The R.A.C.E. Marketing Cycle drives our client marketing solutions.
A/B Testing (and other types of tests) coupled with smart and actionable Analytics review and reporting is the whole key to the ROI game. But a good percentage of B2B Marketers are missing smart ways to look at Analytics, and also just not finding the time to improve results through testing. We have articles on the why and how.
On the R.A.C.E. marketing cycle chart at left, we are looking at tactics in these articles from section 04 Conversions, and also section 06 Analytics.
Google analytics has come a long way in the last few years. You should be using realistic $ goals, custom channels, and smart attribution to help you decide where to place your online marketing budget among other advanced techniques including filtering, dashboards, and geo targeting (which you can then use in a remarketing campaign). We’ll discuss these concepts here.
We often get asked, with so many reports and KPI's in Google Analytics, what are the most important ones to review and track? Great question. We like to break it down into four questions: Where did people come to your site from? What page did they land on? What did...
How did we increase a pop ups lead gathering results by 670%? We’ll tell you how — and it did not require a major redesign for the B version. In split testing, often a simple change using persuasion psychology makes all the difference.
Businesses of all sizes should regularly be performing site audits. If you’re not sure where to begin, read on to discover what site audits are, why they are important, and how to perform one.
Site speed optimization. If your home page does not load in just a few seconds, don’t be surprised when visitors hop on to the next site instead of yours. You’ll also get a down bump in your SEO. Read on and we’ll show you how we optimized a client site who’s on one of our Business Care and Growth plans, to be faster than 82% of tested sites
How confident are you in your A/B test results? There’s always a certain amount of risk when you pick a winner because, statistically speaking, testing not a binary proposition with one version a clear loser and one a clear winner. We look at some tools that help you understand the degrees of probability that the version you pick will be the true A/B test winner.
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