Frequency Distribution for Pharma Geo-Marketing
In this article, we delve into the concept of frequency distribution, a fundamental statistical tool crucial for pharma geo-marketing. This method involves organizing data into specified groups or classes, enabling us to discern patterns and trends in pharmaceutical product pricing and purchase rates within different geographical areas.
Let’s take an example with Dr.Helena
Imagine a table, not of drugs or supplements, but of numbers and groups. This table, known as a frequency distribution table, categorizes data into classes and records how many values fall into each class. Dr. Helena uses it to analyze the range of food supplement prices in her pharmacy.
Dr. Helena, with her ledger in hand, notes down the prices of various supplements: [10, 5, 6, 15, 21, 16, 23, 7, 29, 27, 30, 11, 12]. Her goal? To determine how many products are priced above and below $20.
Key Points in Frequency Distribution
- No Overlapping: Each class is distinct; overlaps are a no-go.
- Class Width: Here, it’s $21, the range of each class.
- Class Boundaries: These are the fine lines between classes. For instance, between the $20 and $21 classes lies a boundary at $20.5 👇🏼
- Upper-Class Limit (UCL) and Lower Next Class Limit (LNCL): The highest and lowest values that define class limits.
Why does this kind of data is valuable for pharmaceutical Industry ?
In the context of a pharmaceutical company, understanding data related to pharmacy segmentation is invaluable. It offers insights into the availability of products in each pharmacy based on different price ranges.
This information enables the development of targeted strategies for medical representatives, focusing on specific products that align with the pricing structure of individual pharmacies.
Additionally, providing geographical data to wholesalers can aid in the creation of customized packages for pharmacists, tailored to their specific consumption patterns.
This data is also essential when launching new products in the market, as it assists in identifying the most suitable geographical areas based on product pricing.
Furthermore, this approach can be applied to data dimensions included in your business unit.
The Relative Frequency
Dr. Helena, currently serving as a Marketing Manager in a pharmaceutical company, is keen on utilizing relative frequency to gain insights into market trends within a specific geographical area.
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She describes relative frequency as a percentage-based concept, representing the portion of data within a given class. To calculate it, she divides the frequency of each class by the sum of all frequencies, a figure she denotes as ’n’.
Let’s take an example to have a better idea
Consider this scenario: we’re examining the annual purchase rates from various pharmacies across a particular geographical area. Our dataset looks like this: [68, 69, 69, 69, 69, 70, 70, 70, 70, 70, 70, 70, 71, 71, 71, 71, 71, 72, 72, 72, 72, 72, 73, 73, 73].
- Helena’s initial approach was to sort this data into ascending order, starting with the lowest values and proceeding to the highest.
- Next, she crafted an organized table. In this table, each unique purchase rate was listed in one column, with a parallel column dedicated to recording the frequency of each rate.
- With the basic frequency table in place, Helena’s next task is to add another element to her analysis: a column for cumulative frequency. This column will accumulate the frequencies as we move down the table, providing a running total. This total is key to understanding the scope of her study, specifically, the total number of pharmacies involved.
- Helena then shifts her focus to calculating the relative frequency. This measure will give deeper insight into each purchase rate’s participation within the total dataset. Relative frequency is calculated by dividing the frequency of each purchase rate by the maximum value of the cumulative frequency.
By executing these steps, Helena gains a comprehensive understanding of the purchasing behavior across the pharmacies in her study.
This frequency table can help you quickly answer your question about the market you want to study and have a quick insight of it, For instance, if we want to determine the percentage of pharmacies that make more than 70 purchases in a year, we simply refer to the relative frequencies. By adding them up — for instance, in our case, 20% + 20% + 12% — we find that it amounts to 52%. This means that 52% of the pharmacies, which equates to 13 out of the 25 pharmacies in our dataset, are making more than 70 purchases annually in that specific region.
Integrating this data with the average purchase frequency in the same geographical region lays the groundwork for assessing the TAM in that area. This comparative analysis is vital for a comprehensive understanding of market dynamics, both in a standalone context and relative to other regions.
Armed with such insights, we’re positioned to distribute our marketing budget in a more targeted and strategic manner. Additionally, it informs the shaping of our medical promotion strategies, aiming to achieve the highest Return on Investment (ROI) possible.
As we approach the conclusion of this article, I trust you found the information valuable. Please feel free to share this piece and discuss your experiences related to field studies and market analysis. I’m curious to know ; have you ever utilized frequency distribution in your work? If so, how did you apply it?
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