Cases - Business

The goal of database marketing is to increase marketing efficiency & Customer lifetime value, with the smart use of Customer data. In example, use Customer data t

According to USFDA, a combination product is one composed of any combination of a drug and device; biological product and device; drug and biological product

o identify Customer groups, which would yield high response to offers, in order to address them directly.

Database marketing is based on Customer information rel

; or drug, device, and biological product and fixed dose combination would include two or more combinations of drug.

Examples of combination products may in

ated to:
• Customer behavior
• Customer profile & demographics

Based exclusively on behavioral information, one can classify customers into RFM (recen

lude drug-coated devices, drugs packaged with delivery devices in medical kits, and drugs and devices packaged separately but intended to be used together.

cy - frequency - monetary) or RF cells. The goal is to identify Customer groups with high expected response rates. Different RFM cells are expected to provide sig

here is enormous increase in the number of combination products entering the market in the recent years. Combination products have proven advantages but fixe

nificantly different expected response rate (especially the ones linked to the most recent Customers). The more significant the statistically expected difference

d dose combinations are still in the process of convincing regulatory authority on their advantages over the single ingredient formulations.

Combination pro

is, the higher potential business value this grouping yields. In order to apply RFM, one does not need statistics skills. Therefore this approach is less costly,

ucts have become life saving products for the pharmaceutical companies who doesn’t have many innovative molecules in their product pipeline and have been inc

since it is simpler and requires only customer behavioral information.

Predictive models based on both behavioral & demographic data, can outperform Customer gr

easingly used in the product life cycle management. Even the companies having product patents are trying to extend their product life cycle through the combi

oupings based solely on behavioral data (like RFM).

In order to develop such a model, one needs to use behavioral & demographic data of a set of Customers, which

nation products and maximize the revenues. But the companies involved in this practice are overlooking that they are burdening the patients both economically

have been monitored vis-?-vis their responses to a specific offer. The Customer set is divided into two subsets of equal (or comparable) size & similar types of

and physically. They need to rightly judge the benefits of the combination products and they have to even look at the risks involved when combining the produ

Customers (in respect with profile & behavior): a test set (or model train set) and a validation set. The model shall be developed against the test set and vali

ts. Some of the combination products were well accepted by physicians while others suffered. Companies involved in development of combination products are fi

ated against the validation set. A modeling algorithm can be applied (e.g. logistic regression analysis), against the test set data, in order to identify the vari

ding difficulty in defining their combination products and facing various challenges from selecting a combination to marketing it.

Following aspects would a

ables, that influence significantly ‘the probability to respond to an offer’ (which is the dependent variable). Validation of the model follows. It involves ident

dd to the challenges in developing combination products:

Which markets to tap where the combination products can do fairly well?
Which combination prod

ifying most of the actual responders in the validation set, given that these Customers are known. After being validated, the model can be used in a test campaign.

cts are meaningful and rational?
Which therapeutic categories to select?
Which Combinations can address unmet needs of the patients?
Do combin

Various obstacles may appear during this modelling process:
• There may be no capture of customer reactions to previous offers, therefore no data to model

tions increase the patient compliance?
What would be the developing cost?
How to tackle the risks encountered during combination product developmen

on.
• If the model does not validate sufficiently against the validation group, then the model may be a failure. This may mean that factors affecting signifi

t?

As combination products don't fit into the traditional categories of drugs, medical devices, or biological products, the USFDA is in the process of devel

cantly the customer behavior, are not captured among the data available or are not used in the model.
• Many customer databases hold Customer behavior info,

ping new procedures for reviewing their safety, efficacy and quality.

Professional from academic institutions, pharmaceutical industries, health care indust

but limited demographics on the Customers. Lists with consumer demographics (offered by many in the USA), can be used to enrich Customer data with demographics.

y and representatives from various regulatory agencies are working out to design the regulatory requirements for manufacture and sale of combination products

A validated model can be applied on the whole Customer database, to identify a group of Customers with high propensity to respond positively to a similar offer.

.

As there is an increasing trend of the combination products companies manufacturing such products should be able to tackle the problems involved in the de

Having produced this Customer list, the next step is to run a test campaign in order to verify the expected response and analyse again the results. Any attempt t

elopment. They need to be wiser in analyzing the market trends and the regulatory requirements.

Companies that provide selfless information through particip

o execute a fully blown campaign without a prior test, may lead to a failure, since market conditions are constantly changing.

Copyright 2006 - Kostis Panayotaki

tion in industry events and feedback to regulatory authorities would be able to face the challenges and will be successful in developing combination products

HTTP = HTML link (for blogs, profiles,phorums):
<a href="http://www.cases.org.ua/article/30744/cases-Database-Marketing--in-Search-of-Statistical-Significance.html">Database Marketing - in Search of Statistical Significance</a>

BB link (for phorums):
[url=http://www.cases.org.ua/article/30744/cases-Database-Marketing--in-Search-of-Statistical-Significance.html]Database Marketing - in Search of Statistical Significance[/url]

How much does a new car really cost. When factoring in insurance, resale value and the time Value of money.

Are you enjoying your work, or just going through the motions? Life is short - too short not to love what you do.

It was not possible for businesses to properly collect and analyze data before the 20th century. In 1970, decision support systems were introduced in business. Decision support systems can analyze one department at a time. In 1980, executive information systems were introduced. The executive information system can effectively summarize ongoing transaction within an organization. By 1990, business intelligence improved with the introduction of computer technologies. Customer relationship management also improved. Advanced management techniques combined with new technology improved the planning, reporting and analysis in business. These new developments gave rise to an integrated methodology known as corporate performance management. Corporate business management is a holistic approach in strategic planning.

Tags

Links

Cases - Database Marketing - in Search of Statistical Significance

Related Articles: