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.
Predictive maintenance is a proactive approach to maintenance that uses data science and AI algorithms to predict equipment failures before they occur. By analyzing historical data, monitoring equipment conditions in real-time, and detecting early signs of potential issues, predictive maintenance helps businesses anticipate maintenance needs and schedule repairs at the most opportune times. This strategy minimizes downtime, reduces maintenance costs, and extends the lifespan of equipment, ultimately improving operational efficiency and maximizing productivity for our business.
The process begins with comprehensive data collection from various sources such as sensors, equipment logs, and maintenance records, encompassing parameters like temperature, pressure, and usage patterns. This data then undergoes preprocessing to ensure accuracy and consistency, including handling missing values and outlier detection.
Subsequently, domain expertise is leveraged in feature engineering to identify key indicators of equipment health or performance, such as temperature trends or vibration patterns. Machine learning algorithms are then deployed to develop predictive models, utilizing historical data to discern patterns and relationships between input features and equipment failures. These models are trained and refined to optimize performance, adjusting parameters to minimize prediction errors.
Once validated, the models are deployed into production systems for real-time monitoring of equipment health. They analyze incoming data streams to detect anomalies or signs of potential failures, enabling timely intervention to prevent downtime. Finally, the process is iterative, with continuous monitoring of model performance and refinement based on feedback from maintenance activities and new data, ensuring adaptability to evolving operating conditions and maximizing the benefits of predictive maintenance.
Striking a balance between false positives and false negatives is critical. While false positives can lead to unnecessary maintenance interventions and associated costs, false negatives risk overlooking genuine maintenance needs, potentially resulting in equipment failures and downtime. Achieving an optimal ratio between false positives and false negatives requires thorough evaluation and tuning of the predictive model, considering factors such as the cost of maintenance actions and the potential consequences of equipment failures. By addressing these considerations, organizations can enhance the accuracy and efficiency of their predictive maintenance efforts, maximizing equipment reliability and minimizing operational disruptions.
Demand forecasting is the process of predicting future customer demand for a product or service based on historical data, market trends, and other relevant factors. Demand planning is a crucial part of doing business as it impacts a lot of decisions in your company, like: production planning, inventory management, staffing, or even investment decisions to enter new markets.
Having efficient and accurate demand forecasting can deliver a competitive advantage, increased efficiency and better customer service.
Demand forecasting in retail businesses, particularly in industries which sell in items with limited shelf-live like the food industry, is essential for maintaining optimal inventory levels and maximizing sales while minimizing waste. Achieving this balance requires accurately predicting future demand for products, which is often influenced by various factors such as historical sales data, seasonality, weather conditions, promotions, and external events.
Manual forecasting methods become impractical as the scale of operations increases, leading to inefficiencies and inaccuracies. Implementing AI-driven forecasting solutions allows businesses to leverage machine learning algorithms to analyze large volumes of data and identify complex patterns that human planners may overlook. By considering multiple factors simultaneously, these models can generate more accurate forecasts, enabling businesses to optimize inventory management, reduce waste, and improve overall operational efficiency.
To ensure the effectiveness of demand forecasting, businesses must tailor their approach to account for specific product characteristics and store dynamics. For instance, certain products may exhibit seasonal peaks or fluctuations in demand based on external factors like holidays or promotions. Moreover, each store may have its own unique sales patterns and customer preferences, necessitating a granular approach to forecasting that considers store-specific data. By building predictive models at the product and store level and incorporating a broad set of shared drivers such as past sales, seasonality, holidays, promotions, and weather, businesses can enhance the accuracy and relevance of their forecasts.
Leveraging AI techniques like regression analysis allows for a more nuanced understanding of demand drivers and enables businesses to adapt quickly to changing market conditions. Ultimately, AI-driven demand forecasting empowers businesses to make data-driven decisions, optimize inventory levels, and enhance customer satisfaction, leading to improved profitability and competitiveness.
When conducting demand forecasting, it's essential to remain vigilant of concept drift, i.e., the statistical properties of demand fluctuating over time due to factors like market trends. Transparency in the model's decision-making process and identification of key drivers are crucial for understanding forecasted outcomes. This transparency allows stakeholders to make informed decisions based on a comprehensive understanding of the forecasted outcomes and associated uncertainties.
Additionally, striking the right balance between false positives and false negatives is vital for effective forecasting. While false positives may lead to overestimating demand and unnecessary inventory costs, false negatives can result in stockouts and missed sales opportunities. Achieving an optimal balance between these errors requires careful calibration of the forecasting model, considering factors such as the cost of inventory holding and lost sales.
Ensuring accurate treasury forecasts is crucial for any business, as it directly impacts financial planning, liquidity management, and risk mitigation strategies.
By accurately predicting cash flows, a company can optimize its capital allocation, manage working capital effectively, and make informed investment decisions. Additionally, reliable treasury forecasts enable proactive management of financial risks such as liquidity shortages, interest rate fluctuations, and currency volatility, reducing the likelihood of financial crises or disruptions to business operations. Furthermore, it optimises collection management to prioritize customers with highest likelihood and importance to be contacted.
In our approach, we utilize both regression and classification algorithms to generate predictions for treasury forecasting. With regression algorithms, we predict a numeric value, while classification algorithms allow us to predict multiple classes with associated likelihoods. Given these options, we opted for multiple classification predictions with predefined buckets, offering a broader understanding of potential delays and their severity.
By leveraging these forecasts, our customer can optimize cash pooling strategies, ensuring efficient allocation of funds across accounts and minimizing idle cash balances. Additionally, they can strategically request loans in specific currencies based on predicted cash flows, mitigating currency risks and optimizing borrowing costs. Lastly this provide prioritization to the collections team within their contact strategy by defining a prioritized call list for each customer.
In contract fulfillment, discrepancies between specified volume commitments and actual order quantities can occur due to various factors, including end-product customer demand or alternative supply sources. Sales departments are committed to achieving higher contract fulfillment rates to meet client expectations and maximize revenue.
To address this challenge, we set up a detection model that automatically sends alerts directed towards account managers. These alerts serve two primary purposes:
By implementing these automated alerts, we streamline the process of managing contract discrepancies, optimize inventory utilization, and ultimately enhance overall contract fulfillment performance. This proactive approach ensures that we not only meet our contractual obligations but also capitalize on opportunities to drive additional revenue and strengthen customer relationships.
Employability scoring for recruitment involves the use of machine learning algorithms to predict the likelihood of job candidates being successfully matched with available positions.
At element61, we recognize the importance of streamlining the recruitment process to efficiently identify the most promising candidates in the HR and interim business. Drawing from a past use case with Vivaldis, a Belgian interim office, we understand the challenges of processing a high volume of job candidates without a prioritization system in place. By implementing an employability scoring model, we were able to rank candidates based on their likelihood of finding a job, allowing recruiters to focus their efforts on reviewing the most relevant resumes first.
In order to scan the most relevant resumes, we have built a machine learning model which predicts which candidates are most likely to be matched with a job. The employees can then scan through the most relevant resumes and as such, tackle high value candidates as fast as possible. At this point, the machine learning model is not scoring the candidates for a specific job but rather the overall employability of a candidate.
In order to define a score, we consider different drivers for the employability score such as age, language knowledge, education level, driving permit, experience, etc. Important when working with sensitive data, is that the model is built to avoid any discrimination or bias. Among others, this means we don’t include certain drivers like gender & region. The tooling developed is working hand-in-hand with Salesforce, the customer tool in place to collect and store all the information about the candidates (both information received in the offices as on the website itself). This means that Salesforce remains the end-user tool & embeds our solution.
