The Science of Customer Experience Management

White Paper

Today there is a lot of buzz around customer experience. Many companies have realized that investments in customer experience improvement is important not just because it helps to boost the bottom lines of their businesses but because it takes at least 4 to 6 times more cost to acquire a new customer than to retain an existing customer.

Modern day communicating platforms and tools have given more muscle power to customer's voice. On one hand, your customers could become brand ambassadors of your business if they are delighted about doing business with you. On the flip side one sad customer would soon turn to thousands of unhappy customers at the blink of an eye. Now, businesses have an added advantage unlike a few decades ago. They can derive long term success by leveraging technology retain their

customers longer by providing them a better customer experience. With the help of technological improvements, businesses can understand their customers like never before.

A proactive strategist would not only leverage the information to meet today's customer needs but also align the business for tomorrow. Though businesses now have more data about their customers, it is not always effectively utilised to manage customer experience.

Analytics per-se should be applied at every customer touch point to derive insights which could be used to devise strategic initiatives that can increase customer retention and enhance customer life time value by providing better business experience and value to the customers.

Is Customer Experience Innovation being done right?

At a broad level, businesses have not leveraged technology to understand the customers as much as customers do to evaluate a product / service. This is also a reason as to why the average tenure of companies in the S&P 500 has come down from 61 years in 1958 to 18 years in 2012. At the current churn rate, about 75% of S&P 500 will be replaced by 2027.

Some companies have a notion that they pay key attention to customer experience improvement efforts. In a research study conducted by Forrester, among 100 customer experience professionals, about 73 % of the Interviewees said they plan to launch innovative customer experience in the upcoming year and about 66% of them told that they have already launched innovative customer experience program. If so much of innovation is happening in this space and happening right, we should have probably seen most of the companies achieve a good Customer Experience Index rating by its customers. But in 2013, only 8% of the companies received top grade from the customers in the annual Benchmarking Survey. A lot of them certainly missed the bus here!

The Science of Customer Experience Management

The scope of this white paper

In this white paper we shall talk about:

Data sources for customer experience management Steps for effectively managing customer experience Approach note on quantitative data analysis Approach note on unstructured data analysis for mining insights about customer experience

with examples. Sample industry use cases that showcase benefits of implementing customer experience

management solution. Note on the need for real time customer experience tracking and management by enabling real time

analytics infrastructure While in this paper we attempt to cover reasonable depth on basic principles and approaches on

customer experience management using analytics, details about the working of advanced analytics algorithms are not under the scope of this white paper.

DATA SOURCES FOR CUSTOMER EXPERIENCE MANAGEMENT

We will look at various data sources and categorize them under:

1. Internal – Both structured and unstructured2. External - Structured and unstructured

1. Feedback Surveys: (Internal data - Structured)

Periodic and trigger based surveys form the core of customer experience management as these surveys give us the most direct information about customer's perception about the quality of product /service. Periodic surveys help measure overall customer experience by time, whereas trigger based surveys help capture the most recent customer experience as soon as the customer interacts with the business. This event could be: product purchase, product service or product enquiry etc.

2. Transaction information: (Internal data – Structured and Unstructured)

Structured dataProduct Information such as product purchase details, purchase frequency, product type, purchase value, product returns /abandonments, order renewals, renewal dates, etc.Website Information such website navigation pattern, most visited web pages, inbound / outbound page visits, product views.

Unstructured data:Reason for product return, customer service feedback, reason for service request, searched terms in the website, customer support information – both voice and text data from call centres etc.

3. Customer demographics: (Internal data – Structured)

Personal Information such as - gender, age, job, title, residence information etc.

4. Social Media Information: (External data – Unstructured)

Facebook likes, shares, twitter sentiment and other customer interactions with the business on social media. Customer profile data can be of incredible use for improving customer experience. Many innovative companies have started to tap the data that is available in customer's social profile. The data is also highly accurate and reliable as this information is declared by customers themselves. Demographic and psychographic details about individuals from social profiles provide a more rounded view. This information can also be leveraged for customer segmentation. For example, a typical Facebook profile would contain information such as birth date, marital status, location, interests and a lot of other personal data. A LinkedIn profile contains professional credentials about the individual. This information can be used to segment and profile customers to tailor offers and promotions that can tremendously improve customer experience.

5 Simple Steps To Effectively Manage Customer Experience

1. Create a customer centric data repository

Customers form the core of any business and this should be reflected in the way we collect and manage data. Information captured from all the data sources should be stored in a customer centric way. We must move away from channel centricity. All the data sources and customer touch points should be mapped to the customer. These customer touch points could be internal (managed by the company) or external (social media) touch points. The touch points should in turn mapped to channels of communication.

