Category : Data & Analytics

How predictive analytics is driving personalized banking experiences

Jul 23,2024 / By Vivek Patil / No Comments

Today, consumers have greater control over their financial journeys. Therefore, banks must adapt to customers’ evolving needs by providing seamless, end-to-end experiences.

Data plays a critical role in this transformation. Robust data foundations enable banks to efficiently assess transaction details, stakeholder information, payment processing, compliance, and documentation. Given the increasing use of smartphones and the constantly evolving fintech landscape, it is important to focus on addressing three key areas:

Cost reduction
Improved decision-making
Enhanced customer experiences

Now, let’s dive into how predictive analytics can assist in achieving these objectives.

Understanding the role of predictive analytics in modern banking

Prebuilt predictive analytics platforms aim to enhance personalization. But these platforms continuously fall short due to constantly changing customer behavior.

Banks need real-time analytics capabilities which helps them understand spending patterns linked to major life or financial events, enabling banks to predict and implement the next best actions (more on this in the next section).

Creating real-time predictive models allows banks to tailor hyper-personalized offers, recognizing the unique motivations behind each customer’s activities and events. This approach ensures more accurate and relevant customer engagement, ultimately driving better results for the customer and financial institution.

Types of predictive modeling

Predictive modeling automates targeting, minimizing manual data analysis and dependence on human intuition. Here are a few common types of predictive models:

Benefits of predictive analytics

Predictive analytics in BFSI offers significant benefits for leadership aiming to boost profitability and efficiency:

It reduces costs by preventing fraud, lowering loan defaults, and retaining customers who might otherwise churn.

Real-time data updates enable better decision-making, accurately representing risks and boosting confidence.

Hyper-personalization allows targeted customer segmentation and personalized communication, enhancing overall customer experience and satisfaction.

Banks understand the necessity of establishing a top-notch customer experience. However, many still have crucial operational data confined within legacy IT systems.

How are banks adopting experience driven banking?

Banks and NBFCs are embracing experience-led banking by analyzing customer data from digital banking activities, customer interactions, and transaction records. They use transactional, behavioral, and demographic details. Integrating data from both digital and physical channels is crucial for creating a comprehensive customer profile (360-degree view) and omnichannel experience.

Hyper-personalization is driving a 75% increase in customer engagement in one of our BFSI projects at Robosoft, as shown in the image below.

Next best action model

The next best action model (next best offer) uses AI to suggest the most appropriate decision or action for each customer interaction. We have published a detailed blog on building best-in-class recommendation systems – save it for later reading.

In contrast to the past, today’s customer journeys are non-linear and highly dynamic due to frequently changing personal financial situations. Banks can significantly improve results by proactively addressing customer needs with suitable alternatives.

Outcome-driven personalization in BFSI

BFSI brands can use predictive analytics to improve website personalization, thereby increasing onboarding completion rates and decreasing drop-offs. Brands can nurture long-term relationships by providing guidance and support during the setup process.

Tailored messages, such as reminders for bill payments, updates on loan qualification, credit card offers, or information about nearby branch locations based on past transactions and browsing history, have the potential to re-engage inactive customers and enhance overall engagement and conversions. Same goes for mobile app personalization.

Use cases of predictive analytics in banking

Collateral management: Predictive analytics helps banks forecast payment flows and anticipate end-of-day and intra-day positions, identifying potential collateral shortfalls. For example, HSBC uses predictive models to improve collateral management, ensuring accurate and timely forecasts to mitigate risks. It leverages NLP and machine learning within its PayMe app to understand transaction intent quickly. Their platform also offers personalized recommendations to customers to reduce irregular activities.
Cash management: Predictive analytics enables banks to forecast cash and manage working capital efficiently. For instance, Bank of America compares a company’s working capital and payment efficiency with industry benchmarks. Predictive analytics provides them with deposit balance notifications, dynamic data visualizations, and metrics for assessing payment efficiency, optimizing supplier payments, managing strategic cross-border payment flows, and protecting against account fraud.
Risk management: Predictive analytics helps take proactive anti-fraud actions, enhance internal audits, and refine credit and liquidity risk evaluations. For example, Wells Fargo bank uses analytics to notify customers about unusually high recurring payments and suggests transferring excess funds by checking savings accounts.
Marketing and sales optimization: Predictive analytics helps banks optimize their marketing and sales strategies by identifying the most effective channels, messages, and offers for various customer segments. For example, HDFC and many other banking players use predictive analytics to segment customers and tailor marketing campaigns, leading to higher engagement and value-building for top customers.

