In the 2000s, the business world was driven by marketing. In the 2010s, it was technology. This decade is all about optimizing your customer’s experience.
Customer Experience (CX) is the overall perception your customer has of your business and offerings. A good customer experience can boost customer loyalty and customer lifetime value. A bad customer experience can cause customer distrust and churn.
Marketers know that happy customers make loyal customers who stay longer and spend more with your brand. If your business isn’t making the customer experience enjoyable or helpful, they’ll leave and go to your competitor.
The answer to optimizing your customer experience begins with tracking customer experience analytics. In this article, we’ll look at what customer experience means and how to use customer experience analytics to get actionable insights, minimize customer churn, and boost business outcomes.
- CX vs UX
- The 3 main components of customer experience
- Customer experience and the customer journey
- Benefits of tracking customer experience analytics
- How do data analytics help improve customer experience?
- Important customer experience metrics to measure and optimize
- Using customer feedback to measure customer satisfaction
- Using machine learning for customer experience analytics processing
- Customer experience analytics FAQs
What is CX vs UX?
Customer Experience (CX) refers to all interactions a customer has with your brand. It’s calculated based on metrics like customer retention rate or churn rate, customer satisfaction (CSAT), and likelihood to recommend your brand to others (NPS).
User Experience (UX) refers to the customer interactions with your product or service. This customer data is derived from success, error, and abandonment rates. It can also include data related to the time it takes to complete a task, clicks to completion, and ease of finding information. It falls under the purview of the overall customer experience (related to their impressions of your product).
CX is all about the overall experience with your brand. Customers expect helpful, professional, and easy interactions with your marketing, sales, support, and helpdesk teams. Customer Experience insights can help identify your customer’s pain points so you can be the solution to their struggles.
What are the 3 main components of customer experience?
Customer experience is broken into three components. Your customer will subconsciously (or consciously) use their experience in these interactions to form their opinion (aka satisfaction) with your company. The three main components of customer experience for any company are:
The first part of the customer journey is through your sales and marketing. It’s one of the first touch points you get with your customer, so it needs to be strong, accurate, and honest. If this first critical touchpoint sets the wrong tone or doesn’t accurately represent your offering, it can domino and taint the rest of the customer experience. It can be difficult or impossible to bounce back from a bad first impression.
Many companies like to stretch the truth or oversell in their marketing. You instantly lose trust when customers buy the product and don’t get the same result your marketing promised them. This could be enough to convince a customer to cut their losses and leave. A few may stick around to see if you can remedy the situation.
For example: If your marketing indicated that you provided 15GB of free cloud storage space, but your system was only made to support 5GB, there isn’t much you can do to give the customers what they expected. The only way to earn their trust back is to change your marketing to reflect the actual offering more accurately, admit your error, and offer current customers a credit or bonus apology.
Some people may overlook your marketing and customer experience faults (which we’ll talk about below). But, if they have a terrible experience with your product, they’ll likely walk away. That’s why ensuring you have a solid product or service is so important. This is the portion of customer satisfaction that deals with UX.
For example: If you sell a SaaS product, but the login screen gives people an error message 50% of the time they try to log in, that’s a bad user experience. Your product is not performing as expected due to faulty software, programming, or system.
Another example: If you sell a monthly subscription box, but half the items in the curated box are not on theme or are broken, that’s not meeting customer expectations and can create a negative experience for the customer.
3. Service Experience
Even the most successful businesses get help desk inquiries or customer complaints. It’s how they handle them that helps create a good customer experience. This includes how quickly you respond to dissatisfied customers, how easy it is for customers to contact your support or help teams, how helpful your brick-and-mortar staff are, and if you can resolve the customer’s inquiry or complaint to their satisfaction.
For example: A customer may contact you because they can’t log in to their online account. If your support desk staff provides easy-to-follow password reset instructions that work, that creates a good customer experience that doesn’t negatively affect their image of your company.
On the other hand, if your customer service rep has trouble accessing your customer’s file to help them reset their password, is rude or condescending, or speaks too fast for the customer to understand them, even if the password gets reset, it may still create a negative experience for the customer. In this case, it’s likely not enough to encourage customer churn, but enough that if they have another negative experience with your company, they could leave.
