Customer Churn Prediction and Streamlining Customer Experience

Harness the power of Machine Learning for data-driven insights:
Employing advanced analytics and machine learning empowers organizations to gain a deeper understanding of customer behavior. Recent studies indicate that machine learning can lead to a 5% monthly reduction in churn, revolutionizing company growth over the course of a year. Effective predictive analytics can identify early indicators of churn, enabling timely intervention. For instance, analyzing specific customer behaviors can reveal dissatisfaction, prompting proactive measures to address their concerns.

Use customer feedback results in training AI models:
Feedback loops are instrumental in enhancing products and services. Proactively utilizing customer feedback to refine services is crucial. Have you considered incorporating CSAT results into an artificial intelligence model?

Optimize Customer Service:
Efficiency is paramount in customer service. Automated assistants expedite issue detection, resulting in increased customer satisfaction.

Deliver a personalized experience, implement incentives and loyalty programs:
Understanding hidden customer patterns is pivotal in crafting effective marketing and campaign strategies. For example, rewarding loyal customers throughcampaigns like discounts can strengthen customer loyalty. Tailoring communications and interactions foster loyalty and trust, especially for customers who are considering to churn.

Ensure competitive pricing and foster product innovation:
Maintaining competitive pricing is essential to stay appealing to both existing and new customers. This is critical for attracting and retaining customer loyalty. Broad Street Analytics can assist in determining the right pricing policy by leveraging the power of AI.

Broad Street Analytics will lead you to gain new customers while keeping the current ones happy and satisfied.

Customer Segmentation

People as individuals are very special, but people as part of groups are not so much. They are similar.

There are several ways for classifying your customers such as demographics, behavioral, psychographical, geographical etc. However, it's not very simple to pick which for which circumstances. Or should you pick a combination of two? Broad Street Analytics AI models will identify patterns for you.

Once customer segmentation is provided accurately, you as a company can decide
which segment to target,
how to tailor your products and services,
how to communicate with the audience,
where to share your messages,
and possibly more...

Broad Street Analytics helps you clustering customers in the most effective way based on your company goals.

Predictive Marketing

How are your sales going to be effected when you change your marketing strategy?

Predictive analytics merges current and/or past data, predictive modeling, and machine learning to anticipate specific events and outcomes. In contrast, descriptive analytics works on historical data to comprehend the factors and anomalies influencing performance. While descriptive analytics focuses on the past, predictive analytics is geared toward anticipating future developments.

Make data-driven, better-informed decisions and more accurate budget allocation.

Forecast trends and trend shifts. Also, their impact on your revenue.

Optimize your marketing strategy to lead more sales.

Broad Street Analytics helps you understanding the impact of your marketing decisions on revenue and sales.

Sales & Claim Prediction

Sales prediction:
Sales prediction, commonly known as sales forecasting, involves the process of projecting future sales by estimating the quantity of products or services that an individual salesperson, a sales team, or a company is expected to sell within a defined time period, such as the upcoming week, month, quarter, or year.

A sales forecast is instrumental for guiding businesses in making informed decisions. It plays a crucial role in comprehensive business planning, budgeting, and risk management. Through sales forecasting, companies can strategically allocate resources for future growth and adeptly manage their cash flow.

Insurance claim prediction:
According to Willis Towers, more than two-thirds of insurance firms state that predictive analytics has played a crucial role in lowering their expenses and addressing underwriting challenges. The surge in Artificial Intelligence has led insurance companies to increasingly embrace machine learning for pivotal objectives such as reducing costs, refining underwriting processes, and enhancing fraud detection. Specifically, through the utilization of machine learning, insurers can efficiently screen cases, assess them with exceptional accuracy, and make precise cost predictions.

To emphasize the significance of integrating machine learning, McKinsey estimates that German insurers alone could save approximately 500 million Euros annually by incorporating machine learning systems into healthcare insurance. This statistic underscores the compelling need for insurance companies to adopt machine learning methodologies.

Broad Street Analytics will fast forward the time for you to help calculating your cash flow and/or taking other data-driven decisions.

Fraudulent Activity Detection

The global average rate of losses caused by fraud for the last two decades represents 6.05% of the gross domestic product. Additionally, companies have reported that cyber breaches have caused financial damages equaling 3% to 10% of their revenue. Moreover, global digital fraud losses are projected to exceed $343 billion between 2023 and 2027.

Given the estimated amounts, it is a crucial question for any organization to build up an efficient fraud management system. Fraud management is identifying, preventing, detecting and responding to fraudulent activities within an organization.

