Systems | Development | Analytics | API | Testing

Latest Posts

Tackling shopping cart abandonment with data analytics

Today, the average shopping cart abandonment rate in online retail hovers at a disheartening 69.57%. To put that into an even more ominous perspective, that’s $18bn lost every single year. All because customers simply didn’t want to check out. What’s going on? There are dozens of reasons why you could be experiencing a high shopping cart abandonment rate. Yet understanding the exact reason isn’t an easy task.

The 9 biggest big data challenges for ecommerce directors

Every effective ecommerce director knows that big data is critical to creating a great customer experience. Yet with big data comes big challenges. Data-driven ecommerce teams are much more likely to turn a profit. They are a remarkable 23 times more likely to acquire customers than their non-data-driven peers. They are six times more likely to retain customers. And they are 19 times more likely to be profitable as a result.

The 4 best metrics for Twitter analytics

Effectively using Twitter analytics is critical to driving success on the social media platform. But there are dozens of metrics for Twitter you could be tracking, so finding the right ones for your team and company can be difficult. We’d recommend keeping it simple. So, we’re sharing the 4 best metrics for Twitter analytics, to help you truly understand your audience and supercharge your Twitter presence. First off, let’s take a quick look at the state-of-play on Twitter today.

10 stats on the challenges of data analysis today

We’ve come a long way since the phrase ‘big data’ entered our vocabulary back in 2005. Now data is absolutely everywhere, in absolutely every industry. Today, we will generate about 2.5 quintillion bytes of data and, by 2025, it’s predicted that the figure will reach 463 exabytes (that’s the equivalent of 212,765,957 DVDs a day).

5 key benefits of anomaly detection

Collecting, analysing and reporting on company data takes up a lot of time, resources and manpower (and this is only set to increase as we collect more and more data). Take marketers, for example, they spend on average 3.55 hours a week collecting, organising, and analysing marketing data from different sources. That adds up to 250 hours a year. This energy spent on understanding data and KPIs is why business intelligence (BI) tools that streamline the process are booming right now.

7 essential Google Analytics custom alerts

If you’re committed to making data-driven decisions, then you already know that staying on top of fluctuations in your Google Analytics dashboard is critical. But analysing data and building reports is only one small part of your job. You know that, as much as you know you need to stay on top of these figures, you can’t be glued to Google Analytics all the time. But if there are sudden changes, anomalies or fluctuations in your data, you may need to know about them immediately.

What to do when there's too much data

With the mass digitisation of businesses over the last 20 years, data has become an industry obsession. We use data to understand our customers better, understand our businesses better, understand ourselves better. And today, a whopping 90% of enterprise analytics and business professionals say that data-driven decision making is absolutely key to their digital transformation initiatives.