A case study showing the effectiveness of Tapdaq within Raki Bench's application, Guess The Flag. To sign up to Tapdaq, visit www.Tapdaq.com

1. Raki Bench Case Study

2. • Raki has been developing applications for 6 months
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• Raki currently has 60 applications spread across a number of
niches.
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• Raki has a re-skin business model. He focuses on building and
publishing multiple, similar applications in a short space of
time.
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• Using this method, he has generated healthy revenues and
sustainable growth on the App Store.
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• Raki was one of the first entrepreneurs to use Tapdaq.
Developer Overview

3. Guess The Flag is a trivia-based application.
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Here are a few screenshots of the application.
Application Overview

4. Monetization User Acquisition Analytics
SDKs Installed

5. • Every application starts with no user-base at all, and developers often focus on
the size of their revenues immediately.
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• However, focus should be placed on the average LTUV. The higher your average
LTUV, the more you can afford to spend on acquiring new users and the faster
you can scale your user-base.
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• Developers can improve their average LTUV by iterating and improving their
application but crucially, this takes time and resource.
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• Pre Tapdaq, and based on the first month of existence the average LTUV on
‘Guess the Flag’ stood at $0.022
What’s Raki’s problem?
Taking Raki’s average LTUV of $0.022, if we drove 1,000,000
users to Guess The Flag, we’d make Raki $22,000.

6. App Performance With Tapdaq

7. The Tapdaq Objective
Raki’s Problem
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Taking Raki’s LTV of $0.037, he would be unable to acquire users on any other network
because their average CPI (cost per install) far exceeds this figure. Typically, being in
excess of $2.
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Our Objective
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We wanted to help Raki generate more daily boot ups for his application which would in
turn maximise his in app and ad revenue. To do this, we placed Tapdaq interstitials in
unobtrusive locations within Raki’s apps, all pointing to ‘Guess the Flag’.
On the 22nd April, 2014 - we turned Tapdaq on for Raki’s
applications.
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On the following pages, the Tapdaq analytics are from 22nd April to
13th May. The previous analytics data is from 22nd March to April 13th.
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Tapdaq metrics are always in blue. The previous data is in red.

8. Installs
Installs Comparison
When you break that down into a % difference it works out as 19.5% increase in
installs.
Installs are only valuable if they convert into cash, so let’s look at the other statistics…
Before Tapdaq With Tapdaq
970 1,159

9. Bootups
Bootups Comparison
When you break these bootups into a % difference it works out as 47.1% increase.
Before Tapdaq With Tapdaq
6,721 9,893

10. Revenue
Revenue Comparison
Before Tapdaq With Tapdaq
$38.28 $55.17
This is a growth in revenue of 44.1%
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Yes, $55 is still a small amount of revenue in the grand scheme of things but don’t
forget the growth Raki has seen here has not cost him $1 and furthermore, it’s
scaleable.

11. LTV
Before Tapdaq With Tapdaq
$0.022 $0.048
This represents in the average LTV of a user acquired by Tapdaq being
worth 118% more than before Tapdaq was implemented.
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Understanding this statistic means that when we can scale the traffic that
we have started to generate Raki, the revenue will increase substantially.
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So you are probably thinking, why the sudden jump in LTVU when we
integrated Tapdaq?

12. We selected other applications which were relevant to ‘Guess the
Flag’, and cross promoted within these products, trading Daq,
not dollars.
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With this in mind, the quality of the user acquired was much
higher than compared to a more generic campaign.
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Overview
With a new LTV of $0.048, if we drove 1,000,000 users to Guess
The Flag using Tapdaq, we’d make Raki $48,000.

13. Overview
We’ve provided Raki with a free way of generating higher quality
users, which can scale. Raki is now implementing this model out
across all 60 of his applications which is resulting in far more
impactful changes to his overall revenue.

14. “A pretty remarkable product for generating
installs and traction without spending any
cash to advertise.”
- Raki Bench

15. Raki Bench Case Study