All posts tagged Analytics

Case Study: Results of Context & Drip Emails in CRO

How Focusing on Context & Drip Emails Increased Conversions by 240% for a B2C Mobile App

We’ve completed our first 3 months of testing and optimization with LogDog and I’m excited to share the results:

By focusing on the context, visitors’ intent, and emotional triggers combined with drip email marketing we’ve increased signups from LogDog’s website by 240%.

Over the past 5 years we’ve developed a unique methodology that helps us increase conversions & revenue for SaaS companies substantially more than with traditional conversion rate optimization.

By combining psychology, drip marketing, A/B testing, and continuous optimization with in-depth research…

We’re able to increase ROI and revenues often by hundreds of percent. We call this Revenue Optimization.

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A/B Testing Validity Tests That Could Invalidate Your Results

4 Threats That Could Make Your A/B Test Results Invalid

A/B testing may seem straightforward, but the truth is that there are a lot of “little-known” factors that can shift how your tests perform.

You might have heard about sample sizes, statistical significance, and time periods required for proper testing of landing pages…

But what about the History Effect? The Novelty Effect? The Instrumentation Effect? or the Selection Effect?

Watch to find out why the above threats could invalidate your A/B tests (and how to avoid skewing your data).

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How Startups can Properly use Google Analytics

Startups, You’re Doing Google Analytics All Wrong

We’ve all done this: Launched a new website for our company, installed Google Analytics to track all sorts of data, and proceeded to occasionally keep an eye on our traffic and looking at the sources.

And that’s a problem. Most startup companies won’t use Google Analytics as a key tool to make business decisions, nor will they ever go deeper than looking at the basic reports. They will focus on the metrics that will make them feel good, such as the increase in visitors and pageviews. These are called vanity metrics, and they’re totally worthless at helping make decisions.

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