Forecasting is hard, even when a significant amount of historical data is available. When historical data is limited, forecasting is much harder. But then, what about forecasting when there is simply no historical data?

The no-data situation is more frequent that it looks: for every product launch, a company has to forecast future sales for the new product, while there are precisely no records for this product.

In practice, we have found that many companies - already using robust statistical tools to forecast their regular sales - just guesstimate when it comes to product launches (or one-shot promotions). We have also found that, in many situations, guesstimates are vastly inaccurate.

Obviously, if there is absolutely no historical data, then, indeed, statistical forecasting tools (such as Lokad) are powerless. Yet, in most companies, new products are launched on a regular basis, and this history of launches can be analyzed to figure out patterns of early sales.

Lokad takes advantage of historical product launches (when such data is available) to forecast the sales of a product even if there are no data yet for this particular product. Typically, we estimate that 20 product launches or so are needed to start learning launch patterns. In practice, there is no hard-coded lower limit on the number of product launches in our technology, but with less 20 launches, forecasts tend to become erratic.

In practice, you can use the Safety Stock Calculator to forecast product launches. Note that raw sales data is not enough in the case of product launches, tags and events are needed as well (well, at least tags or events):

  • Tags should be provided in order to describe the product. Tags typically express similarities that exist between products (ex: color, size, category, product family, …). Those tags are used by Lokad to match the new product with existing ones. Typically, a tag is a permanent descriptor of the product: it does not change over time.
  • Events should be (eventually) provided to describe the launch operation itself. Events are just like tags, but positioned at a certain date. Events typically represent marketing operations that support the product launch. An event usually has a lifetime shorter than the product itself (otherwise it should be considered a tag).

The distinction between tags and events helps Lokad to figure the relative position of the product within the distribution channels of the company (tags), from the impact of the marketing operations themselves (events).

Reader Comments (2)

My point is that forecasting sales at launch-time is neither an art nor rocket science. If you “guesstimate” you don’t have any chance to improve your forecast accuracy over time (as you gain experience with product launch) because there is no process. Hence, we suggest to use statistical approaches as it tends to be way more accurate if relevant data happens to be available. 8 years ago | Joannes Vermorel

You say Guesstimates….then how do you think they would be accurate in any way ?? 8 years ago | Ergo Baby Carrier