Superior customer experience in finance – Leveraging the power of behavioral analytics

Superior customer experience in finance – Leveraging the power of behavioral analytics

Imagine this scenario – Two customers dial into a bank’s call center. One of them is looking to explore alternate payment options and the other wants to know their credit statement for the month. You would expect the interactive voice response (IVR) options for both customers to be different. However, they are both presented with the same, frustrating automated responses.

This challenge occurs often because the technology fails to recognize the context or behavior that led to each customer’s call. Therefore, their experience was generic and not personalized to a specific need.

Today, advanced analytics combined with digital technology has the power to provide deep customer insights in the financial world. This can create a compelling value proposition provided banks and financial institutions embrace these insights to analyze behavioral trends. To break it down – in this age of advanced analytics, data will become a differentiating factor. Data enables banks to better understand their customers, edge out competition and push the right products at the right time.

However, just analyzing historical data to derive insights may not be enough to predict future customer behavior. To truly predict the future, behavioral analytics needs to be leveraged to understand customers intimately.

Behavioral analytics is the decoding of customer behavior based on their interactions with the organization across platforms and choice patterns. These platforms could be social media, ecommerce sites, online reviews, mobile applications and more. When banks utilize this data and come up with insights into every customer’s decision-making process, they can predict their customers’ unmet needs and design personalized interventions, or nudges. These personalized nudges towards certain choices can generate loyalty in customers and push them to make the right decisions.

Look what Merrill Lynch does. The bank was aware that retirement was the last thing on their younger customers’ minds. Which is understandable, because most young people in the US are working towards paying off their student debt or saving up for family and real-estate. However, saving up for retirement early helps in getting better returns at the time of retirement. Merrill Lynch worked on putting up an aging algorithm on their website – which allows a user to upload their photo and watch it age 20, 30 or 40 years. This seemed like an odd thing for the bank to do, but customers who used the program started saving for their retirement. Merrill Lynch ended up getting the desired result from the digital program.

While Merrill Lynch is just one example, financial services organizations should aim to use the power of behavioral analytics to enhance customer experience regularly. For instance, behavioral analytics can be used to predict customer patterns and raise a flag when suspicious activity is detected. Banks can also use behavioral analytics to sell a set of financial services depending on what a customer wants at a particular time. Programs can also be designed to specifically target customers that are known to have high CLTV and high lapse rates. By equipping themselves with the right tools, banks can ensure they don’t lose out on customers.

So, the next time a customer dials into your call center, wouldn’t it be great to directly address their problems? After all, a personalized response leads to happy customers.

How can OSG help on the journey towards customer satisfaction?

At OSG, we use cognitive and behavioral analytics to understand what matters most to our clients. Cognitive analytics are leveraged to look at historical data and decision making. Behavioral analytics are used to go beyond the “who” and “what”, to understand the “how” and “why” to predict future buying patterns. When combined together, we enable you to design and deliver superior experiences by nudging specific customer segments. This builds up loyalty, leading to higher customer engagement and improved customer experience.

Through behavioral analytics, OSG helped a leading global health insurer segment their customers and understand their lifetime value to predict future customer engagement. By leveraging our powerful behavioral analytics, OSG analyzed the client’s customer data to create segments and improve customer life time value. A predictive scoring model for segments was created to drive a marketing strategy for each segment. Through this approach, OSG helped increase customer engagement and profitability for the client.

To learn more about how you can get started on your behavioral analytics journey, write to website@osganalytics.com.

OSG Steps to Success

OSG is a “catalyst” that helps our clients be the best at decoding their customers’ decisions. Our clients have seen a minimum 20% improvement in customer engagement by implementing smart insights delivered using our behavioral analytics products.

Revenue Growth in the Digital Age – Using Behavioral Analytics for Smart Customer Retention

Revenue Growth in the Digital Age – Using Behavioral Analytics for Smart Customer Retention

Customers are in a state of constant evolution. They are digitally driven, socially connected and mobile empowered. They are at the center of the universe and have more control over the purchase process and how they choose to interact with organizations/brands. Gartner defines this shift well- “The most disruptive thing in the market is not technology but rather the customer.”

For any business to grow in this customer-centric market, it is imperative to retain existing, most valuable customers through better customer experience and engagement. Recent research shows that improving customer retention rate by 5% can enhance bottom line by 25- 95%. It typically costs any company 5X to acquire new customers as compared to retaining existing ones. This clearly shows the importance of retention in growing revenue and profitability.

