Forecasting is a thankless job. It’s a lot like being a referee or umpire in your favorite sport; the only time a sports game official is noticed is when they do something wrong! Similarly, a forecaster’s primary aim is to stay out of the internal company “news”. Unfortunately, sales forecasting methods for new tech products don’t always allow the desired anonymity.
Make no mistake, Sales forecasting is a very important function in any business. In the software business, your whole business plan could be riding on meeting the forecast. That’s because it allows you to fund revenue growth and new product development. In a hardware business, it’s an even more critical issue. For example, you have to worry about creating too much or too little inventory. Either of these can create a massive problem for your business.
Forecasting is hard in the best of circumstances
It’s bad enough when you are trying to forecast an existing mature product in a mature industry segment. This itself is a difficult and complex task. Even when using well-known techniques such as smoothing, trending, and seasonalit. Fine-tuning the next monthly or annual forecast is always a difficult task.
Early in my career at Hewlett Packard, I spent 4 months on a special assignment dedicated solely to improving our product forecast accuracy. The marketing department was engaged in an ongoing “discussion” (OK-an argument!) with the manufacturing department over inventory levels. Not surprisingly, manufacturing wanted the inventory levels to be lean. Marketing favored a more robust number. This was because manufacturing was being graded on their costs and at that time “owned” the inventory. Marketing was graded on revenue. Low inventory levels often lead to missed sales opportunities and lower revenue.
I became a Lotus spreadsheet guru (the dominant spreadsheet at the time)! We used every sales forecasting method we could find to try to improve our forecast accuracy. Keep in mind that these were high-tech products (computer printers), which can have steep growth curves. But they also were successful product lines with significant historical data available. Try as we might, the best we could ever do was to get within +/-25% of the eventual actual unit sales numbers.
New technology products are the worst-case scenario for forecasters
The main message here is that forecasting any product in high-tech industries is very difficult. I was working on fairly established products, and we still struggled. Forecasting accurately the performance of NEW PRODUCTS with no historical data in technology markets is nearly impossible, IMO. Then add in brutal competition, a tight market research budget, vague notions of market size, an early stage on the user acceptance curve, and often the reality of an unknown brand. Forecasters of new technology products better be able to handle stress well. Otherwise, they may end up in substance abuse clinics. But of course, even though it’s hard, it’s still VERY important. So what’s a forecaster to do?
There are two basic methodologies that I typically utilize when attempting to forecast sales for a completely new technology product:
Top-down sales forecasting methods
The first approach that I usually employ is the “top-down” method. You might also call this the “macro” approach. This is an exercise of defining the size of your total addressable market first. This is done via existing market research or by estimating the total number of potential users. Also, estimate what a reasonable market share will be for your product. Make sure to consider all attributes of your market position in formulating a realistic potential market share estimate. Consider everything you can in your share analysis. Include your marketing budget, brand strength, an unbiased view of how your product stacks up vs. the competition, etc.
It may be helpful to put it all in a spreadsheet. Quantify the various important attributes of your company/product vs. your competition. Be careful about assigning too much precision to these numbers, however. Remember that garbage in equals garbage out! Your assigned numbers are by definition imprecise in this exercise. But if you go through this process thoughtfully, it can be very helpful in analyzing your relative market position. Obtaining your top-down forecast is then as easy as multiplying the market share % you believe you can obtain times the market size that you came up with via research.
Bottoms-up sales forecasting methods
After I’ve done the top-down or macro forecast, I also use a “bottoms-up” or “micro” approach as a sanity check. To do this, you first want to gather practical information on what you think you can sell. Do this by canvassing individual stakeholders in the sales arena: direct field sales reps, e-commerce retailers, traditional dealers, international distributors, etc. It’s helpful to gather info from any channel that will be a significant contributor to sales for this new product.
Usually, it’s impractical to do a complete survey of everyone who may be involved in the sales effort. What’s important is to obtain a representative sample that is both broad enough and deep enough that the data you gather has at least some statistical significance. At that point, you can “normalize” the data. As an example, say you were able to gather data from a broad cross-section of sales points, totaling approximately 10% of the total sales infrastructure (10% of dealers, 10% of direct sales reps, etc.). You would then multiply the total number of units/dollars you obtained from your sales entities times 10, to reach a bottoms-up forecast totaling 100%.
Do you have forecast convergence?
The key to this exercise is to discover whether your two views of the market are close enough that they appear to be focusing on the same topic! If they do, you may be in pretty good shape with your forecast. If they are off by an order of magnitude, or two, it’s probably time to reconsider some of your assumptions and fine-tune your projections.
So that’s my advice on how to approach the unenviable task of forecasting a completely new technology product. It’s a high-risk, high-return activity under the best of circumstances. And ideal conditions are seldom found in this activity in the technology market. But if you are able to construct both a top-down and a bottoms-up forecast and the two numbers at least fall in the same ballpark, you’re probably on the right track.
Give it a shot yourself next time you’re faced with a daunting new product forecast. Feel free to shoot me an email with your questions, or leave a comment to extend this discussion on sales forecasting methods.
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