Unisender.com Success Story: Built a Clear Analytics Model for SaaS and Calculated LTV
Results: Built a clear analytics model and calculated LTV.
The Client
Unisender is a large SaaS-service company, specializing in emailing for businesses all over the world. It is a pleasure to talk about our work on this project as the promotion of large SaaS projects is our ultimate goal.
The Challenge
The client did not have a clear model for evaluating the effectiveness of contextual advertising. Therefore, it was impossible to scale contextual advertising and increase income.
Our goals are likely to be the same for all SaaS projects:
- To build a clear model of analytics, which then shows the effectiveness of contextual advertising.
- Understand the value of CAC (customer acquisition cost) at which we can scale.
- Distinguish the first from repeated payments.
- Understand the time it takes (the number of days on average) after registration for leads to become customers.
- To calculate the LTV (customer lifetime).
The Solution
We concluded that it is the goal of contextual advertising to motivate people to try the service, and it is the task of the service to motivate them to use it.
In other words, context should bring new leads, while analytics in contextual advertising should answer the following questions:
- How high is the quality of leads attracted from contextual advertising?
- What is the payback on contextual advertising?
Here is the analytics model that we built:
1. We created a report analyzing the effectiveness of context by cohort (source/channel, down to the level of the keyword that led the lead). At the time of registration, we recorded the source that led the lead. Then, for the first and repeated payments, the value was assigned to the source that led to the lead, not the source that led the payment itself.
What do the reports look like (filtered — leads only from the PPC channel):
Advertising budget and revenue by cohort
Registrations and payments
Allowable and actual CAC
Important: The report was updated daily and we could observe how metrics changed over time, e.g., CAC allowed, number of first payments, income/LTV.
2. We made a report with a division of all payments on the first and repeated payments. It's also worth recording the month in which the lead moved into the category of those who pay. For example:
i. Registered in September, started paying in September — assign value.
ii. Registered in September, started paying in October — assign value, and so on.
3. Next, we created a report estimating payback on actual and predictable CAC. As we had data on registrations for a particular month and the number of customers who immediately started paying, we could predict the total of paying customers per cohort and the LTV obtainable from that cohort. This allowed us to estimate the return on advertising at the end of the reporting month. Re-estimation of payback was done after 2 to 3 months.
4. We made a report with a preliminary assessment of the quality of leads through the sales funnel.
5. Detailed reports by sources, regions, months, types of campaigns, etc. We detailed the reports described above down to the keyword/advertisement level. Each source had its own average LTV and therefore, its own maximum allowable CAC. This was the case for each country/region.
The Results
- We have built a clear analytical model that can evaluate the effectiveness of contextual advertising.
- Understood the value of СAC (customer acquisition cost) at which we can scale.
- Distinguished the first payments from repeated payments.
- Determined how many days on average after registration leads become clients.
- Calculated LTV. Our analytics model makes it possible to assess the quality of leads more quickly, which reduces the time and budgets spent on tests.
Testimonials
Julia Gordienko, Team Lead at Netpeak
We started to work with an already formed understanding of the metrics we were going to monitor. At the same time, colleagues from UniSender were open to any revision, which would improve the model of analytics. Thanks to this, we now have the opportunity to test hypotheses and draw conclusions both quickly and with small budgets. I hope that our joint experience will be useful for those SaaS users who are looking for the perfect analytics model.
Read more:
- 4B Ukraine success story — in a year we have increased organic traffic by 50 000% through blog.
- Informburo.kz success story: organic traffic increased by 569%, the number of audience rose by five times.
- Eda.ua success story — growth of organic traffic by 331.5% in 6 months.
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