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What is it and why is it interesting for your business?

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.

How does it work?

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.

What do we need to pay attention to?

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.

What is it and why is it interesting for your business?

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.

How does it work?

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.

What do we need to pay attention to?

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.

What is it and why is it interesting for your business?

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.

How does it work?

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.

What do we need to pay attention to?

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.

What is it and why is it interesting for your business?

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.

How does it work?

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.

• Step 1: predicting if an invoice will be paid on time, yielding a binary outcome of "Yes" or "No."
• Step 2: if the invoice is predicted to be unpaid, we predict whether it will be settled before the end of the month, recognizing the significance of month-end, quarter-end, and year-end deadlines in finance.

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.

What is it and why is it interesting for your business?

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.

How does it work?

To address this challenge, we set up a detection model that automatically sends alerts directed towards account managers. These alerts serve two primary purposes:

• To prompt account managers to encourage customers to proceed with purchasing the goods despite deviations from the contract terms
• To facilitate the selling of excess goods to alternative customers.

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.

What is it and why is it interesting for your business?

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.

How does it work?

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.