Churn prediction is the process of identifying customers who are likely to discontinue their relationship with a business. It involves analyzing customer behaviour, and other relevant features to predict the likelihood of customer churn. By predicting customer attrition, businesses can take proactive measures to retain valuable customers, such as personalized re-engagement campaigns or improving customer experience.
Churn prediction is often crucial for businesses as the cost of acquiring a single customer is generally greater than the cost of retaining one. This is especially the case in industries with subscription-based business models.
Insights from churn prediction can lead to several benefits, among which:
Combining churn prediction with customer segmentation, helps you to identify and target high, medium & low value customers that are likely to churn. This allows businesses to allocate resources more efficiently to where they generate the most value.
We use a set of customer features to recognize patterns in customer behaviour and predict the likelihood of churn in the next month, quarter, semester.
Some of the most frequently used features are:
Features should always be defined together with business-savvy stakeholders to assemble a set that is capable of predicting churn. Depending on the nature of the dataset and the complexity of the features, the applied algorithm can vary from a logistic regression to a neural network.
Ultimately, we use the model to benchmark a customer's behaviour to historical feature data that was gathered for similar customers, and use this to predict how likely they are to churn.
Model interpretability is crucial when using AI to predict customer churn. It refers to the ability of a business to understand the model and the factors that contribute to churn. Without a thorough understanding of the model outcomes, it becomes challenging to take appropriate actions to prevent attrition.
In industries characterized by stiff competition, such as media and publishing, effective customer retention strategies are indispensable for sustained success. In a climate where advertising revenues face significant challenges, the balance between customer acquisition and retention becomes even more critical, with retention often proving to be up to five times more cost-effective than acquisition efforts.
To tackle this, a comprehensive churn prediction model was developed, utilizing both subscription and behavioral analytics data. The model accurately forecasts churn probabilities over various timeframes, such as 1, 3, and 6 months, enabling companies to proactively address attrition risks.
With solid performance demonstrated across these periods, the model's insights are now being leveraged across advertising and marketing initiatives to optimize customer engagement and loyalty, contributing to the long-term viability of businesses in the sector.
Lead scoring is a methodology used by sales and marketing to rank and prioritize potential customers (leads) based on their likelihood of converting into paying customers.
The goal is to retain customers that already have interest and convert them to "real" customers. For instance, if someone visits your website, you want them to revisit your website and potentially buy something or use your service.
Lead scoring can be a simple classification algorithm that gives a user a score between 0 and 1 to become a potential lead or not. Based on the scores, you can split your customers up into tiers: low, medium or high potential leads. The ones you then definitely want to target are the medium potential leads as they just need a bit more of convincing. The high potential leads do not necessarily need more efforts, but do need to be followed up quickly.
In this way, your marketing efforts can be bundled and no resources are wasted. Identifying your leads means also identifying the customer behavior, tracking your website, your sales, looking at the user demographics and so on. As such, one can identify a lead early on in the process and does not have to wait for them to click on an add, a whitepaper or a form to be informed by their interest. You can then anticipate and target more leads in advance.
In most cases, identifying your leads means identifying criteria that indicate a qualified, potential lead for your business. Useful criteria can be found by looking into questions like:
Each criteria is seen as a potential factor to indicate whether a user could be a potential lead. The scoring model then assigns a score based on previous behavior and the characteristics of the user. This is a predictive model that is mainly based on patterns in the past that are linked to a conversion of a lead or not.
In the B2B market, GDPR regulations are generally not a cause for concern. However, in a B2C use-case ensure compliance with data privacy regulations when collecting and processing lead data. Additionally, consider ethical implications of lead scoring, such as potential biases in the data or model predictions, and take steps to mitigate them.
Establish a feedback loop to continuously monitor the performance of the lead scoring model in production. Collect feedback from sales and marketing teams on the quality of scored leads and their conversion outcomes. Use this feedback to iteratively improve the model by retraining on updated data or adjusting model parameters.
Propensity scoring is a concept often used in the customer or subscriber management. Propensity scoring can be used to determine the likelihood of a customer buying, up-selling or churning. While all related they can be used throughout the customer lifecycle and require different strategies for management. As customer churn has its own intricacies, we dedicated page specifically to this topic.
Propensity to renew refers to the likelihood that a customer will continue or prolong their contract with the business upon its expiration. This metric is particularly relevant for subscription-based businesses where customer retention is essential for sustained revenue growth. Factors influencing propensity to renew may include customer satisfaction, perceived value of the service, ease of use, customer support experience, and competitive offerings. Rather than defining yourself who is the audience most (un)likely to renew, AI scoring helps marketeers continuously analyses all historical data to automatically gather a relevant target audience at all times. Updating the scoring of each customer over time.
Every customer exhibits unique behaviors and characteristics, ranging from the specifics of their contract (duration, installments, etc.), the product or service they've engaged with, to their individual attributes (customer type, age, past renewal history, etc.). The challenge here is to sift through this diverse array of data and identify patterns that can accurately predict whether a customer is likely to renew their contract or not.
As a proof of concept, we approach this problem as a classification task. We aim to develop a machine learning model capable of discerning whether a customer will renew their contract or not. This involves analyzing the data to determine if there are strong correlations between various factors and the likelihood of renewal. The model outputs the probability of a customer renewing their contract within a given timeframe (e.g., next month, next three months). If the proof of concept generates satisfactory results, the scope can be expanded to answer questions such as when will a customer renew or will the customer renew in the same product/service to determine up-sell possibilities.
Utilizing time-series data necessitates a careful train-test split to prevent data leakage, ensuring that information in the training set doesn't inadvertently aid predictions in the test set. For instance, when segmenting contracts into monthly data points, it's crucial to assess if contracts overlapping between training and testing sets lead to data leakage.
Additionally, in the B2C market, strict adherence to GDPR regulations is paramount. It is essential to implement rigorous measures to safeguard customer data privacy and uphold ethical standards throughout the analytical process.
