisfied with the system. Or employees may be quitting because of a salary freeze even though they love the system, giving you a negative proxy measurement (turnover) while employees are very happy with the system. So choose the proxy wisely and, when possible, use more than one. Do not avoid intangibles altogether but strive to apply reasonable measurements to them. It's usually very difficult to translate intangible benefits into hard dollars, although some brave souls have tried[1]. Therefore, intangible benefits usually don't belong in an ROI analysis.

[1] See the work of Frederik Reichheld, for instance Loyalty Rules.

Strategic vs. Operational

You may find that the business functions involved in the CRM project focus too much on internal productivity metrics. This is often the case with call centers, which seem to be obsessed with such items as the average speed of answer (the time callers spend on hold) and the average handle time (the average time of a conversation). While both metrics are relevant and useful to managers, such internally focused, operational metrics completely fail to address the outcome of the activities in the call center. Did the callers get what they wanted? Did the company get value from the interactions? Some call center managers even argue that the operational metrics they love are appropriate strategic metrics. For instance, they believe that the average speed of answer captures customer satisfaction. Their reasoning is that if a customer waited less than 30 seconds, then the customer is happy and the call center did its job. Wrong! While we can all agree that a customer who is forced to wait on hold is likely to be less satisfied than one whose call goes right through, a customer who stays on hold for a few minutes but then gets a complete answer will likely be more satisfied than the one who has no wait but also does not get a satisfactory answer within that first call. Beware of operational metrics pretending to be strategic metrics! Why are operational (productivity) metrics overused? One reason is that call center supervisors and managers often have a limited view of the business strategy, hence a limited view of metrics. The other reason is that it's easy to get operational metrics, since all the data is nicely logged in the tracking tools. In fact, the vast majority of canned reports offered by the CRM vendors are operational metrics. I must admit that it's hard to tell whether this is because the vendors lack an understanding of strategic metrics or because customers only demand operational metrics. Operational efficiency (making lots of sales calls, generating lots of proposals, answering lots of service requests) is an important component of effectiveness (closing lots of business, increasing customers' loyalty), but it's not enough. Think of effectiveness as efficiency combined with quality. Making lots of sales calls is efficient. Calling on the right prospects is quality. Capture both efficiency and effectiveness in your metrics, that is, include both operational and strategic metrics. Operational metrics are often stuck within a particular business function. Measure results across business functions whenever possible. For instance, instead of counting the number of leads that the telemarketers deliver to the sales team, measure the value of the leads as they turn into bona fide customers. (It takes a while to work through the sales cycle so you may need to be patient.)

Short-Term or Long-Term?

The metrics that measure success for the CRM project focus on the reasonably short term, while the metrics you need to manage the business will be with you for a long time. So it's a good investment to focus the requirements definition on the business metrics, and to do so early in the process (starting at the kickoff workshop) so that you can count on the data you are interested in to be collected and available in the tool. Aside from the data collection issues, the other decisions about metrics are fairly easy to change based on changing needs so don't worry too much about getting them exactly right the first time around. This includes the specific mathematical analysis of the data and the formatting of the reports or graphs. In that sense, metrics are a work in process.