New product sales forecasting without past sales data

New product sales forecasting without past sales data

New product sales forecasting without past sales data The most difficult task a forecaster is asked to perform is that of projecting sales for new pro...

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New product sales forecasting without past sales data The most difficult task a forecaster is asked to perform is that of projecting sales for new products. This paper reviews various types of approaches to building forecasting models that can be used to forecast sales prior to the product introduction. The paper is not a comprehensive review of all models, but the models included does represent the range of types of approaches.

Michael D. G E U R T S Department of Business Management, Brigham Young University, IVovo, tiT 84602, U.S.A. James E. R E I N M U T H College of Business, University of Oregon, Eugene, OR 9 7403, U.5.A.

Received April 1978 Revised September 1978

Problems in forecasting

A common approach is to utilize past sales of similar products to estimate new product sales. However, the product differences and the difference i1.~circumstances often lead to large inaccuracies in the sales estimation process. A second difficulty in forecasting sales for a new product is competitor actions. With many new product introductions, there is a tendency for competition to intensify its marketing efforts in selling existing products. These increased efforts may substantially reduce the new product sales. The forecaster must therefore anticipate competitor reactions and their probable effect on the new product sales. The third problem encountered in forecasting new products is determining the effect planned promotions will have on sales. Often the new product will receive an extraordinarily large amount of salesmen's time and promotional funds during its introductory stage. Yet the outcome may not be commensurate with these input resources. The introduction of a new product should be viewed by the forecaster as an atypical period shrouded in uncertainty. The uniqueness of the new offering requires careful and specific analysis to create a success. ful sales forecast.

1. Introduction

Thousands of new products are offered to consumers each year. In 1961, O'Meara reported that the failure rate of new produ,ts was 80% (~ee [ 10]). An article in the March 2, 1972 issue of Business Week found that the failure rate remained at 8 out of 10 (see [91). The capital, materials, time, and human effort used in producing 80% of the new products are wasted. If the farmers in America wasted 80% of their crops, it wc uld be a national scandal. If lumber companies only used lumber from two of every ten trees logged, env2~onmentalist.~ would have the public in an uproar. It's ,~nthinkable to put 80% of our resources to unproductive processes. Yet, that is exactly what happens in the marketing of new products. Particularly unpalatable is the fact that many resources wasted in new product marketing are nonrenewable. Time, capital, and thinking energies can never be renewed. The reasons for the failure of new product offerings are varied and include poor marketing, poor product design, and/or bad timing. Whatever the reason, if the company had had an accurate sales forecast, most of these products would never been marketed. Forecasting wlfich overestimates sales can lead a company to lose money through overproduction. The equally tragic experience of underestimation prevents a potentially profitable product from being fully marketed.

The adoption process

Sociologists have developed a theory dealing with how and why individuals accept a new idea or product [ 11 ]. This theory focuses on how new products and new ideas flow through a group of individuals. They have found that there are new product characteristics that accelerate the adoption process. Since it is the

© North-HollandPubLishingCompany European Journal of Operational Research4 (1980) 84-94 84

M.D. Geurts, ZE. Reinmuth / New product salesforecasting

forecaster's goal to successfully predict the rate of acceptance of new products, an awareness of these traits is extremely useful. Briefly, they are: (1) Readily perceived benefits by prospective buyers which can easily be explained by sales personnel and advertising media. (2) Capability of being used on a trial basis rather than a complete conversion to the new product. (3) Compatibility when existing life styles. (4) Use is easily understood - technical knowledge not needed for operation. The forecaster must evaluate his product in light of the above characteristics to determine the quantity of sales that can be expected.

2. Forecasting without a relevant data base

Much has been written on forecasting sales, demand, or the outcome of any other time series when a historical data base for the time series is available. However, very little appears on forecasting in the absence of a historical data base. Two common cases illustrating this type of forecasting problem are the introduction of an entirely new product and the introduction of a product into a new market area where the market conditions are sufficiently different from current market areas. The latter illustration suggests that historical data is available for the product, but that the data is not relevant to the current market study. A third illustration relates to a product which has been on the market for a long time but which its m a r ket environment has suddenly changed. That is, a historical data base is available but soon becomes suddenly worthless. Take, for example, the sales of certain large automobiles in the face of today's energy crisis. Can Ford Motor Company expect demand for their large station wagons to continue to follow his. torical sales patterns in light of new trends in advertising, inflation, per capita income, etc.? Certainly not. Ford is suddenly in a position where much of the past sales data is irrelevant for forecasting sales of new ears. With the volatility of today's economy, numerous forecasting models relying on a historical data base must be constantly reevaluated. As forecasts are esseno tial for budgeting, planning, and control, they must be clear reflections of the latest information and not simply an evaluation based upon outdated, historical relationships. Now more than ever before, company forecasting models require periodic examination to

