Since Solo Liquid is a new product at the introduction stage, there is no existing data for the performance of Solo. Therefore, methods of projecting project performance must rely on comparative data.
In the short-term planning, which focuses on the first 13 weeks of the fiscal year, involves the development of a new-product introduction model to predict the brand awareness (BA), initial purchase rate (IP), repeat purchase rate(RR), and the market share (MS).
The following four equations are used to predic each variable.
1. BA = f (DAR, AF)
| BA: Brand Awareness |
| DAR: Day-After Recall |
| AF: Average Frequency |
The interaction of the above two variables, DAR and AF, effect on brand awareness. BA could be explained by DAR variable for commercials used to introduct a new brand during the first thirteen weeks and the AF of the media schedule utilized to introduct the brand during the initial period.
2. IP = f (BA, DN, PK, FB, CU)
| IP: Initial Purchase Rate |
PK: Quality of Packaging |
| BA: Brand Awareness |
FB: Family Brand |
| DN: Distribution |
CU: Consumers Using |
The interaction of five variables, BA, DN, PK, FB, and CU, effect on initial purchase (IP). In this relationship, the distribution and packaging are expected to have an effect on each other, so the two variables would be multiplied with each other.
3. RR = f (IP, PS, PF, RP)
| RR: Repeat Purchase |
| IP: Initial Purchase |
| PS: Product Satisfaction |
| PF: Purchase Frequency |
| RP: Relative Price |
This equation constitutes the repeat purchase response function. It states that RR is predicted by the IP, the percent of target market consumers who have excellent product satisfaction, the purchase frequency of consumers of the product, and the relative price of the new product compared to its existing competitors.
4. MS = f (RR)
| MS: Market Share |
| RR: Repeat Purchase Rate |
This equation is a semi-long term function that suggests that the market share for the new product at the end of the first year on the market can be explained solely by the levels of repeart purchase rate at the end of the first thirteen weeks.
Five current effects functions were applied to above each equation in order to determine which one produced the best fit to the data, and therefore which had the lowest percentage of error. As one can see, each step in the theory has the potential to utilize a different current effects model.
Please click here for the five current effects functions.
Once a specified functional model is defined that quantifies the relationships between these conceptual variables, it is tested on specific data. Both of these tasks are developed and tested in the data analysis stage of this presentation. These data comprise both successful and unsuccessful brands and the breadth of the data are intended to illustrate and aid understanding of the range of potential results.
Please click here for the data
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