Optimize trial conversion rate

Accelerate customer growth with faster analysis of the factors changing trial conversion rate.

Analytics Challenge

Business Challenge

KPI Impact

Analytics Challenge

Depending on the free trial structure, the key drivers of conversion aren’t simple to analyze. Trials can last for 48 hours or 30 days, and customer activity and engagement during the trial is the difference between growth and decline. Multiple factors influence conversions, like marketing, sales activity, and A/B tests within the product. Without analyzing the driving factors behind each conversion, you have nothing to act on.

The other problem is the activity of a user. Ideally, every signup actively uses a product during the trial period, but many signups are dead on arrival, adding noise to your calculation. To find actionable insights in your trial conversion rate, first define what active trials are; you can remove the “dead on arrival” accounts and set a baseline for trial conversions.

By continuously testing and measuring this new baseline, you’ll put a finger on what’s working and what needs improvement without delays from manually slicing and dicing the data.

Trial Conversion Rate

Trial Conversion Rate varies by trial structure but is typically assessed by dividing the number of conversions by the total number of trials in a given time.

TCR = # of conversions / total # of trials 

Analytics Challenge

Depending on the free trial structure, the key drivers of conversion aren’t simple to analyze. Trials can last for 48 hours or 30 days, and customer activity and engagement during the trial is the difference between growth and decline. Multiple factors influence conversions, like marketing, sales activity, and A/B tests within the product. Without analyzing the driving factors behind each conversion, you have nothing to act on.

The other problem is the activity of a user. Ideally, every signup actively uses a product during the trial period, but many signups are dead on arrival, adding noise to your calculation. To find actionable insights in your trial conversion rate, first define what active trials are; you can remove the “dead on arrival” accounts and set a baseline for trial conversions.

By continuously testing and measuring this new baseline, you’ll put a finger on what’s working and what needs improvement without delays from manually slicing and dicing the data.

Business Challenge

It’s common for subscription-based businesses to offer a “try before you buy” version of their products, allowing potential customers to understand the value firsthand before they buy.

While these offers are a proven way to maximize signups and potential sales leads, the challenge is figuring out the difference between a tryer and buyer. In many free-trial business models, the average conversion rate hovers below 5%.

But when you’re continuously diagnosing not only your conversion rate but also the factors behind a changing trial conversion rate, you’ll be able to regularly optimize and finetune your conversion strategy while also mitigating risk.

KPI Impact

When you can quickly and accurately diagnose conversion rates, you can better advise marketing, sales, and product on where to focus their attention.

Let’s take a look at how a small increase (1%) in conversion can have on your top-line growth. Imagine you’ve calculated that your SaaS company has a 10% conversion rate, where out of 1,000 active new free trials 100 users move to paid each month.

As you look at the factors impacting your trial conversion rate, you notice those who convert to paid also consistently complete email onboarding and share this insight with marketing. Optimizing for this factor leads to a 1% increase in your trial conversion rate the next month. If your service costs $150 per month, that translates to:

    100 new paid users/month = $1500
    + 1% increase = +101 new paid users/month
    Annual user growth = +144
    Net new sales for new users: $21,600 (12%)

Finding the factors that increase trial conversion rate by even 1% each month can add another 1,344 new users. That’s 12% growth YoY.

Designing datasets for conversion rate

Get a comprehensive picture of what factors, or combinations of factors, are key drivers for conversion rate, by tracking trial conversion rate at the account or user level. Each row should represent a unique user, with one record per trial period.

When setting up your data, make your datasets as wide as possible. Start with the recommended fields below and add as many descriptive variables as you can to augment your analysis.

Schema Blueprint:

  • One row per user
  • Metric column =
    Converted (T/F)

Recommended

Account ID

Account Creation Date

Customer Age

Customer City, State, Zip

Household Income

Product SKU(s)

Onboarding Completed (T/F)

Product Usage

Session Durations

Optional

Company Age

Company Size

Company Valuation

Customer Age

Customer Tier

Loyalty Program

Account Source Promotion

Number of Users on Account

# Customer Success Tickets

Ticket Response Time

CSAT

Schema Blueprint:
  • One row per user
  • Metric column =
    Converted (T/F)
Recommended

Account ID

Account Creation Date

Customer Age

Customer City, State, Zip

Household Income

Product SKU(s)

Onboarding Completed (T/F)

Product Usage

Session Durations

Optional

Company Age

Company Size

Company Valuation

Customer Age

Customer Tier

Loyalty Program

Account Source Promotion

Number of Users on Account

# Customer Success Tickets

Ticket Response Time

CSAT

Housecall Pro Logo

See how Housecall Pro uses Sisu to get insights on their Trial Conversion Rate, faster.

Read the case study

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