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Numerous studies have pointed out that while almost all Fortune 500 companies have great investments in "Web Analytics" they still struggle to make any meaningful business decisions. Most people complain that there are tera bytes of data and giga bytes of reports and mega bytes of Excel and PowerPoint files. Yet no actionable insights, no innate awareness of what is really going on through the clutter of site clickstream data.
We all wish that our key internal partners, business decision makers, would use Web Analytics data a lot more to make effective decisions. How do we make recommendations / decisions with confidence? How can we drive action rather than pushing data? The challenge is how to separate Signal from Noise and make it easy to communicate that distinction. This is where Excellent Analytics Tip #1, a recurring series, comes in.
The title of my presentation at the Washington DC Emetrics summit was: Creating a Data Driven Web Decision Making Culture – Lessons, Tips, Insights from a Practitioner. My hope was to share tips and insights that might help companies move from just having lots and lots of data to creating cultures where decisions are made not on gut-feel, or the proverbial seat of the pants, but rather based on data.
It is only fair to follow up a post titled " Stop obsessing about conversion rate " with this post. (Just in case you have not read the Stop Obsessing post that please read that first for more context). Conversion rate is a very important metric, used properly. Here is my point of view on the basics and best practices for measuring conversion rate.
AI adoption is reshaping sales and marketing. But is it delivering real results? We surveyed 1,000+ GTM professionals to find out. The data is clear: AI users report 47% higher productivity and an average of 12 hours saved per week. But leaders say mainstream AI tools still fall short on accuracy and business impact. Download the full report today to see how AI is being used — and where go-to-market professionals think there are gaps and opportunities.
This post is a primer on the delightful world of testing and experimentation (A/B, Multivariate, and a new term from me: Experience Testing). There is a lot on the web about A/B or Multivariate testing but my hope in this post is to give you some rationale around importance and then a point of view on each methodology along with some tips. I covered Experimentation and Testing during my recent speech at the Emetrics Summit and here is the text from that slide: Experiment or Go Home: Customers ye
Imagine walking into and out of a supermarket. If you did not purchase anything then the supermarket managers probably don't even know you were there. If you purchased something, the supermarket knows something was sold (they know a bit more if you use a membership card). Visiting a website, you leave behind a significant amount of data, whether you buy something or not.
Some of you have heard me speak at a conference , I always have a deep passion and excitement when I talk about the “Trinity” I wax and wane about it and go on and on about how fantastic the “Trinity” is. But it took a comment from Lisa Seaman to make me realize that I had not written about the “Trinity” on this blog.
Some of you have heard me speak at a conference , I always have a deep passion and excitement when I talk about the “Trinity” I wax and wane about it and go on and on about how fantastic the “Trinity” is. But it took a comment from Lisa Seaman to make me realize that I had not written about the “Trinity” on this blog.
The topic of my speech at the E-consultancy Online Marketing Masterclasses 2006 in London ( sign up here ) is "Conversion Rate Optimization: What, Why, How" While working on one of the slides (Tip # 9) the realization dawned that we measure conversion rate rather sub optimally and in a way that grossly overestimates the improvement possibilities.
How many metrics can you call adorable? Site abandonment rate is an adorable metric, to me : ), for these reasons: Money, money, money baby. IMHO there isn't a metric out there that can tell you a lot so quickly and any improvement you make to it will directly and immediately impact the bottom-line. It measures the customer interaction in a very small number of web pages, probably one for cart and two to three for checkout.
I am often asked what we look for when we hire Web Analysts or what quality do good Analysts possess or how to measure if a resource that already exists is optimal or how to mentor / motivate / guide our more junior Analysts to propel them to become great Analysts. This blog post is an attempt to answer all those questions wrapped into one. We all agree that reporting is not analysis.
On this blog we have talked about the importance of the “Why” often. Web Analytics usually simply helps us understand the “What” Clickstream data typically does not tell us why something happened. We have stressed how important it is to know the Why in order to derive actionable insights around customer behavior on the website and outcomes from that behavior.
Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage
When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m
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