Qorus Banking Innovation Awards 2021- Nominated

Call Center - Customer Analytics

Submitted by

Emirates NBD

Logo of Emirates NBD

Emirates NBD, a leading banking group in the MENAT (Middle East, North Africa and Turkey) region,...

29/09/2021 Banking Innovation

About

One of its kind Call Center analysis in the region, a customer centric framework to improve call center efficiency by moving from Servicing Calls to Servicing Customers.

Innovation presentation

Why Customer level Analytics in Call Center?

Great customer-centric brands recognize that when employees and systems know everything about every customer interaction over time, they can provide better service and support.

In this complex and challenging contact center world, Customer level analytics holds the key to working smarter and having real impact across channels on customer experience.

During our exploratory analysis, we have observed stubbornly CCO anchored customers, who prefer the CCO channel by looking at their call frequency in a year and the time difference between every call. More than 35,000 customers who called for more than 10 times in a year and more than once in less than 30 days were identified.

The objective of this study is to understand customers’ calling behavior, cluster them into similar categories and develop a framework that enables better customer management at the Call Center

The methodology developed is based on a customer centric framework where the objective is to move the call center inbound system from servicing calls to servicing customers. This is achieved using a three pronged strategy as described below

1. Link analysis to predict next customer call

2. Developing customer clusters with similar call reasons

3. Reverse Scoring framework (C2S2)

Link Analysis:

To predict the next call reason from a customer based on his previous call and proactively communicate about the next call even before the customer calls.

An exploratory data analysis reveals that customers calling for specific reason with high linkage to another reason, have a high likelihood to call again for the linked reason. A proactive communication to customers would prevent these linked calls in the future. A detailed analysis to bring out the possible linkages between call reasons is done to define actions that will reduce future calls with proactive customer communication.

Link analysis is used to discover associations between the various call reasons. It is the process of discovering and examining the connections between items in a complex unorganized data system. Association discovery rules are based on frequency counts of the number of times that item occur alone and in combination in the database. The rules are expressed as “if item A is part of an event, then item B is also part of the event X% of the time.” These rules are denoted as A->B.

For instance, there were 7000+ cases where customers who called for enquiring about their Credit Card Payment, called again in the next 7 weeks to enquire for Credit Card Installment. This shows the high linkage between these two call reasons along with sufficient instances in the last 12 months. So next time when a customer calls for card payment enquiry, he will be proactively communicated about card installment products through alternate channels like SMS or mail post the call informing about relevant credit card installment plans.

Developed Customer Clusters using Unsupervised Learning Techniques:

To classify customers into different clusters with common calling behavior.

Customers are first segmented into 3 categories based on their call volumes in a year into:

1. High engagement (8+ calls in a year)

2. Medium engagement (3 to 8 calls in a year)

3. Low engagement (1 to 3 calls in a year)

The next step is to cluster customers within each of these three engagement categories into clusters such that customers within a cluster have similar calling traits. This is done using the K-means clustering technique on SAS Enterprise Miner.

K-means algorithm is an iterative algorithm that tries to partition the dataset into K pre-defined distinct non-overlapping subgroups (clusters) where each data point belongs to only one group. It tries to make the intra-cluster data points as similar as possible while also keeping the clusters as different as possible.

Top 5 call reasons capturing 43 percent of the calls are mostly around credit card transaction & statement enquiry, card installments, online & mobile banking general queries, general enquiries & credit card block & replace requests

All customers within each engagement group are quite different from each other. For instance, Cluster H3 in figure3 has all customers covering 14% of the call volumes among high engagement group calls calling mostly for card installment products.

All these 25 clusters based on calling behavior were further defined by various customer attributes including behavior, revenue, product holding, financial segment, digital activity and persona including nationality and income.

Customer Calls & Service Score (C2S2):

To build a framework for scoring customers which can be used to prioritize customers to speak to Call Center Agents when they call on the IVR.

A multi attribute value model (MAVT) is used to compute a score for each customer. Multi−attribute value theory is a method that transforms and aggregates available data into useful information. It makes the ‘incomparable’ attributes comparable, prioritizes them by assigning weights and finally reduces the amount of information by aggregating the weighted standardized scores.

The process to be followed to carry out MAVT is listed below:

1. Definition of parameters: identify parameters which are to be compared with each other.

2. Assessment of scores for each alternative: assign values to each effect or indicator.

3. Standardization of the scores in order to make the criteria comparable with each other.

4. Weighting of criteria, in order to assign priorities to them.

A total score is calculated by multiplying the standardized scores with its appropriate weight, followed by summing the weighted scores of all criteria.

The parameters used in the model are as described below.

1. Call behavior

2. Revenue

3. Persona

4. Financial segment

5. Customer behavior

6. Digital score

7. Call reason

Weights are assigned for each of these parameters and based on the value of each variable customer get a score – Customer Calls & Service Score (C2S2).

The model is built in a reverse scoring mechanism where higher C2S2 score implies that customer would be assigned a lower priority on agent call transfers. This score would help agents prioritize calls in times of call outages or limitation of ground staff services.

Uniqueness of the project

A bank’s call center plays a pivotal role in interacting with their customers and adhering to their requirements regularly. Even with the shift towards digital adoption, the importance of call center cannot be overlooked. To keep up with the competitive pace, today call centers are not just seen as a channel for customer service but also for business growth through cross sell, acquisitions, building customer relationship and retention.

