SMART Forecasting Concept

Classic Consumer Adoption Process

Rogers, et al, demonstrated that innovators decide to adopt an innovation independently of the decisions of other individuals in a social system. We might ordinarily expect the first adopters to be innovators. Innovators will spread the idea to ultimate users or adopters. Five classes of adopters have been identified, based on the timing of adoption by the various groups:

Innovators have been described rather arbitrarily as the first 2½% of adopters; early adopters are the next 13½% who adopt the new idea; and so on.

Innovators interact with other innovators, are venturesome & daring, and are willing to try new ideas at some risk.
Early Adopters are guided by respect, are opinion leaders, and adopt new ideas early but carefully.
Early Majority are deliberate and adopt new ideas before the average person — although they rarely are leaders.
Late Majority are skeptical. They adopt an innovation only after a majority of people has tried it.
Laggards are tradition bound. They are suspicious of changes, mix with other tradition-bound people, and adopt the innovation only because it has now taken on a measure of tradition itself.

Relation Between Purchase Intention & Actual Adoption A proven model for adjusting stated purchase intention is utilized to improve the predictive accuracy of initial purchases of new technologies (Jamieson, Linda F. & Bass, Frank M., "Adjusting Stated Intention Measures to Predict Trial Purchase of New Products: A Comparison of Models & Methods," Journal of Marketing Research, August 1989). This model is based on the largest and most comprehensive database on purchase intention & actual purchase behavior for new products, such as home computers & cordless phones. This model takes into account that many factors will affect trial of new products, such as awareness, liking, availability & affordability. By providing a valid framework for including these variables rather than relying only on stated purchase intentions (e.g., the percentage of consumers who say they will "definitely" or "probably" acquire DSR), more accurate predictions of initial purchases are made. Rate of Diffusion The Bass model of diffusion is used to estimate the speed new technologies will be adopted (Bass, Frank M., "A New Product Growth Model for Consumer Durables," Management Science, January 1969). The Bass model of diffusion has been widely used to successfully predict the growth rate of numerous new & innovative technologies, including color TV, VCRs, telephone answering machines, overhead projectors, mainframe computers, direct broadcast satellite television, and recording media (records, tapes & CDs). The Bass model for forecasting first purchase has had a long history in marketing. It is most appropriate for forecasting sales of an innovation (more generally a new product) for which no closely competing alternatives exist in the marketplace. The Bass model offers a good starting point for forecasting the long-term sales pattern of new technologies and new durable products under two types of conditions: The firm has recently introduced the product or technology and has observed its sales for a few time periods, or The firm has not yet introduced the product or technology, but is similar in some way to existing products or technologies whose sales history is known. The basic assumption of the model is that the probability of a consumer's initial purchase is related linearly to the number of previous buyers. Buyers are composed of both innovators & imitators. The number of previous buyers does not influence innovators in the timing of their initial purchase, while imitators are strongly affected by the number of adopters. Innovators therefore have greater importance in the early stages of new product adoptions than after the product becomes more widely disseminated. This model implies exponential growth of initial purchases to a peak and then exponential decay. The slope of the diffusion curve is therefore a function of the rate that awareness of the new product is developed, as well as the product's appeal. The model attempts to predict how many customers will eventually adopt the new product and when they will adopt. Bass suggests that the likelihood (L(t)) that a customer will adopt an innovation at time t (given that the customer had not adopted before) could be characterized as: where...
	=	the number of customers who have already adopted the innovation by time t;
	=	parameter representing the total number of customers in the adopting target segment, all of whom will eventually adopt the product;
	=	the coefficient of innovation (or coefficient of external influence)
	=	coefficient of imitation (or coefficient of internal influence)
The equation above suggests that the likelihood that a customer will adopt at time t is the sum of two components. The first component (p) refers to a constant propensity to adopt that is independent of how many other customers have adopted the innovation before time t. The second component is proportional to the number of customers who have already adopted the innovation by time t and represents the extent of favorable interactions between the innovators and the other adopters of the product (imitators). After transforming the above equation into one that looks at the number of adopters at t (n(t)) we get... If q > p, then imitation effects dominate the innovation effects and the plot of n(t) against time (t) will have an inverted U shape. This is likely to be the case for new movies, new records, or such new technologies as cellular radios. On the other hand, if q < p, then innovation effects will dominate and the highest sales will occur at introduction and sales will decline in every period after that (e.g., blockbuster movies). Furthermore, the lower the value of p, the longer it takes to realize sales growth for innovation. When both p and q are large, product sales take off rapidly and fall off quickly after reaching a maximum. Bass, Krishnan, and Jain (1994) proposed a general form of the equation that incorporates the effects of marketing-mix variables on the likelihood of adoption, so that by increasing marketing effort, a firm can increase the likelihood of adoption of the innovation — that is, marketing effort (increasing advertising or decreasing price) speeds up the rate of diffusion in the population.