How to Use Data Analytics for Better Customer Experience

Illustrator: Anna Galvañ
data analytics in customer experience management

The current competition sets a high bar for user experience delivery, and companies now focus deeply on improving customer experience (CX). To this end, businesses and startups fine-tune the consumer’s journey and interaction with their product, from discovery to conversion.

However, managing the customer experience should align with every company’s internal business processes instead of being a stand-alone operation. That’s why you need data collection and management systems to track user experience data and spot avenues for improvements.

In this article, we discuss the relevance and importance of data analytics in CX management. But first, let’s understand what customer experience analysis entails today.

What is Customer Experience Analysis in 2022?

Customer experience analysis means collecting, managing, and evaluating consumer data to improve their experiences with your business. 

Nowadays, CX analysis covers customer reviews, pain points, interactions, behavior, social media engagement, and support.

Who Benefits from Customer Experience Analytics?

Marketers often assume that customer experience analytics help only the consumers, but in reality, businesses and their employees also benefit from this process. 

  • Business executives and shareholders: The top-brass in every company always considers key metrics before signing off on a product (or project). Armed with comprehensive insights into consumer behavior, they can finalize crucial business decisions.
  • Marketing specialists and CX leaders: Marketers, especially CMOs, need up-to-date consumer information to make strategy tweaks. Without detailed CX insights, marketers and CX experts would rely on guesswork to address consumers’ needs.
  • Support agents: Agents need detailed and drilled-down information to streamline customer support. Otherwise, the customer experience effort crumbles due to redundancies and bottlenecks.
  • Customers: Of course, the users benefit from data analysis because the inferences drawn from the data improve their interaction with your business. 

How Does Data Analytics Improve Customer Experience? 

Analyzing data is one part of the puzzle, but interpreting the data and making decisions complete the entire customer experience picture. For this, you need to have a stack of tools for data collection, management, and analysis.

According to Finances Online, 19% of consumers prefer an optimized customer experience over other brand-building strategies. And most users won’t mind paying extra bucks for a better customer experience.

finance online chart landbot
Source: Finance Online

Here are other advantages of using data analytics to improve customer experience.

Personalize the Experience

When you collect data from multiple users across multiple platforms and demographics, you will notice repeating patterns and themes. From these recurring themes, you can craft a customer persona for your company’s products.

And why is a persona or profile necessary?

Marketers and customer support agents can adjust their playbooks to suit most customers’ expectations with a user profile in mind. Besides, McKinsey estimates that personalizing the customer experience bumps the revenue by 5-10%.

Let’s say most users dislike the new checkout feature on the company’s website; this means that the figurative user disapproves of this feature, making it a pain point for them.

With this information, you can redesign the checkout page to address the virtual consumer’s pain point.

On the consumers’ end, they feel a personal connection with the company because they addressed this issue.

Identify Bottlenecks

We already mentioned one pain point, but what if users have more issues with your company? 

That’s where data analysis tosses your business a life jacket. 

The checkout flaw might be the least of your worries; the app update might be unresponsive on older devices as well. Only user reviews and insights from a survey maker can point out CX issues with your brand. 

Similarly, customer support agents can unclog their logs faster since they understand user pain points better.

Predict Market Changes

With a well-crafted user persona, coupled with an array of vital pain points, marketers can predict consumer behavior with near precision. 

Companies like Apple and Amazon excel because they are always one stride ahead of the curve, thanks to customer experience data. Users love companies that help them solve problems they never knew existed. And this applies to smaller companies too.

So, any company that wants to outperform the competition in a saturated market must use data-based predictions to determine future trends — and capitalize on them.

Improve Customer Support

Customer support is inevitable when users interact with your product. Hence, you need to gather analytics to assist support agents. 

Social media ‘temp checks’ and direct customer engagement can predict the primary issues customers face when using your product. 

As a result, your agents will obtain contextual, data-based information regarding the origin of key issues as well as their solutions.

You can also use chatbots as part of the marketing strategy to relieve the support agents’ workload.

Solidify Brand Loyalty

Your company identifies changing consumer behavior, predicts market changes, and personalizes the experience — this is the blueprint for a viable company that consumers love.

But that’s not all; data-based CX analytics helps you spot exploitable gaps to improve the customer experience and foster your brand’s online reputation.  

One common practice for most companies is to use a webinar platform to host educational sessions that help customers better understand your business and strengths. Doing this will make you a thought leader in a niche and increase brand loyalty. 

Also, social media still has a great potential of improving brand awareness and loyalty.

Users prefer interacting with brands on social media, which presents an opportunity to gather more personalized data. To grow your social media following, use the best solutions to gain followers.

Make Data-Driven Decisions

Let’s pan the camera away from the customers for a moment... 

Executives, marketers, and other contributing members of the decision funnel need properly-analyzed data to make decisions. 

