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Koyo Jeans - Case Report for Abb Electric

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Case Report for ABB Electric

Despite the fact that the market for electrical equipment was stagnant, ABB Electric suffered a decreasing profit due to the poor sales performance. This troublesome situation was partly attributed to:

  • the poor salesforce lacking a focused selling methods;
  • and the little penetration among the RECs and small municipalities.

To solve the problems, ABB had to boost its sales and enlarge its market share, which means that the company needed to win the customers from its competitors. In order to achieve this new strategy, ABB created a marketing information system (MKIS) to guide its marketing strategies. The key questions of which the company wanted to find the answers through the MKIS are:

  • the variable needs and wants of its potential customers;
  • the important product attributes valued by the customers to satisfy their needs;
  • the different segments of the electrical equipment market based on different weights on the different attributes and the actual choices made by customers.

1. Targeting without Model A budget for a supplementary direct marketing campaign is allocated to target 18 companies out of 88 in one particular region. If the decision is only based on the location of customers and their purchase volume, the choice is shown as the Chart 1. The two main rationales behind this decision are the ABB’s new strategy and high sales revenue. Firstly, ABB’s new strategy is to win customers from its competitors and increase penetration in rural electrification cooperatives, municipalities and industrial firms, because ABB’s existing customers are mostly investor-owner electrical utilities whose sales were expected to drop as much as 80 percent per year for the next two or three years. Therefore, the customers who chose A should be excluded. Next, the top 18 companies in terms of purchase volume are selected, because they can provide great income to the seller. For districts, it’s found that District 2 should be mostly focused, since customers from District 2 contributed nearly half of the total annual sales volume earned by ABB’s competitors. (Chart 2) [pic 1][pic 2][pic 3]

2.1 Key Drivers Based on Chart 3, it’s found that the variables with the most significant positive impact on the choices are energy loss, quality, price, problem solver, warranty and ease of install. However, the coefficient table only shows the overall impact on all alternatives. In order to see how these variables affect the customer’s choice on ABB, the analysis of elasticities has been done (Chart 4). From the elasticity table above, it shows that the change of energy loss, quality, problem solver, price and warranty have much greater impact on the choice probability increase of ABB. In conclusion, these five attributes are the key drivers for ABB in this market.[pic 4]

[pic 5]

Based on the utility function involving customers’ perceptions on 9 attributes of 4 brands, the purchase probability of each alternative can be inferred. According to the MNL Implications, the relationship between the marginal impact of marketing action and choosing probability is shown Figure 1.

The underlying principle illustrated in Figure1 is that Competitive[1] and Swithable[2] customers would have stronger response to marketing actions, because the more intermediate the probability of choosing the option is, the higher the marginal impact of attribute improvement. Therefore, to find the appropriate targets to focus ABB’s marketing efforts, firstly segmentation by switchability is used.

2.2 Segmentation by Switchability The definition of segmentation by switchability is based on the difference of choosing probability between the first choice and the second one (or an alternative), thus the 88 customers are separated into two groups: Group 1 in which the purchase probability of ABB is the highest, and Group 2 in which ABB is not the 1st choice. Then for the Group 1, the difference of probability is calculated by:

               D1i= ABB Probability – 2nd Choice Probability

And for the second group, the difference of probability is calculated by:

               D2i=1st Choice Probability- ABB Probability

Then the mean of the probability differences in each group (M1=0.593, M2=0.814) is calculated, and by comparing D1i with M1, and comparing D2i with M2, 4 segments is identified: Loyal segment where D1i >M1, Competitive segment where D1i < M1, Switchable segment where D2i< M2, Lost segment where D2i >M2. So the 18 targets to focus will come from the competitive and switchable segments.[pic 6]

2.2 Targeting by Profitability Change To identify the 18 targets, the profitability change should be considered to determine the economic benefits brought by each competitive or switchable customer and the effectiveness of the marketing program. However, “sales” is used as a substitute for profitability to measure, because of lacking profit margin data. So by multiplying purchase probability and purchase volume, the potential sales ABB may gain can be inferred.  And then the increase in sales potential is calculated by S1-S0[3]. Finally, by reordering these data of potential sales change in descending order, the top 18 as target customers who are both highly-responsive to marketing actions and revenue-supporting are identified.(Appendix: Table 1)

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