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Unisender.com success story: built a clear analytics model for SaaS and calculated LTV

Service: PPC, web analytics.
Niche: SaaS, email marketing platforms.

Result: built a clear analytics model and calculated LTV.

The Client

Unisender — is a big SaaS-service, specializing in emailing for businesses all over the world. It is a special pleasure to talk about this case because the promotion of big SaaS projects — is our ultimate goal.

The Challenge

The client did not have a clear model for evaluating the effectiveness of contextual advertising, without which it is impossible to scale contextual advertising and obtain more income.

Our goals are probably the same for all SaaS projects:

  1. To build a clear model of analytics, which will show how effective contextual advertising is.
  2. Understand at what value of CAC (customer acquisition cost) you can scale.
  3. Distinguish the first from repeated payments.
  4. Understand how many days (on average) after registration leads become customers.
  5. To calculate the LTV (customer lifetime).

The Solution

We concluded that it is the task 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, analytics in contextual advertising — should answer questions:

  1. How high quality is the leads attracted from contextual advertising?
  2. What is the payback on contextual advertising?

What analytics model 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 record the source that led the lead. Then, for the first and repeated payments, the value is assigned to the source that led the lead, not the source that led the payment itself.

What do the reports look like (filtered — leads only from the PPC channel):

reklamnyj-budzet-i-dohod-po-kogorte1

Advertising budget and revenue by cohort

Registrations and payments

Registrations and payments

Allowed and actual CAC

Allowable and actual CAC

Important: the report is updated every day and you can observe how metrics change over time: 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. 1. Registered in September, started paying in September — assign value 2. Registered in September, started paying in October — assign value. And so on:

We made a report with a division of all payments on the first and repeated payments

3. Created a report estimating payback on actual and predictable CAC. If we have data on registrations in a particular month, the number of customers who immediately started paying, we can predict how many total paying customers per cohort will be and what LTV we will get from that cohort. This allows us to estimate the return on advertising at the end of the reporting month. Re-estimation of payback is done after 2-3 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 has its own average LTV and, therefore, its own maximum allowable CAC. This is the case in each country/region.

The Results

  1. We have built a clear analytical model that can tell how effective contextual advertising is.
  2. Understood at what value of СAC (customer acquisition cost) can scale.
  3. Distinguished the first payments from repeated payments.
  4. Determined how many days on average after registration leads become clients.
  5. 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

Julia Gordienko, Team Lead at Netpeak

We started to work with an already formed understanding of what metrics we were going to monitor. At the same time, colleagues from UniSender are 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 with small budgets and quickly. I hope that our joint experience will be useful for those SaaS users who are looking for the perfect analytics model.

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