Some Prospects are Easier to Convert Than Others

The two targeting criteria that matter most

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1. Category potential– A customer’s potential for using products or services in your

defined category

2. Brand share– The market share that your product or service enjoys with

each customer

1. Category potential– A customer’s potential for using products or services in your

defined category

2. Brand share– The market share that your product or service enjoys with

each customer

The two targeting criteria that matter most

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1. Category potential– A customer’s potential for using products or services in your

defined category

2. Brand share– The relative market share that your product or service enjoys

with each customer

2. Propensity for adopting new products

New product adoption pattern for a late entrant into the US diabetes market

Source: IMS New Prescription Audit 4

0-6 6-12 12-18 18-24 24-30 30-36 36-420%

5%

10%

15%

20%

25%

30%

35%

40%

3.1%

7.9%10.4% 11.8% 13.1% 13.8% 14.9%

Months post-launch

Mark

et

Sh

are

Adoption of product by physicians

Groups

1

3

2

4

TRx Share36%

10%

20%

2%

Analysis of Customers revealed four distinct product adoption patterns

0-6 6-12 12-18 18-24 24-30 30-36 36-420%

5%

10%

15%

20%

25%

30%

35%

40%

Month Post-Launch

Mark

et

Sh

are

Source: IMS New Prescription Audit

10x

12x

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Could these early adopters have been identified prior to the launch?

A key learning about new product adoption behavior is that it’s not brand specific*

• So, early adopters for products in one category also tend to be early adopters for products in other categories that are relevant to them

• Propensity for adopting new products appears to customer dependent rather than category or brand dependent

• We can leverage past behavior in one category to predict the future adoption behavior of the same customer in another category

7Note: Learning from pharmaceutical industry. Data not available for other industries

One method for scoring adoption propensity is to average performance on other recent launches

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We then segment customers into quartiles based on their market shares @ month 6

9

4

3

2

1

We then average customer scores for other recent launches to develop a single adoption score

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To develop a single score for each customer, we need to convert the two variables to a similar scale

Customer

Category Potential

Adoption Ranking

1 20,000 4.0

2 75,000 3.7

3 150,000 3.3

… … …

Average 62,000 2

Raw data

Zip Code

Category

Potential

Brand Share

Avg.

1 0.3 2.0 1.2

2 1.2 1.9 1.6

3 2.4 1.7 2.1

… … …

Average 1 1

Normalized Score

Caveats related to use of adoption criteria

• Tendency for early adopters in one category to be early adopters in other categories should be validated before using this approach

• You need the market share by customer from previous new product launches to establish an accurate ranking of adoption potential

• Absolute usage of a new product can be valuable directionally but without a denominator can be misleading

– Customer A: 5 purchases out of 10 total purchases (50%)

– Customer B: 5 purchases out of 100 total purchases (5%)

• If your ranking of adoption potential is more directional in nature, you may want to apply a lower weighting to this criteria (e.g. 25% vs. 50%)

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Alternative approach

1. Rely exclusively on category potential criteria to guide promotional efforts at launch

2. Survey actual customer market share at month 6, reprioritize customers and adjust promotional efforts accordingly

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Summary

• The two targeting criteria that matter most are a customer’s category potential and brand share

• Brand share cannot be predicted at launch but a customer’s propensity for early adoption can

• You need the market share by customer from previous new product launches to establish an accurate ranking of adoption potential

• If this data is not available, you may want to profile customers again shortly after launch to include actual adoption behavior as a valuable second targeting criteria

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