Objectives of the targeting study

  • The French pharmacy inventory remains very heterogeneous (profiles of pharmacies) and dynamic (evolution of points of sale, groups, etc.)
  • Each market responds to its own dynamics, the control of which often represents a real competitive advantage
  • Each pharmacy, depending on the segmentation to which it belongs, has potential in a given market
  • Targeting your market allows you to better understand the universe of its customers in a defined market and strengthen its commercial actions by prioritizing the action of its sales forces
  • Better targeting means allocating resources more efficiently by freeing up space to develop prospects and by maximizing turnover and margin on existing customers

Prerequisites

  • Validation of the definitions of the markets targeted by Openhealth proposal ( classes )
  • Receipt of your Sell-in file with all of your products included in each target market, detailed by point of sale with 36 months of history

With the information necessary for matching pharmacies
Specifications: " IN-OHC 160106-Pre-requisites Matching Pharmacies_IC.pdf file »

Interest of Sell-in for targeting studies

Source

It is important to have the details at the point of sale to take into account only those of the concerned perimeter.

And for example to make sure in this case to exclude the parapharmacies of GMS, independent ...

Geographical territory

In a similar way, this makes it possible to distinguish the points of sale of the different geographical territories and make sure to take into account only those concerned by the perimeter.

Certain territories have their own atypisms.

Please note that sometimes our customers also send us data from foreign countries, some of which we have a partnership with (example: Germany, Switzerland, Belgium, Italy, Spain, Portugal, Poland)

Market

It is important to have your sell-in at product granularity order to control the list of codes GTIN of your aggregates such as the brand.

This will allow us to identify the possible absence of a GTIN code in the file received or in the market taken into account for the analysis

Example: a code with sell-out sales for which we would not observe a sell-in in your data or vice versa.

Knowing the composition of aggregates also allows more advanced analyzes such as assortment.

Time period

The fact of only having the Sell-in over one year of history does not in all likelihood make it possible to take into account all the disparities in terms of customer behavior (old / new / strong changes ).

A 3-year history enables statistical abnormalities to be detected

Examples

  • Pharmacy that stocks very heavily in 2018 and does not buy in 2019

Very large gap in 2018 between the Sell-in (0) and the Sell-out carried out during the year

  • Pharmacy that does not buy in 2018 and starts buying for one year of stock in 2019

The Sell-out will be very low in 2019 compared to the Sell-in

  • The Sell-in thus makes it possible to detect these pharmacies with very strong evolutions

2 or 3 pharmacies in this case can sometimes alone carry the growth of a laboratory.

The detail in volume and value is necessary, because the "value" indicator is calculated and dependent on the price.

At OpenHealth, the calculation method for sales values (sell-out) is as follows: Volume x Transaction price = Sales values. (as opposed to the use of a benchmark price by other data providers)

This method also allows us to know the price disparity by product in pharmacies, in particular via the module: Price Dispersion

Indicators

The detail in volume and value is necessary, because the "value" indicator is calculated and dependent on the price.

At OpenHealth, the calculation method for sales values (sell-out) is as follows: Volume x Transaction price = Sales values. (as opposed to the use of a benchmark price by other data providers)

This method also allows us to know the price disparity by product in pharmacies, in particular via the module: Price Dispersion


Custom option HUB: CUSTOMER TYPE

The segments selected in the context of this targeting / segmentation study can be implemented in market monitoring on the Hub.

The Customer type functionality allows you to view your data on homogeneous pharmacy groups and not on the total pharmacy, the HUB user being free to define the pharmacy group himself.

Definition of a type of pharmacy and its modalities (eg: gold, silver, bronze / small, medium large / grouped, non-grouped).

Attention, even if the statistical method is very reliable on the "ESSENTIAL" modules, the "CUSTOMER TYPE" option will have a more restricted precision on the "ADVANCED" modules.

Our technical teams study our coverage of the target and validate the feasibility.

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