Forecasting sales for a B2B product category: case of auto component product

Purpose – The purpose of this paper is to improve the capability of managers to forecast revenues and develop marketing plans for B2B component products. Design/methodology/approach – The methodology used is a dynamic market simulation at the product level. A previously developed consumer goods speciality product forecasting model is extensively modified to capture the different parameters (i.e. direct selling) relevant to a business‐to‐business (B2B) component goods product category. A dynamic simulation is developed using a set of equations developed to capture the marketing mix. Using just the demand equation (total supply exogenous) and employing the entire model (supply endogenous), sales are predicted. Findings – The key findings are that the simulation produced more accurate (lower error) forecasts. The dynamic simulation for total demand for B2B auto components produced a mean absolute percentage error (MAPE) of 8.5 percent, comparing favorably with the average MAPEs of 30 percent achieved by 168 companies forecasting B2B products. Research limitations/implications – The main research limitation is that the model is limited to B2B component products. Practical implications – The practical implication of the model is that it improves the ability of marketing managers to successfully reach revenue targets. Originality/value – This improved ability adds value to the B2B component marketing manager's planning process by providing a method of specifying a marketing plan that is likely to result in revenue that achieves or exceeds the target revenue and knowledge of what marketing mix levels would move present sales to meet or exceed target.