How to improve customer experience with data analysis (2022)

The customer forms an integral part of almost any business. Any organisation offering a product, service or experience requires someone to purchase their wares. And for a customer to keep on purchasing goods from you, you must keep them satisfied! The motivations behind achieving customer satisfaction can vary from industry to industry; sometimes you must differentiate yourself from or outperform a competitor, other times you must maintain a level of service or risk punishment from regulators. If you’re reading this article, I’m sure you already know all of that – what you really want to read about is data, and how you can use yours to make achieving customer satisfaction even easier.

I want to draw on two ways of working with customer personas – one a more traditional, journey-focussed approach to identifying personas, and one that uses data to take a deeper dive into the customer journey. Let’s see why taking that closer look with data can help you improve your customers’ experiences…

The traditional approach: Identifying personas using customer journeys

Traditionally, you can identify customers by examining the journeys that they take in their contact with you and the interaction points that they touch, sometimes inadvertently. The more seamless and personal the journey that you can offer your customer, the more satisfied you’re likely to make them.

A mobile application is a great example of this. Apps can be simple and clean to use for many customers, but what if a customer doesn’t own an appropriate device? One simple element of the journey has already divided our customers into two distinctly identifiable segments – those who can complete it using the app, and those who can’t.

(Video) How Big Data Analytics Can Be Used to Improve Customer Experience?

By applying this kind of thought process to customer journeys we can begin to identify personas based on other factors too, like age, gender, location, profession, education and technology used.

However, by using these as criteria to segment customers, it is likely that you’ll carry forward some element of unconscious bias and pre-defined misconceptions into your personas.

The alternative approach: Understanding personas using data

So, how else can we segment and understand our customers, other than by using the basic demographic and technological information that we capture about them at their interaction points?

The data that we capture about customer journeys can be so much better at telling us the real story. We can use data to segment happy customers from dissatisfied customers. Even more importantly, we can identify the parts of the customer journey or the operational features that are contributing to negative experiences.

Often, it is not a lack of understanding or incorrect profiling of a customer that is leading to their dissatisfaction, but rather a poorly performing element of the service you’ve provided. It’s crucial to be able to recognise problems like this if you want to make improvements.

(Video) How Can Your Data Analytics Improve Your Customer Experience?

By analysing the data from real customers’ journeys, along with known outcomes from previous interactions and customers journeys, you can begin to identify those who are experiencing negative outcomes. The crux of this is that, rather than simply analysing the characteristics of a customer persona, you’re also analysing the experience that they are provided by your product or service.

Applying analytics to improve the customer experience

Taking this a step further, how can we use this data about customer experiences to improve customer satisfaction? This is where you don’t just want to be reactive in your use of analytics. Building a predictive model for the outcomes of your customers’ journeys enables you to identify what a customer might be likely to experience at various points along that journey. Where these predictions differs from your expectations, or from the reality that a customer is experiencing, you know you’ve got an ideal area to make improvements.

Furthermore, using real data to understand what customers are experiencing on their journey, rather than making assumptions based on the information we know about them, reduces the effects of human bias and pre-conceptions and lets the truth do the talking.

How to improve customer satisfaction in the water industry

Here’s an example of the process I’ve just described in action in the water industry…

Water companies hold data including customer information records (in a CRM system), customer billing data, complaints data, and data about specific jobs and operations they’ve conducted. By piecing together job data and customer data, we can get a much more complete picture of what a customer’s experience is like – from their first call to report a problem, to the operative logging this in the system, to sending out a repair team, to the arrival and departure times of that team.

(Video) Better Customer Experiences with Data Analytics

By doing this, we can start to identify points in that customer experience which can be associated with a particular outcome. And by doing this across a whole set of historical data about jobs and customers together, you can begin to see the scenarios which repeatedly lead to a positive result – and those which don’t.

It might sound obvious that if the service team arrives on time to a job, the customer will be much more likely to leave a positive review of the company, but some results from data analysis like this will be more unexpected. For instance, let’s say the team arrives on time to a job (meeting the timeframe defined in their SLA, Service Level Agreement), but the customer still leaves a negative review. Why might this be? Perhaps the problem is so dire that resolving it will never turn it into a positive experience, no matter how effective or efficient that resolution is; or maybe the SLA timeframe that’s in place for this type of job needs reassessing.

