Back in the day, the joke about weathermen used to be that it must be great to have a position like that since they can forecast wrong more than 50% of the time and still keep their job. That certainly wouldn't happen in the hardscrabble, real world of Sales where salespeople and their managers are expected to forecast every week, and, as the month progresses, define every day what the end of the month or quarter will be within very narrow ranges.
Salespeople live and die by the accuracy of their forecasts and resulting win rates...and yet data from Sirius Decisions states that only 54% of B2B salespeople-in general-achieve their quota. Which says that there's a major disconnect here...
- So, either the quota was wrong and never had any buy-in
- Or, the experience and selling skills were lacking
- Or, the forecasting process wasn't much more than "good guessing"
In the world of forecasting the weather, we now look to our weather forecasters to not only accurately predict the storm's intensity, its arrival...and departure times, but in blizzards like Juno and Neptune, the depth of the snow within an accuracy level of a couple of inches. For example, WBZ predicted 18"-24" inches last Friday at the NH beach. By Sunday morning, I went out and measured the result which came in at a not-surprising 22 inches.
Are there big misses in weather forecasting? Sure...but rarely, and nowhere near the level of certainly ten and even five years ago, when "good guessing" was "good enough".
So, how did the level of forecasting accuracy improve?
In a world where the entry ticket for weather forecasters still seems to be good looks, stylish clothes and perfect speech delivery, what happened is that data-really BIG DATA-arrived on the scene coupled with massive computing power.
First, for those who aren’t in the know...
Most of the U.S. weather forecasts out there are originally based on data from the U.S. National Weather Service, a government-run agency tasked with measuring and predicting everything related to weather across all of North America. Commercial companies like The Weather Channel then build off of that data and try to produce a “better” forecast — a fairly lucky position to be in, if you consider that the NWS does a good portion of the heavy lifting for them.
Second, many private companies like The Weather Channel, Weatherbug, and countless other online businesses such as the Weather Undergound each have have thousands, and often tens of thousands, of volunteers providing active data points along the path of every storm. Add in hundreds of sophisticated ocean buoys and mega supercomputers from IBM and Cray, which refresh data every 2.5 seconds on average, and forecasting accuracy skyrockets. The end result is relatively accurate forecasting when compared to the results just five and certainly ten years ago.
Imagine what our sales forecasting accuracy rates would be if we were to be more analytical, more data-based, more reliant on trends and algorithms and less on "What do you think?" or "How are you feeling about the month?"
If there were uniformity in the actual process of sales forecasting, and if there were formal standards of using a uniform sales process, the forecasting accuracy of the average sales team would improve significantly by reducing the variability of "the personal touch" and providing much greater objectivity. Think about what happens too often today:
- The salesperson presents a forecast based on "their gut"
- The managers shave that number to give themselves more protection
- The CFO does the same thing when preparing their financial forecasts
Somewhat similar to the the personal interpretations of some of the TV weatherpeople who don't use all of the data available to them since they figure the public won’t believe them anyway. Also, just like some salespeople who are always living on the edge of marginal performance, some meteorologists admit that they purposely fudge their rain forecasts. What’s a better way to keep the public tuning in every day than to make them think it’s going to rain more frequently or at a higher velocity. When the rain does not appear, or it's less than was predicted, everyone actually feels more positive, there are more views and ratings go up.
Timing & Predictability
Given all of the factors that influence it, the weather is an undeniably complex process—and like any process, it can exhibit a lot of variation. However, if you’re going to make any big plans based on weather and you want to minimize the variation, the market data about weather forecasting suggests that it’s best to rely on the next-day forecast than 10 days in the future. Nothing surprising there since the same situation exists in the world of Sales. How many times has every sales manager heard in the Monday morning sales meeting, "Not really sure about the forecast, but why don't we talk on Friday, and I can give you a better idea".
There is not much we can do to accurately predict 10 or more days into the future, so relatively speaking, the 1-day and 5-day forecasts come a lot closer to doing that than most other aspects. As for the 10-day forecast, meteorologists (just like salespeople) already know exactly how unpredictable the weather conditions 10 days in the future can be, but they provide it to us-the public-nonetheless because we still want some sense of what the future holds, despite the unreliability of the predictions. Having said that, it’s good to know which weather forecasts we can really count on, and which come closer to fortune-telling! The same applies to who on the sales team can be relied on for practical and predicable forecasts and which salespeople who are only good at "good guessing."
A few tactics to dramatically improve your forecasting
- Require everyone to use the same sales processes. Same process, same definitions, same tools and the same level of formality.
- Measure everything along the path of that process. Time, frequency of tool usage, variability of process, and ultimately, the accuracy of the various forecasts provided over the period of days and each month
- Record all of the data points along those sales processes and use them in your algorithms to assess accuracy and best practices being used.
- Employ only market-leading CRM platforms such as SFDC and MSCRM
- Require everyone to consistently use CRM in the same manner
- Yes, note the word, "require" above. This is a business, not a game
- Use highly integrated sales productivity platforms, such as Brainshark's Sales Accelerator that tie directly into CRM
- Measure the weekly trends of forecasting accuracy over time
- Refresh and share that data in real time to promote best practices
What to Measure?
One of our partners that we highly respect in the Sales Optimization Ecosystem is Insight Squared. For a number of years, Zorain Rotenberg, who co-authored this document on The CEO’s Guide to Sales and Marketing KPIs, has been an instructor at our semi-annual Sales Management Boot Camps. Zorian has since moved on to lead a new startup, but the guy's a genius at sales and marketing analytics. If you're considering improving your forecasting accuracy and wondering just what to measure along the path of your sales process, this is an excellent piece to read and to circulate among your sales team before you think about beginnng this journey.
Recommendation if I want to hit the Bullseye...
In a winter where we're all glued to the 6:00 PM news desperately trying to figure out everything from expected temperatures over the next five days to if and when we need to shovel our roofs, let's see if we can adapt some of the same data-based analytics and uniform processes used by the professional weather forecasters to impact the accuracy and predictability of our own sales forecasts.
My personal recommendation would be to take half of your sales meeting this coming Monday morning-after you've listened to everyone's end-of-February forecasts, and spend concentrated time discussing how you and the team could improved your forecast accuracy.
Get all of the improvement areas identified and listed on the whiteboard next Monday. Then during over the next two weeks of Monday morning meetings, put together a six month plan to begin to execute on this. Once, you get into the process, you'll quickly realize that it's not that difficult to achieve significantly improved predictability. All it takes is...
- defining the data,
- creating a uniform sales process,
- agreeing on the metrics to be measured
- figuring out a way to crunch the numbers
- and openly discussing trends and best practices.
Will your forecasting be perfect in six months? Probably not, but it will be a heck of a lot better than what you're doing now. Plus, you never want to suffer like Dilbert when the questions of accountability of your forecasts get asked.
Welcome to 2015. Going be a great year...as long as you're planning for it.