Forecasting Without Data

 

When it comes to preparing a forecast for a new product that has no historical or corollary data, pure analytical techniques don't help much. So what do you do when you have a new product/service idea and are trying to figure out how much of it you can sell?

The answer is to develop a disciplined, methodical process that best identifies and approximates the many influencing factors and market conditions facing your product launch. Here are a few approaches to consider:

 

Delphi Method
This method utilizes several expert panels (functional or cross-functional; preferably a mix of insiders and outsiders) who are asked to provide a forecast. Each forecast is weighted equally and the results are shared between panels, who then continue the discussion in an effort to fine-tune their forecast with points raised by other panels. If the panels' forecasts begin to converge, a final forecast can be drawn from the average of all the forecasts.

For an expanded definition and explanation of the Delphi Method, click here.

BASES Model
If you have a new product to be distributed in supermarkets, drug stores, or discount stores, you might find that ACNielsen's BASES database of 25 years of product launch history might prove to be a good starting point. The methodology looks at thousands of products with similar characteristics, consumer receptivity, and marketing plans, and forecasts sales under flexible sets of assumptions. Unfortunately, BASES is limited to just these kinds of products. It doesn't help with new paints, sporting goods, electronics, etc.

For more information, click here.

Diffusion Model
This widely used mathematical model uses rates of product adoption and usage spread from other established products to predict the rate of new product acceptance into the market. (See Forecasting Market Size for a Unique New Product.) It is very applicable to both durable and non-durable goods across a wide variety of categories.

Monte Carlo Simulation
This analytical method, used in conjunction with a spreadsheet, is a form of "what-if" analysis. The name is derived from Monte Carlo, Monaco, known for its casinos and games of chance. Monte Carlo simulation is based on the probabilities of an outcome, much like the probabilities of rolling dice, and the predicted outcomes that are expected over time. Monte Carlo methods randomly select values from a defined set of possible outcomes to create scenarios of a problem. The process is repeated many thousands of times (via computer simulation) to establish probabilities of various outcomes. (See Just Give Me A Number: How to Avoid the Flaw-of-Averages.)

Agent Simulation
Monte Carlo on steroids, Agent Simulations are highly advanced statistical models that identify and isolate the likely behavior of "agents" who are integral to the success of the product (e.g., consumer segments, competitors, distributors, etc.) based upon sets of rules found to explain past behaviors. Used for everything from air traffic modeling to war games, these sophisticated tools are very insightful where there are a large number of variable behaviors among a moderate number of agent parties.

These approaches can be combined and supplemented with market research to develop a custom process that best suits your needs. Be careful to check your preferred approach with someone experienced in new product forecasting before you go too far down the road to ensure your efforts are sound before you invest too much of your time, money, or reputation.

Also see Forecasting With Data.

For more information on forecasting without data, see New Product Forecasting.

For additional information and analysis of forecasting applications, see The Forecasting Report.

For forecasting software reviews, see the Forecasting Software Review Database.