Artificial intelligence (AI) has a significant role in fraud management. AI technologies, such as machine learning (ML) algorithms, can analyze large amounts of data and detect patterns and anomalies that may indicate fraudulent activities. AI-powered fraud management systems can identify and prevent various types of fraud, such as payment fraud, identity theft or phishing attacks. They can also adapt and learn from new fraud patterns and trends, improving their detection over time.

AI-based solutions can also integrate with other security systems, such as identity verification and biometric authentication, to provide a more comprehensive approach to fraud prevention.

Reference: How can artificial intelligence help detect fraud?

Broad Street Analytics helps banks and insurance companies identifying transaction frauds, illegal payments and identity thefts.

Anti-money Laundering

AI and AML have significant crossover — both rely on patterns in what are often very layered data sets. In AML, important patterns in variables can include frequency, size, and location of deposits, as well as jurisdictions of companies, controllers, owners, and politically exposed persons (PEPs). Machine learning is well-equipped to unearth the buried nexus points between the variables.

In the current economic landscape, where banks and financial institutions are keen on tightening operational expenses, areas heavily reliant on manpower, such as AML and compliance, undergo close examination. Simultaneously, these entities grapple with increasingly stringent and dynamic regulations.

CFOs are actively seeking cost-effective approaches to enhance programs and achieve more with less. While strategies like off-shoring and relocating teams to tax-friendly states contribute to cost reduction, automation emerges as the ultimate frontier in this pursuit.

Broad Street Analytics helps banks and insurance companies train AI models for AML, and readily available location based demographics database will go a long way in your trainings for sure.

Predictive Maintenance

One of the most popular and modern maintenance policies is Predictive Maintenance (PdM), which is also known as condition-based maintenance. PdM measures parameters in the condition of equipment in order to carry out the appropriate tasks to optimize the service life of machines and processes without increasing the risk of failure.

PdM as a term is mostly used for equipment related maintenance strategies which excels in
Preventing imminent and unexpected equipment failure,
Reducing the total time spent in maintenance,
Reducing maintenance costs by avoiding catastrophic defects,
Increasing availability and reliability of your equipment,
Extending life of equipment and processes,
and possibly more...

Broad Street Analytics helps you reducing equipment failures for higher customer satisfaction and reduce your maintenance costs.

Household Energy Optimization

In the recent era, for most sustainable smart cities energy conservation is a major point of consideration as urbanization is been carried out at an exponential rate. Out of that most of the energy consumption is diverted toward households, and there is a huge scope for optimization of this energy. Hence predicting this household energy with the advancement of AI and Machine Learning techniques is considered a social contribution and area of interest for most researchers. But only predicting the energy consumption will not solve the problem of energy optimization for a city, it is also important to understand the factors responsible for such predictions so that all possible recourses could be carried out to those factors, and it becomes more accountable, trustworthy and justifiable its energy optimization decisions towards its all stakeholders.

Reference: Explainable AI for predicting daily household energy usages

Broad Street Analytics helps energy companies predicting monthly electricity consumption of households in addition to energy optimization suggestions for your customers, customer segmentation and predictive marketing for your company.

Stock (Inventory) Optimization

One of the main applications of AI for stock optimization is demand forecasting, which is the process of predicting future customer demand based on historical data, market trends, and other factors. AI can use machine learning algorithms to analyze various data sources, such as sales records, customer behavior, weather, seasonality, promotions, and more, and generate accurate and timely forecasts that can help businesses plan their stock levels and replenishment strategies. AI can also adjust the forecasts dynamically based on real-time data and feedback, and account for uncertainty and variability in demand.

Another application of AI for stock optimization is stock allocation, which is the process of distributing the available stock across different locations, channels, and products to maximize customer satisfaction and profitability. AI can use optimization algorithms to find the optimal allocation of stock that minimizes costs, such as holding costs, ordering costs, transportation costs, and stockout costs, and maximizes benefits, such as revenue, service level, and customer loyalty. AI can also consider various constraints, such as capacity, lead time, demand, and stock policies, and provide recommendations or solutions that can help businesses optimize their stock allocation.

Reference: What are the challenges and benefits of using AI for inventory optimization?

Broad Street Analytics runs simulations to fast forward time to your company's future to tell you about future stocks, products and services in demand, and help you allocating your resources, deciding lead time etc.

Dr. John Snow and Story of Broad Street Pump