Customers don't follow a straight line to purchase anymore — and if you're not analyzing the full journey, you're missing revenue.

Customer journey analytics is the fine art of gathering, managing, optimizing, and analyzing behavioral data across every customer touchpoint. It's about extracting the full context behind Key Performance Indicators (KPIs) like conversion rate, Customer Lifetime Value (CLV), and customer drop-off rate. 

This will allow you to fine-tune and create more profitable processes through:

• Multi-channel path optimization — Benchmark performance across customer acquisition channels (e.g., search, ads, social media, email, and mobile app) to refine your resource allocation.
• Improve Customer Acquisition Cost (CAC) — Track how much you spend per interaction to make sharper optimization decisions. 
• Fix holes in your lead generation funnel — Identify bottlenecks in your customer acquisition channels to minimize marketing waste. 

This guide will show you how ecommerce brands can use customer journey analytics to build airtight marketing and sales funnels. 

Let's get started. 

What is Customer Journey Analytics? 

Customer journey analytics is all about dissecting the steps that customers take toward a conversion or purchase decision. 

The goal is simple: double down on what works and patch up weak points that cause you to bleed sales. 

This means going beyond session-based or channel-based analytics and retracing every interaction in the customer journey, including but not limited to: 

• Ad clicks
• Organic click-through
• Product page view
• Cart abandonment
• Retargeting email click-through
• Add-to-cart click
• Purchase
• Review submission

Implementing a thorough approach connects the dots from marketing, product, and support touchpoints all the way to a conversion decision.

To ensure accuracy, customer journey analytics should also pull in cross-platform and cross-device data. It also utilizes a cocktail of quantitative and qualitative data — leaving no stone unturned until you fully grasp how each customer interaction ultimately impacts your bottom line. 

Why Customer Journey Analytics Drives Revenue 

Before we get to the good stuff, here's a more detailed look at the specific ways customer journey analytics can spur business growth. 

• Pinpoint Drop-Offs — Use data to underline customer journey steps that lose the customer's interest or shopping intent. Customer journey analytics also aim to dig into the reasons behind these drop-offs, including slow page performance, unclear CTAs, or confusing navigation structure. 
• Real-Time Personalization — Present personalized content, product recommendations, or special offers at the most opportune moments. Using customer journey analytics, you can triangulate these opportunities to ensure your personalized content hits at the right time. 
• Optimize Marketing Spend — Pull the plug on ineffective touchpoints unless data suggests they'll be more profitable to fix. At the same time, you can funnel more of your budget on touchpoints and marketing channels that generate the highest, most reliable downstream revenue. 
• Improve Retention & CLV — A thorough customer journey analysis reveals missed upselling opportunities that could've boosted your CLV and customer retention. It can also highlight spotty customer journey stages that inflate churn risk, giving you a head start for addressing them.

Let's say, through customer journey analytics, an international ecommerce brand notices an anomalously high number of drop-offs on your freshly redesigned Product Detail Page (PDP) — despite maintaining strong conversion performance from ad clicks that went straight to checkout.

Upon closer investigation, the company realizes that the newly added product demo videos and high-resolution images bogged down your PDP's loading speed. 

With this information, they took their case to Nostra AI and implemented the Edge Delivery Engine to compensate for the PDP's higher bandwidth overhead. In turn, the drop-offs on their PDPs were significantly reduced while sales almost doubled.

That's customer journey analytics in action, similar to Glamnetic's case which resulted in a 77.48% reduction in bounce rate after evaluating the problem and implementing Nostra AI as a quick, effective, and permanent solution. 

Key Metrics to Track in Journey Analytics

Customer journey analytics requires you to pin down important key metrics and KPIs describing what goes down during each phase of the lifecycle: 

1. Conversion Rate by Journey Segment 

Conversion rate is a critical piece of information that gauges how profitable your marketing efforts are — not just how much traffic you can get. 

With customer journey analytics, you need to go even further than that and track conversion rates across different journey segments. Each segment is broken down by each customer's source, device, behavior, and URL path to a conversion decision.