Conclusion

The growing demand for super apps, embedded finance, and personalized services has prompted banks to upgrade their digital banking platforms.

To leverage predictive analytics effectively, banks must update their application environment. Key steps include aligning IT and business initiatives, unlocking core systems, securely integrating data, and optimizing APIs through automation. By following this approach, banks can tap into previously unused capabilities to deliver seamless digital experiences much faster.

Many financial institutions have established AI and machine learning innovation centers to enhance data utilization through predictive analytics. This shift requires building in-house capabilities or collaborating with external tech partner to develop advanced fintech products and tailored digital experiences.

Data & Analytics

How to build a best-in-class recommendation system

Jul 9,2024 / By Jim Griffin / No Comments

Recommendation systems are powerful tools that guide customers to relevant products and services. Yet, they often face significant challenges, particularly when dealing with sparse data from new customers or unexplored product offerings. In this blog, we’ll dive into four types of product or service recommendation systems:

User-based collaborative filtering

Item-based collaborative filtering

Trending

Preference-based systems

Next, we’ll review how a hybrid system works to curate the best recommendations from multiple models, like those mentioned above. Finally, we’ll explore why applying hybrid systems at the customer-segment level can improve accuracy even further, rather than using a generalized approach across the entire customer database.

Types of recommendation systems

1. User-Based collaborative filtering

User-based collaborative filtering is a popular recommendation engine algorithm that connects similar users based on their past behaviors. Essentially, it identifies users with comparable tastes and recommends products that have interested those users. However, it requires a large dataset to work effectively, as it relies on finding significant overlaps in user activity. A more recent approach within this category is genome matching, which creates a detailed profile of a customer based on numerous attributes, even if they are new. By comparing these profiles using methods like cosine similarity, we can infer what new customers might like based on what similar, more established customers have enjoyed.

User-Based Collaborative Filtering - Robosoft Technologies - Recommendation systems

Genome matching

Genome matching is a more advanced technique under user-based collaborative filtering. It involves creating a detailed profile of customers using numerous binomial variables (e.g., time of day of purchase, discount usage, etc.). By mapping new customers to similar profiles of existing customers, marketers can derive meaningful insights and make more accurate recommendations, even with limited initial data.

If you’re a marketer deeply invested in understanding your customers, read our blog about the “Customer genomes approach” – Solving the cold start problem in recommendation systems.

2. Item-based collaborative filtering

Item-based collaborative filtering takes a different approach by focusing on the relationships between items rather than users. This method suggests products that are frequently bought together. For example, if a customer buys a hamburger, the system might recommend fries. This approach is effective in providing relevant suggestions based on item-to-item correlations and is reliable for delivering useful recommendations.

3. Trending (Hot selling)

The trending or hot-selling approach highlights what is currently popular among users. By stack ranking products or categories, marketers can feature items that are in high demand. This method works well for surfacing popular items and can be particularly effective in categories with high turnover or seasonality. For instance, dynamic environments where trends change rapidly, such as fashion or tech gadgets. Determining the right level of granularity and excluding less relevant attributes (like size) are key to making this approach work.

Recommendation systems

When implementing it’s crucial to consider the granularity of the categories. For example, while stack ranking by size – may not be effective, identifying trending categories and then tailoring recommendations within those categories can be highly impactful. The “last mile” in this process involves fine-tuning recommendations based on additional factors like size preferences or introducing the latest variants of popular products.

