There’s an old axiom: You can’t manage what you don’t measure. John is the sales vice president of a major manufacturing company and exactly what you imagine when you think about this role: a seasoned veteran, politically astute, protective of selling resources and selling responsibilities. He had two questions: What does all this CRM technology and process investment buy us? And how long, realistically, will it take to kick in?
Every quarter, and especially each year-end, senior management turns to John (and his contemporaries throughout the world) and asks a series of pointed questions. Primary among these questions is, “Are we going to make plan?”
Having been around the block a time or two, people responsible for sales know the drill. So before anyone asks Question No. 1, they have asked their direct reports sales managers, business managers, key account managers the same question: “How’re we doin’?
There are several problems with this approach of managing and measuring sales productivity based on revenue. Many of the difficulties fall into two areas. First, results are, by definition, historical. That is, by the time you actually know what the number is, it’s cooked. And much to everyone’s dismay, if the number is short, your goose may be cooked right along with it.
Unlike the economists’ index of economic leading indicators, revenue is a trailing figure. So, you can’t manage results, you can only monitor them. Clearly, the value-add from this activity is not very high and, in an increasingly competitive and global environment, is not the formula for continued success.
The second problem is that the revenue figures are often hysterical. That is, these numbers are completely imaginary and can be arbitrarily assigned to territories through biases or misconceptions. Even though these numbers are not helpful, they do matter, and management is not going to stop looking at them any time soon. The question John was really asking is, “Do we have anything better to look forward to?
This is where the notion of measuring and analytics come into play. You can check out our video on “The Right Things to Measure” for an overview of process.
After mapping a company’s sales process, it was surprising to tell the group, “This is now our common yardstick and it is almost certainly wrong.” What I meant was, you can bet this yardstick is not exactly 36 inches long, But, if we all measure with it and come up short or long, then we’ve learned something. This approach helps establish baseline performance. It provides a common unit of measure for what people are currently doing.
The idea, of course, is to get to leading indicators. Examples of such gauges in sales include pipeline %full, flow, sales cycle length, close rates, average deal size, and prospect quality criteria, to name just a few. Getting a handle on such measures can tell sales management how the current revenue production system is working, how individual reps are doing within this system, and how to keep it under control.
When such information becomes available, a whole new field of management possibilities opens. The principle here is to model how the system operates, then measure against this model as a basis for coaching to improve performance.
Absent such process measures, sales managers essentially have one gauge: We’re there/we’re not there yet. Is it possible that better information could be made available? And how would this information be compiled and reported?
First, it’s worth noting there is a difference between data and information. We collect data but convert it to information through analysis. Companies have been collecting data forever. The problem is, nobody’s been looking at it. For example, managers often require the field to submit call activity reports, but it’s not unusual for reps never to receive any feedback on these. Similarly, many sales campaigns generate paper trails, either via specific forms or less formal records, yet rarely does anyone bother to analyze these records.
This is the equivalent of recording football games then never looking at the game films. In the case of game films, you may not be getting any smarter, but at least you didn’t interrupt or burden the players in any extraneous way. In sales, reps create the records with required tedious reports they’re not certain are ever actually reviewed.
Is there a meaningful alternative? The answer is yes, and the reasons are compelling for everyone involved.
Let’s go back and look at the list of possible leading indicators. Speaking to groups of executives I’ll ask, “Who here wants to shorten their sales cycle?” They all raise their hands unanimously. Then I’ll ask, “By your company’s definition, when does the sales cycle begin and end, and how long is it today?” Only a small percentage of companies have actually defined these parameters so process operating metrics are impossible.
Groups, in this case, will revert to averages or, more likely, guesstimates. For example, the prevailing opinion may be that average sales used to take six to eight months, but now take more like nine to 12 months. If this were true and all else remained the same, then productivity would have decreased and vulnerability would have increased. However, prevailing opinions are not facts.
Second, some insights discovered may be counter-intuitive. For example, the highest revenue producers, with higher average deal size, higher close rates, and higher customer satisfaction levels, may also have the longest average sales cycle time!
Assuming none of these other conditions accrued, the question arises, what has caused the sales cycle to lengthen? Again, ready answers may include increased competition, more demanding or tentative buyers, lengthy and expensive evaluations but are any of these the actual culprit? Without more definitive information, nobody can really say.
Enter metrics. With actual process definition and ongoing performance measures, we might reveal that the sales cycle is indeed eight months. Further, that the shortest cycle recorded is two months and the longest 18. This raises the notion of both mean sales force performance and normal variance above and below the mean.
Now, finally, we’re getting somewhere. If we’re all measuring sales cycles with the same begin/end definition, and we record mean and normally varying performance, suddenly a picture emerges of how the system is operating. In this discussion, improving performance means raising the mean while reducing the variance. This provides a consistent approach for reps to manage their business and develop their territories, managers to manage and develop their sales teams, and top management to get a handle on the overall health of the business.
The final observation to make here is that performing the process well is the individual’s responsibility. Improving the system or environment to reduce the variance and increase the mean performance capability is management’s responsibility. This is not an insignificant point it will require management to improve the operation’s effectiveness over time, not simply dictate higher and higher quotas.
The use of technology to non-intrusively capture and analyze ongoing operational data will result in clear roles and responsibilities, and meaningful feedback that is based on facts, not opinions and judgments.