Forecasting New Product Potential Market

Author Name(s): *Alireza Mamaghani, Gang Zong
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New-product plansneed dynamic processes to forecast market behavior during product life time. Thispaper intent to present an innovated model to predictnew product potential market by renewed method. Accordingly, the model parameters organized by product attribute values and developed by analogical insight based on the adoption patterns through Bass modelover product life time.The findings highlighted that the suggested model is reliable enough for forecasting optimum demand of potential market without any market historyin pre-launch phase. Therefore, the results provide valuable viewpoint for planners to investigate product attributes and evaluateeffective parameters respect to the optimizing market size on product life time.


BassModel, Bass Coefficient’s Estimation, Automotive Pre-launch Forecasting.


Firms needs to adjust their products to keep customer advantagesaccording to changes in customers’ needsvia products development in potential market. New products demand Forecasting is an iterative method for calculate approximately the number of a product or quantity of services that the customer will purchase or consume [1]. Anticipate of demands which include the formal and informal procedure of forecasting used in areas such as making pricing decisions, assessing the future requirements capacity or making decisions on whether to implement new production line for the new target market. It is essential for companies in particular to produce the required quantities at the right time and arrange well in advance for the several elements in the production processes [2].

Evaluation of product diffusion is the procedure to know whether a new idea or new product is successful in market or not. The concept of diffusion still remains complex phenomena and it is aninnovative way to findthe rate of adoption process which is the speed of the new idea broughtfrom one consumer to the next. Adoption (the reciprocal process as viewed from a consumer viewpoint rather than distributor) is similar to diffusion but it deals with the psychological processes an individual goes through rather than a cumulative market process. Thus, various approaches, methods and computer models have been developed to investigate the market diffusion of new products.

Theory of innovation diffusion investigated by researchers of consumer behavior, marketing management, economics and management etc. to find in what way a novel idea of a new product and/or a new service isaccepted among the members of its potential consumer population over time [3] Although the above studies ofpotential market provide valuable viewpoints inestimating new products adoption rate, howevermost of them are based on qualitative estimates or focused on the technology aspects only. This paper intends to use a mathematical modeling approach to consider the market scope, user adoption behavior, and future outlines of previous technologies which are making product development in target market. In this regard, forecasting market diffusion has been verified as anauthoritative tool for predicting the market for new products.Furthermore, these models can estimate market potential and describe how the new product will penetrate in market as well. The model results provide important information for marketing plans to implement their strategy with respect to market behavior.

In the following, the second section summarizes the literature review and previous findings also the model and methodology of research illustrated in the third section then the model findings and sensitivity analysis results are presented in the last section of this study. Generally, new product demand or potential market forecasting is a process that describes sales rates under a given set of conditionsjust like an estimating game result. New product can be explained by various concepts to different people and can be classified into six dissimilar categories which are new-to-the world, new-to-the firm orsettingnew product line, developing or modifying the existing products, repositioning and cost reductions. Forecasting potential market of new product is similar todemand forecasting of new product.Sometimes, instead of demand complexity because of local strategy or monopoly reasons it might beeasier to forecast. In short, modelsarethe best way for modeling the conditions and concepts to estimate the theories.

Bass Model (BM) (1969) stems mathematically from the social behavior. The assumptions are similar in certain respect to the theoretical concepts emerging in the literature on adoption and diffusion of new-product as well as in the application categories [4]. The model which is investigated in this paper differs from other models in term of the assumptions are explicated.In addition the model presents a rationale algorithm for calculating new product demand. Therefore it has found wide-ranging applications in many industries.

The Bass model is the most common diffusion model used in marketing and it is a mixed model capturing both innovative and imitative effects. Themodel assumed that the potential adopters are influenced by innovation and imitator behavior [5].

In automobile industry during the elapsed few decades, the number of automakers increased by more than 50% because of diversity of vehicle types produced by companies and some of them joint together for making core-competence. This variety provides complexity in market models respect to innovator and imitators behavior and make higher car demands.

The past studies in this industry merely focus on the value of customer only by investigating on customers’ purchase history (Gupta & Lehmann, 2003; Ho, Park, & Zhou, 2006, Gupta, Mela , & Vidal-Sanz, 2006).Otherstudies consideredthe customer behaviourin life cycle whereas several studies have Investigated Adoption rate in the technology and market changes. The previous studies based on Bass model in automobile industry investigated by  Dimmick, Patterson, & Albarran, (1992)hadmodifiedBM model to Nich markets and some studies attempt to forecast the diffusion of new technology and demand in a market by BM. Kim, Shin, & Park, (2009); Radojicic & Markovic, (2009)and Litman, (2014)predicted the implementation analogy of vehicles based on previous vehiclerelated technologies (automatic transmission, air bags, hybrid vehicles, subscription vehicle services and vehicle navigation systems) changes in market rate.Jensen et al. (2014)used Norwegian new car registration data from 2003 to 2013 to estimate the electronic Vehicles demand model in Denmark market, and studied these findings directly aslifestyle and economic conditions of the two countries were generally very similar. Furthermore, Park, Kim, & Lee, (2011) used Japanese sale volume of HEVs to estimate diffusion model for HFCV in Korea byadjustingthe imitation parameter based on other previously estimated models in the two countries.

However, the past studies investigated the market but Bass parameters haven’t used as exclusive method for synthesis forecasting market diffusion whereasthe diffusion model represented in previous studies forecasted the parameters in Bass model by the product market data history over continues time for local markets. This studyaims to fill this gap,as limited studies beencited in theliterature on new product vehicle demand forecasting, this paper attempts to improve bass model by parameter analogy estimation in potential market and forecasting new vehicle demand with optimizing potential market size bythefollowing methodology.


This research used basic Bass model to analysis new brand diffusion rate and estimate marketdemand behavior respect to product features.Hence, we have found some similar products (for different vehicle brands) to make analogy through their diffusion models and forecast optimum market size respect to new brand life time base on sensitive analysis in different potential marketdimensions.

Theproposed studyusefulto the literature by providing a quantitative approach to new vehicle lifeand estimate market penetration based on product attributes by adoption experiences. Research results showed that p, q and M significantly effects on product life time and the annual sales increase up to its highestsales rateatearlier point of product half-lifetime.Furthermore, two coefficients p and M in model which have positive effect onraising product demand (potential sale curve) over time. In addition, the parameter estimates fitness derived from ordinary least square regression providesreliable descriptions for the new product penetration sale in market. Meanwhile, Sensitive analysis results haven’t investigated any significant relations among three category of peak time, potential market size and new product time life. Therefore, the study outcomes provide valuable insights in terms of the possible market diffusion patterns and the impacts of different factors on user adoption.

For covering presentresearch limitations, future researches investigate model development by updating the assumption in internal variables effects on the Bass model and develop model by considering external variables to forecast time life changes over time.

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