CRM Analysis: A Fundamental Analysis to Boost Sales
Posted: Tue Dec 03, 2024 7:02 am
If the majority of your customers are 35 years old, is it correct to identify your main uk phone number list target as the 30-40 age group? If a certain book is the best seller of the moment, is it correct to propose it as a "suggested product" to those who are purchasing other products on your e-commerce?
In both cases, the intuitive answer would be yes. The correct answer, however, is quite different. As often happens, in fact, intuitive answers correspond to biases rather than truth.
And to get the right answers, then, what do we need to do? Obviously, query the data. In this case, the information we are looking for is found within our corporate CRM: that's where there is a real mine of information about our users and their purchasing habits.
Doing CRM Analysis means categorizing, correlating, reorganizing our CRM data to have clear and precise indications to support marketing strategies, as well as having the opportunity to create personalized communications that will strengthen brand loyalty in users, quickly improving sales results.
Let's see how together.
An Introduction to CRM Analysis
Today, almost all companies have equipped themselves with CRM, that is, computerized systems that bring together information from the website's CMS, from salespeople's phone calls, from information collected from individual points of sale (perhaps via a loyalty card), or in other ways.
We have already written several times, moreover, about how these data today have an increasing importance also due to the increasingly stringent regulations regarding the collection and use of "third party" data, that is, those collected, for example, through website cookies.
In fact, the company CRM contains all First Party Data, for which the company has direct consent to use by the interested user:
Data relating to USERS, such as personal data.
PRODUCT data, such as product category, price, purchase cost, brand…
ORDER data, which is where the first two entities meet. For example, the receipt total and the list of items purchased in a single order.
An expert analyst, by questioning this data, will be able to carry out even very in-depth analyses, which can provide new food for thought and investigation or confirm (or deny) what the company already believes it knows.
In particular, among these data we find:
An accurate picture of users and buyers, which will help to confidently define the target of the company's marketing initiatives (and, above all, to create effective segmentations).
Reliable information on purchasing paths, from which to start to improve business processes or to insert into these paths with effective initiatives.
Ideas for improving product assortment, profit margins, bundling, or other initiatives.
It is no coincidence that online sales giants invest a lot of resources in CRM Analysis and use it to segment their customers in an extremely refined way, create personalized communications, update prices and layouts.
It is no exaggeration to say that much of their success is due precisely to this ability to capitalize on the information in their possession, to translate it into new sales.
DON'T KNOW WHO TO ENTRUST WITH YOUR CRM ANALYSIS? DISCOVER OUR ANALYTICS SERVICES FOR YOU
Correct data, beyond personal biases
A problem we often encounter, both with entrepreneurs and with some consultants, is the inability to relate to data correctly. Out of superficiality, numerical data are ignored in favor of personal impressions or are evaluated with intuitive but substantially incorrect methodologies.
CRM Analysis allows us to overcome these incorrect assessments, which would lead to wrong strategies.
To understand how easy it is to fall into this type of error, let's consider as an example a seemingly extremely simple piece of data, namely the age of your target audience.
A common mistake is to rely on personal feelings. The entrepreneur sees that many of his customers are 35 years old and therefore takes this value as a starting point. Obviously, the reasoning is wrong because it is subject to confirmation bias; it also does not consider the possibility that 35-year-olds represent a good portion of the clientele, but not particularly relevant to the total.
Another mistake is to simply refer to the average. Knowing that the average age of my buyers is 35 tells me nothing about the actual distribution of purchases. It doesn't tell me if the buyers are mostly people between 30 and 40, or if they are distributed in a range from 20 to 50 or even from 20 to 57.
So the correct data can be extrapolated only with a statistical procedure that takes into consideration both the mean and the variance, so as to determine with certainty what is the age range that covers approximately 66% of my clientele.
Basket Analysis and RFM Analysis
Basket Analysis and RFM (Recency, Frequency, Monetary value) analysis are two of the most important components of CRM Analysis.
The online sales giants (which we mentioned earlier as among the companies that
In both cases, the intuitive answer would be yes. The correct answer, however, is quite different. As often happens, in fact, intuitive answers correspond to biases rather than truth.
And to get the right answers, then, what do we need to do? Obviously, query the data. In this case, the information we are looking for is found within our corporate CRM: that's where there is a real mine of information about our users and their purchasing habits.
Doing CRM Analysis means categorizing, correlating, reorganizing our CRM data to have clear and precise indications to support marketing strategies, as well as having the opportunity to create personalized communications that will strengthen brand loyalty in users, quickly improving sales results.
Let's see how together.
An Introduction to CRM Analysis
Today, almost all companies have equipped themselves with CRM, that is, computerized systems that bring together information from the website's CMS, from salespeople's phone calls, from information collected from individual points of sale (perhaps via a loyalty card), or in other ways.
We have already written several times, moreover, about how these data today have an increasing importance also due to the increasingly stringent regulations regarding the collection and use of "third party" data, that is, those collected, for example, through website cookies.
In fact, the company CRM contains all First Party Data, for which the company has direct consent to use by the interested user:
Data relating to USERS, such as personal data.
PRODUCT data, such as product category, price, purchase cost, brand…
ORDER data, which is where the first two entities meet. For example, the receipt total and the list of items purchased in a single order.
An expert analyst, by questioning this data, will be able to carry out even very in-depth analyses, which can provide new food for thought and investigation or confirm (or deny) what the company already believes it knows.
In particular, among these data we find:
An accurate picture of users and buyers, which will help to confidently define the target of the company's marketing initiatives (and, above all, to create effective segmentations).
Reliable information on purchasing paths, from which to start to improve business processes or to insert into these paths with effective initiatives.
Ideas for improving product assortment, profit margins, bundling, or other initiatives.
It is no coincidence that online sales giants invest a lot of resources in CRM Analysis and use it to segment their customers in an extremely refined way, create personalized communications, update prices and layouts.
It is no exaggeration to say that much of their success is due precisely to this ability to capitalize on the information in their possession, to translate it into new sales.
DON'T KNOW WHO TO ENTRUST WITH YOUR CRM ANALYSIS? DISCOVER OUR ANALYTICS SERVICES FOR YOU
Correct data, beyond personal biases
A problem we often encounter, both with entrepreneurs and with some consultants, is the inability to relate to data correctly. Out of superficiality, numerical data are ignored in favor of personal impressions or are evaluated with intuitive but substantially incorrect methodologies.
CRM Analysis allows us to overcome these incorrect assessments, which would lead to wrong strategies.
To understand how easy it is to fall into this type of error, let's consider as an example a seemingly extremely simple piece of data, namely the age of your target audience.
A common mistake is to rely on personal feelings. The entrepreneur sees that many of his customers are 35 years old and therefore takes this value as a starting point. Obviously, the reasoning is wrong because it is subject to confirmation bias; it also does not consider the possibility that 35-year-olds represent a good portion of the clientele, but not particularly relevant to the total.
Another mistake is to simply refer to the average. Knowing that the average age of my buyers is 35 tells me nothing about the actual distribution of purchases. It doesn't tell me if the buyers are mostly people between 30 and 40, or if they are distributed in a range from 20 to 50 or even from 20 to 57.
So the correct data can be extrapolated only with a statistical procedure that takes into consideration both the mean and the variance, so as to determine with certainty what is the age range that covers approximately 66% of my clientele.
Basket Analysis and RFM Analysis
Basket Analysis and RFM (Recency, Frequency, Monetary value) analysis are two of the most important components of CRM Analysis.
The online sales giants (which we mentioned earlier as among the companies that