85

determine their relevance in light of the firm's economic and market environment. There are four common forecasting schemes which can be employed without an available historical data base. Associated with each scheme will be an analysis of the inherent problems associated with the application of the method. Short examples are offered to motivate the use of the methods. The noted success in application of each method w ~ also be reported for short term forecasts (0-.~ months), medium term forecasts (3 months-1 year), and long term forecasts (greater than 1 year). The panel consensus method

This technique assumes that the organization contains experts who have special knowledge and experience enabling them to effectively evaluate the uncertain effects of the future. It further assumes that these experts will recognize one another's special areas of expertise and, by supplementing each other's knowledge, arrive at a consensus as to the company forecast. For instance, a company marketing director has special knowledge regarding competitors, distribution channels, and social attitudes of the buyers toward the product of interest. The firm's economist evaluates the effects of pricing strategies, advertising, promotion, and the impact of national economic policies on company sales. The sales manager will have special insight into salesforce motivation and other activities. Each recognizes the special competence of the others and, thus, effectively assimilates the evidence. This in turn causes each member of the panel to modify his sales e:;timate in light of the evidence supplied such that u!ttimately a consensus sales estimate is agreed upon by all members of the panel. One obvious difficulty of the panel consensus method is that personality factors may make a consensus agreement impossible. Perhaps certain members of the panel are simply not willing to compromise. Further, a hierarchial bias may exist within the group° Suppose a parcel consists of the company president and mid-level line and staff employees. A lower level expert may be quite reluctant to criticize the president's position. Perhaps the president's best interest are served by adhering to a specific forecast value in the hope of generating additional equity capital into the firm. Should his f~-ecast prove overly optimistic, he can, in the end, i:~tame the sales force and absolve himself of responsi~i~ity. He is ultimately responsible.

~ M.D. Geurts, J.E. Reinmuth ] New product salesforecasting

The panel consensus method has generally proven poor to fair for short or medium term forecasts, and has also typically shown poor results in long term fore,~asting exercises. The Delphi method

A refinement of the panel consensus method of forecasting is the Delphi method [7]. It is a method of forecasting using a group of experts who make individual forecasts. There is no group meeting;, as a result, the forecasters are not influenced by personalities or group membership in making their forecasts. Generally a pooling of the forecasts is conducted by a mediator who then sends ot~t the results of the first attempted forecast. The forecasts receive information from the mediator and are asked to make a second revised forecast and to explain reasons for their forecasted values. The process can continue a number of times until a consensus is reached or the forecasters no longer change their forecasts. The primary advantage of the procedure is that through the mediator the forecasters exchange information but their geographic distance prevents social pressure from developing to influence the forecasts. Historical analogy

This approach assumes similar products exist that have preceded our product and that the firm can use the sales history of the previous product to gauge the success of the present product. A natural assumption underlying this approach is that the earlier product had a similar economic and market environment during its introductory stage as does the current product. !f thi:; assumption is not valid, there is no justification for the analogy. Consider, for instance, a company which produces and markets packaged breakfast foods. Suppose in early 1972, this firm introduced a candycoated breakfast food called Sugadeops. A certain sales record was observed in 1972 that thi~ company wishes to use as an analogy for the e×pected 1978 sales of a similar product they will call Oaties Under what conditions is such a historical analogy justified? One must consider similarities of economic conditions, market conditions, competitive factors, and the psycholo~/of buyer behavior. With all other factors stable, the competitive factor will be somewhat different as the new product is now competing with Sugafloops wh~:h was not an established competitor on tile market ka 1972.

