TABLE OF CONTENT
What is Customer Experience Analytics?
Customer experience analytics is the process of collecting, processing, and analyzing customer experience (CX) data for the purpose of driving customer journey design and creating meaningful engagement for maximum retention. In very simple terms, CX analysis aims to analyze customer data to drive meaningful changes.
CX or customer experience data can include any interaction that takes place between the user and the product. The data can include phone support interactions, web chat, social media, SMS, email, and reviews.
Apart from these interactions, experience analytics goes beyond - to drill down into the customer journey to understand different touchpoints and monitor customer behavior. Mapping out the entire customer journey will allow developers to create intelligent designs to ultimately have an effect on the entire Digital Customer Experience (DCX).
The main outcome of pursuing experience analytics, in any case, should yield higher conversion rates, reduce any friction points and increase your revenue.
Why quantifying CX can be difficult?
Since CX management is the full-fledged collection of processes to manage customer interactions and optimize them to derive favorable outcomes - it can be difficult to effectively implement and devise strategies related to customer experience.
AHarvard Business Review Analytics Surveyfrom 2014 describes customer experience analytics as very challenging where more than 50% of the organizations report an overall lack of processes to support CX management programs.
In simple terms, it can be extremely hard to build systems that support continuous evaluation, analytics, and management of customer experience to then connect them to tangible outcomes.
Creating a uniform and singular view for each customer to track their interaction with the product and in turn track the essential parameters are the basis of customer experience analytics. We will further explore the exact steps for this process in the corresponding sections.
How to collect Customer Experience (CX) data?
The first step in the long process of customer experience analytics will naturally involve data collection. Here, the goal is to identify the key metrics or key performance indicators (KPI) that your business will benefit from tracking.
1. Set a goal for CX analytics
The first step in the data collection process would be to set a clear goal for performing CX analytics. The goal will inform all the further processes and will guide the team to determine and track the metrics that directly affect this goal.
The goal here is to identify friction points in customer interactions, increase conversion rates and identify customer needs to then create a personalized customer experience.
2. Determine CX data sources
A good starting point to begin analyzing customer data is through all the preexisting data companies already have from various customer interactions. With this pre-existing data, businesses can start identifying pain points or any friction customers experience.
This can prove to be a far more efficient and cost-effective way of collecting data - as businesses can save resources that they might expend on market research or external surveys.
Here, apart from direct customer interactions, teams can also use metadata like purchase history, membership details, quantifiable metrics like clicks and visits, and sign-ups to identify the trends.
Some possible data sources include -
- Survey data.
- Website or physical store traffic.
- Interactions on different social media platforms.
- Purchases, repurchases, and returns history.
- Cart abandonment.
- Coupon or discount redemption.
3. Identify the points of entry your customers use
In the next step, businesses need to identify the various channels or entry points the customers interact with to come across your brand. Any possible direct or indirect interaction that takes place between customer and brand can prove to be data-rich.
The goal here is to track all possible interactions to create a holistic and accurate customer journey map that will account for the key metrics your business is tracking. Each channel or source of customer interaction will account for a network route in the customer data journey.
4. Aggregate data across all channels and sources
Once you have identified and outlined all the data sources and channels rich with customer interaction data, the next step is to organize this data. Organizing data across channels while retaining the key derivable insights from that channel is important.
To get a holistic view of the trends and to understand what is causing friction at a particular touchpoint, it is important to consider data from all channels. Any data point missed will result in a skewed analysis.
Here businesses will have to understand what actionable data can be derived from a particular channel. This will inform the strategy used to organize the data. Teams can also choose to opt for an external tool that helps with CX data collection and analysis.
The steps to managing and aggregating the data are as follows:
- Collecting and unifying data from different channels and sources.
- Clean and update the data by removing any duplicates, incorrect or irrelevant data.
- Organize the data to create a single customer view.
How to analyze the data?
Now that you have all the data you can possibly need from all the possible customer interactions, you can start analyzing the data. It’s important to objectively and subjectively derive insights keeping in mind the context of interactions taking place.
1. Find points that cause friction in CX
Start by analyzing each channel - prioritizing the channels with the most interactions. Once the analysis is done try to identify the basic trends and conversions for each channel. These trends should point to the possible pain points of friction in customer experience. By sectionalizing your data based on viable groups and looking for accurate trends will help derive clean insights.
Any particular keyword mentioned in conjunction with a negative keyword like “return”, “difficulty”, “error” can point to a friction point. Interaction can be analyzed to look for these keywords.
2. Gather more context
Once you have found the possible friction touchpoints in your customer journey the next step is to determine the cause of these issues. The said issue should be analyzed in the right context. The scale and source of the issue or any particular trend should help contextualize the problem.
Now based on the severity of the said issue, teams can prioritize and devise solutions.
3. Quantify the impact of these solutions
Next, the teams will aim to create solutions that directly address the friction points identified by the experience analytics. To make sure the solutions are sustainable with a long-term vision, teams need to tie their impact with key business metrics. This will demonstrate the importance of these solutions and their ability to produce quantifiable business outcomes. The performance of the solutions can also be tested based on the performance of these metrics. Some metrics that companies can choose to track are:
- Customer Satisfaction - it measures how happy customers are with a company’s product or service
- Customer Effort Score - the amount of effort a customer has to exert to get an issue resolved
- Net Promoter Score - it is a metric that measures a customer’s loyalty to a brand by asking them how likely they are to recommend the brand to others on a scale of 1 to 10.
- Customer Sentiment- this is a key performance metric that indicated the overall sentiment of a customer towards a brand. This is measured by evaluating customer interactions.
Final solutions should aim to directly impact these KPIs - which means they should improve customer satisfaction and net promoter score, and reduce the customer effort score. Finally, the changes made should improve the overall sentiment of the customer.
Ways to improve CX with experience analytics insights
Ultimately all customer experience analytics insights should yield an improvement in the customer experience or customer journey. Common ways to do this are as follows:
1. Define your ideal customer segment
Once the analysis is done, it should be very apparent which demographic accounts for maximum sales and revenue outcomes. This segment of your customer can be considered as your ideal user base or target audience. Understanding what motivates them to make purchases and trying to directly expand this segment of customers will possibly yield better outcomes.
2. Create personalized customer experiences
Since the previous steps of customer experience analytics successfully identify the trends pertaining to each channel or segment of customers it will allow businesses to address these pain points and provide personalized solutions to them.
For example, if a segment of users are facing a common issue or are looking for a particular topic, then creating content related to that topic or issue can be an attempt to become more personalized.
3. Identify customer needs
Analyzing the interaction will give businesses an opportunity to correctly identify customer needs if any. Any good customer experience will constantly aim to cater to their audience and all their changing needs. If a customer is engaged they will constantly tell you their expectation and ways your brand can improve. Actively listening to this chatter on social media or through direct interaction and trying to work them into your CX can be one use of CX analytics.
4. Differentiate yourself from your competitors
Once businesses have performed CX analysis for their own brand they can then decide to go hunting for more CX insights by analyzing their competitors. CX analysis of competitors can provide teams with valuable solutions. The teams need to look out for customer journey segments where their competitors are doing better than them and try to devise a solution.
5. Continuously monitor experiences
Finally, the customer interactions with a brand are extremely dynamic and ever-changing so companies need to be on top of it all the time. The final step in customer experience analytics is to continuously collect data and analyze them to monitor these experiences.