By identifying the subset of features from every job which consistently lead to an outcome, whether positive or negative, we can then begin to investigate which features (or combinations of features) are representative of a particular outcome. Using historical data, you can predict what outcome you will likely get from a known scenario. Then, you can use this to test out new scenarios and establish the likely outcome from these too.

What’s the point of all this in improving customer experiences? Well, now you can start to identify those customers and scenarios which are more likely to result in a negative outcome and work proactively to turn those customer experiences into positive ones.

Here’s what data analysis can actually do for customer experience

Still, there are some limitations to what we can understand with data analysis. For instance, customers who always give negative feedback with no apparent reason, or customers who give positive results regardless of the outcome – data analysis can’t get to grips with these ‘illogical’ choices.

(Video) Data, Analytics and Improving the Customer Experience

What analysis can do though is to pinpoint and help organisations to address the scenarios and customers whose outcomes can be swayed; whose score will change based on the experience they're provided.

Imagine the following: David has a burst pipe in his garden, and your customer service team promises to be there on Tuesday. But on Tuesday morning you’re alerted to a bigger job – a burst water main at a school – and you can’t make it to David’s property. A quick courtesy call to inform David that your team will be over later that day could be the crucial factor in ensuring that David’s feedback is much more positive. But how do you know exactly which action will have the most impact? This insight is what the data analysis I’ve described can give you.

In the new world of C-MeX (Customer Measure of Experience) for the water industry, customers score their experiences on a scale of 0 to 10, so it is vital to improve those experiences. Customers who give a score of 4, for instance, may have had a slightly negative experience which could be very easily improved, likely transforming them into a more satisfied customer with a score of 5 or above.

Data analysis can pinpoint the exact actions or features of scenarios that can make this difference – and which customers and scenarios are most likely to have their experience transformed from negative to positive if these actions are included. Don’t make assumptions – figure out exactly the most important actions to take, and what the cost of implementing them is, rather than just rolling out new actions (like a courtesy call when you’re running a bit late) at scale.

How to make the most of your data model

Of course, data alone will not solve the problem of understanding why customers have the experience they do – context is always important when looking at results. For example, uncommon outcomes are not particularly well served by this kind of data analysis. Why? Because the journey and experiences involved are not covered across the rest of your data set. If data about this outcome isn’t already in existence, then a predictive model won’t know how to handle it. It is not that the model is wrong, it simply doesn’t have enough information to know – a little like us sometimes!

(Video) Using Real-time Data Analytics to Improve the Customer Experience

The more data you can feed your model with, the better and more accurate its insights will be.

When data is combined with human domain knowledge and is given the appropriate value in your analysis, you can understand so much more about the products and services that you provide. No matter how you go about it, understanding this is the key to ensuring that you provide the best experience for all your customers, always.

Using data analytics and machine learning, the Aiimi team has helped several of our customers in heavily regulated industries to better understand the experiences that they provide to their customers. This has helped them to identify the causes of negative outcomes and better retain and satisfy their consumers.

FAQs

How do data analytics helps improve customer experience? ›

Big Data analytics removes the guesswork when it comes to understanding customer needs, pain points, goals, and interests, and it creates total visibility into the buying process. Companies can now review thousands of data points in real-time that help them understand their customers in context.

How data can improve customer relations? ›

Data makes it possible to understand what customers have to go through and where the friction points are without real-time interaction. Data allows you to watch behaviors and trends that lead up to, occur during, and happen after purchases, giving you better insight into the purchase journey.

Why is data analysis important in customer service? ›

Here are three ways data analysis can improve customer service: Enables your team to be agile and quickly react to problems. Improves your bottom-line by helping teams better understand customer needs. Allows teams to be proactive and get ahead of future challenges.

What are customer experience analytics? ›

What is customer experience analytics? Customer experience analytics is the process of collecting and analyzing customer data, with the goal of better understanding customer needs, viewpoints, and experiences with your products and services.

