sales forecast

  • June 18, 2014
  • weather2I don’t know anyone who was ever great at sales forecasting. This is not to say that it can’t be done but it’s a hard problem, like forecasting the weather. Today they can tell us within a few degrees the weather for many days in advance because we’ve invested in many data gathering and analysis tools like satellites, computer modeling, and analytics.

    It’s not that weather forecasting has been perfected though. Instead, forecasters have been able to bring the forecast down to a range of probabilities through the use of all that data and models for how the oceans and atmosphere, the jet stream and other major parts of the earth work. So we typically see something like a 30 percent chance of rain and a temperature range of about 4 degrees and those ranges are enough to enable us to make plans like going to a picnic or not, or remembering to carry a hat or umbrella.

    Sales forecasting has none of this going for it at the moment in part because while a sales forecast is a much smaller problem than the weather five days hence, we still do it much as we did a hundred years ago. Too often that means a senior person reviews all the deals, applies his or her experience and follows a gut instinct. By the way, that person is known affectionately as a HIPPO because the decision was the result of the Highest Paid Person’s Opinion. How’s that for science?

    But what would the forecasting process look like if we had as much science employed as a local TV weather person? Like the weather person, the sales manager would have a model based on many iterations of patterns that account for all kinds of variables including deal stages and their inputs, sales rep and that person’s track record, the product and its adoption patterns and much more.

    If the interest is in total revenue intelligence, the model would also include inputs from the current customer base including propensity to churn, cross-sell and upsell opportunities, and customer lifetime value. To manage all this there would also be powerful analytics to process all the data and the result would not be a single answer such as a dollar number or the determination of whether or not you’ll make your target.

    The output would be a set of ranges. Just like the weather forecast you don’t know until the forecast is in the past, so there’s no such thing as 100%. But also, like a weather forecast you don’t need absolute certainty, you simply need to know the probability of rain on Saturday so you can figure out if you want to golf.

    Right now human behavior plays a big role in sales forecasting. We have a number to shoot for and often we try to figure out ways to make it regardless of soundness of our reasoning. So we assume deals are going to close even if it means we have to ignore telltale signs of trouble until it’s too late. But imagine using modeling and analytics to evaluate your company’s position in each deal. The model can tell you the warning signs because with analytics it can tell you how closely any deal fits with the model’s known history of success. As with weather forecasting there’s no value judgment just a probability of rain. Managers still need to apply their reasoning. But armed with this kind of knowledge sales people up and down the organization can evaluate scenarios constructed to make their deals match up with the ideal.

    Here’s a trivial example. A company I know has a sales process, which is a model itself, which at a certain point has the rep meeting with a key decision maker. If the key decision-maker is not onboard they know the chances of getting the deal done are greatly reduced, so the meeting is an important gating factor. Yet, time after time, reps skip this step. Perhaps the coach in the deal reassures the rep that the decision-maker has already approved, or maybe the decision-maker doesn’t have time to see the rep, or any of a hundred other reasons.

    But it might be hard for management to discover this, if for instance, the rep simply indicates on the forecast that the deal is at forecasting stage. A manager with 5 reps each working 50 deals in various stages might not have the time to go through every piece of deal data so the mistake would get through but it wouldn’t improve the forecast one bit.

    Now, take a more scientific approach. The sales process stage is one of the variables in the model that the company uses to evaluate deals. More importantly, the business applies analytics to the deal data rather than expecting managers to review all of it. Analytics would have no trouble spotting the incomplete deal stage and would down grade the forecast appropriately. A report would then show the variance between the forecast and the model.

    If you apply this logic to every deal in the forecast then you have that range of probabilities that weather people rely on to tell you about Saturday’s conditions. More importantly, and unlike a weather report, the forecast is also prescriptive because it shows you how you can improve it. Get that meeting with the decision-maker if you want to close the deal!