To ensure seamless customer service and offer products & Services proactively through the Call Center, Analytics plays a crucial role in not just optimizing the entire process but also predicting customers needs for personalized services. We, at Emirates NBD, decided to move from the traditional way of Servicing Calls to Analytics driven approach of Servicing Customers.

Instead of just looking at the type and reason of calls, we used a statistical approach to assess similar customers based on their calling behavior such as reason for call, call duration and addressed them as segments with similar needs to provide effective and consistent service. Most importantly, our approach also takes into account customer relationship with the bank to prioritize calls on IVR to be transferred to Agents. Additionally, we also utilize the vast call center data to predict customer’s next call based on his last call for proactive communication.

The objective of this customer level analytics in the call center space is manifold:

- Customer Centricity – We focus on satisfying customer needs instead of just solving the problem in hand

- Process Optimization – Our approach aims at migrating irrelevant calls to alternate channels along with prioritizing calls basis relevant and customer importance

- Digital Adoption – In this era of digitization, evolving to current trends is of utmost importance. Proactive communication through digital channels is not only time saving but also drives engagement & CX

- Revenue Growth – Expanding the realm of call center from just servicing calls to building customer relationship by equipping them with data driven insights

As Shep Hyken has rightly said “Make every interaction count, even the small ones. They are all relevant”, we need to realize the power of the Call Center for Customer Excellence.

In-Depth Analysis

The Reason Behind

Response to a specific issue

With 200K+ calls received by Call Center Agents every month, during COVID-19 lockdown, it became difficult to manage calls with lower ground staff. The need of the hour

was to immediately Reduce these interactions by

o Migrating Customers calling behavior from IVR to digital Channels

o Proactive Customer Communication to avoid calls

o Restricting Agent Call Transfers on IVR but at the same time maintaining customer satisfaction

A 2020 analysis on the type of calls we get in our call center revealed that more than 17K customers called at least twice to enquire about credit card transactions and account statements when information is already available on online channels. We wanted to funnel out least priority calls with queries that can be solved through alternative channels while focusing more on acquisition requests.

Challenges

Budget

While we developed the framework and the prioritization of customers on the IVR logics.

The implementation needed an IT transformation and high budget approvals.

This delayed implementation but it finally got approved

Results

Benefits

- 15% reduction in Call Volumes

- 10% reduction in FTEs

- 20% Probable Shift towards Digital Channel

Earlier to handle the high number of IVR calls, all calls that have high frequency of IVR intent including account opening, product benefits, investment and offer enquiries are being restricted from going to the agent. Post sending their enquiry on email, customers need to wait for 24-48 hours for a reply. This is irrespective of their relationship with the bank including revenue/segment-based. This is leading to drop in CX and customers looking out for other primary banks

Benefits from the framework: Better CX, Lower Calls, Reduced Costs, Higher Revenue

- With C2S2 score on the system, the customers are now prioritized basis their reason to call and their banking relationship on the IVR.

- Moreover, customers calling for basic enquiries would have to wait longer to the IVR as per the score are now moving to digital platforms to address their queries.

- Since we have identified customer segments with similar calling behavior, we are proactively communicating with them on their probable reasons for calling through emails, digital banners, info on website, popups, etc

- Our algorithm to assess the next call reason of a customer based on his current call, helps us proactively communicate on the next probable reason to reduce calls significantly

- Helps understand our customer issues better and the reasons where the call volumes are very high becomes our action items to resolve for those customers who dint call but are facing similar issues. This further enhances CX

Key Dates

  • Launch date 30 June 2021
  • Time to Market 0-1 year
  • Conception Duration 3 months
  • Implementation Duration 6 months
  • Testing Duration 3 months

Interested in learning more? Speak to Boris, Qorus's Content Lead

Qorus has a library of almost 8,000 innovation case studies across critical areas like customer experience, sustainability, marketing & distribution and more that can be used to inform your decision-making.
Contact us

Related innovations

26/03/2024 Insurance Innovation

Insurance Infrastructure-as-a-Service for Inclusive Insurance

B4E Insurtech's Value Proposition Problem Statement: Insurance can be complex and costly, leaving many without coverage. This is especially true...

25/03/2024 Insurance Innovation

BankaBot Project

As AgeSA Bancassurance team in the company, our one of crucial professions is being a guider for sales coaches who...

25/03/2024 Insurance Innovation

Amana Takaful Motor Insurance App - Enhanced Version 2024.

Our innovation, the Amana Takaful Motor Insurance App - Enhanced Version 2024, represents a groundbreaking evolution in the realm of...

22/03/2024 Insurance Innovation

Repair Manager

Repair Manager is an all-in-one platform for Repair Management for motor insurance claims. Insurance companies have all the tools in...

Related news & insights

Digital Reinvention
25/03/2024 Interview

Česká spořitelna recognized for financial wellness initiatives

Česká spořitelna won bronze in the Environmental, Social and Governance (ESG) category at the Qorus Reinvention Awards – Europe. Veronika...

Digital Reinvention
23/03/2024 News

MAPFRE explores Generative AI's impact on society and insurance industry

Through thorough research, MAPFRE has outlined four plausible scenarios and the role insurance might play in each.

Digital Reinvention
21/03/2024 News

Scotiabank launches Money Style: A tool for improved financial conversations

Scotiabank introduces Money Style, an online tool available through Scotia Advice+, aimed at fostering empathy and emotional intelligence in financial...

Digital Reinvention
20/03/2024 News

Fidelity Bank launches ANDI, a digital banking solution for Louisiana communities

ANDI, short for “A New Day In Banking,” offers a range of digital financial services tailored to customers across the...