Definition of distribution probabilities
The probability distribution is a principle based on mathematics. It highlights the fact that there is a probability for each result that will depend on a completely random variant.
In today's world, where companies are under very strong competitive pressure, the distribution of probabilities can play an essential role, especially when companies want to develop a marketing strategy for a new product or service.
One of the main objectives, but we will come back to this in more detail later, is to be able to predict to a certain extent the purchasing behavior of consumers, to know what drives them to buy but also to know the main obstacles to these purchases.
Thanks to the distribution of probabilities, the company will be able to develop strategies that are more in line with the needs and expectations of consumers, especially when certain situations appear to be uncertain. This is particularly the case when a product is new, but already has many competitors on the market, or when it is an innovation, and the company does not know how it will be received by the customer.
Key features
Distributions can be of several kinds, they can be considered as discrete, for example when the results are limited, the number of views on a video presenting a product for example.
They can also be continuous, i.e. when they refer to a period, namely the time spent by Internet users on the page of a product or service.
Formulas applied to distribution probabilities
There are two types of formulas, those that are applied to discrete variables and those that are applied to continuous variables.
For the first, it must be considered that it follows a probability that is associated with several values. For example, how many consumers have become customers following a video presenting a product on the net.
For the other, it is a question of considering variables, but continuously. In terms of marketing, it is thus possible to study how customers spend their money on the site in each area.
Which types of distribution are particularly suitable for marketing?
Applied to discrete distributions
Type 1: Binomial
This type of distribution allows you to highlight whether a campaign has been a success or a failure.
This allows businesses to calculate the number of clicks or views that have turned into actual sales over a given period.
Type 2: Fish
The aim here is to see how many Internet users have visited a site in a day, or over a shorter period, in one or two hours.
This type of distribution can also be used to analyze the number of returns on a new product, or the number of customers who have called customer service for a breakdown.
For continuous distribution
Type 1: normal
The aim here is to take more account of consumer behavior during a purchasing process.
From the point of view of companies, it is above all a question of analysing how much time consumers spend on a site, and therefore of being able to regulate the activity of the site in question, by offering more appropriate content, especially if consumers do not spend much time on it. This will allow companies to find the right balance between content that is too long and content that is not long enough.
It is also a question of understanding the discrepancies that can exist between several purchasing habits.
Type 2: Exponential
For example, how long does it take for a customer of a company to buy something from the same site?
When a company knows the time gap between two purchases by the same customer, it is more possible to offer promotions at the right time, and therefore increase the chances of selling in the long term.
One of the main objectives is to be able to collect as much data as possible, to predict behavior and increase sales significantly. Companies will then be able to increase both their sales and their competitiveness.
Concrete examples:
We were talking earlier about the normal type of distribution. The latter makes it possible to study how a brand's customers make their purchases. For example, it is a question of calculating the variations in the average basket of consumers and using this analysis to improve customer segmentation at a given time of the year. Indeed, during certain periods, this could be distorted, such as Christmas or Easter depending on the sector of activity.
In addition, the so-called exponential distribution will make it possible to analyze how long there is between two purchases, but from two different customers. Companies can then send more personalized emails to customers to increase their chances of selling a product.
Companies, thanks to the probability of distribution, can also forecast their inventories more accurately. Indeed, to perfect the productivity and performance of a group, it is necessary to anticipate possible stock shortages but also to avoid losses. This is what the distribution of fish will allow.
Finally, when a company defines a new advertising campaign, the probability of distribution makes it possible to analyze the number of clicks and to be able to predict the number of clicks over a given period.
Conclusion
In today's highly competitive environment, probability distributions are a major asset for a more effective understanding of customers' purchasing behavior. The different types of distribution and their associated formulas are a way for companies to optimize the management of their resources. The main objective is to be able to develop a more optimal consumer experience, to improve long-term customer loyalty and satisfaction.
Distribution probabilities can help companies be more accurate in their marketing research and more efficient, remaining more competitive in the long run, while adapting to an ever-changing environment.
Understanding customer behavior has become paramount for companies, and risks are significantly reduced through prior analysis and calculations.
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