How Can a CDP Help Minimize Business Churn Rate?
Customer churn refers to the amount of consumers who have quit purchasing your company's services or products over time. It is the most important statistic for assessing and improving client retention, and it is a big challenge for any company.
Unfortunately, organizations attempting to expand their client base sometimes overlook churn, regarding recruitment and retention issues as two separate issues. Despite this, according to a well-known Harvard Business Review study
"Acquiring a new customer is anywhere from five to 25 times more expensive than retaining an existing customer"
Existing and loyal customers are more easily persuaded by your brand's offering than potential new customers, thus retention is crucial. Despite this, we continue to see businesses ignore their client retention initiatives in order to seek new customers.
The first step in reducing churn is to create a synergy between recruiting and retention.
This is why a customer data platform (CDP) gives churn rate optimization a new level. ACQUIA CDP can improve the customer experience while also putting churn rate monitoring at the forefront of corporate growth analysis.
A CDP gives us a new perspective on the customer journey
A high turnover rate as a result of a non-optimized client journey must be considered.
Acquia CDP unifies all of your data sources and reimagines how teams use it to boost business efficiency and improve customer satisfaction.
A CDP unifies consumer identities and insights across all channels by connecting data sources in a single platform. Businesses can execute personalisation efforts and consistent consumer experiences at scale if all of this data is brought together in a single source of truth.
From IT to data engineers to marketers, data now drives all essential business processes. To realize the full value of their consumer data, departments must collaborate from a consolidated platform.
SQLI, an Acquia partner, works with all of these people in a firm to create a virtuous circle that focuses on minimizing churn rates across all marketing efforts.
This is how that procedure looks:
- SQLI ingest your data sources into the Acquia CDP:
- SQLI ensure Identity reconciliation;
- SQLI create addressable segments;
- SQLI built a machine learning to address in real-time the churn rate optimization;
- SQLI set-up Dashboard & Report for better cross-channel customer analysis:
- SQLI built adapted real-time journey orchestrations depending on the customer "churn risk"
To ensure real-time churn risk monitoring, SQLI developed an integrated machine learning methodology
The SQLI technique is backed by Acquia CDP's out-of-the-box machine learning algorithms, which significantly speeds up the development process.
Model for predicting churn
The machine learning method entails creating a model that can calculate output from input. Using historical data, the model is developed without being explicitly programmed. The customer characteristics are the inputs to churn prediction, and the output is the probability that the customer will churn.
Because it involves time, the prediction is more properly referred to as a forecast. Is a client likely to churn in the near future if you know what his current and prior traits are? As a result, the model input is data collected over a period of time that begins in the past and ends at a given moment in time (this is called the observation window). And the model's output is assessed using data from a temporal span that begins at the same point (called the response window).
The time point is in the past when the model is trained and evaluated. However, is the present period when the model is deployed in production to predict if a client is likely to churn.
The replies window data is simply used to determine whether the client is still interacting with the brand. If the answer is affirmative, the customer is classified as "no churn." Otherwise, they're referred to as "churn." The type of technique used to build the model is considered to be supervised because the data is labeled. The model's output is divided into two categories: "churn" and "no churn." As a result, the algorithm is a binary classification algorithm (categories and named classes in ML context).
Data
Customer information can be divided into two categories:
- Customer attributes that aren't dependent on the targeted business are one type of data. This category, for example, comprises demographic characteristics such as birth date (from which age is calculated), education level, location, and income...
- Customer contacts with the brand are the subject of the other data category. What products were purchased? What channel was it that was used? The client journey is crucial and should be meticulously documented. Interactions with customer service are extremely crucial, especially when it comes to predicting turnover (e.g., queries sent, number of interactions, history of customer satisfaction scores).
It matters in both areas. Because the data from the first is rather static (it doesn't change significantly over time), it won't provide a significant benefit in terms of attrition. It will, however, provide useful data because it will assist the model in learning customer kinds that are linked to consumer actions.
The usefulness of data is determined by the business purpose.
Methodology
The model is created automatically, with no prior knowledge of its performance. To improve it, findings must be analyzed, which leads to the improvement of input data or/and model. These enhancements will result in fresh results, which will need to be analyzed again if they aren't satisfactory, and so on. It is an iterative procedure to create a model. The CRISP-MD (Cross-industry standard process for data mining) is an iterative approach that is well suited for the machine learning task.
Conclusion and perspectives
To be successful, a churn detection project needs to be sustained by:
- A good understanding of the business and of the available data.
- Data completeness and accuracy, on the customers, the products and how customers interact with your brand.
- Understanding machine learning models and parameters to adapt them to the business needs.
By incorporating all channels and capturing the whole customer experience, Acquia CDP gives a single 360° view of the consumer. Brands may achieve greater marketing success, lower customer churn, and retain more customers over time when they use the SQLI approach and expertise to rationalize and speed up machine learning applications.
The focus of this article was on churn prediction, which is critical for corporate growth. Machine learning, on the other hand, can be used in a variety of marketing scenarios, including:
- Predictions:
- Likelihood to engage
- Likelihood to convert
- Likelihood to respond to a discount offer
- Likelihood to make a repeat purchase
- Likelihood to make a return
- Likelihood to churn
- Personas:
- Product cluster
- Behavioral cluster
- Seasonal cluster
- One-to-one personalization:
- Next best product
- Next best action
- Send time optimization
The team behind this project
Damien Sonnerat - Data Scientist