How to Analyze (and Improve) Customer Journeys
We walk through everything you need to know to measure, analyze, and optimize the customer journey.
What are customer journey analytics?
Customer journey analytics is the process of identifying customer touchpoints and understanding how they affect customer experiences and business outcomes. It includes interactions that take place before, during, and after the point of sale.
3 reasons companies are focusing on customer journey analytics
Measure engagement across channels
Customer journey analytics help identify the channels that are effective at engaging customers.
Glossier, a beauty and skincare brand, analyzed customer behavior between its eCommerce platform and its publication Into The Gloss. By bringing this data together into a unified customer profile, Glossier discovered that people who browse both sites tend to spend more than those who browse only one. Digging further, Glossier found out which articles led to purchases, and was able to replicate that recipe for success.
Optimize omnichannel customer experiences
In an omnichannel approach, you connect all of your customer engagement channels to provide a continuous experience for customers.
Nike has been lauded for its omnichannel experiences across its app, online shop, and brick-and-mortar stores. The inventory and events in Nike Live stores are tailored to suit the shared traits and user behaviors of local NikePlus members. Shoppers are encouraged to use the Nike app in stores to scan QR codes and learn more about products, or to have clothes delivered to a fitting room.
Reduce customer churn and improve retention
Amid economic uncertainty and rising digital advertising costs, customer retention has become a crucial growth strategy.
Being able to recognize the telltale signs of customer satisfaction or dissatisfaction helps businesses proactively launch personalized campaigns to either keep users engaged and prevent them from churning.
One way to do this is by identifying the interactions that increase customer value and loyalty. For instance, at Segment we studied the behavior of our loyal customers and identified a set of high-value actions they all had in common. Using this data, we crafted personalized messages and engagement campaigns to nudge other customers to follow suit.
How to analyze customer journeys in 5 steps
1. Identify touchpoints and define interactions
First, organize customer touchpoints by journey stage: awareness, consideration, conversion, service, and advocacy. Here are a few examples:
Awareness: clicking on a link from another website
Consideration: watching a product demo video
Conversion: setting up a paid account
Service: chatting with customer support
Advocacy: sending a referral code to potential users
2. Measure how customers interact on each channel
Identify all the channels where you interact with potential and current customers (e.g. social media, email, your website, or app) and connect these to a business intelligence tool or a customer data platform (CDP).
From there, you can start comparing engagement rates across channels, and identify the most important metrics to track depending on your business goals.
3. Set up an attribution program
Attribution helps determine which touchpoints lead a customer to convert (e.g. creating an account, signing up for a free trial, etc). We recommend using multi-touch attribution because it takes into account the entire customer journey.
To set up an attribution program, identify the events and user traits significant to your model, as well as the channels that generate this data. For example, if you have a blog and also advertise on LinkedIn, you’d want to pull data from Google Analytics and LinkedIn, focusing on actions like scrolling down a page and clicking on an ad.
4. Identify where and why customers churn
Sometimes, customers will fill out a survey explaining why they decided to churn. Other times, they drop off without explanation. For the latter, businesses can dig through customer support interactions or session analytics to find the cause of their dissatisfaction (e.g. frequent bugs, rage clicks).
Customer journey analytics can also unearth the signs of someone who’s about to churn. For example, you may find that in the weeks prior to churning, customers tend to unsubscribe from your email list, and leverage this insight to re-engage at-risk users.
5. Use your data to create a customer journey map
Using the data from customer journey analytics, you can create customer journey maps, which Forrester Research defines as “documents that visually illustrate customers’ processes, needs, and perceptions throughout their relationships with a company.”
Some maps capture the entire customer experience, while others focus on specific stages, like illustrating the path to adopting a new product feature.
Here’s an example of a journey map we created when we launched Personas.
7 metrics for analyzing the customer journey
Customer Satisfaction Score (CSAT)
A customer satisfaction score (CSAT) measures users’ satisfaction with your product or service, typically on a scale from 1-10.
Calculation: average CSAT =sum of satisfaction ratings ÷ total # of responses
Customer Lifetime Value (CLV)
CLV reflects the total revenue you expect to earn from a customer throughout their relationship with your business.
Calculation: CLV = (annual revenue per customer x customer lifespan in years) – customer acquisition cost
Customer Effort Score (CES)
CES measures how easy or difficult it is to do business with you via surveys where customers rate ease of use on a numerical scale.
Calculation: CES = sum of effort ratings ÷ total # of responses
Touchpoint & engagement metrics
Tracking the average session time helps you identify trends or outliers in app usage (e.g. drastic drops can point to bugs or outages, while a steadily declining drop can signal waning engagement).
length of session time = time when the user leaves the app − time when the user enters the app
average session time = total session time ÷ # of sessions
Bounce rate refers to the percentage of single-page sessions – where a visitor leaves your site after viewing just one page – out of the total number of sessions. The optimal bounce rate is 26% to 40%, according to SEMrush. Keep in mind that a bounce isn’t always bad (a bounce off a FAQ page may indicate the reader got the answers they needed).
Calculation: bounce rate = (single-page sessions ÷ all sessions) x 100
Open rate measures the percentage of emails that were opened among all emails sent in a campaign (a study by MailChimp found the average open rate is 21.33%).
Calculation: open rate = (# of opened emails ÷ # of emails sent) x 100
Conversion measures the percentage of people who perform an action you’ve asked them to take. If 100 people see your CTA to download an ebook and half of them did so, you have a 50% conversion rate.
Calculation: conversion rate = (# of users who completed a specific action ÷ # of users you exposed to that option) x 100
5 tools for customer journey analytics
Customer Data Platforms (CDPs)
CDPs collect data on customer interactions across multiple channels. They unify the data into a single customer view and update customer profiles in real time based on the latest touchpoints. They also help you identify customer segments and implement tailored workflows based on each segment’s shared journeys.
Customer Engagement Platforms (CEPs)
CEPs like Twilio do everything a CDP does while also letting you orchestrate customer engagement workflows. For example, if you answer a support call from a customer and send updates on their issue through WhatsApp, you can do both on a CEP instead of switching from one app to another.
Attribution tools like Adjust, Criteo, and Singular analyze how much each touchpoint contributes to a conversion event. Choose a tool that supports your preferred attribution model. Better yet, get one that supports several different models, including multi-touch attribution.
You don’t have to stop at analyzing the first conversion event. Integrate your attribution tool with a CDP so you can measure the impact of different marketing campaigns throughout a customer’s journey.
Behavioral analytics tools
Behavioral analytics tools like Indicative and Mixpanel identify patterns and changes in customer behavior. They also spot behavior “cohorts,” which are customer segments formed on the basis of shared actions.
With this data, you can predict customer behavior and lifetime value, and create workflows that are automatically triggered when a customer performs a certain action.
Business intelligence (BI) tools
BI platforms like Holistics, SAS, and Tableau collect and process unstructured data from various sources to help you make sense of it through reports and visualizations. BI reports are descriptive, as they’re based on past and present events. So while they’re useful for spotting trends, they’re more powerful when integrated with other analytics and customer-facing tools.
When you integrate a BI tool with a CDP, you can use the data to enrich customer profiles, inform customer segmentation, and create effective customer engagement campaigns.
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