Here's How to Optimize Customer Experience with User Data
Optimizing customer experience is the easiest way for your company to grow.
According to Forrester, companies that harnessed and applied data and analytics to differentiate their products and customer experience are forecasted to grow 27% to 40% annually. This is twice as fast as companies that neglected harnessing user data in customer experience optimization.
You may be wary of using user data in customer experience optimization because of government regulations around personally identifiable information (PII).
However, 96% of all data you collect on your customers on your website is completely anonymous, making A/B testing and personalization a must for developing a competitive advantage.
In this article, you will learn:
- What customer experience optimization is
- How to optimize your customer experiences using user data
- The benefits and risks of doing user experience optimization
- Anonymizing PII and how to use it in your industry
1 What is customer experience optimization?
Customer experience (CX) optimization is the process of improving the overall experience that customers have when interacting with your brand or business.
The goal of customer experience optimization is to create a positive, seamless, and engaging experience for customers across all touchpoints, from visiting your website to talking to a customer service agent to exploring your social media profiles.
Customer experience optimization typically involves gathering customer feedback, analyzing data, and using insights to identify areas for improvement and optimize the customer journey.
This is where user data comes in. User data from interactions with your business can tell you where you can optimize the experience for your customers, which we’ll explore a bit later in the article.
2 Why is customer experience optimization important?
Customer experience optimization is important because it helps businesses retain customers, increase revenue, and differentiate themselves from competitors.
Because your customers expect to leave satisfied and informed every time they interact with your company, customer experience optimization is a must.
Benefits of customer experience optimization
Customer experience optimization offers several benefits including:
Retention and loyalty. By creating a positive and memorable customer experience, businesses can increase customer loyalty and retention. This can lead to repeat purchases, positive word-of-mouth referrals, and a stronger brand reputation.
Differentiation. In today's competitive marketplace, businesses need to differentiate themselves from their competitors. A superior customer experience can set a business apart and give it a competitive edge.
Revenue. A positive customer experience can lead to increased sales and revenue. Customers are more likely to make repeat purchases and to spend more money when they have a positive experience with a brand.
Cost savings. By optimizing the customer experience, businesses can reduce the costs associated with customer acquisition and retention. Satisfied customers are less likely to leave and more likely to refer new customers to the business, reducing the need for costly advertising and marketing campaigns.
Insights. By analyzing customer feedback and data, businesses can gain valuable insights into customer needs, preferences, and pain points. This can inform product and service development, as well as other business decisions.
Knowing exactly what your customers want can help you make business decisions that provide better experience for customers and drive revenue for your company.
3 How is customer experience optimization applied in different industries?
The way you optimize experiences for your customers depends on the industry you’re in and the type of business you run.
Because customer experience optimization is unique to your industry, here are some examples to help you get some ideas of how to improve user experience.
E-commerce
In an e-commerce business, you can optimize your product/content recommendation to improve customer experience.
Using historical behavioral data and product affinity data, you can make product recommendations even more effective for each customer that peruses your product category pages and elsewhere on the web.
Say your e-commerce store sells sneakers. You can recommend socks to customers who add a pair of sneakers to their cart and similar design of socks to visitors who are looking at socks on your site.
You can even take this product recommendation further by using 3rd party cookie data, for example, to show your sock products to users searching for socks elsewhere on the web or to even bring back customers who have previously bought footwear or socks on your website.
This can help you drive revenue and keep your customers coming back.
Automotive Industry
You can optimize customer experience using contextual data in the automotive industry.
You can run ads for new car accessories on an article titled “When to get a first oil change” because the context is clear - visitors who are reading this article probably bought a new car recently and will need accessories for said car.
Also, you can target users based on geolocation and weather. If your automotive store sells tires, you can use contextual targeting to advertise to users in Canada and the USA in the winter who will be needing winter tires to make their cars and commutes safer when it snows.
This will help you create a base of loyal customers who know they can trust you to anticipate what they need.
Health industry
If your company is in the health sector, you can still run customer experience optimization for your users.
With rules-based targeting, you can show returning visitors brochures about treatment options for conditions they have previously researched on your website or articles on those conditions they have read in their previous visits. This method doesn’t involve any PII and keeps you above board.
Another customer experience optimization you can run is on content recommendation. You can invite visitors who are reading about a particular illness to a private online discovery call with a specialist who can answer questions visitors have and their specific concerns.
This can help your visitors feel heard and that your health-based company takes their health concerns seriously.
4 How to improve your customer experience with experimentation
Before starting customer experience optimization, you need to understand your customer’s journey across your website. You want to find the areas that give customers issues and then work on fixing them.
Here are some steps to take to optimize your customers’ experiences:
1. Leverage quantitative and behavioral analytics data
Understanding your customer comes from digging into user data from your analytics tools.