How do you use customer experience data? ›

How to Use Data to Improve Customer Experience
  1. Collect an Inventory of Current Customers. ...
  2. Determine Where You Stand With Your Clients. ...
  3. Map and Analyze Customer Profiles. ...
  4. Put Data Into Practice to Appeal to Clients. ...
  5. Measure Customer Satisfaction Results, and Make Changes Accordingly.
28 Jul 2020

What are the 3 main components of customer experience? ›

The three main components of customer experience are:
  • Discovery. This component is all about how companies contact customers and how they make that contact relevant and meaningful. ...
  • Engagement. This component is about how customers interact with the company and company products. ...
  • Delivery.
13 Feb 2022

What makes the best customer experience? ›

In short, good customer experience can be achieved if you: Make listening to customers a top priority across the business. Use customer feedback to develop an in-depth understanding of your customers. Implement a system to help you collect feedback, analyze it, and act on it regularly.

What is a customer experience framework? ›

A customer experience management framework is the model, strategy, or structure you use to measure, analyze, and improve your customer experience. It's fundamental if you want to take your CX seriously.

Can data analysis or data analytics introduce customer service improvements? ›

By using advanced data analytics you can understand your consumer base, deliver on or exceed expectations and proactively identifying opportunities for improvement. Your business can establish a connection with customers that has the ability to stand the test of time.

How do you approach customer service from a data analytics perspective? ›

Below are four key strategies the customer service manager can implement to leverage analytics to maximum effect.
  1. Differentiate the customer experience. ...
  2. Deliver personalized service. ...
  3. Develop a singular view of the customer. ...
  4. Deliver consistently exceptional service.

How data analytics can improve organizational operations customer experience productivity and performance? ›

Data analytics help to improve business management by helping leaders assess the effectiveness of current workflows, analyze the outcomes of the processes, automate new workflows, and refine them over time. Data also allows leaders to determine if processes are burdensome, draining the budget, or challenging to use.

What is the goal of customer analytics? ›

The goal of customer analytics is to create a single, accurate view of an organization's customer base, which can inform decisions about how to best acquire and retain future customers. It can also identify high-value customers and suggest proactive ways to interact with them.

How customer analytics can help a business? ›

At a micro-level, customer analytics allows companies to understand who their users are as individuals. They can segment users by demographics, interests, and behaviors and view their unique journeys. This knowledge helps businesses better cater to each customer persona.

How does analytics help customer segmentation? ›

Customer Segmentation Analytics Address Critical Business Questions. Effective customer segmentation uncovers consumers' need states, mindsets, behaviors, demographics and social profiles to identify their profit potential so that you can tailor your marketing strategies to align with each segment at a micro level.

What is a CX data analyst? ›

The work of a CX analyst is the intersection of customer service and business/data analytics. These analysts focus their work on collecting and parsing data, specifically focused on customer service and experiences. They then turn that data into something useful.

What are the activities in customer analytics? ›

As the backbone for all marketing activities, customer analytics comprises techniques such as predictive modeling, data visualization, information management and segmentation.

Which analytics allows you to develop a quality and customer experience for all customers? ›

Customer Experience Analytics – Definition

Customer experience analytics is the systematic examination of a company's customer data to understand the customers, their pain points, and their experiences which will, in turn, give you the confidence to update and optimize existing customer experience solutions.

How do you collect data from customer service? ›

Data collection methods
  1. Surveys. Asking direct questions to your customers is probably one of the most popular, and also an effective method of gathering data. ...
  2. Interviews. ...
  3. Focus group. ...
  4. Online tracking. ...
  5. Marketing analysis. ...
  6. Monitoring social media. ...
  7. Subscription and registration data. ...
  8. Monitoring in-store traffic.
5 Mar 2020

What is data analytics in simple words? ›

Data analytics (DA) is the process of examining data sets in order to find trends and draw conclusions about the information they contain. Increasingly, data analytics is done with the aid of specialized systems and software.

What are the four components of customer experience? ›

- To truly understand customer experience we have to understand the four components required to build one. There are archetypes, activities, interactions, and principles. An easy way to remember these is to think about the different parts of a relationship.

What are touchpoints in customer experience? ›

What is a customer touchpoint? Customer touchpoints are the various moments at which a customer will directly, or indirectly, come into contact with your brand. These touchpoints make up the customer journey, and are key to influencing the customer experience.