However, most companies struggle to handle customer pain points, resulting in poor/unsatisfactory customer experience and engagement, thus losing them. This is more so for companies that are not well-equipped for the digital and social age. 68% customers leave a brand because they feel it is indifferent to them. Gone are the days when a one-size-fits-all approach can keep customers engaged. Templatized reminders through emails, text messages and tele-calling without a deep understanding of customers’ needs is a redundant and ineffective way to retain customers. Despite the time and effort invested, the response rate is low, leading to minimal ROI.

The key to significantly improve retention is by adding intelligence to the process and making it smarter. This can be done by uncovering the customer’s decision-making process through behavioral analytics. Behavioral analytics helps gain a deeper understanding of what matters to customers to granularly identify “how” and “why” they make decisions. This approach uncovers customers’ unmet needs and sets the stage for an organization to address their concerns before they decide to be on their way out. By uncovering what is important to customers (expectations), behavioral analytics helps organizations/brands deliver superior customer experience driving enhanced customer engagement. A typical Customer Experience (CX) revamp exercise should take the following path to facilitate ‘smart’ retention:

  • Use behavioral analytics to understand what matters most to your customers & why
  • Create actionable micro-segments by marrying ‘what’ & ‘why’ with ‘who’ (demographics)
  • Identify your most important customers and uncover their choice patterns
  • Develop communication strategies that address these choice patterns
  • Identify unmet needs for innovation opportunities that will delight your customers
  • Identify cross-sell and up-sell opportunities keeping in mind the above findings
  • Implement systems and capabilities that support and deliver insights on CX

A smart retention strategy delivers enhanced customer engagement and profitability and is the starting point for sustainable growth. OSG Dynamo our AI based big data analytics platform gets to the root of understanding what behavioral triggers can help customers stay motivated and engaged with an organizations’ products and services. By measuring customer expectations and designing tailored experiences, Illuminate ensures that customers stay engaged, dropping churn and increasing retention.

For a global health insurer, OSG’s behavioral analytics solution delivered a 30% reduction in churn and a 700-bps growth in revenue.

We hope this information has been interesting and valuable to you. Please, feel free to share it with colleagues and other people in your network. We welcome discussing this topic further with you and understanding your specific challenges.

 

OSG Steps to Success

OSG is a “catalyst” that helps our clients be the best at decoding their customers’ decisions. Our clients have seen a minimum 20% improvement in customer engagement by implementing smart insights delivered using our behavioral analytics products.

Customer Lifetime Value – Is that all we’ve got?

Customer Lifetime Value – Is that all we’ve got?

Not all customers are the same – their contribution to revenue and the cost to acquire or retain them varies. How then do businesses identify the right acquisition and retention strategy for their varied customers? Designing a marketing strategy without sufficient understanding of revenue/cost impact of each customer is like shooting at a target blindfolded.

 

Uncovering the economic value of each customer, also called Customer Lifetime Value (CLTV), is an effective first step to efficient marketing. Let us consider an example to understand this better. A health insurer that has customers’ CLTV and churn/lapse rate data can segment its existing customers. The four segments of this 2×2 matrix (X-axis: Lapse Rate, Y-axis: CLTV) will look like this:

Segment 1: High CLTV, High Lapse rate

Segment 2: Low CLTV, High Lapse rate

Segment 3: Low CLTV, Low Lapse rate

Segment 4: High CLTV, Low Lapse rate

 

Understanding the behavioral drivers of customers across these segments will allow us to develop marketing programs that drive profitable interventions with individual customers. For example, if there are more customers in Segment 1 (High CLTV & High Lapse), the organization should aggressively pursue a retention strategy to ensure they do not lose existing customers. Similarly, if there are more customers in Segment 3 (Low CLTV & Low Lapse), the organization should look at understanding the drivers of low CLTV to develop an appropriate intervention. In the case of this health insurer, low CLTV could be driven by high claims cost. This insight can help the organization focus on two specific interventions for customers in Segment 3 – Health and wellness programs for high-risk customers to mitigate claims risk, and differential product pricing to account for claims risk.

While any organization should prioritize interventions based on the spread across these segments, the interventions when rolled out may not resonate and gain traction as expected with customers. What could be wrong? Is there still a missing piece to this puzzle? The unequivocal answer is yes!