Perhaps subtle changes like a higher milk price in 1978 than in 1972 has caused buyers to disregard breakfast foods requiring milk in favor of a breakfast of fruit and toast or eggs. Once the changing factors have been identified, historical data must be modified to accomodate the influence of fluctuating environmental conditions. However, the varying behavior of too many economic and market conditions may render historical data useless, reducing the task to a judgmental estimate or a consensus opinion from a pane! of experts. When the comparable time series relates to a tempe'ring company (not one of your company's prior product introductions) the task is even more complex. Now one must consider the possible impact of differ¢r~t dist.dhuti_onal channels, pricing strategies, brand lo~'alty of the competing company's products versus that of yours, as well as all the other factors mentioned earlier. Historical analogies are extremely risky endeavors, but generally produce fair to good results in medium and long term forecasts, not ~o much because the method itself is foolproof, but because users are usually very judicious in its application. That is, when underlying conditions surrounding a certain historical series are clearly different from those associated with a new product, a historical analogy is not undertaken. Because of the unpredictable activity of most products shortly after introduction, a historical analogy usually produces poor results for short term forecasts, even though underlying conditions may be stable. Models used ]'or forecasting new products

Due to the large number of variables that must be considered in forecasting new products, many models have been created. Most models are specifically applicable to individual products or situations. A description and example using several models follows. These examples are intended to give the user an overview and introduction to the various approaches used to construct individual models. For the mathematical models, an example is worked out using one of two products. One example is an electric food dryer. This is a consumer durable type product which ~emoves moisture from fruits and vegetables. Actual sales for the first year turned out to be an average 12 per month. Another example that will be used in L'eggs pantyhose. Data for this product are derived from L'eggs Products, Inc. (A), (B) and (C) (see Harvard Business School Cases, 1974). Since

M.D. Geurts,J.E. Reinmuth / New product salesforecasting the product's introduction in 1970, L'eggs has accounted for over 25% of the hosiery volume done by retail food and drug outlets and has a total market share of between 5 and 6%. Total annual market potential is approximately 1.35 billion pairs.

Fourt and l¢oodlock This model was proposed in 1960 and was developed to forecast the sales of grocery products [4]. The model views the penetration of new products as the paramount variable to use in forecasting sales. There is a maximum penetration possible. As a firm's sales increase, potential declines. The object of the forecast is to predict the increase in penetration Qt, where:

Qt=rQ.(1 - r ) t-I , m

constant; Q is the potential sales expressed as a proportion of all buyers who are expected to buy; t - 1 is last time period; t is current period. From the above, it can be seen that Qt, the forecast of incremental sales, is a function of two variables; r and Q . If a forecaster estimated that 30% of the potential market would eventually adopt the product, then Q would equal 0.3. If the forecaster also estimated that during each period 50% of the non-triers would try the product (r = 0.5), then in the first period the forecast of initial buyers would be" - r)

= r0 = (0.5×0.3)

practice the value of Q is always substantially less. The major limitation of the Fourt and Woodlock model is that it ignores many significant variables affecting sales. For example, the economic environment, the number and strength of competitors, promotional expenditures and the characteristics of the products can all have a significant impact on market penetration. Their model assumes sales are a function of time only. The usefulness of this model is that it identifie~ significant variable, i.e., potential penetration as a variable to be contended with in developing other models. Fourt and Woodlock also did empirical work to estimate penetration rates which can be useful in model building.

Fourt model forecast of sales for the food dryer

r is the penetration rate for potential sales and is a

= r0(1

87

= 0.15.

For period one, the forecaster would estimate that 15% of the potential buyers would try the product. For the second period, the forecast of triers would be" Q2 = (0.5X0.3)(1 - 0.5) 2-1 = (0.15X0.5) 1 = 0.075, 7.5% of the potential buyers would in fact buy the product. The forecast can b ~=extended for an infinite number of periods, but as t increases, Qt decreases. After a number of periods, Qt approaches 0. In order to translate ,*he number of new triers into actual sa!es for the period, tke f~iecaster has to estimate the proportion of one-time triers who will buy a second time and the length of time between purchases. The real difficulty of this model is i~ estimating Q and r. Initially, these, uada01es must bc subjectively estimated. Only after sales have occurred can the fo~:ecaster use data to estimate the variable r, Q can never be estimated from past data. In theory, Q is potentially equal to the total population. However, in

Subjective estimate that potential market is 500,000. Subjective estimate that 10% would eventually buy the product. Estimate that 0.5% would try who had not tried each month, then r = 0.005,

Q = 0.10

and

Q = 500,000.

The proportion buying for the first month would be given by: Ql - rQ(1 - r) t-1 = (0.005)(0.10)(0.955) 1-1 , Ql = 0.005 X 0.10 - 0.0005, (of all the potential buyers, 0.005 will buy the first month). Sales for the first period would then be Q, times Q or 0.0005 X 500,000 = 250. The sales forecast for the first month would be 250. During the second month the penetration rate Qz would be 0.0005 X (0.10)(0.995) 2-~ = 0,0004975. Sales for the second month would be f~)recasted as 0.004975 X 500,000 = 248.