Quantitative data analytics tools like Google Analytics, Heap, and Amplitude provide a treasure trove of non-PII user data that you can dig into to find issues plaguing your customers. Your analytics data can tell you so many things about your customers. It can tell you:
- How much time your customers are spending on different pages
- Where they’re coming from (traffic sources)
- How they navigate your website - which pages they go to after they land on your website
- The pages where they leave your website
To further dig into what exactly your customers are having issues with, you can use behavioral analytics tools to see heatmaps, session recordings, analyze forms, etc. Kameleoon integrates with behavioral analytics tools like Hotjar, Fullstory, Crazy Egg, and Mixpanel which are useful in helping you analyze customer behavior on your website pages.
User analytics tools can show you:
- Heat maps that tell you what part of a page your visitors spend the most time on.
- How customers behave when filling out forms - you can confirm if the forms you have on your website are causing frustrations for your customers.
- How user sessions typically go through session recordings.
Armed with both quantitative and customer behavioral data, you can postulate a hypothesis on issues making your customers’ experience worse on your website.
For example, if customers are dropping off when they encounter a sign-up form at checkout on your site, your quantitative and behavioral data can tell you that your sign-up form is a prime candidate for customer experience optimization.
2. Create a hypothesis
Once you have identified issues in your customer experience, you can posit a hypothesis on how you can solve the issues.
Take the example above, after confirming that your sign-up form at checkout is the problem that your customers are having. You can hypothesize that optimizing the form at checkout will improve customer experience and boost revenue.
Positing a hypothesis will form the basis of what you will be testing in your customer experience optimization.
3. Choose an experimentation method
In optimizing user experience, there are different experimentation methods available to you:
A/B testing can be a quick and easy-to-understand optimization method to optimize customer experience as a marketing team. Take the form sign-up drop-off example, you can design a new form and test it against the original form.
Multivariate testing allows you to test multiple elements at once to identify the multiple variables on a page that can rapidly improve user experience for your business. For example, you can use multivariate testing to test all the customer flow and journey through your ecommerce store.
Server-side testing lets you experiment and implement changes on the back end without affecting the functionality and load times of your website on the front end. With server-side testing, both marketing, product, and development teams work together to test and push features that directly optimize customer experience without any further damaging the experience of your users.
4. Test your hypothesis
After you choose the testing method you want to use, you design your variation using either client-side or server-side testing.
With Kameleoon, you can design and launch experiments client-side, server-side, or even hybrid experiments. Hybrid experiments are a perfect marriage of server-side and client-side testing where you can launch server-side testing leveraging client-side capabilities.
After you design your experiment, choose your audience segments which you will bucket into groups for your test. Decide on the triggers for your experiment. Simulate your experiment and launch it.
At the end of your experiment, you can analyze your results and implement the changes.
5 Is it risky to practice customer experience optimization?
Too many brands don’t do customer experience optimization because they think data is risky.
The most important thing to note is that most of the data you will use for A/B testing is not risky or sensitive at all. In fact, 96% of all data you collect on your customers on your website is completely anonymous.
It’s not covered by any regulations, because it counts as non-personally identifiable data. There’s no way anyone can tie this information back to the user’s identity. You can use the data to help you achieve your business KPIs without worrying about regulatory risk.
However, using personally identifiable data (PII) can be done with very little risk as well. Using PII opens the door to dozens of targeting, testing, and customer experience optimization techniques. It lets you run more advanced tests after you’ve already exhausted basic A/B tests and multivariate tests.
Let’s discuss the use of data in customer experience optimization below.
6 Customer experience optimization and personally identifiable information (PII)
Personally identifiable information (PII) is any data that can be used to identify a specific individual, such as a person's name, email address, phone number, date of birth, or any other information that can be used to identify or contact a person.
The use of PII is heavily regulated by laws such as the GDPR and CCPA. For that reason, much of the data used in customer experience optimization is not PII.
Ryan Koonce, Founder of Mammoth Growth, explains below.
Examples of non-PII customer data you would commonly use in your customer experience optimization efforts include:
- The device type they use to access your website (mobile phones, tablets, and computers)
- How much time customers spent on the page
- How far down they scrolled on each web page (scroll depth)
- Where your customers are coming from (traffic source)
- Which pages customers progress through
- How customers use the search bar on your website
- How much time customers spent on each page on your website (time on page)
What about using data that contains personally identifiable information (PII)?
With that being said, using PII opens the door to more in-depth targeting, A/B testing, feature management, and customer experience optimization techniques.
You can run more advanced tests and personalizations that help you become more precise with your targeting and optimization - improving your customers’ experiences on your website.
A few pieces of PII on their own are not very sensitive. Using a customer’s IP address alone for example will not reveal the customer’s identity.
However, it has the potential to identify a customer’s identity if you combine it with other PII. When you combine IP addresses with other PII data in your CRM, you could reveal the user’s identity.
To prevent this, companies do what’s called “anonymization” when working with PII.
Data anonymization
Data anonymization is the process of removing personally identifiable information (PII) from data so that it can no longer be linked back to an individual.
