Step 1: Connect your data source

First, you will need to make sure that your customer interaction data is properly sent to Intempt Platform. Go to Sources -> Create a source and set up an Intempt JS tracker on your website.

Step 2: Create funnel events

To start tracking your customer actions, you will need to create specific funnel event collections. When considering what events to track, go granular. That means tracking not only whether a visitor saw your payment page, but also tracking how many form fields they filled out, what they clicked on, and how far into the payment page they scrolled.

Here’s the step-by-step process of how you should define funnel events:

1. Go to your website and pretend to be a customer
2. List out every major click you make and page you visit
3. Leave only the essential events on the list

The funnel event list could look like this:

Step 1: Visited landing page

Step 2: Pricing page

Step 3: Company information page

Step 4: Plan details page

Step 5: Payment information page

Step 6: Visited dashboard

Step 7: Paid after trial

Step 3: Analyze your funnel data

Next, we need to analyze how often each funnel event was triggered among all users who have visited the site and the app. Here we will need to create 7 different event-based metrics that will showcase the funnel drop-offs.

Go to “Metrics” -> Create a new metric, define the period you want to analyze, select the format and choose the required metric creation options from the dropdown:

Step 1 formula: Visited landing page event count / Total User Count

Step 2 formula: Visited pricing page event count / Total User Count

Step 3 formula: Visited company information page event count / Total User Count

Step 4 formula: Visited plan details page event count / Total User Count

Step 5 formula: Visited payment information page event count / Total User Count

Step 6 formula: Visited dashboard event count / Total User Count

Step 7 formula: Paid after trial / Total User Count

After all metrics are in place, you can start filtering the funnel drop off events. List 3 events that have the lowest event count/ total user count ratio among the funnel steps.

To get even more granular, you can also calculate the drop-off event list metric change from the starting month to the latest date. This would enable you to check the change (decrease/increase) of the metric during the defined period.

Note: user count concept is dynamic and dependent on the identify function. A single user can have multiple profiles (e.g. when using different devices or browsers). However, once a user enters information that identifies him/her (like email) all unidentified profiles are merged into 1 master user profile. Due to this reason, in some cases, the most recent total user count might be lower compared to the initial months’ number.

Step 4: Analyze why users dropped off

Here are the steps you should take to try to figure out why visitors drop out (in order of priority):

1. Make sure the data is accurate
2. Walkthrough the site
3. Check for mobile issues
4. Look for anomalies in the data
5. Use heatmaps to study your users’ behavior
6. Segment by referrer
7. Run user surveys to ask why they dropped off

Step 5: Use segment editor to resolve drop-off points

Identifying the funnel bottlenecks is step one in the overall process. The next step is leveraging real-time segmentation to better move users along the funnel — and to win-back those who’ve dropped out.

For example, after analyzing your funnel, you realized that most users only use your app if they’ve viewed your onboarding page first. You solved this issue by adjusting your onboarding sequence to encourage people to visit that page first.

But what if you want to reach out to those users who already missed visiting the onboarding page?

Use Intempt Segment editor to create segments to target users matching the criteria. Then follow up with an automated email.

Here are a few more examples of how you can leverage real-time segmentation to prevent funnel drop-offs:

• Dynamic messaging: Email product offerings for the specific cart products that were abandoned pre-purchase.
• Support information: Unify user behavior data with the information you have in your CRM on that customer to proactively help customers before they ask for it.
• Intent-based offers: Identify the users most likely to convert and automatically reach out with a personalized offer.
• Re-engage former users: Automatically email users who haven’t used your app in the last 30 days.