Assessor (product policy)

The assessor model is a simulation model that helps to forecast the future market share of a new product . The focus of the model is on the modeling of repeat purchases. Accordingly, the model refers to consumer goods whose regular repeat purchases are crucial for the success of the product. Assessor is less suitable for situations of unstable market conditions and the resulting quickly shifting consumer preferences.

procedure

First, test persons are invited to a laboratory experiment in the form of a test market simulation .

In the first step, an evaluation of products already on the market, the competitors of the new product, is carried out, revealing the product selection preferences.

In the second step, the test subjects are presented with the new product using the same communication concept. Then the test persons again give their product choice preferences, taking into account the new product. By comparing the market shares gained in this way and assuming that a certain percentage of people would buy the new product again in the long term, the market share forecast can be determined.

Structure of the model

By combining two sub-models, the trial repeat model and the preference model , which provide market share estimates independently of one another, the model predicts the long-term market share of the new product. This consists of the mean of the two estimates of the partial models. It is assumed that the future market share of a new product consists of an initial purchase rate and a repeat purchase rate. ${\ displaystyle M (*)}$

Trial repeat model

The trial repeat model is based on the formula:

{\ displaystyle M (*) = T \ cdot S}

to estimate the market share . It shows the first purchase rate and the repurchase rate. ${\ displaystyle M}$ ${\ displaystyle T}$ ${\ displaystyle S}$

The first purchase rate consists of the probability of attempted purchase , the level of awareness , availability at the point of sale , the probability that a customer will receive the new product free of charge on the basis of a coupon and a conditional probability that the customer will actually try the product received free of charge : ${\ displaystyle T}$ ${\ displaystyle F}$ ${\ displaystyle K}$ ${\ displaystyle D}$ ${\ displaystyle C}$ ${\ displaystyle U}$

{\ displaystyle T = (F \ cdot K \ cdot D) + (C \ cdot U) - (F \ cdot K \ cdot D) \ cdot (C \ cdot U)}

The last term must be subtracted in order to subtract those customers who buy the new product with and without a coupon. These would be counted twice without subtraction.

The repurchase rate consists of the probability of transition from an established product to the new product observed in the test market simulation and the repurchase probability of choosing the new product again ${\ displaystyle S}$ ${\ displaystyle P01}$ ${\ displaystyle P11}$

{\ displaystyle S = {\ frac {P01} {1 + P01-P11}}}

Preference model

In the preference model, data is processed that is generated from pairwise comparisons of individual, already existing, products with the new product.

For this purpose, the test persons take a number of already existing products into a kind of product preference list in the first run, whereupon two of these products are compared with each other and the purchase probability is then determined with the help of an arithmetic mean . ${\ displaystyle i \ (i = 1, \ dotsc, N)}$ ${\ displaystyle I \ (I = 1, \ dotsc, k)}$ ${\ displaystyle \ alpha i (I)}$

After the first run, the test persons are exposed to a simulated buying situation in which they decide either for or against the new product. The test persons who have decided on the new product will now carry out the pairwise comparisons again, but this time taking the new product into account. They are again compared with each other in pairs and the purchase probability is determined . ${\ displaystyle i \ (i = 1, \ dotsc, n)}$ ${\ displaystyle \ beta i (I)}$

${\ displaystyle E}$ is the proportion of consumers who have chosen the new brand. For the brands that already exist:

{\ displaystyle M_ {1} (I) = {\ frac {1} {n}} \ cdot \ sum _ {i = 1} ^ {n} \ beta i (I)}

{\ displaystyle M_ {2} (I) = {\ frac {1} {Nn}} \ cdot \ sum _ {i = n + 1} ^ {N} \ alpha i (I)}

a long-term estimated market share of:

{\ displaystyle M (I) = E \ cdot M_ {1} (I) + (1-E) \ cdot M_ {2} (I)}

The result for the new product is: ${\ displaystyle *}$

{\ displaystyle M_ {1} (*) = {\ frac {1} {n}} \ cdot \ sum _ {i = 1} ^ {n} \ beta i (*)}