    Mark Twain once said, “Everyone complains about the weather but no one does anything about it.” If you’re tired of complaints about sales forecast accuracy, consider building an accurate model and applying analytics. It works for the weather.

    Published: 9 years ago

    Modeling is a big idea and one that I started noodling on many years ago.  In the last couple of weeks I’ve looked at the cloud computing model, or what it ought to be.  Today I am looking at a broader paradigm.  One of the great things about new paradigms is that there is no model per se, in a way a new paradigm is an opportunity to create the model; it’s an incipient model perhaps.

    The most interesting models or modeling I can think of are those that take place in four dimensions.  You will recall without much prompting that we live in four dimensions, time plus the three that define space and from this we get Einstein’s space-time.  People who study the big bang speak of matter, space and time as “condensing” out of the event.  That choice of word has always fascinated me.

    There might even be more dimensions that we don’t know about or comprehend.  How can that be?  I don’t know the only analogy I can make is that my dog lives in the same four (or more) dimensions as I do but he’s only apparently aware of the ever present now.

    Four-dimensional modeling is not hard to comprehend, but like a dog, we routinely fall back a dimension to deal with reality, especially in business.  Let’s use the analogy of riding a bike.  The bicycle stands against a wall in four dimensions like everything else though it can be comprehended in three very easily.  But riding the thing is definitely a four dimensional experience.  You can’t ride a bike unless you make a conscious effort to go through space-time balanced on those two wheels.

    Learning to ride a bike requires modeling — either training wheels or the expert hand of an adult or perhaps an older sibling who models for you the feeling of keeping your balance.  No amount of discussion before hand is very instructive for a first time rider because words fail to communicate the feeling in your stomach as you take your first ride.

    In business our models are typically three-dimensional.  The two that make the most interesting contrast for me are the accounts receivable (AR) report and the sales forecast.  The AR report tells you in a two dimensional grid about what was done in the one dimensional past.  It gives you an accurate representation of what is owed and what will come in barring some future four-dimensional miscue.

    The sales forecast is much different.  We treat is like the AR report but with far different expectations.  The forecast is purely four-dimensional but we insist on treating it like the AR report which, though not perfect, is physics compared to the sociology of the forecast.

    My point, and perhaps it is not a big one, is that good forecasting requires a four-dimensional model and that can’t be done with a report.  You might disagree and the evidence is on your side, mostly.  For decades we’ve used a standard forecast report to predict future revenues but honestly, it’s been far less than satisfying.  We get our forecasts wrong quite a bit.  My data invariably shows that sales forecasts rarely have a ninety percent confidence level.  It’s a narrow range — fifty percent is as good as a dartboard so there isn’t a lot of room to work in.  We complain about forecast accuracy with the same frequency we complain about the weather, but as Mark Twain wryly observed about the weather, we never do anything about it.

    That standard sales forecasting via reports and spreadsheets has worked so well for so long is not a tribute to the method but an artifact of times when we were able to sell standardized products into huge markets.  Demand was usually sufficient to backfill one opportunity with another when necessary.

    But today’s markets are a bit different.  We are doing far more cross- and up-selling than ever.  Product lines are expanding but categories are relatively stagnant.  Getting the forecast right has never been more important because margins are smaller and there are fewer deals with which to backfill.

    The solution for the sociology of the forecast might be the same as the solution for the weather — a model moderated by computer processing power.  Rather than focusing on a single report that amounts to a three-D snapshot in time, we need two things.  First, a model that captures past information and integrates it into the present but also the model has to offer enough predictive value from prior experience to enable us to self-correct and avoid a crash.

    A child (or a trained circus animal for that matter) on a bike can do this and it should not be terribly difficult for us big-brained adult humans to do so with a forecast.  I have been impressed with the advances made by analytics companies in this area in the last few years though they often discuss everything in terms of analytics.  I would rather they approach this in terms of a model or riding a bike though.  Perhaps that would make the idea of using analytics less daunting.

    Published: 13 years ago