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.
After receiving a $500k budget increase, someone in the C-suite must decide where to allocate these funds: remarketing budget, R&D, events, website redesign, etc.
What should they do? Do they need more information to decide? Even with new information, will that clarify the percentage allocation of funds?
As consumer needs and technology become increasingly complex, so too do the decisions. Modeling is critical for complex decision-making. Relying on intuition alone can set you up for failure.
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.
Subscriptions are an increasingly common way to buy products online, whether consumables like coffee and energy supplements, or personalized lifestyle boxes and fashion.
Traffic attribution identifies which sources drive visitors to a web property. And it’s impossible to credit a conversion to the correct source without first knowing how a visitor got to a website.
In other words, the foundation of conversion attribution is traffic attribution. Simple as it may sound, attributing a session to its traffic source can be tricky, even impossible.
There are hundreds of services for site tracking, advertising, customization, and, in general, souping up your ability to measure, reach, and convert your visitors.
How do you coordinate the analytics setup of a web shop that sells their products all over the world—if you have to handle 10+ languages and currencies in over 80 countries?
Kaggle, the Google-acquired data science platform, started as a virtual meeting point for machine-learning geeks to compete on predictive accuracy scores.
It evolved into a Swiss Army knife for data science and analytics—one that can help data professionals, including data-driven marketers, elevate their analytics game.
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.