This is typically done by applying techniques such as data masking, data aggregation, or data perturbation to modify or remove specific data elements that could be used to identify an individual. The purpose of data anonymization is to protect the privacy of individuals while still allowing the data to be used for analysis, research, or other purposes.
Anonymized data is often used in fields such as healthcare, finance, and marketing where sensitive data must be protected while still allowing for data-driven insights and decision-making.
Once you anonymize the PII, you remove the risk associated with it, and you can use it in your customer experience optimization efforts. What kind of data you choose to use, whether it’s PII or anonymized, will depend on what you’re trying to achieve.
7 Types of data used in CX optimization
With this in mind, let’s take a look at three different types of data that are commonly used in customer experience optimization and the risks associated with each.
1. Anonymous data
On your website, 96% of visitors are anonymous, meaning that you cannot identify them and recognize them on their next visit. They generate “hot data,” covering everything they do when browsing. Normally this is anonymous unless the visitor is logged in (and consequently identifiable).
You should use anonymous data because it’s low-risk, high-reward. As James McCormick of Forrester points out in the analyst’s “Adopt AI for Personalization Safely and Smartly to Win European Customers” report, “anonymized data such as visitor website behavioral data is relatively low risk to use.”
Here are some examples of anonymous data:
- What people have people clicked on
- The frequency of their clicks
- Where they have come from (organic vs direct vs social media)
- How much time they have spent on specific pages
- How their journey has progressed through your site
- Their search bar usage
According to a recent Forrester report, only 31% of healthcare organizations run A/B tests all the time, despite the fact they are an essential part of a strategy that makes HCOs 5x more likely to grow revenue.
There’s so much you can do with anonymous data, including basic A/B testing, multivariate testing, and rules-based targeting which delivers specific content and experiences based on “business rules”.
2. Pseudonymized data
Pseudonymized data is a broad category. Essentially, it is any type of data where the company makes efforts to cloak personally identifiable information (PII). It includes:
- Transaction history
- IP addresses
- Browser history
- Posts on social media
- Geographic information
An effective, experienced data scientist can anonymize personally identifiable data in a way that it cannot be linked back to the customer, significantly reducing your regulatory risk.
Regulators will look at data on a case-by-case basis to determine whether it fits the definition of PII. However, it is clear that companies must ask for users' consent when using PII, whether it is pseudonymized or not, for marketing purposes. In other words, regulatory requirements apply to pseudonymized data.
Pseudonymized data will not give your organization an entirely different set of tools than you have with just anonymous data.
However, those who already have A/B testing and experimentation programs can extend their capabilities to more advanced techniques by adding pseudonymized data including product/content recommendations, contextual targeting, and profile-based targeting.
3. Identifiable data
Identifiable data refers largely to customer and user information that you have in your CRM, or transactional data. Here, PII is readily available.
Organizations starting out on their data governance and optimization journeys should focus their efforts on leveraging anonymous and pseudonymous data.
However, identifiable data becomes indispensable for organizations that perform advanced algorithmic personalization. That’s because algorithms require large amounts of customer data to work. Connected with a company’s CRM, these algorithms and programs will have access to sensitive information which the organization must manage.
Remember: Data governance is just a fancy way of saying that you know which data you plan to use for what.
To leverage algorithmic methods, an organization must know in advance which kinds of user data it plans to use for what kind of marketing. That way, when collecting the data, they can ask for consent, so that only that subset can be used by the algorithm for certain tests. However, data-compliant tools—like Kameleoon—can help you algorithmically test without the risk.
8 Using 1st, 2nd, and 3rd party data in customer experience optimization
1st party data is any information you obtain about users on your own website, as well as information you personally obtain by form, email, or even phone conversation—because you collected it yourself.
If you sell that information to another company it becomes 2nd party data.
3rd party data refers to data sold by vendors who collect, recycle and sell millions of people’s data through giant marketplaces and platforms.
Until recently, many businesses have been able to commercialize customer data without much oversight. But regulations are changing as customers rightfully begin to understand that their consent and any data they supply is theirs unless agreed otherwise.
There are two issues at play: privacy and consent. Respecting consent and privacy usually means some combination of anonymization techniques and consent forms in your customer experience optimization.
There is a risk spectrum for the kinds of information you can and cannot use for marketing. As a marketer who wants to start optimizing, you must create a data governance strategy that considers the kinds of data you need in order to run different types of tests.
9 Getting started with CX optimization with user data
Customer experience optimization is now the standard of running a business in today’s world. Your customers expect to leave your website, social media pages, and other places they interact with your business satisfied.
Optimizing experiences for your visitors starts with user data from your behavioral and quantitative analytics tools. By building a more mature governance strategy, you can stop being overwhelmed by data and move from the most basic, least risky types of testing to the more advanced ones.
Once you know what data enables which customer experience optimization methods, you can decide how to collect it in accordance with security and privacy regulations.
To learn about customer experience optimization in a highly regulated industry like banking, check out our original research on BFSI and A/B testing.
