If you're looking to understand the Happy Path analysis report, this article is for you!

Prerequisites

• You know what the event mapping is
• You know what the conversion mapping is

What is this?

The happy path analysis is an insight report that helps you understand the customer journey and what sequence of events is the most interesting for your leads to become customers.

The goal of this report is to give you some insights with regards to the different events that are performed after sign-up so you can adjust your messaging and your onboarding in your product or focus on the most effective campaigns: get your users to perform the events correlating the most to conversion in the timeframe that matters.

Where do I find this?

In the App, head to the tab Insights.

If the report has not been yet configured for you, it will display as locked. Keep reading to have it configured.

If the report has already been configured for you, click Enter. Jump to the key takeaways!

How do I configure this?

To configure the Happy path, you need to decid on which customer journey to analyze and which success metric to use.

First, click on 'Request Access', and then notify our Support team by submitting a request, where you provide the following parameters:

What is the starting point of the journey?

We recommend the sign-up event.

Provide the Support team with the MK Event name from your event mapping corresponding to your sign-up event.

What is the endpoint of the journey?

Which conversion definition will define the success of the journey?

Is it Open Opp, Closed Won or SQO?

We recommend to use Closed Won.

If you don’t have a lot of users, we recommend to rather use the Open Opp conversion definition.

What are the key takeaways?

What is this report computing?

This report looks at the users who signed up in the past year (in the limit of max 10,000 users) and analyzes which events they performed after they signed up and when they performed them. For each event, you can see:

• Is this event usually performed right after the sign-up or more likely 7, 14, or 30 days after? Is the event performed more by people who convert or didn’t convert? We look at the conversion rate of the population who performed this event, for different durations between the day when the event is performed and the sign-up.
• What is the ideal number of occurrences of this event after the sign-up? Is the lead more likely to convert if they log in to the product at least 5, 10, or 68 times within the first 30 days after the sign-up? We look at the number of occurrences of each event to determine the frequency sweet spot.
• Is the volume of people who performed this action large enough, for its analysis to be relevant and actionable?

How do performed events correlate to conversion?

The first table is the raw data and it can be exported into csv if you want to further analyze it. It looks at all the events performed within X days after the sign-up. In the screenshot below, the table is configured for 30 days after the sign-up event.

• meta_event is the event defined from the event mapping

• Here is the legend for each 'n_XX' column:
  • n means “number”
  • The first number represents a boolean: has performed the event or not
    • 1: has performed
    • 0: has not performed
    • x: all leads
  • The second number represents a boolean: has converted or not
    • 1: has converted
    • 0: has not converted
    • X: all leads
• This gives us the meaning for each column:

  • n_11 is the number of people in the sample that performed the event and converted

  • n_10 is the number of people in the sample that performed the event and did not convert

  • n_01 is the number of people in the sample that did not perform the event and converted

  • n_00 is the number of people in the sample that did not perform the event and did not convert

  • n_1X is the total number of people in the sample that performed the event

  • n_0X is the total number of people in the sample that did not perform the event

  • n_X1 is the total number of people in the sample that converted. n_X1 = n_11+n_01

  • n_X0 is the total number of people in the sample that did not convert. n_X0 = n_10+n_00

• prob_conversion_if_not_this is the conversion rate of people not performing this event

  = n_01 / n_0X * 100
  

• prob_conversion_if_this is the conversion rate of people performing this event

  n_11 / n_1X * 100
  

• percent_of_people_who_did_action = n_1X / total number of leads * 100

• average_conversion_rate = n_X1 / total number of leads * 100

Which events correlate most strongly to conversion?

Below this table, you'll find a graph, that can be understood by dividing it into 4 sections:

Activation Events:

• Strong negative impact if someone does not perform this event
• Minor positive impact if someone does perform this event
• Activation events happen comparably often. They are necessary for conversion but not sufficient

Engagement Events:

• Low negative impact if someone does not perform this event
• Low positive impact if someone does perform this event

Catalyst Events:

• Strong negative impact if someone does not perform this event
• Strong positive impact if someone does perform this event

Delight Events:

• Low negative impact if someone does not perform this event
• Strong positive impact if someone does perform this event
  
• Delight events don’t happen very often

How does conversion correlation evolve with occurrences over time?

The drop-down list on top lists all events performed on day 1, day 5, day 7, day 10, day 14 and day 30 after sign-up.

Select one event in the drop-down list to see its correlation to conversion depending on the number of times leads performed this event in the given timeframe after sign-up.

What are the most important milestones to have hit by day X since sign-up?

Select a number of days in the drop-down to see the list of potential milestones to have accomplished by this day, by order of correlation to conversion.