A simple segmentation exercise like this based on CLTV is a great start but does not throw any light on drivers of customers’ purchase behavior and hierarchy of needs. This means that any message delivered or intervention deployed may or may not resonate with customers as it is not aligned with their true behavioral motivations. To ensure customers engage with the marketing programs deployed, it is imperative for organizations to understand their expectations. Richard Thaler recently received a Nobel prize for his work in behavioral economics recognizing that humans can be nudged and the right nudge and the size of this behavioral nudge must be designed correctly.

Organizations can uncover customer expectations (or behavioral nudges) by doing a better job of understanding customer behavior. Behavioral analytics helps organizations go deeper into what truly matters to their customers, helping create actionable micro-segments based on customer behavioral profiles and the nudges they would best respond to. OSG’s Illuminate can accurately identify “how” and “why” customers make decisions and which nudges can help create better behavioral engagement. This hybrid data set results in a view that is not only reflective of past behaviors but is also highly predictive of future customer behavior. As a result, organizations can craft microsegment-based go-to-market strategies that are truly customer-centric and address segment specific needs. Stronger relevance leads to improved engagement from customers, enhancing success of interventions deployed.

A global insurance company saw 30% drop in churn and a 7% increase in revenue through growth opportunities identified by Illuminate.

We hope this information has been interesting and valuable to you. Please, feel free to share it with colleagues and other people in your network. We welcome discussing this topic further with you and understanding your specific challenges.

 

OSG Steps to Success

OSG is a “catalyst” that helps our clients be the best at decoding their customers’ decisions. Our clients have seen a minimum 20% improvement in customer engagement by implementing smart insights delivered using our behavioral analytics products.

Data Analytics in Banking and Financial Services

Data Analytics in Banking and Financial Services

In today’s data-driven world, data analytics play a crucial role in informed decision making to drive organizations forward, improve efficiency, increase returns, and in turn achieve business goals. For the uninitiated, data analytics is the process of discovery, interpretation, and conveying meaningful insights from the data to help in the decision-making process.

According to the latest Worldwide Semi-Annual Big Data and Analytics Spending Guide from one of the top research firms, worldwide revenues for big data and business analytics will go up to more than $203 billion in 2020. The applications for data analytics are significantly growing day by day because of various innovations in the field. Out of this $130 billion market share, the banking sector leads revenues with a contribution of $17 billion in 2016.

In the Banking and Financial Services sector, through data analytics, institutions can monitor and assess large amounts of customer data and create personalized/customized products and services specific to individual consumers.

For example, when a customer buys a vehicle, the bank sends promotional offers of insurance to cover the customer’s vehicle. In the future, such applications could be expanded even further. One way this could happen is if a customer got a large bill, the bank could offer an EMI conversion or a loan to cover the cost.

Some of the areas where banking and financial institutions are increasingly using data analytics include:

  • Fraud detection
  • Managing customer data
  • Risk modelling for investment banks
  • Personalized marketing
  • Lifetime value prediction
  • Real-time and predictive analytics
  • Customer segmentation
  • Customer spending patterns
  • Transaction channel identification
  • Customer feedback analysis and application

 

The importance of data analytics in the banking and financial services sector has been realized at a greater scale and most of the established banks have already started reaping the benefits.

For instance, an American bank used machine learning to comprehend the discounts that its private bankers were providing to customers. Bankers were claiming that they offered discounts only to important/ valuable customers. However, when the data was assessed through analytics, it showed a different story. It showed the discount patterns which were not needed, and which could easily be corrected. The bank adopted the changes, leading to an increase in revenues by 8% within few months.

A leading industry survey conducted for 20 banks across the EMEA region revealed that there were certain areas of improvement, which if worked upon could deliver great returns. Some of the areas included were:

  • Aligning the priorities of analytics to the strategic vision of the banks
  • Incorporating decision making with analytics practices
  • Developing advanced-analytics assets on a large scale and investing in the roles which are critical to analytics
  • Enabling the user revolution with clearly defined data ownership and maintenance of high-quality data

To gain competitive advantage, banks should recognize the importance of data science, incorporate it in their decision-making process, and develop strategies based on the actionable insights from their customers data. Start with small, doable steps to integrate data analytics into operating models and stay ahead of competition.

OSG Steps to Success

OSG is a “catalyst” that helps our clients be the best at decoding their customers’ decisions. Our clients have seen a minimum 20% improvement in customer engagement by implementing smart insights delivered using our behavioral analytics products.