What are the types of customer experience? ›

There are two types of customer experience—direct and indirect contact:
  • Direct customer experience refers to any interaction initiated by the customer. ...
  • Indirect customer experience refers to the passive encounters with your company.
18 Mar 2022

What are the 7 qualities of good customer service? ›

7 Must-Have Qualities of a Stellar Customer Service Rep
  • Problem-Solving Skills. The number one skill you need to excel in for good customer service is problem-solving. ...
  • Clear Communication. ...
  • Friendly Attitude. ...
  • Empathy. ...
  • Business Acumen. ...
  • Product/Service Knowledge. ...
  • Strong Time Management.

What are the 3 most important things in customer service? ›

Essentially, the 3 important qualities of customer service center around three “p”s: professionalism, patience, and a “people-first” attitude. Although customer service varies from customer to customer, as long as you're following these guidelines, you're on the right track.

How do you deliver good customer experience? ›

8 Ways to Deliver an Outstanding Customer Experience
  1. Design the Experience. Every company has a mission and its own set of goals. ...
  2. Show Empathy. ...
  3. Be Friendly. ...
  4. Provide Value. ...
  5. Never Stop Improving. ...
  6. Be Consistent. ...
  7. Leverage Technology. ...
  8. Appreciate Your Customers.
18 Mar 2021

What does a customer experience strategy look like? ›

A CX strategy comprises the plans you put in place to provide positive experiences at each customer touchpoint along the customer journey and the purposeful ways to measure those experiences – both online and off. A good CX strategy creates meaningful experiences that can improve customer loyalty.

How do you build the right CX strategy? ›

How to build a customer experience strategy: A deep dive
  1. Research and create buyer personas.
  2. Understand your business objectives.
  3. Manage your stakeholders.
  4. Adopt an omnichannel approach.
  5. Use the right technology tools.
  6. Review your customer experience strategy on a regular basis.
10 Mar 2022

What are five examples of customer focused behavior? ›

Some of the customer focus examples include quality customer support, developing the best solutions for clients rather than the best products in general, using various data to understand customer behavior better, asking for customer feedback and taking it seriously to improve, focusing on their satisfaction, etc.

What analytics are important for customer service? ›

Customer service analytics offer rich insights into two crucial aspects- first, how customers perceive your customer service, and second, how well your team performs to meet the rising customer expectations. Metrics like Average Response Time, First Contact Resolution, CSAT, etc.

What is the importance of data analysis? ›

Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business and by storing large amounts of data.

What is the purpose of data analysis? ›

The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics has become increasingly important in the enterprise as a means for analyzing and shaping business processes and improving decision-making and business results.

Do data analysts deal with customers? ›

Data analyst role

They create reports, dashboards, and other visualizations on data associated with customers, business processes, market economics, and more to provide insights to senior management and business leaders in support of decision-making efforts.

What are the different types of customer analytics? ›

There are 6 different types of customer analytics: customer journey, customer experience, customer engagement, customer lifetime, customer loyalty, and voice of customer.

What is predictive customer analytics? ›

Predictive customer analytics helps businesses identify customers at high risk of churning. To identify customer attrition before it happens, look at the traits of customers who have churned in the past using churn rate cohort analysis. You can also look at a customer's lifecycle for clues about who will likely churn.

How can data analytics benefit a company? ›

Businesses collect customer data from many different channels, including physical retail, e-commerce, and social media. By using data analytics to create comprehensive customer profiles from this data, businesses can gain insights into customer behavior to provide a more personalized experience.

Which is best tool for data analysis? ›

Top 10 Data Analytics Tools You Need To Know In 2022
  • R and Python.
  • Microsoft Excel.
  • Tableau.
  • RapidMiner.
  • KNIME.
  • Power BI.
  • Apache Spark.
  • QlikView.
22 Jul 2022

Why is data analytics important in marketing? ›

Data analytics provides the opportunity for companies and marketing teams to gain more insight to help make their business more relevant and establish themselves within saturated markets. Standing out is the biggest goal for your brand to attract your customers.

How can you provide effective customer service by capturing data to track interaction? ›

The key to effective quality monitoring includes six crucial steps:
  1. Listen to your customers by monitoring interactions. ...
  2. Capture all of your customer feedback channels. ...
  3. Ask your customer what they think. ...
  4. Use quality monitoring to help agents improve skills. ...
  5. Do not view agent development as a one-off activity.
11 Feb 2013

What are the uses of data in the hospitality industry? ›

Data analytics in the hospitality industry can help hoteliers to develop a strategy for managing revenue by using the data gathered from various sources like the information found on the internet. Through analysis of these data, they can make predictions that will help owners with forecasting.

What is data analytics in simple words? ›

Data analytics (DA) is the process of examining data sets in order to find trends and draw conclusions about the information they contain. Increasingly, data analytics is done with the aid of specialized systems and software.

How could insights from that data solve a problem in data analysis? ›

Data analytics is a sub-field of data science and plays a major role in problem-solving. It makes it easy for us to extract and gather information from various sources and combine it to use it in our solutions effectively. More the information we can gather, the easier tackling the problem gets for us.

What ways can you record and manage customer data? ›

A simple way to store customer information is to use an electronic spreadsheet. If you have more detailed information, a customer relationship manager (CRM) database might be more suitable. A CRM can help you analyse customer information to find purchasing trends and identify your best customers.

How do you gather customer data? ›

Some of the simplest ways to collect data are through:
  1. Surveys.
  2. Newsletter and blog subscriptions.
  3. Promotions, competitions, and offers.
  4. Customer orders.
  5. Transaction history.
  6. Web-tracking.
  7. Marketing analytics.
  8. Social media.

What are the five ways to monitor your customer service? ›

Analyze service interactions across all channels
  • Ask customers for their feedback. Asking your customers for feedback on their preferred channels is a great start. ...
  • Consult with agents to improve practices. ...
  • Offer regular employee training and mentoring. ...
  • Provide regular channel maintenance.

What is the significant role of data analytics in hospitality industry? ›

Data analytics in the hotel industry is key to marketing strategy, building customer loyalty, and enhancing productivity. It enables hotels to personalize experiences for their guests, introduce better hotel pricing strategies, and expand their customer base.

What are the benefits of having data analytics in hospitality industry? ›

Proper data analysis can allow the hotel industry to build a tailored marketing strategy plan that is targeted to specific customer types and maximize revenue. This enables advertisers to build a more personalized user experience. They can identify key consumer groups and develop more content to serve their needs.

What is the data analytic in hospitality and why it is important? ›

Data analytics will help organizations gain real-time insights that will enable them to understand their areas of improvement, understand customer sentiments, optimally manage revenues, predict demand patterns and optimize pricing, real-time observation of internal processes via operational analytics, competitive ...

Which is best tool for data analysis? ›

Top 10 Data Analytics Tools You Need To Know In 2022
  • R and Python.
  • Microsoft Excel.
  • Tableau.
  • RapidMiner.
  • KNIME.
  • Power BI.
  • Apache Spark.
  • QlikView.
22 Jul 2022

What is the benefit of data analysis? ›

The rich variety of data that enterprises generate contains valuable insights, and data analytics is the way to unlock them. Data analytics can help an organization with everything from personalizing a marketing pitch for an individual customer to identifying and mitigating risks to its business.

What is the key objective of data analysis? ›

The main purpose of data analysis is to find meaning in data so that the derived knowledge can be used to make informed decisions.

What business problems can be solved by data analytics? ›

Analytics can be used to solve issues across a myriad of different complexities, like sinking revenues, inefficient risk and fraud reporting, poor KPI management, plummeting marketing ROI and more.

What are the important data analyst skills? ›

Here are the eight most important data analyst skills:
  • Data cleaning and preparation.
  • Data analysis and exploration.
  • Statistical knowledge.
  • Creating data visualizations.
  • Creating dashboards and reports.
  • Writing and communication.
  • Domain knowledge.
  • Problem solving.
27 Jun 2022

What is problem solving in data analysis? ›

Problem Solving and Data Analysis includes using ratios, percentages, and proportional reasoning to solve problems in real-world situations, including science, social science, and other contexts. It also includes describing relationships shown graphically and analyzing statistical data.

Videos

1. Improve Customer Experience with Data Science
(Data Science Foundation International)
2. Data analysis to improve service quality and customer satisfaction:suzuki's Group
(慶應義塾 Keio University)
3. Advanced Analytics for Better Customer Experiences
(Looker)
4. Data Analytics: Getting closer to your customer
(Fujitsu)
5. Customer experience optimization: What we learned from data analysis - Tara Robertson at CXL Live
(CXL)
6. Top 5 Customer Analytics - Demonstrated
(Data Science Demonstrated)

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