2. Capture transactional and service data

Capture all the transaction information such as number of products purchased, order/ subscription value, order/renewal dates, product abandonments, product returns, product purchase channel, product enquiries etc. All inbound and outbound service requests must be mapped with request details, communication date, communication channels, resolution details, customer satisfaction rating, and customer feedback. If there are any interactions with the customer on social media, it is imperative to capture those interactions as well.

3. Track customer experience scores

Surveys are the most effective tools used by businesses today to capture feedback from their customers. A well designed survey will give enough information about your customer's comfort level of doing business with you. You can drill down to the factors that your customers appreciate about doing business with you and to the factors which the customers want you to improve. You can also get an idea about the reasons / factors responsible for your customers moving on to your competitor.

In order to derive the insights mentioned above, it is not just enough to analyse the survey data in isolation. Integrate the survey responses (at the customer level) with your internal data repository and conduct data analytics.

Slicing and dicing of responses by various dimensions of your survey data will help you identify the customer segments of importance.

Advanced analytics (key driver analysis, statistical significance testing, correlational analysis, banner tables) done on quantitative responses after integrating survey responses with your data repository will help you to identify the drivers of engagement/satisfaction.

Advancement in technology has made it possible to have a structured approach to mining insights from comments captured from the open ended questions captured in the surveys unstructured data. (We will get into that later in this white paper).

Companies must have a good understanding of the key drivers of engagement/satisfaction of the stake holders involved with their businesses in order to provide a better stake holder experience. Key driver analysis done on the survey data (satisfaction score as a dependent variable) allows us to zero down on the key drivers of engagement.

This exercise typically is complex, exploratory involving a lot of statistical analysis needed to be performed by data scientists and generally takes couple of weeks to get the results. BRIDGEi2i's survey analytics application Surveyi2i hides all the complexities at the backend and provides you with a simple interface to choose the algorithm of interest to perform the analysis. It has algorithms which combine machine learning and statistical techniques, which could be used for analysing a variety of surveys.

One should also perform key attribute analysis to identify the features of demographic profiles (of stakeholders) that influence engagement/satisfaction. Cross tabulations and banner tables with significance tests should then be performed to validate the parameters that influence overall experience. Such analysis when done by the customer segments (identified by analysing various sources of data) will help us accurately identify drivers and sentiments specific to the customer segment

4. Track behavioural changes and communicate them timely Businesses must allow the customers to communicate in the channel of their comfort. Information about customer's preferred channel of communication must be made available for every customer. Track behavioural changes such as changes in: purchase pattern, purchase frequency, service request type, request frequency, preferred purchase channel, payment mode etc. Establish a real time analytics framework to track these changes in conjunction with the most recent customer feedback (surveys). Real time analytics capabilities enable businesses to quickly react to changing environment and facilitate timely interaction with the customers. Timely interaction with the customers through their most preferred channel of communication is the key for conversion and provides businesses with the best opportunity to improve customer experience.

5. Evaluate the impact of your customer experience improvement efforts

It is necessary to quantify the impact of your customer experience improvement efforts. The metrics to choose for quantifying customer experience are subjective and vary by industry vertical. For example, in the retail industry, the yardstick to measure the impact of customer improvement efforts would be the measurement of net change in: sales / revenue, customer attrition rates, average cost to serve a customer, number of new customer additions. These numbers will certainly help you identify if you are in the right direction with respect to your customer experience improvement efforts.

A Novel Approach For Mining Insights From Unstructured Data Using Analytics

There is certainly no doubt that there are a lot of insights hidden in comments collected from various customer touch points. It is very difficult to manually eyeball all the comments given by the customer and derive insights from it. We can however design a process, where we get an understanding of our customer's voice, without losing information or putting in labourious manual efforts for reading the comments.

Putting math behind unstructured data This idea is derived from the concept of “collective Intelligence”. It is like the voting system in a democracy, where the nation is expected to make the right decision of choosing a political party. Before we go deeper in to the steps, let us cover the business context with 5 basic components of unstructured data analysis:

1. Word cloud - Provides a view of recurrent words appearing in the comments. Might act as the first step in building a taxonomy.

2. Theme cloud – Will give a gist of topics mentioned by the respondents, again aiding development of a customized taxonomy.

3. Word Associations – It gives an idea words / phrases that has strong associations with the key features.

4. Sentiment Analysis - It takes the tonality and mood of the sentences in to consideration and gives the distribution for positive and negative sentiments.

5. Category Analysis – Provides the summarised view of respondents’ thoughts after a taxonomy is built (Find topic clusters by categorizing synonymous/ representative words under each cluster).

4 Useful Steps For Extracting Insights From Unstructured Data

Step 1 – Analysis of key words and phrases

Start applying the word cloud and noun phrase analysis to get an idea of the subjects discussed by the respondents. Do this analysis across various customer segments such as customer segmentation based on: age, revenue, average purchase value, purchase frequency, geography, products purchased, family size, last satisfaction rating, purchase channel etc.