The Bass model This model was developed to forecast sales of durable consumer products [2]. Durable products like refrigerators are long lasting, hence there tends to be very little repeat buying. It is a type of "epide, rfic" model in that the basic assumption is that product use spreads like a disease. Buyers expose others to use of the new product. Bass's model focu~e~ on measuring the proportion of people who are inrmvators and the

88

M.D. Geurts, £E. Reinmuth / New product salesforecasting

proportion of people who are imitati,~rs of these innovators. The Bass model is a probabilistic model. The important variable is the probability of a purchase for those who have not already purchased the product being made during time T. That is, the probability that someone who will ultimately buy the article ectually buys it in period T. This variable, by def'mition is P(T), and is dependent on the number of past purchases: 1'(2") ffi p + (q/m) r(T)

where the parameters p and q/ra are constants; p is fraction of all adopters who are innovators and q/m times Y(T) reflects the proportion of the population that are imitators. Y(T) is number of past buyers. Bass labels p as the coefficient of innovation and q as the c~fficient of imitation. The forecasting equation of period T is: $(T) = pm + (q - p)Y(T) - q/m[y(T)] 2.

S(T) is sales during period T expressed in units and m is the quantity of product expected to be purchased during the time period under consideration (i.e., the life of t~.e product). Since p, q and m are constants and Y(T) is history, the problem is to estimate p, q and m. This estimation can be done from the p, q and m of other products or after the product has been on the market for 3 or more years, past scales data can be used. Bass has derived p, q and m values; for a large number of products these values are listed in hi,~article I2]. B~s tested his model on color TV sales. He obtained an estimate of m, p and q using the first 3 years of sales. His estimates were: q = 0.67,

p = 0.018,

m = 37.4.

The technique for estimating these variables is to use regression ST = a + b YT-~ + c Y~,_l,

for T = 2 and 3.

ST = sales at time T, and I-:I

si;. These are the accumulated sales up to time T. The coefficient " , " estimates pm, "b" estimates q - p , and "c" esti:aates - q / m . In the color TV forecast, the model was ai)le to do a reasonably accurate job of forecasting. Bas:iwas very accurate for the years 19681970; howei~er, in later years, color TV sales rose and

then fell giving a continued cyclical pattern. There are drawbacks in the Bass model. It ignores several important variables such as marketing efforts, economic conditions and consumer attitudes. An example of a consumer durable that ~ould probably have been overforecast using the Bass model is the trash compactor. Consuwer attitudes toward this product have been very unenthusiastic. Innovators adopted while imitators ignored the product. In addition, the poor economic conditions of 1974 and 1975 had a dramatic effect on most durables, including trash compactors. The Bass model o f food dryer sales The forecast equation is: S(T) = pm + (q - p) Y(T) - q/m[Y(T)] 2 .

Using Bass's values for p, m and q for home freezers, derived from research, p = 0.018,

q = 0.17,

m = 21,973.

The second year forecast yielded S(T)= 396 + (0.152)(396)- 21,973 ~ [156,8!6] = = 455/year or 38/month. The Massy model

This model is designed to forecast the sales of new products which are consumer convenience goods [8]. Convenience goods are products which will be purchased by consumers every few weeks or months. Sales for the period t can be estimated by a formula such as: S t -- SFtlVFt ÷ SltlVlt ÷ S 2 t N 2 t ÷ "'" ÷ ~itlVi~

where Sit = average purchase volume of buyers in the ith repeat purchase class at time t and Nit = number of repeat buyers in the ith repeat purchase class at time t. Massy calls his model STEAM, whichstands for Stochastic Evolutionary Adoption Model. The model is based on measuring the degree of product loyalty that is developed after the initia1 purchase. Panel data is used to establish the S and N variables. Panel data is data gathered by having several families (the panel) keep a diary of all their purchases over a period of time. The families are usually cc,mpensated for their diaries. The process is to make a statistical inference of the future sales volume during period t + n by examining a sample of specific consumer buying behavior in past

M.D. Geurts,J.E. Reinmuth / New product salesforecasting period t - 1, t - 2, t - 3, ..., t - m. The buyer behavior is quantified by frequency and quantity of purchase. The procedure is theoretically quite simple and straightforward. However, the statistical procedure is somewhat complex and tedious requiring a computer ~or execution. From the panel data, the program determines what proportion of the people who have bought the product x times, y times, z times, etc., are likely to buy the product in the t + 1 period. The number of times a consumer has purchased the product categorizes him into a Nit class. The sales potential of each class is measured and represented by Sit. Frequency of purchase is used to determine probability of future purchases, the Sit variable. Massy tested his model on a product he calls B. He developed the parameters from panel data for the first 26 weeks that the product was marketed. His forecasting accuracy was disappointing. Massy attributed the large forecast errors to management marketing activity which increased sales.