A simple way to look at a journey is through Google Analytics, specifically using the "Path exploration" report. 

Path explorations will help you identify journeys that are more likely to lead to conversion goals. 

You can also use Google Analytics to conduct cohort analysis as well as explore funnels and segment overlaps. This allows you to track customer behavior and profitability by pre-defined groups. 

If you're looking for more advanced tracking features, here are Google Analytics alternatives that will help you track customer website behavior and conversions per segment: 

• Hotjar
• Crazy Egg
• FullStory
• MixPanel
• Kissmetrics

2. Time to Purchase 

Time to Purchase is a straightforward KPI that does exactly what it says on the tin.

It measures the total time it takes for a customer to complete a conversion goal from their first interaction with your business.

The shorter it is, the more effective you are in attracting decisive buyers. This also sometimes correlates with lower CAC. 

Longer Time to Purchase, on the other hand, presents more lead nurturing opportunities. This gives you more room to unload your upselling, cross-selling, and other strategies to maximize Average Order Value (AOV) and CLV.

You can measure Time to Purchase with Google Analytics and by analyzing data in built-in reports from platforms like Shopify (measure the time between a customer's first interaction and their purchase date.

3. Drop-Off Rates at Each Funnel Stage

When it comes to customer journey analytics, it's important to understand that drop-off rate is not the same as bounce rate. 

Bounces are users who leave a page without clicking anything. Drop-offs, on the other hand, pertain to users who abandon a multi-step journey or funnel. 

To optimize the customer journey, you need a firm handle on where these drop-offs tend to take place. 

A visual website analytics tool like Hotjar should do the trick. 

Using the "Funnels" tool, you don't need to look at rows of data to identify where customer engagement tends to fall off.

4. Touchpoint Frequency & Sequencing 

Touchpoint frequency measures how often users interact with a specific stage of the customer journey. Sequencing, on the other hand, tracks the order in which these stages or touchpoints are interacted with. 

In customer journey analytics, it's imperative that you ideal the ideal touchpoint frequency and sequencing leading to the best outcomes. 

For this, you may need a combination of analytics tools like Google Analytics, Hotjar, Optimizely, and HubSpot.

5. CLV by Journey Type

Finally, you need a foolproof way of accurately measuring the long-term impact of specific customer journeys on your business. 

Measuring this KPI is a tad more complex than the rest. 

In a nutshell, you need to start by identifying the "journey types" that customers typically take on their way to a conversion. You then have to track the CLV generated by these journeys and use them as benchmarks for further optimization. 

To measure CLV, simply multiply your AOV, Average Number of Transactions (ANT), and Average Customer Lifetime (ACL). 

For example, if you have an AOV of $49, ANT of 3 transactions, and ACL of 6 months, your CLV would be: 

Just remember to do this per customer journey type, making your CLV data valuable for customer journey analytics. 

How to Build a Journey Analytics Framework

Now that you know what customer journey analytics is and which metrics to track, it's time to put together your journey analytics framework. 

This will help you establish a streamlined and reusable workflow for thoroughly analyzing customer journeys. 

Of course, the exact steps you need to take will depend on your business type, marketing channels, and a handful of other factors. But there are four essential steps that every ecommerce business needs to do in order to create a solid customer journey analytics framework: 

Step 1: Map the Journey Stages

First and foremost, you need to map the journey stages your customers go through before (and after) making a purchase. 

The customer journey is typically broken down into five stages:

• Awareness — Customers learn about your brand for the very first time (through social media, paid ads, organic search listings, forum posts, etc.). 
• Interest — Customers express interest in your products by viewing PDPs, reading reviews, and subscribing to your newsletter.
• Evaluation/Consideration — At this point, customers are almost ready to make a purchase decision, which shows when they start adding products to their carts or directly inquiring about discounts. 
• Purchase/Action — Here, you've accomplished your initial goal of securing a sale (but the journey doesn't stop here). 
• Loyalty — The next challenge post-purchase is to drive customer loyalty and maximize their lifetime value through repeat purchases or upgrades. 