Step 2 – Building a taxonomy of key features

Now that we have an idea about the most frequently repeated words and nouns, the next step is to categorize synonymous words and noun phrases in to clusters and give a name to each cluster. Each cluster name must collectively denote the words represented in the cluster.

Create an exhaustive list of the unique subjects discussed by the respondents by iteratively running the word cloud and noun phrase analysis across various dimensions identified earlier.

Step 3– Analysis of associated sentiments

In order to get an idea about respondents’ mood and tonality, conduct sentiment analysis for each category by identifying the degree of positivity or negativity for each sentiment (sentences) and sum up the sentiment scores for each category.

Customization of the sentiment dictionary and continuous enhancement of the business rules applied can make such analysis more accurate. Sentiments observed at a feature level is far more actionable than a high level sentiment analysis.

Step 4 – Key driver analysis on verbatim sentences

It is possible to find the drivers of satisfaction from unstructured data by considering the percentage of people who spoke about specific category. Identify the presence of such categories in each sentence (given by the customer in surveys, service feedback etc.). Now regress the presence or sentiment of each category against the customer satisfaction rating (captured in latest customer satisfaction survey).

Following this, we can achieve drivers of satisfaction for each segment. However such insights have to be supplemented with average satisfaction scores, sentiment scores and themes discussed across segments.

Now, at an aggregate level, we would not only be able to identify the drivers of satisfaction (from unstructured data), but also find the sentiment for every category. You save precious time without the need of manually going through individual sentences without missing much of information but at the same time.

Let us see the above steps in action with a few use cases.

Use Cases

Use Case 1 – Unstructured data analysis done for one of the world leading logistics service provider

Snapshot of the steps followed

Sample word cloud and word phrases

Sample Categories

Sample of top issues raised and negativity associated for each category

Key drivers of satisfaction derived from unstructured data

What kind of insights am I expected to get? Use Case 2 – Telecom Service Provider

A communication service provider wants to improve the customer experience in its business. BRIDGEi2i designed a customer experience measurement survey and analysed it in conjunction with the internal / external data sources.

Following are the snapshots of the insights provided:

Dynamic customer experience tracking, via reports shared at daily frequency. Identification of the root cause or sources of satisfaction / dissatisfaction.

Identification of Product / Service gap through unstructured data analysis done on the Customer's feedback through surveys and social media.

Identification of non-profitable customer segments and deriving a rate plan change for achieving profitability

Customer segmentation based on: Big ticket customers, churn rate, life time value, interaction channels etc., and identification of drivers of customer experience for each segment.

Most preferred interaction channel analysis at individual and customer segment level analysis. Recommendation of yield optimisation strategies that can boost revenue and customer experience. Product bundling, up sell cross sell suggestions that are likely to improve customer experience and

revenue - based on customer usage patterns Optimisation of IVR service and recommendation of self-service portals based on call centre data

analysis. This is done by analysing the call drop out trends at IVR paths, identifying repeat calls. Changes to the IVR are validation of customer satisfaction levels post implementation and number of repeat incidences.

Use Case 3 – Retail groceries chain in US

One of the leading retail chains in the US wanted to measure customer experience levels and improve the customer engagement and experience. BRIDGEi2i set up their customer experience management infrastructure and delivered actionable insights.

Identified the reasons for customers’ preference for a particular retail store and reasons for customers preference.

Customers’ retailing experience measurement across various channels. Channels that met their expectations vs. channels that did not meet his expectations. Reasons for satisfaction and dissatisfaction.

Relevant product bundling, cross sell, up sell opportunities and discount coupons were suggested for each customer segment based on purchase pattern and purchase frequency.

Proactive follow ups, reminders to improve customer experience and pave way for customer loyalty. Customer rewards program that incentivise loyal customers and boost customer experience.

Use Case 4 – User experience for technology support services

A global technology company wanted to improve customer experience for their IT services. They wanted to create elevate the user experience by converging user feedbacks across different platforms. Millions of user feedback entries came in regularly through emails, forums, tickets and surveys. The challenge was to collate this large amount of structured and unstructured data to understand the problems addressed at different points in time and cluster them across different services and regions.

BRIDGEi2i performed extensive text mining on the unstructured data of emails and threads between the initiator and the IT resolver platform to gain insights like top conversations, sentiments etc. on the customers requiring immediate attention and systems requiring immediate resolution. BRIDGEi2i also analysed the structured big data of service tickets to identify the common IT related issues and performed survey analysis to highlight customer rating and sentiments along with key areas of improvement.

The user experience analysis provided a detailed drill down into their IT services & products across different regions/departments. Insights about challenges for the user experience across different regions along with areas of improvements and sentiments across top conversation categories with high priority incidences helped the client make user experience much easier to maintain, reducing their maintenance costs and improve user experience.