89

of somewhat sophisticated statisticrd techniques and a computer program. The model can be viewed as a queuing approach to sales forecasting. Arrival in the queue and serving in the queue constitute sales. Arrivals are from several classes with different arrival rates. Massy's parameters measure the different arrival rates. Service is not a problem since everyone who arrives is immediately served. The model is interesting in that it uses past panel data to forecast sales from a probabilistic approach. A Massy model forecast of food dryers would not be appropriate. There are very few repeat purchases of electric food driers and the model requires measuring repeat purchases. The model is designed for nondurable products.

t.

Massy model forecast for L "eggsproducts The Massy model is usually used in combination with a computer. But for purpose of example, a simplified illustration will be demonstrated. It must be remembered that this forecasting model is only applicable to the first year of sales. Afterward, a different technique or method must be used. Suppose from the data: 50% of the consumers will repurchase the product within 1 month. 25% of the consumers will repurchase the product again in 2 months, ignore repeat purchases over 2 months. Sales on a monthly basis are: (in millions of pairs). Line (1)represents forecasted monthly sales. Line (2) represents z 50% repvrchase rate within 1 month. Line (3) represents a 25% repurchase rate within 2 months. Line (4) represents total forecasted monthly sales. The forecast for May is 4 ne~v sales for the month plus 0.5 × 3 which is 50% of April new purchasers buying in May = 1.5 plus 0.75 which is 0.25 × 3. This is 25% of the March new buyers buying again in ~lay.

Limitations of the Massy model The major limitation of the Massey model is that it ignores marketing and economic a,'tivities. A basic premise is that the past pattern of sales is sufficient to predict the future. The idea is that a sales forecast is like forecasting the path of a bullet. If we take the trajectory of the bullet's path for a short distance and can measure the velocity, then we can accurately forecast where the bullet will be in each future period. Physicists have been extremely accurate in estimating bullets and spaceship paths s~th this technique. However, when it comes to sales forecasting, such techniques which ignore potential detracting forces often produce inaccurate sales iorecasts. Forecasting sales is an art as well as a science. Most business management activities can use scientific techniques to aid in the decision process, but managerial

wisdom and insight are critical inputs for making optimal decisions. The Massy model also requires the use

Jan. Febr.

March April

May

June

July

August Sept.

Oct.

Nov.

Dec.

3 1 0.5

3 1.5 0.5

4 1.5 0.75

4 5 2 0.75

5 2 1

5 2.5 1

5 2.5 1.25

6 2.5 1.25

8 3 1.25

8 4 1.5

4.5

5.0

6.25

6.75

8.0

8.5

8.75

9.75

12.25

(1)

2

(2)

-

(3)

-

2 1 -

(4)

2.0

3.0

I3.5 = 88.25 million parrs

90

M.D. Geurts, ZE. Reinmuth / New product salesforecasting

rf, =A=

The Assmus n~odel The Assmt~5 model is designed to forecast sales for new products which are consumer nondurable goods [1 ]. It is a sinliulation model that traces the number of persons in the~various stages of the purchase process. Consumers ar!,'classified as "Unawares", "Awares"," "Triers", and!"Repeaters''. The model simulates the weekly flow t:i'omone category to another for the first year after intJitoduction. Market share can be derived from this for¢icast sales information. In or,der t(i find the total number of purchases in any wec~, t, lhe model adds up the final values of eqs. (3), (6), (7), ,~8)and (10). Market share is derived by dividing the ftxm's annual consumer purchases by"he purchases maite in the total market. The relationships expressed in .iqs. (1), (2), (4), (5) and (9) are assumed. at - ( 1 - e

l:]at)(Ut- sut)

(1)

where at is tt:t~ number of new awares in week t, I is an advertisin effectiveness variable, dt is the amount of advertisin dollars spent in week t, Ut is the total number of"l! fnawares" left in week t, and sut is the number of sai nples distributed to those who were previously unaw re in week t. t °

At* = ~

t=l

at

(1

-

e-I"at)(Vt sut)

(2)

-

f t = &Tt + SUt

Ps " RAt + FI" AAt

t

a~. c(1 -

(4)

-es)

where c is th( purchase cycle in vumber of weeks. f

=

i=l

where rft is the number of people who repeat purchase and who were new triers one purchase cycle previous, and/'2 is the assumed percentage of the new triers (ft) who will repeat. rSt = S t - ~ " P3