Customer experience and the customer journey
Did you know that 54% of consumers feel the customer experience of many businesses needs improvement? Customers are already coming to you with negative expectations, but you have an opportunity to earn their trust throughout the full customer journey:
This is the most volatile time for a customer of your brand. You have yet to build brand loyalty, so every interaction is highly scrutinized. Optimizing this stage to ensure their experience is as flawless as possible is essential to acquiring new customers.
This is when you nurture your current customers to keep them engaged, happy, and coming back for more. Get detailed information about all customer interactions with your product and their interactions with the help desk or support team members. This data can be used to optimize their experience so you can nurture them into becoming long time, high-value customers.
Your high-value customers are those who spend more and are brand loyal. They are the ones who will refer you to others and are generally more forgiving of the occasional “bad” experience with your brand—as long as you can correct it.
Benefits of Tracking Customer Experience Analytics
Why are businesses tracking customer experience analytics in their processes? There are many benefits of knowing more about your customer’s experience:
- Understanding your customer journey map: You can correlate experience data through customer feedback and other analytics to understand the customer’s experience from acquisition to loyal customer. Knowing this helps you optimize your business offerings, messaging, and services to boost customer retention and lifetime value.
- Increasing customer loyalty: Happy, satisfied customers are the ones who remain brand loyalists. Provide them with a consistently positive experience, and, all other things equal, they’ll help you boost your average customer lifetime value as loyal brand evangelists.
- Lowering customer churn: Customer churn is the percentage of customers you lose over a certain period. Again, the best way to encourage customers to stay a customer of your brand is to keep them happy and not give them a reason to leave for your competitors.
- Identifying operational, policy, or product problems: CX analytics give insights into where you may not be performing optimally in your business. This is an effective way to see what business processes and procedures are not meeting your customer’s expectations and needs. It helps you identify bugs and errors in your product as well as the human interactions causing the most stress for your customers so you can take steps to improve these interactions for the betterment of your customers and business.
- Removing siloed business practices: Customer experience analytics show us the overall picture of our brand. Then, we can dive deeper into the business units that need the most improvement to boost customer experience scores.
- Identifying customer interaction trends: All customer touchpoints are an opportunity to collect customer data on interaction satisfaction. As you collect more data, you’ll start to notice trends. You can use these trends to optimize your business offerings and experience to reverse negative trends and encourage or maintain positive ones.
- Improving customer support agent training: Knowing the most commonly asked help desk questions can help you hone your customer support agent training to cover real topics they’re likely to get asked on the job. Create a cheat sheet or script for customer support agents to use when faced with these common questions, so everyone is on the same page to create a more consistent customer experience.
How do data analytics help improve customer experience?
When you collect customer experience analytics from multiple data sources over time, you can use this along your operational data to get a high-level picture of your customers’ happiness. You can correlate this with demographic data to identify:
- Overall customer sentiment
- Customers likely to churn
- Business changes likely to lower customer churn
- Product or experiences that are producing negative customer experiences
- How many customers are loyal or disloyal to your brand
- If your product is meeting consumer expectations
- Where to reallocate company resources and existing systems to boost customer satisfaction
Important customer experience metrics to measure and optimize
Without accurate customer-provided data, it’s challenging to use customer experience analytics. You can take educated guesses based on metrics like the number of support inquiries per topic, but it won’t give you the whole picture.
To build a deeper understanding of your customers and build a robust customer experience analytics program, you need customer data from other sources, including:
NPS customer data
Net Promoter Scores (NPS) measure how likely a customer is to refer you to a friend or colleague. Good NPS scores indicate customer happiness and loyalty as only those who are satisfied with your brand will refer you to others. Net promoter score questions should cover specific aspects of your brand and product so you can get a detailed picture of any areas your customers think need improvement.
A Customer Satisfaction Score (CSAT) measures how happy your customers are throughout their customer journey. Answers are collected from numerical values (such as satisfaction scales) or are turned into numerical values using sentiment analysis.
CSAT scores include questions such as:
- “Rate your in-store experience today.”