4. Preference-based recommendation systems

Preference-based systems analyze individual purchase patterns to identify tendencies toward certain categories or types of products. If a customer frequently buys formal pants, the system will continue to recommend similar items, keeping in mind the latest trends within that category. This method personalizes recommendations based on observed preferences, ensuring that suggestions remain relevant over time.

The hybrid approach: Coalition recommendation systems

A coalition recommendation system, also known as a hybrid recommendation system, combines multiple recommendation methods to enhance accuracy (as shown in the below image).

Recommendation systems

By evaluating and integrating suggestions from user-based, item-based, trending, and preference-based models, it determines the most relevant products to present. The hybrid approach increases confidence in recommendations, especially when multiple models suggest the same item or when a model has a high confidence score based on recent performance metrics like Mean Average Precision (mAP).

Enhancing accuracy with customer segments

Applying hybrid systems at the customer-segment level rather than a one-size-fits-all approach can significantly boost accuracy. Customer segments can be based on various factors such as geography, shopping frequency, recency, or product category preferences.

Segments in product recommendation systems

By tailoring recommendations to these specific segments, marketers can deliver more personalized and effective suggestions. This nuanced approach combines customer segmentation with the precision of coalition recommendation systems, leading to more accurate and engaging customer interactions.

Product recommendation systems

Conclusion

We explored various recommendation models and how combining them into a hybrid system can significantly enhance accuracy, especially when tailored to specific customer segments. This approach allows marketers to better understand and meet their customers’ needs, leading to improved engagement and business success.

In summary, using a mix of different recommendation techniques enables the creation of highly accurate and personalized customer experiences. When combined with customer segmentation, the coalition approach ensures each recommendation is relevant and effective, driving engagement and satisfaction.

By continually refining these systems and incorporating the latest techniques, marketers can stay ahead in the ever-evolving landscape of recommendation systems and deliver the best possible experience to every customer.

Next steps

Refer to this eBook for more details on retail transformation and consumer solutions. At Robosoft, we’re revolutionizing retail space by focusing on hyper-personalization and dynamic user experiences. If you’re a marketer, partnering with us will redefine:

Omnichannel experience: Immerse your customers in a unified commerce shopping experience with seamless online/offline integration.

Real-time analytics: Enhance your inventory management, demand forecasting, and personalized customer recommendations.

Customer Data Platform (CDP): Organize, segment, and digitally enhance your customer data for better insights and actions.

Connect with us to unlock the power of data-driven retail marketing.

Data & Analytics

Solving the cold start problem in recommendation systems

May 16,2024 / By Jim Griffin / No Comments

cold start problem in recommender systems

Interacting with recommendation systems has become an integral part of our daily lives, whether shopping on Amazon or discovering new music on Spotify. These algorithms work silently in the background, guiding us towards our next favorite choices.

Yet, businesses relying on these recommendation systems to drive revenue have an obstacle: the cold start problem.

Picture a scenario where a customer makes their first purchase, or a new item is introduced without historical sales data—this poses a daunting task for conventional recommendation algorithms.

This blog will discuss the fundamental challenges marketers encounter with recommendation systems and show how customer genomes can be a solution.

Understanding the cold start problem in recommendation systems

The goal of any recommendation system is to predict what a customer might want to buy and showcase those products to guide their purchasing decisions. The algorithm analyzes customer behavior and product characteristics to estimate the likelihood that a customer will be interested in a specific item. It involves creating detailed profiles for each customer and product. This approach is useful when figuring out what products a customer might want to buy again.

Imagine a new customer making their first purchase on an e-commerce platform. This customer’s profile is essentially a blank slate. This is where the Cold Start Problem emerges. It occurs when there is limited or sparse data of these newcomers.

Traditional recommendation systems, including popular methods like content-based and collaborative filtering, heavily rely on historical behavioral data to generate recommendations.