(7)

where rst is the number of "Repeaters" who were sample triers, b- is the average number of w.oeks it takes to use the sample up, and Pa is the assumed percentage of sample triers who become "Repeaters" each week. rrt=P4.rt_

c

where rrt is the number of repeaters who were repeaters one purchase cycle previously, r t - i is the number of repeaters one purchase cycle past, and/'4 is the percentage of repeaters who repeat again after one purchase cycle. t

SKt =

Lti_e(l -/)2) + &_~-(l - Pa) + i=l

+ r/_e(1 - P 4 ) ] "(1 - P s ) t - i where SKt is the number of "Skeptical Awares" or people who purchased and did not repeat after one purchase cycle.

(10)

where rkt is the number of repeaters who come from the "Skeptical Awares", and Ps is the percentage of "Skeptical Awakes" who become repeaters each week.

where ft is th number of first-time triers in week t, sat is the number 0f samples distributed to those who were aware oi the product, RA t is the number of people who h lye been "Awares'" for more than on~ purchase cyc[ and are classified as "Reluctant ° Awares", AA i is the number of "Active Awares', or those who ha e been "Awares" for one purchase: cycle er less, )l is the percentage of Active Awares that purchase in week t (assumed to be a comtant), and Ps is the )ercentage of "Reluctant Awares" that purchase in ,'ek t (arbitrarily p2).

i=l

(6)

rkt-- es " S g t

where A t , is le cumulative total of "Awares" in week t.

RAt= ~

-e2

[a - ai_.c(1 - P L Y ] ( 1 -

t-i,

(s)

Limitations o f the Assmus model One of the p;oblems with this model arises in the ~stimation of the parameters/, and Pl through Ps. Assmus found that I ranged from 0.01 to 0.07. Test market data or linear regression can be used to estimate Pl through P~. Assmus model forecast for L'eggs products From the test market data: Pl = 0.6,

Ps = 0.1,

RAt = 20 million,

AA t = 80 million. Therefore, using eq. (3) and with no samples distributed, the forecasted sales for the year are 50 million pair. Since Assmus model is designed for nondurables, a forecast of durables like food dryers should not be made.

M.D. Geurts, £E. Reinmuth / New product sales forecasting

The Esla'n model This model was suggested by Gerald Eskin [3]. He advanced the Massy model by simplifying the parameter estimation procedure. The Eskin model forecasts sales up to a period t and uses past purchase behavior to estimate St, sales in that period. The equation is: t

st =

[Rt(J)" V t ( O l •

i=0

The volume of sales to time t is the summation of

Rt(J) times Ut(J). Where Rt(f) is the cumulative number of consumers repeating at least J times, and Ut(J) is the average units purchased on the Jth repurchase. Rt(J) is further divided into Rit(J), the time at which a last purchase was made. Then

(1) durability of the products in months, D; (2) number of potential users, U; (3) number of major competitors, M; (4) number of potential competitors, P; (5) the expected proportion (share) of users who will be made aware of the product during 1 month, S; minimum 1/144, maximum 1/48, expected 1/96. 12 U 1 Yearly sales forecast = ~ - × ~ - X ~ X S. The inventor estimated D, U, M and P; the variable S has three values: 1/144, 1/48, 1/96. This results in a minimum forecast, a maximum forecase, and a most likely forecast. If the forecaster uses S = 1/144, he is indicating that he expects that during the year one out of every 144 potential users (U) will be made aware of the product.

Explanation of variables

t

Rt(J)=~

91

[Rit(d)'R~d- 1)]

i=1

where Ri(J - 1) is the number of products purchased in the t period by individuals who had also made (Jr - 1) purchases in the ith period. Rit is therefore the proportion of buyers who bought their (J - 1) purchases in the ith period and then made their (J) purchases in the next period (t), i.e., the percent who, say, bought their fourth widget last month and a fifth widget this month. Eskin uses autocorrelation procedures to estimate % T,/~, and T. The advantage of Eskin's Model is that it simplifies the estimation of parameter procedures proposed by Massy.

.~sMn model fo, e,~astfor L 'eggs products For period one: Rt(J) = 20 million,

Ut(J) = 2,

St---40 million pair.

Again, this example is simplified for purposes of illustration. Normally, a computer is used to calculate the sales forecast of the particular product.