- “What was your biggest roadblock to using our product?”
- “How likely are you to purchase from us again?”
Customer Effort Score (CES) measures how much effort a customer goes through to resolve their problem. A customer effort score can be subjective, but it can still help identify areas of customer engagement that could be improved.
This includes questions such as:
- “Rate how easy your experience was with our customer service teams.”
- “Rate how easy it was to find your answer in our helpdesk library.”
- “Rate your experience with our chatbot today.”
Anonymous customer feedback is one of the most honest sources of feedback a business can get. Knowing they’re answering anonymously usually encourages them to be completely honest in their review without worrying about repercussions.
Include customer surveys throughout your business, including during onboarding, annually from current customers, when a customer makes an account change, when a customer interacts with a customer support member, and when the customer terminates their relationship with you.
Survey data can be collected, analyzed and summarized manually or through customer experience management platforms. The benefits of using analytics software include fast, actionable results, and access to predictive analytics algorithms to identify changes and trends in customer satisfaction before they cause a problem.
Voice of the Customer (VOC)
Voice of Customer (VOC) is what customers are saying about your brand or products. Documenting and distilling customer needs and expectations helps you improve your brand offerings and experience. Any time someone is speaking about your brand or product, this is VOC data.
Customer surveys are a strong source of VOC customer experience analytics data, as are online reviews, and social media. Collect what people say when tagging your company, using your branded hashtags, on review sites and marketplaces, and in all customer surveys. You can then use a machine learning platform like Idiomatic to look for common words and phrases and what part of the customer journey it relates to, to build a VOC strategy or program.
Other customer experience data
Your business should incorporate some kind of customer data collection with every customer interaction experience. This can be as basic as “how many support inquiries we got in August” or as detailed as the specific customer ID who instigated the inquiry. The more detailed you can get, the better you’ll understand the nature of these interactions.
Here are some examples of customer data you can collect related to customer experience:
- The exact “problem” the customer had
- If their problem was satisfactorily solved
- What steps a customer took to solve the problem (ie: visiting the online help library before using a help desk chatbot)
- Time stamps of all interactions
The more data you can collect regarding their interactions with your brand, the clearer and more specific actionable insights you can get when reviewing your customer experience analytics data.
While not essential, knowing which customers are experiencing problems can help you look for trends in your data. For example, you may find that most customers in Lisbon, Portugal, have initiated help desk tickets due to trouble accessing the cloud server data, but customers in New York City don’t.
Based on this data, you may hypothesize that there may be issues with accessing your American-based servers from overseas locations. Knowing this, you can run internal tests to verify this hypothesis and correct the problem.
Using customer feedback to measure customer satisfaction
Using all your data, you can see where customers are most dissatisfied. Then you can summarize and provide customer satisfaction overviews for your business as a whole or for a specific business unit or interaction type.
Combine your customer experience analytics with data from other sources (including NPS, CSAT, CES, and VOC) to get a detailed picture of customer satisfaction. Then, take appropriate steps to improve where needed.
The best way to take such large amounts of data is to use customer experience software like Idiomatic to process and use machine learning algorithms to help you see the relevant trends in the data. Customer experience analytics solutions like this can help you sift through large amounts of data, quantify feedback results quickly, and pull more accurate customer analytics. This helps inform a more detailed and appropriate customer experience strategy.
Using machine learning for customer experience analytics processing
Idiomatic is a customer experience analytics solution to help you boost customer engagement, increase customer satisfaction and loyalty, and impact your business in a positive way. It takes all your customer feedback data and uses sophisticated algorithms to look for trends and provide actionable results. As you continually add more data from customer experiences, it learns and provides even more accurate insights and predictions.
It’s faster than manual data analysts to collect, review, and summarize the data, removing human errors from the process. It helps businesses like yours use customer feedback to create a better customer experience.
With Idiomatic as your customer experience analytics solution, your team can focus on using results and recommendations from the analysis to improve the customer experience.
Request a demo of the Idiomatic solution to learn how to take your customer experience to the next level.
Here are some quick FAQs about using customer experience analytics to improve your customer’s experience: