One thing many people forget when dealing with data: outliers.
Even in a controlled online A/B test, your data set may be skewed by extremities. How do you deal with them? Do you trim them out, or is there another way?
As is tradition with our other tool comparisons, this is not a dry feature-by-feature comparison of Segment and Mparticle. We won’t proclaim one is the ‘best’ customer data platform (CDP) for which every business, including yours, should use. Many factors, including your budget, company size, and current data workflow, will determine if either platform is the right fit.
If you need a line by line feature breakdown, a quick Google search will serve you best in that case.
Instead, we’ll be covering the core use cases that customer data platforms address and explore whether Segment or Mparticle ultimately deserves a spot in your tech stack.
Like many young SaaS startups, we had no shortage of marketing and sales data, but it wasn’t easy to comprehend. The information was there, but it was scattered all over the place.
Some bits and pieces could be found in Google Analytics, while other data was stored in BigQuery and ProfitWell. This arrangement made it challenging to give a quick answer to basic questions on user conversions or to comment on traffic rates and MRR. It wasn’t until we began creating custom dashboards to visualize our data that everything started to click.
Are you a CMO who thinks accurate attribution is a pipe dream? Or a customer experience director who has to hack together data to create something resembling a customer journey?
For a web analytics analyst or a data-driven marketer, these are words to live by: “Without data, you’re just another person with an opinion.”
Optimization isn’t about educated guesses and hunches, no matter how many years you’ve been in the industry. It’s about doing the research, asking the right questions, digging for clues in problem areas, paying attention to the signs when they appear, and running smart A/B tests.
Web analytics analysis is a big part of that. It helps separate the optimizers from just another person with an opinion.
Google Analytics helps us identify conversion uplift opportunities. Traffic is precious, and we don’t want to waste it on tests that don’t result in learning or uplifts.
That’s why we want good data for:
Google Analytics is widely used. But most marketers only scratch the surface when it comes to reports.
You can find insights for conversion optimization from tons of reports—and the juicier reports are lesser known.
I asked some of my friends in the industry to share underutilized reports that they often turn to when looking for insights.
Peter Drucker famously said, “What gets measured gets managed.” But what if your data is wrong? What if you’re not measuring correctly or completely? What if there’s a whole pile of things you think you’re measuring when really…you’re not?
A lot of the people relying on Google Analytics are relying on bad data. No, not because Google Analytics is awful. Because their configurations are broken. That’s why you need to conduct a Google Analytics audit.
According to a study, 71% of website visitors complete their purchases offline.
Online, we have plenty of ways to track visitor behavior—cookies, heat maps, click tracking, retargeting, etc., but as soon as that person picks up the phone, we’re lost. We don’t have to be.
Ever wonder why your site has a lot of visitors but not enough transactions, purchases, or inquiries? In this post, we look at marketing and UX metrics from a slightly different angle.