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What Sales Forecasting & Jumbo Shrimp Have In Common

Authored by Barry Trailer on August 22, 2017

Barry Trailer is the Research Principal and Co-Founder of CSO Insights.

The number 1 metric used to measure (and reward) the performance of sales managers is their teams making their number.  Number 5 on the list is forecast accuracy. No wonder “forecast accuracy” continues to be an oxymoron, that is, a contradiction of terms (ala jumbo shrimp).

Who cares, so long as you’re making your number? In the chart below, the performance of 2 companies (A & B) is shown over a 3-year period, versus the straight line representing quota. Company A’s performance is ±50%, while Company B’s is ±5%--which means both companies made their number. However, if these were publicly held companies, B could have 3 or 4 times the market cap of A, on the exact same revenues.  If privately held, lenders would want to lend Company B more money, for longer periods on more favorable terms. Why? B is more predictable.

Assuming these companies were in the same industry (i.e., same seasonal variance, relatively same size deals, etc.), B would be more productive and more profitable. Company A would have more rush shipments, more overtime, more sick time, and generally higher expenses simply because of the volatility and lack of consistency.

I talked about the importance of sales process in my prior blog. It could also be said that Company B has its processes more under control; Company B has less variance.

How would you like to be the CFO of Company A? How would you give guidance prior to the end of a period? As the head of HR, would you staff for the peaks and risk overstaffing, or the valleys, risking delays and massive overtime?

If it takes 6 months to recruit, hire and train a product specialist, as CEO, would you authorize beginning that process 6 months in advance of actual sales based on the sales forecast? If not, would you risk poor customer satisfaction ratings downstream because customer implementations are delayed or poorly implemented? Poor sales forecasting takes the bat out of every other functional leader’s hands. Profitability can be lost as well.

Making the Number versus Making the Forecast

As stated above, it’s all about making the number, right? Maybe, but there’s a difference between making the number and making your forecast.

For example, a rep has a quarterly number of $400K. The forecast shows 5 deals (A, B, C, D & E) each averaging $150K and each with a 60% probability of closing (with a win). First, what does “60%” mean? Sounds better than 50/50 but the rep’s not going out on a limb and committing to 90%. Without a defined sales process and buyer evidence “toll gates,” one rep’s 60% could be another’s 30% (whatever that means). But as this rep’s manager is thinking, 5 X $150K X 60% = $450K, so we look OK.

Second, what closes is B for $75K and surprise mega-deal H for $350K. Final quarterly number: $425K. Hooray, ring the bell, made the number! This “hero” is in a flat spin, completely out of control. She made her number but blew the forecast completely.  Again, who cares?  Here’s one example.

A capital equipment manufacturer in the semiconductor industry made a test machine that sold for $2.5M. The basic box was the same but Samsung, Phillips, AMD, etc., would each have custom requirements. In January, an order would be forecast for 4 machines for Samsung to be delivered in April. In early March, Samsung says, we want those boxes but now don’t need them until August.

Sales hustles around and finds new orders for 2 machines with Phillips and 2 with AMD. The customization for Samsung is undone and the new client specs begin being built. Mid-March, Phillips says we only need 1 and AMD pushes their 2 off until Q3 at the earliest.  A new home is found for the 3 orphaned machines at Intel, which agrees to accelerate a July order for 3 machines but at a discount to take them before the end of April. Again, sales’ heroic efforts are celebrated but all the profit from the 3 machines has been eroded. Sales made their number but blew their forecast.

There are other ways that “forecast” inaccuracies can hurt the bottom line. For now, look closely at your sales process, how your managers reinforce/enforce this process, and how their review uses this to improve “forecast accuracy” beyond numeric mumbo jumbo—or shrimp gumbo.

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