The Geurts model This model was developed to aid the independent inventor who wants to estimate the first year sales of his product [5]. Ii is oriented for use by the inventor and incorporates the competitive envi,onmer.t into the sales forecast. The formula used for forecasting is a function of:

D - Durability refers to expected length of time between purchases expressed in months. This may be the expected life of the product or time between purchases. As the value decreases, the forecast increases. U - is the number of potential users of the product; those who have both the financial capacity and the potential use for the product. The first two terms in the equation give the maximum that would be sold if there were no competitors and everyone know of the product. D X U = maximum who would buy during a year. This figure is reduced by existing competitors who will divide the market with the new innovation. Historically, the division has resulted in the new entry getting a smaller share than the existing firms. Thus major competitors are squared and divided into the potential users figure. Squaring is not a!ways appropriate and the exponent for U can take on a value as low as 1 in which case the competitors will divide the market proportionately with the new entry. The exponent can take on values as high as 3 in which case the new entry will get an extremely small market share. The forecasters' experience and evpectatio,: should be used to change the value from two. The maximum that could be sold alsb must be redu :ed by potential users and by those who are unaware of the new product M - is the number of major competitors. Positioning theory states that a firm's share of the mar'ket is an exponential function of the number of strong competitors. The integer could take on any value greater than one. Very little empirical work has been conducted to estimate the most likely value. Initial investigation indicates a value close to two performs best.

M.D. Geurts, :I.E. Reinmuth / New product sales forecasting

92

P - i. the number of potential competitors. As new cor tpetitors enter t~e market, total sales will decreas~. P can t ~ e on ,alues greater than zero. If p = O, t]e 1/1"term shou!d be dropped from the equation. S - fiefs to the expected proportion of U individuals w~ ~ will be made aware of the product during a year. Tills parameter can take on values of: 1/144, 1[48, 1~96. "[hese values are subject to change at the discreti]m of the forecaster.

Limita~!ons of the model Thelprimary limitation of this model is its lack of refinentent. It was designed for a non-corporation produ¢]: and accuracy is limited. A range rather than a speci]ic number is generated. The forecast doesn't yield nl0nthly or weekly projections,,only the first year es]:imate~ Also, the model doesnt incorporate consud~ler research data and all parameters are subjectively eltimated.

aeurt~,nmodel forecast for L "eggsproducts Frclaa the data given in the case: D = 0.l; or 2 weeks, P--- 3,

U= 100 million,

M = 3,

S = 0.0667.

Thl s"

12 mX 0.5

00 million 1 X -j X 0.0667 = 5.93 millio~ pair 32 per month.

The r~ rrket research new product forecasting proce-

0.e., the introduction of a new breakfast food into New Zealand - each of the country's 3 million residents is a prospective buyer). Therefore, market research methods employing a sample survey are recommended for use almost exclusively in cases where either the buyers are somewhat limited in the number of distribution agents or the survey is likely to provide reliable information about the buying behavior of the general public. Typical market research methods (a) identify the population of prospective buyers of the product, (b) select a representative sample of size n from this population, and then (c) find the proportion, p, of this sample who indicate that they would take an affirmative action if the product were presented to them. The sales forecast is then: Forecast = (N)(p) where N is the total number in the population of prospective buyers. It is essential that the sample be a stratified sample, reflecting all important characteristics of the population in the same proportions. That is, if it is thought that women are much more likely to react favorably to the product than men, the male-female split of the sample should reflect the male-female split of the population. On the other hand, if men and women are thought to react to the product in a similar manner, it would make little difference what male-female split is determined for the sample. In a similar manner, other socio-economic and demographic characteristics of the population must be evaluated before determining the dimensions of the sample.

dures i The Juster approach method is to ask potential buyers if they wouldl buy the product, then evaluate the proportion who '~Iauld buy and the variance associated with the estimal :e. Based on the sample, an inference is made conce3i ning the total universe. As!tmple survey of opinions from those best able to ass( ss the potential sales of your product, the prosp e c t i v buyers themselves, is the most logical approach for obl aining an accurate new product sales forecast. This a ~proach is easy to manage and quite reliable when he prospective buyers are few in number and easy t, identify (i.e., the Boeing Company determinin potential sales for the 737 aircraft in Austral. asia- they need cci~tact only about ten airline companie~, P. But the method is quite unmanageable when the pl ~spective buyers are many and widely distributed

Most consumer surveys seek to obtain a definitive yes or no response from the respondent. Commonly, a subject is not sure about expected purcase intentions, a fact born out by numerous follow-up surveys. Realizing the difficulty with a required yes-no response to a consumer survey on purchase intentions, Juster [6] has devised a novel technique which allows the market research survey to gather probabili~tic information from the respondents. The Juster approach approach assumes that the respondents' uncertain state of mind about purchase intentions is really probabilistic and not deterministic and that this probabilistic information can be measured. As a measuring device, the Juster approach suggests that each respondent identify with one of a set of descriptive words.