When mapping the journey stages, it's important to consider both digital and traditional touchpoints. This ensures the impact of all marketing activities, be it billboard advertising, TV commercials, and so forth. 

Step 2: Integrate Data Sources

The next step is to pool your data sources into a centralized location — be it a Customer Data Platform (CDP) or Business Intelligence (BI) solution.

This will enable you to standardize and clean your data while packaging them into more readable, visualized reports. Modern tools like Twilio Segment will also help you organize customer data into well-defined segments and journeys.

Some of the usual ecommerce data sources are:

• Website analytics (e.g., Google Analytics 4, Nostra AI, and Hotjar)
• Customer Relationship Management (CRM) platforms
• Email marketing platforms
• Paid media attribution (e.g., Meta Ads and Google Ads)

What about offline marketing channels? 

There are several creative ways to collect data from traditional marketing. Some examples are utilizing specialized attribution software, platform-specific promo codes, or digital polls (i.e., "How did you learn about our product?"). 

Step 3: Segment by Behavior, Not Just Demographics

While it may make sense initially to group customers based on their demographics, you can extract more meaningful insights using behavior-based categorizations. 

For instance, rather than grouping customers by age, you can create segments for:

• Cart abandoners — Which path usually results in cart abandonment?
• "Lookers" — What traits do users who visit frequently but don't buy have in common?
• Fast and high spenders — Where do customers who convert fast and spend big usually come from?

Feel free to create as many customer segments as needed. 

Behavior-based segments enable more in-depth marketing personalization and automation. It's also more cost-effective to design personalized experiences for an entire segment, especially for enterprise ecommerce brands.

Step 4: Act on the Insights

Of course, customer journey data means nothing unless you turn it into actionable steps that boost revenue.

Measure, adjust, iterate — that's the name of the game.

Take one step at a time to ensure every move pulls you in the right direction. Some examples are: 

• Retargeting campaigns — Roll out advertisements targeted at customers who previously interacted with your brand. 
• Onsite personalization — Use AI-powered platforms like Dynamic Yield or other page optimization tools to tailor experiences to the customer's activities. 
• SMS and email sequences — Engineer personalized drip campaigns that address common pitfalls for certain customer segments. 
• Page speed or UX optimization — Engage savvy customers by providing buttery-smooth experiences using UX and website optimization solutions like Nostra AI. 

How Site Performance Impacts the Journey (and Revenue)

Speaking of website optimization, there's one universal aspect of the customer journey that matters every time: speed.

Whether you like it or not, today's customers are highly driven by the need for instant gratification. And ensuring your website is up and functional as soon as possible satisfies this need. 

An eBay study revealed that a 1-second improvement in loading speed can increase add-to-cart clicks by as much as 5%. 

Keep in mind that it's also difficult to gauge the individual impact of customer journey elements if your website doesn't deliver frictionless experiences. 

For example, you may think that a landing page redesign is costing you conversions. But, in reality, people are dropping off because the page takes more than five seconds to load, which is an eternity in the eyes of modern consumers. 

The good news is, there are plenty of plug-and-play solutions that can result in measurable improvements in loading speed.

Nostra AI's Edge Delivery Engine, for instance, moves data storage and processing to a network of edge servers around the world. This, on top of technologies like smart caching and AI-powered optimization, enables websites to load almost instantaneously regardless of the user's location. 

Next Steps

Customer journey analytics allows you to take a comprehensive look at the entire lifecycle — from first interaction to long-term engagement. More importantly, it surfaces hidden or missed optimization opportunities that lead to more impactful customer experiences, reduced costs, and higher revenue. 

All it takes is to have a scrutinous and disciplined approach to customer journey analytics. 

…with the help of the right tools, of course. 

Aside from analytics platforms, don't forget about performance optimization solutions that can start generating results from day one. 

Pair Nostra AI to speed up every step of the customer journey and unlock full-funnel growth. Book a demo here to discover how Nostra AI delivers lightning-fast performance that converts!

‍