When making business decisions, the CX manager collates business objectives with the customer needs to provide a clear pathway to success. 

Another reason why your company needs data analytics on the CX front is that teams often clash due to a difference of opinion. 

But here is the thing: numbers don’t.

Instead of relying on sentiments and seniority, you can base your decision on data obtained from the field.

Track Performance

Every CX data analysis effort should boil down to one thing — tracking the performance.

You can use AI-powered tools or similar data synchronization options. For example, if you're operating in different types of sheets Excel to Google Sheets integration can help to gather your data. But the most important thing is to generate a report that states whether the customer experience improved or worsened over a specific period.

Essentially, tracking performance helps you identify key metrics and opportunities for improvement.

6 Customer Experience Metrics to Track

Every information you collect about your consumers is essential to the customer experience. But let’s face it: some information is more important than others.

Here are the metrics (or key performance indicators) to prioritize when gathering CX data.

Net Promoter Score (NPS)

Net promoter score is a metric that evaluates customers’ loyalty to your brand. This score ranges from -100 to +100 — your target is to hit the highest positive number possible.

qualitrics NPS score
Source: Qualitrics

You can also determine the net promoter score using a simplified 10-point scale that determines detractors, passives, and promoters.

To collect the proper information, you may use specific surveys or questionnaires.

Customer satisfaction score (CSAT)

Customer satisfaction evaluates the degree to which you fulfilled the consumer’s needs or solved their problem.

NPS CSAT score landbot

Unlike NPS, CSAT focuses on specific products or services. The common question prompt for a CSAT evaluation is, “how satisfied are you with this service/product?”

The possible answers on a corresponding 5-point scale are as follows:

  • Very unsatisfied — 1
  • Unsatisfied — 2
  • Neutral — 3
  • Satisfied — 4
  • Very satisfied — 5

Customer Engagement Score

The customer engagement score shows how your users interact with your products. This metric is vital when conducting A/B testing on various marketing channels after a product upgrade.

If the engagement increases, the customer experience has improved. Otherwise, you need further improvements to get consumers to interact with your product.

Customer Churn Rate

If you want to track the pace at which you lose clients, look at your customer churn rate.

Why is this metric vital to business growth and CX?

statista churn rate
Source: Statista

Statista figures show that the churn rate for general retail companies is 24%, while online retailers experience a 22% churn rate. So, if you want to stop losing customers, always monitor the churn rate.

Customer Lifetime Value (CLV)

Customer lifetime value is a metric that shows you the monetary value of a customer pool (or single customer) throughout their interaction with your brand.

To calculate CLV, you need the following information:

  • Customer lifespan
  • Purchase frequency
  • Purchase value

Determining a particular segment’s CLV informs your decision to address their problems.

Customer Effort Score (CES)

Customer effort score survey measures how easy it was for the customer to perform a particular action. This survey often contains a single question, accompanied by a 7-point difficulty scale.

InMoment CES customer score
Source: InMoment

How to Choose the Right Data Management Tools 

In the age of Big Data, many data management tools flood the market with promises of collecting and interpreting data in real time.

Unfortunately, only 4% of CX leaders believe their CX measurement system enables them to calculate ROI.

mckinsey customer experience score
Source: McKinsey

Before purchasing a data management system for CX analysis, here are key factors to consider:

  • Customer management – This functionality contains all the customer’s activities throughout their lifespan with your brand. 
  • Seamless integrations – The CX management system must integrate other services. You should also be able to integrate custom APIs with CEM and other software.
  • Centralized data storage  – The entire data should appear on a centralized dashboard with unbridled access for internal team members.
  • Self-service tools – Instead of waiting for support agents, the CEM system should have self-service tools to assist with recurring issues.
  • Automated responses – Like self-service tools, automated chatbots can relieve the pressure on support agents by offering template-based responses to generic questions.
  • Reporting – Beyond the centralized dashboard, the CEM system should generate detailed reports for export in different file formats — CSV, XLS, PDF, etc.
  • Inventory – Modern CEM systems should contain a local or cloud-hosted database for taking inventory.
  • Task tracking – Support agents should be able to manage customer tickets from creation to completion. 
  • Longevity – Look for solutions that can scale with your business in the long term. Otherwise, you will need to halt operations to upgrade the system or migrate consumer data to new platforms.
  • Security – Since you are handling sensitive user data collected via specific surveys or questionnaires, you need a secure platform with multi-factor verification.

Final Words

Data analytics is the bedrock of customer experience. Every company needs to gather, manage, and interpret data before making any business decisions. 

From this data, customer support agents can personalize their solutions, marketers can predict market changes, and managers can identify workflow bottlenecks.

You also need to determine the key metrics that define the customer experience — and explore how to track them. For that, you need a secure, optimized, and easy-to-use AI-powered CEM platform.