The failure of these traditional recommendation systems means missed opportunities to engage with and cater to new customers effectively. Without accurate recommendations, new customers may feel less connected to the brand or may not discover the full range of offerings, potentially leading to lower retention rates and reduced customer satisfaction.

Check out this blog to learn more about different types of recommendation systems.

Now, let’s try solving the cold start problem in recommendation systems!

The ‘cold start’ issue intensifies with a growing customer base and expanding product inventory. Visualize a vast matrix where rows represent customers and columns represent products. This matrix becomes really large and complex to manage as more customers join and products are added. Now, dealing with such massive data requires a lot of computing power and resources.

The matrix is also quite empty in many places. This happens because not every customer buys every product, leading to uneven activity. So, it’s tough for the system to figure out what products to recommend when there are so many empty spots in the matrix.

That’s where the customer genome algorithm comes in. Which we are going to talk about in the next section.

Watch this video to understand the cold start problem in Recommender Systems.

The customer genome approach

This approach uses a special algorithm to create a unique string of zeros and ones for each customer based on their data. Then, it matches this string to other customers who are more experienced with the brand to get insights on what might interest the new customer.

Think of the customer genome as your unique DNA for shopping preferences—the traits determining your choice. It captures every interaction and breaks it into a genetic code representing more than just product names. Even marketing emails have their own DNA, including subject lines, offers, recommended products, and visual and messaging elements.

Let’s use apparel as an example to understand how the customer genome approach works. Every time you browse, purchase, or engage with a product, it adds to your genome in various ways. Viewing a product doesn’t mean the same commitment as buying it. When you view an item, it goes on your wish list—you’re showing interest with your time. But when you buy it, you’re saying, ‘Yes, I’m a Zara shopper.’ This principle applies to everything, whether it’s training shoes, health drinks, or groceries. Over time, common attributes emerge, reflecting themes like fitness or specific dietary preferences. This concept holds true across all product categories.

So, in this conceptual example, we have various attributes such as purchase behavior on weekends versus weekdays, buying items on sale or at full price, and many more—around 150 to 200 different variables like these.

Let’s break down the data from this image. If a customer buys something on a weekend, we mark that specific attribute as ‘1’ for ‘transaction 1’ in our dataset. The same applies to the customer’s overall profile or ‘genome’.

Now, in ‘transaction 2’, if the weekend purchase doesn’t happen again, it still remains marked as ‘1’ since it’s occurred at least once in the customer’s history.

Here’s another scenario from the above image. Suppose a customer didn’t buy anything on a weekday previously, but in the second transaction, they do. Now, this new piece of information is added to their profile. This method helps create detailed customer profiles or ‘genomes’ that become richer over time as more transactions occur.

Our platform helps you collect and use various types of attributes—like demographics, transaction details, product preferences, and more—to build these customer profiles. For instance, we can identify customers who are discount seekers based on their purchase behaviors.

In a real-world implementation, such as in retail and consumer services, we applied this approach to 4.6 million customers, resulting in 2.7 million unique customer profiles or ‘genomes’. This means we’re essentially targeting each customer individually with personalized recommendations.

Our platform provides a comprehensive view of each customer, incorporating personal details, transaction history, loyalty program engagement, and other derived variables. These variables are then used to create the detailed customer profiles mentioned earlier.

By matching these profiles to those of more established customers, we can generate highly effective recommendations. Hence, this method has proven very successful in improving customer engagement and satisfaction.

Summary

This fresh approach is a welcome relief for marketers deeply invested in understanding their customers. We often feel overwhelmed by the sheer volume of data and the gaps in our knowledge. Terms like ‘customer 360’ and ‘business intelligence’ can be exhausting when we’re still uncertain about our customers’ behavior.

What sets the genome approach apart is its capability to dive deep into a customer’s preferences, providing not just detailed insights but also a broader understanding. The customer genome approach offers far more meaningful insights than the typical “if you liked these, you’ll also like this” kind of recommendations.

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