You may have heard the term customer journey analytics before. You may well also be doing the majority of your tracking via Google Analytics. While this is a great first step to tracking your customers’ journey and creating your customer journey map, there are more robust solutions for more detailed data.
Let’s talk about how to use consumer journey data analytics in your business, why you need to track consumers’ behaviour, and the customer satisfaction benefits for businesses that successfully leverage this data.
What is Customer Journey Analytics?
Customer journey analytics is the process of analysing data related to your customers’ interactions with your business, in order to identify patterns and predict future behaviour. The customer data you collect can come from any number of sources, including customer service calls and surveys, social media interactions, website analytics, and more.
The aim of customer journey analytics is to help you understand what’s going on with each individual customer, as well as how they differ from other customers. Because every customer has a different experience with your company, it is important to take into account all of those different factors when making decisions about how to improve your business.
Types of Customer Journey Analytics
Comprehensive analysis is the most in-depth type of customer journey analytics and it provides you with a view of the full customer journey. This can include information on how customers interact with your company and its products, as well as their feelings and experiences throughout the process.
Priority analysis focuses on one specific aspect of the entire customer journey, e.g. their intent to buy something or their experience with your product or service. This type of analysis allows you to focus on one piece at a time and makes it easier for you to find out what’s going wrong and how to fix it.
Real-time analysis helps track what’s happening as it happens so that you can react quickly if necessary. This type of analysis might be useful to help identify any issues with your website or app that need immediate attention, like an outage or a problem with shipping orders on time.
Scale analysis focuses on identifying what works best for different types of customers based on where they fall on a scale between extremes like “very dissatisfied” or “very satisfied”. It is a useful way to measure the impact that your product or service has on sentiment and customer satisfaction as they progress through their buying journey.
Narrative analysis helps you understand customer behaviour and why people take certain actions in your app or website. It does this by breaking down their journey into smaller steps that let you see where they are struggling and what’s causing them to struggle. This type of customer data allows you to understand which factors are contributing to your success or failure and how you can improve them in the future.
Customer Journey Mapping vs Customer Journey Analytics
Customer journey maps are a visual representation of how your customers move through the sales funnel. They show key touchpoints and experiences that impact a customer’s purchase decision. The purpose of mapping the customer journey is to identify where they get stuck and what they need to move forward in their journey.
Customer journey analytics, on the other hand, helps you understand how customers interact with your brand across multiple channels, including website, social media, advertising, and email. You can use this customer behaviour data to optimise your marketing efforts based on past performance, as well as predict future trends that may influence your marketing strategy.
In essence, while they are both related exercises, one is ineffective without the other. You can’t analyse consumer behaviour without gathering meaningful data using customer journey mapping, and you won’t be able to make adjustments based on the analysis without having a clear picture of how customers move through the funnel.
Why Companies Use Customer Journey Analytics
Customer experience or user journey analytics is all about understanding the way a customer goes through the process of finding, buying, using, and recommending a product or service. Companies can use this information to:
- Identify high-value customers - allows you to focus your marketing efforts on those customers, since they are likely to be more profitable
- Understand what drives repeat purchases - extremely helpful when selling a product that requires regular purchases (e.g. a subscription service)
- Improve customer retention - to figure out why some customers stay with your brand while others leave
- Calculate customer acquisition costs - helps identify how effective your current marketing strategy and plan are and if there is room for improvement
- Calculate customer lifetime value (CLV) - especially useful to conclude how valuable each customer will be over their lifetime if you keep them happy and engaged with your product or service
How Does Customer Journey Analytics Work?
Customer journey analytics usually involves collecting data from multiple sources and then analysing it with a custom-built software program, such as the Google Analytics journey tracking tool.
You start by collecting everything you possibly can about one particular customer’s experience with your company. This can include things like what they searched for on Google, what products they viewed on your website, and even which social media platforms they follow you on (if any). You may also want to collect information about their location, age range, gender, or other demographic factors that might affect the way they interact with your brand.
Next, you analyse that data to see what patterns emerge from it - what kind of things make this particular customer feel excited about your brand? What things make them feel dissatisfied with your brand?
You will also want to look at whether certain segments tend to have similar experiences when interacting with you (e.g. are men more likely than women to leave comments on blog posts?).
The goal of customer journey analytics is to inform your decision-making process and help increase customer lifetime value. By using customer journey analytics tools, you can see where customers are dropping off or struggling with a particular part of the process so that you can essentially tighten the loose ends.
Benefits of Customer Journey Analytics
1. Improve Customer Retention
Customer churn is something that is experienced by most companies without a clear strategy. It is a term used to describe customers who stop using your product or service, and it can be a huge drain on your business. Data-driven customer journey analytics can help you identify potential issues before they become problems, so you can avoid unnecessary churn.
When you know what’s working and what isn’t with regard to your customers’ experiences, it becomes much easier to identify valuable customers who are willing to pay more for your offerings or are more likely to refer others.
Not only will this kind of data help you focus on those high-value customers to ensure they are getting the best possible experience from your business, it will also enable you to attract new customers who are likely to become repeat buyers.
2. Identify Opportunities to Improve Customer Experience
If you are not measuring something, you are missing out on opportunities to learn more about your customers and make improvements. Customer journey analytics provides insight into how your customers feel about the product or service they receive. It helps identify areas where there are gaps in service or where processes could be optimised for better outcomes.
For example, you might notice that customers are abandoning their carts after looking at the shipping costs or that they are signing up for a newsletter but never receiving it. These insights could inform changes to your existing strategy to improve customer experience, which could lead to increased loyalty and repeat purchases.
CLV is a measure of how much money a customer will bring in over their lifetime. By analysing your customers’ journeys, you will be able to see where there are opportunities to increase CLV by making small changes to the process. This could include things like launching a referral program or cross-selling products and services.
3. Create Personalised Experiences
When you track user journeys on Google Analytics, you are equipped with the right data that will allow you to cater to the specific desires of your customers (e.g. showing them products related to other products they have already looked at or purchased). Also, since 80% of consumers are more likely to buy from you if you offer a tailored experience, the best way to create an amazing customer experience is through personalisation.
It helps to understand your customers’ needs, behaviours, and preferences to create more targeted offers, design your content strategy, plan your UI, and optimise your marketing efforts, which will all ultimately improve your conversion rates and positively impact your bottom line.
Best Practices for Customer Journey Analytics
You can use multiple tools in Google Data Studio to improve your customer journey, but if you aren’t doing it right, you won’t find much success - regardless of how many customer journey analytics examples you have read about to get started.
Here are some best practices and actionable insights for getting the most out of your customer journey analytics tools to improve customer loyalty and boost positive business outcomes:
- Always start with the customer experience - Before you dive into any analysis, ask the following questions: Who are we trying to serve? What are their needs? What do they want from us? What kind of experience do we want them to have as they interact with our product / service / business?
- Look at data from multiple perspectives - It is important not just to look at what your customers do, but also how they feel about it - what are their emotions at each step of the journey? Look at what they say explicitly (e.g. “I love this particular feature in this product!“) as well as what they imply implicitly (e.g. when they share that they bought something on social media). You should also measure both quantitative data (how many people did X) and qualitative data (what did people say about doing X).
- Try different types of analytics tools - Some tools are more suited for enterprises while others are more beginner-friendly. So, experiment until you find one that works well for you!