Use Case 5 – Customer service improvement for a group insurance service provider

The client is a large provider of group insurance. Hundreds of queries regarding claims and other related service are made by the customers at the company's contact centre every day. The objective of the project was to analyse the huge volume of unstructured text in contact centre agents’ comments to assess the key drivers of service call volume and other related aspects.

BRIDGEi2i used text mining and advanced semantic analysis tools to understand the typical call initiation reasons and time varying nature of communication between the agent and the customer. Agent notes were textual, highly abbreviated and contained spelling mistakes which needed to be removed. Several publicly available lexicons and grammar dictionaries are used to enable this. The histories of conversations were broken in to single conversation to enable separate analysis as well as analysis of call resolution through multiple calls.

Semantics based text mining methods were used to segment discussions in to various topics which allowed identification of various reasons for calls. Analysis of service instances needing multiple calls provided insights on key bottle necks in the claim management process. Analysis of time taken for issue resolution by issue category helped set the right expectations on resolution time with the customers.

Insights obtained from this exercise helped the insurance company identify key bottle necks in the process and conceptualize measures to significantly decrease service instances as well as improve customer satisfaction.

Use Case 6– Identifying key improvement areas for a global entertainment company

The client is one of the world's largest golf entertainment companies with assets in multiple cities across US. As an initiative to improve brand presence and perception, the client intends to leverage social media as a platform to communicate and interact with its patrons. The client is interested in understanding:

The reach of its social media promotion activities Innovative methods to identify and manage consumer sentiments as soon as a negative event has

been triggered.

BRIDGEi2i identified the appropriate sources of information, the metrics to track and the mechanism to do this. The team developed an automated interactive social media dashboard that identifies consumer sentiments and pin points key offerings that require immediate attention along with a host of metrics that discover patterns in promotion response. BRIDGEi2i created 4 important metrics (share of voice, level of engagement, feature level sentiment and attention areas) that summarizes the client's brand presence and perception in social media.

The client was able to use these metrics to identify focus areas in terms of a brand presence or perception improvement conversation. This enabled separate analysis as well as analysis of call resolution through multiple calls.

Why is it critical to have a real time customer experience management solution in place?

Earlier, we touched upon the need of real-time customer experience management by using real time analytics. Now, let us see what we should do to establish a real time analytics framework.

1. Automate data load to your customer centric data repository. Build APIs to connect with upstream systems. Load the data in to your repository only after data extraction and transformation.

2. Choose metrics that you want to track. Create simple report templates with charts containing the metrics of your interest. It would be a good idea to create report templates based on the report recipient list and reporting frequency

3. Establish various triggers for analysis and reporting. Triggers could be time based or event based. Define the events along with threshold value. (The analysis would be triggered when the threshold limit is reached)

4. Create platform-neutral front end tools for digital report consumption. Keep the reporting interface light and visually rich to ensure report compatibility across multiple devices (laptop, tablets, mobiles)

5. Enable the recipients to forward the report and connect with concerned authorities to decide the next course of action. All the reports should be accessible from your centralized customer experience management platform and all the actions performed / decisions taken must be recorded. This will facilitate bottom up analysis at a later date.

6. Have a tracking mechanism in place. For example if a same survey is executed multiple times across different time periods, it would be useful to save the analysis and reporting settings just once and track the performance over a period of time.

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Such an infrastructure will help your organization take decisions around improving customer experience. The proliferation of data at an individual consumer level across social media and customer touch points, when complimented by advancement in big data analytics using cloud computing technologies allows businesses get a singular view of the customer . Our customer experience management platform ExTrack, enables businesses to effectively manage customer experience. The insights provided by this platform allow businesses to provide better experience to the customer and orchestrate a single view of brand across channels. The objective is simple – Increase in brand loyalty and growth to the bottom line!

Shown below is the high level view of ExTrack solution.

About BRIDGEi2i

BRIDGEi2i provides Business Analytics Solutions to enterprises globally, enabling them to achieve accelerated business impact harnessing the power of data. These analytics services and technology solutions enable business managers to consume more meaningful information from big data, generate actionable insights from complex business problems and make data driven decisions across pan-enterprise processes to create sustainable business impact. BRIDGEi2i has featured among the top 10 analytics and big data start-ups in several coveted publications.

Research has shown that after every bad experience of doing business with a company, 65 % of customers share that bad experience with their friends, 34 % send feedback to the company, while about 25 % express it on social media. Customer experience, if not given enough attention at the right time will lead to exponential revenue loss. It is time to accept the fact that, customer experience management is no more a luxury for businesses, it is now the new norm of doing business.

Reference: FORRESTER Research Q4, 2012, Global customer experience peer research panel, online survey