93

M.D. Geurts, ZE. Reinmuth / New product sales.forecasting

Through a set of follow-up surveys, Juster has recorded the proportion of respondents who take an affirmative action on the product purchase after having identified with each possible descriptive word. The descriptive words and their associated probabilities of positive purchase action are as follows: Descriptive word

Probability

Certain, practically certain Almost sure Very probable Probable Good possibility Fairly good possibility Fair po,~sibility Some possibility Slight possibility Very slight possibility No chance, almost no chance

0.99 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 O. 10 0.01

Using the Juster approach requires an analysis of a set of categorical responses obtained in a sample survey. The analysis is performed by (a) noting the descriptive word selected by each respondent in the sample, (b) recording the affirmative p.ction vrobability associated with each respond~nt's descriptive word, and then (c) finding the average ~f the sample affirmative action probabilities. If the same is truly representative of the population of interest, the average affirmative action probability, i~, should be indicative of the relative frequency of those within the population who would take an affirmative action on the product purchase decision. The forecast then becomes Forecast = (NX/~) where N is the number of possible buyers within the population. Juster has found that the probabilistic approach gives much more accurate results than a survey which requires definitive, yes-no responses. Using the latter approach, many indicating they are nonintenders would report probabilities of purchase greater than zero° Likewise, many answering "yes" to the survey may not be unalterably committed to the decision and therefore indicate a probability of intention less than one. Sknce 1966, the U.S. Bureau of Census has been conducting regular quarterly surveys using the probability responses. They have noted very good results using

this approach and, if anything, their errors of estimation have been off on the low side. This suggests an aaded benefit to using the Juster approach in forecasting new product sales (e.g., an overforecast causing excess expenditures is more costly to the finn than an underforecast which, at worst, results in an opportunity cost of lost sales). Market research surveys should be linctited to cases where the actual buying population is identifiable and controllable and to products to which this population can easily relate and identify. The number of new cars sold in th,~ State of Oregon during the next year and the number of Oregonians who will take a vacation in Hawaii during the next year might prove totally unmanageable using traditional survey methods. As users of market research techniques have been selective in their application, results in short and medium term forecasting applications have been good to excellent. Long term forecasts have been at best fair using these methods. For the food dryer new product, the following sample results were obtained using the Juster procedures (n = 20). Category

Probability of purchase

No. checking

Expected value

Certain Almost sure Very probable Probable Good possibility Fairly good possibility Fair possibility Some possibility Slight possibility Very slight possibility None

0.99 0.90 0.80 0.70 0.60

1

0.6

0.50 0.40 0.30 0.20

1 0 1 0

0.5

0.10 0.01

10 7

0.3 0.0 1.0 0.07 = 2.5

2.20 .__s.s= 0.125 = the proportion who will buy. Estimated size of population is 225,000 exposures per year. 225,000 × 0.125 - 28,125 sales

M.D. Geurt#, .I.E. Reinmuth / New product sales forecasting uct innovation, Oreg. Business Rev. (Fall 1975).

[61 F.T. Juster, Consumer buying intentions and purchasing [1 ] G! Assmus, New products: The design and input implem~3t~tion of a new product, J. Marketing 39 (January

1~75). [ 2] F. ~/. Bass, A new product growth model for consumer dt rabies, Management Sci. (January 1969). [3] G I. Eskin, Dynamic forecasts of new product demand us nga depth or repeat model, J. Marketing Res. (May 1~173). [4] Li ~,. Fourt and W. Woodlock, Early prediction of market ~i :cess for new grocery products, J. Marketing (October

I!~0). [Sj M D. Geurts, A market screen for the noncozporate prod-

[7l Is] [9] [lo] Ill]

probability: An experiment in survey design, J. Am. Statist. Assoc. (September 1966). S. Makridalds and S. Wheelweight, Forecasting Methods and Applications (Wiley, New York, 1978)., W.F. Massy, Forecasting the demand for new convenience product, J. Marketing Res. (November 1969). New products: The push is on marketing, Business Week March 4, 1972). J.T. O'Meat'a Jr., Selecting profitable products, Harvard Busine;ss Rev. (January/February 1961). E. Rogers, Communication of innovations, The New York Free Press (1961).