If you can look at a data set and automatically correlate the findings with a relevant cause, you’re one of the gifted few.
The rest of the world isn’t as lucky. The phrase, ‘a pictures say a thousand words’ has held true for so long because nearly 90 percent of the information humans take in is transmitted and processed visually.
Ultimately, it’s far easier to digest a diagram or chart than analytics and reams of numbers in their purest form. Yet many of the most popular solutions, like Google Analytics, HubSpot and social media channels like Facebook or Twitter, don’t lend themselves to visualising the necessary data to effectively draw out actionable insights.
Statwolf’s data science platform helps marketers overcome that challenge to breathe new life into their campaigns.
Know your limitations with Big Data
There’s a lot to unpack in a Google Analytics dashboard, and straying from pre-configured goals can make your life even more difficult.
Google Analytics, in particular, excels in its primary objective: collecting information about your website’s traffic. But as marketers and data scientists know all too well, gathering data on its own is never enough. Interrogating the data is key to uncovering hidden insights that can have a noticeable impact on your efforts.
Let’s use an example where a marketing team A/B tests multiple landing pages. While Google Analytics can tell which page generated the most click-throughs or converted the most leads, it can be difficult to figure out why that is. Namely, which variation of the control led to success or failure?
Statwolf’s data visualisation tools simplify actionable insights, allowing small teams to work with Big Data. By turning the information into charts, graphs and other interactive displays, marketers can run top-level, comparison and in-depth analysis daily.
The advantage is in the code
Big Data is seen as the competitive advantage that nearly every company hopes to get behind – but many are currently failing to do so. Roughly 74 percent of businesses welcome analytic-driven operations with open arms, but just 29 percent can derive any type of actionable insight from the figures.
One barrier that’s easy to hurdle is the platform in play. Raw data brings with it lots of questions; solutions that can seamlessly unpack that information and form narratives are invaluable. But no two tools are the same, and many require complex installations and provide equally convoluted dashboards.
Statwolf’s data analytics solution is cloud-based, which alleviates any concerns about on-site integration. It connects seamlessly with all well-known web and video platforms, as well as social and paid-advertising channels.
Dedicated support teams, customised training content and a kick-off webinar help businesses get comfortable with the idea of incorporating analytics at the ground-level. A second win is the automated daily reporting that places the most relevant information in the hands of decision-makers.
Start implementing best-in-class practices
Another reason organisations have difficulties finding business value in analytics is because they aren’t employing habits that are conducive to driving change on the bottom-line. There are a few components that need to be accounted for in any effective Big Data strategy:
- Align data with goals: Understand which metrics are closely intertwined with your department’s goals – collecting figures for the sake of having them won’t lead you to the right door.
- Analyse with context and relevance: Data visualisation tools should help correlate results with events so you can easily see how certain actions promoted specific returns. Similarly, sit down and discuss significant KPIs/metrics with stakeholders.
- Keep everything transparent: It’s difficult to get a team to rally behind insights shrouded in secrecy. The reasoning behind every new campaign stemming from an analytic-driven insight should be clearly visible to everyone it will affect.
Driving insights with Big Data that positively impacts the company at a large scale is an obstacle that’s made easier with artificial intelligence. Machine learning algorithms have become more popular as a means of manipulating data to uncover new insights, rather than opting to monitor the usual channels.
Statwolf’s data science platform has machine learning and natural language processing models readily available to tackle the industry’s most challenging goals today.
Don’t waste another second
Every day that passes by without leveraging a tool that supports the simplified analysis of Big Data is another day where actionable insights remain hidden.
Contact our team to learn more, or schedule a demo with our consultants.
