The Power of CRM: Building Lasting Customer Relationships
Greetings to all data science enthusiasts, As mentioned in my previous posts, I will share the most important parts of what we learned in each week of my bootcamp. In this post, i will write about CRM Analytics.
If there is a company of course, it has customers. The management of the relationships with customers is called CRM.
- Customer lifecycle optimizations: for example we have got a website, the first step of the customer is entering the website. The second step is signing up for the site. The third step is making a purchase. These are the initial steps of the customer lifecycle.
- Communication (language, color, visuals, campaigns): It is the communication that will take place with the customer. For example, some companies make jokes on their customers via social media.
- Customer acquisition/finding efforts: It is about finding new customers, regardless of whether it is online or offline. Finding new customers is more costly than retaining existing ones. Therefore, a large part of CRM efforts are aimed at retaining existing customers, which are known as customer churn efforts.
- Cross-selling, upselling: For example, if a person buys a hamburger, they are offered fries (cross-sell). If they buy a cola, they are offered a larger size of the same drink (upsell). In summary, it is an attempt to make more sales.
- Customer segmentation efforts: When a brand addresses its customers communicates with them, instead of treating them all the same, it can divide its customers into different segments and communicate more focused, useful, and efficiently. The basis of these efforts is that resources are limited, but there is a lot of work to be done. For example, if there are 1000 customers, and we want to retain them because finding new ones is more costly, it is quite logical to segment them to prioritize those that are most valuable to us, and those that might be slightly less so. When there is interaction between us and the customer, prioritizing the customers that will provide the most benefit to our company and proceeding accordingly is key.
CRM analytics efforts aim to make the entire customer relationship process more efficient based on data.
KPI, Key Performance Indicators, refer to measurable critical indicators that help an organization achieve specific objectives. They are mathematical indicators used to evaluate the performance of companies, departments, or employees.
- Customer Acquisition Rate: The percentage of customers acquired during a specific period.
- Customer Retention Rate: The likelihood of a customer who has engaged with the company to make a repeat purchase or continue patronizing the company.
- Customer Churn Rate: The rate at which existing customers leave the company.
- Conversion Rate: In the context of an advertisement, if a thousand people see the ad and ten people click on it, the conversion rate is 10/1000.
- Growth Rate: Goals set by companies at the beginning of the year or specific time periods.
RFM Analysis
RFM Analysis is a technique used for customer segmentation. It involves the categorization of customers into groups based on their purchasing behavior, allowing the development of strategies tailored to these groups.
RFM Metrics
Recency: Indicates how recently the customer made a purchase or interacted with us. For example, if one customer’s value is 1 and another’s is 10, a value of 1 is considered better, indicating a more recent purchase, especially when discussing daily transactions.
Frequency: Refers to the number of purchases/transactions made by the customer.
Monetary Value: Represents the monetary value that customers have left with us.
For the Recency metric, a lower value is considered better, for Frequency, a higher value is optimal, and for Monetary, the highest value is considered the best.
We need to convert the RFM metrics into the same format to make comparisons, which leads to the computation of RFM scores.
As you can see in the image below, F stands for Frequency, and R stands for Recency. Based on the R and F scores, we segment the customers
Customer Life Time Value
Customer Lifetime Value (CLTV) is the monetary value that a customer will bring to a company during the entire relationship-communication period established with the company.
CLTV is calculated as follows: CLTV = (customer value / churn rate) x profit margin.
Ultimately, when rankings are made based on the calculated CLTV values for each customer and groups are formed by dividing at certain points based on CLTV values, our customers will be segmented.
To calculate customer lifetime value, we have to do theese steps on below.
Customer Lifetime Value Prediction
Customer lifetime value prediction (CLTV) is a critical metric for businesses seeking to maximize their long-term profitability. Using BD-NBD model and Gamma gamma model to make prediction.
BG/NDB model
As long as a customer is alive, the number of transactions that the customer will perform within a certain time period follows a Poisson distribution with the transaction rate parameter. In simpler terms, a customer will continue to make random purchases around their transaction rate as long as they are alive.
Gamma Gamma Submodel
It is used to estimate how much average profit a customer can generate per transaction. The monetary value of a customer’s transactions is randomly distributed around the average transaction values. The average transaction value may vary over time but remains constant for a single user. The average transaction value follows a gamma distribution across all customers.
By combining the insights from these two models, businesses can make informed decisions regarding customer segmentation, personalized marketing campaigns, and resource allocation.
In my Kaggle Notebook, i explained everything more detailed and did a project about rfm, cltv and cltvp. Feel free to check it on link below;
https://www.kaggle.com/code/huseyinbaytar/crm-rfm-cltv-cltvp
To Be Countinued…
