Part 2. MM4XL Tools > 1. Strategic Tools > Brand Switch > Analysis Case: Hair Loss

Brand Switch Analyst

Analysis Case: Hair Loss

One question managers are often called to answer is whether to put more effort into retaining existing customers, or to concentrate on winning new ones. There is no ready answer to the dilemma, yet Brand Switch Analyst could add some interesting insights in market analysis, which could help to reinforce the logic behind business decisions.

Hair Loss EU Market

This example refers to an existing European market, although product names have been changed for publishing purposes. The products we mention are in direct competition and their switch matrix gives interesting results for highlighting the concept of customer retention. The management asked: How can we prevent the loss of market share in this declining market?

The chart below shows the market share curves for the five products used to treat alopecia (hair loss).

Brand Switch Software Estimates Customer Loyalty and Retention from Sales Performance Data

There is one liquid form and the remainder are capsules. The treatment length varies from one to three months, prices are all quite similar, and all products are sold mainly through pharmacies. Keep2, the market leader, holds some 50% share of voice. A marketing research study has shown that unsatisfied consumers do not jump from product to product, but stop the treatment and start again using a different product, between three and six months later.

When you break down brands into single references, make sure Brand Switch Analyst works with direct competitors so as to reduce, as much as possible, the 'noise' that affects the analysis. Direct competitors are products that share all three characteristics of competition: they (i) offer the same technical performance, (ii) compete in the same market segment and (iii) talk to the same target group.

In such a situation, a brand loyalty strategy must be preferred to that of winning customers from competing products. This concept is also reinforced by the switch values below.

Brand Switch Software Estimates Customer Loyalty and Retention from Sales Performance Data

Keep1, Keep2, and Keep3 claim to stop hair loss. The Liquid form claims to favor hair growth and Fall claims to prevent hair loss. From the consumer point of view there is a lot of hope when buying this sort of product, although they also know that the probability of success is low. However, being sold through pharmacies, although without prescription, may confer to these products a sense of believability, so many with the need try these remedies. Intuitively, based on what has been written so far, one could expect customers:

  • to try one treatment and stop (non customers)
  • to repeat the same treatment (loyal)
  • to repeat, but with a new treatment (disloyal)

There is not really much to do in the first case, unless one really provides a treatment that works. The second instance is captured in the diagonal values of the brand switch matrix and the third case equals 1 minus the loyalty rate of one's brand.

Keep2 and Keep3 show very high loyalty rates. The former is market leader with more than 50% market share; the latter is 2 years old and controls 15% market share. Liquid has lost 25 points of share in the past 10 years and since 2 is below 5%. Keep1 was leader 10 years ago with 50% share and since then it declines regularly; nowadays it is down to 15%. Since 10 years Fall floats between 15-25% share.

Now, the question Keep2's management is asking is How can we prevent the loss of market share in this declining market? The picture below, based on the data from the switch matrix, may add some insight to the decision process. The bubble size relates to the present market share; the arrows show the overall product gain and its origin.

Brand Switch Software Estimates Customer Loyalty and Retention from Sales Performance Data

The market seems to move downwards. It looks like as if users try the product and do not find it useful, so move to an alternative product, whose claim is stronger than the first tried. If this does not work either, they leave the market. The competition is clearly focused on communication (benefit, support and tone). Keep2 has a high share of voice and therefore captures most new users. Its retention rate, however, is becoming weaker than in the past. The declining market is perhaps also playing a role in accelerating this trend.

It seems from the picture above that Keep2's management should target Fall in order to maintain their share safe. Every term they lose around 5% share to Fall and gain 3.5% from it with a net loss of -1.5%. Given Keep2 retains around 95% share, targeting Fall could make available another 8.5%, which could turn Keep2 into a winner again.

An analogous reasoning could be made from the point of view of any other competitor in this market and the conclusions may be completely different. What is important and common to any scenario though, is the ability of the analyst to *see* the latent structure in the data, which is the soul of the data. It is the most extreme and meaningful synthesis of the information squeezable from the data set that each analyst should struggle for.

We hope this brief example has shown why we believe Brand Switch Analyst is a great tool for fostering strategic thinking in business decision-makers. It is however only when analysts use their prior knowledge of the market that Brand Switch Analyst produces the most interesting results.

Other Applications of Brand Switch Rates

Apart from the estimation of brand switch rates and forecasting, there is plenty of other applications where the use of quadratic programming and Markov chains have been reported. Among others:

  1. The application for the evaluation of different business plans is reported in Wroe & Adlerson.
  2. Kotler shows how to use it for drawing what it calls the Competitive Marketing-Mix Model. He shows how multiplying the Marketing Response Vector times the Marketing-Mix Matrix produces the brand purchase probability vector.
  3. In operations management Markov chains and processes are used in a quite broad number of instances, such as in logistics, production, packaging, call plans (reps), and more. Most of the sources cited in our bibliography deal with this sort of problems.

Finally, we suggest using Brand Loyalty indices as a means of comparison for products belonging to the same portfolio (same company). Plotting the Brand Loyalty values against brand investments may offer an interesting perspective of the overall product portfolio competitiveness. In general, products holding large market share have higher retention rates. Therefore, looking at the retention rates is a means of comparing the ability of a firm to gain competitiveness in different market segments.

Do you want to measure the market elasticity of adoption for a new brand? Run two brand switch analyses: the first with the real data and the second adding a column of zeros to the first matrix. Then, multiply the switch matrix by itself as many times as the switch values of the fictitious brand do not go back to zero. Repeat the analysis for several markets, plot on a chart the loyalty rates (diagonal), and find out which market is tougher for new competitors to penetrate.

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