- 16th April 2018
- Posted by: Manolis
According to a recent Harvard Business Review article, big companies are embracing analytics in 2018, but most still don’t have a data-driven culture. Furthermore, if I were to ask you if your company is data-driven, there’s a high probability that you’d say yes — despite substantial industry exaggeration. This is because the industry is only now beginning to put specific parameters around what it means to truly be data-driven versus, say, data-influenced or data-aware.
Many companies confuse the art of data collection with the disciplined science of data analytics. Having access to siloed reporting structures from Facebook, Google and your various marketing, sales and CRM platforms is not the same thing as being able to intelligently answer a question — backed up by data — about your customers and their buyer journeys.
Time is money, and your data speed provides a competitive advantage.
The vast majority of big data is unstructured, which means there’s a fair amount of data wrangling involved before you can analyze, build a hypothesis and test your understanding of what the data is telling you. Even with the help of artificial intelligence, there are often time-consuming steps required to break down the data silos to deliver meaningful and actionable insights from your data.
What’s worse, the larger the company (in terms of the number of employees), the deeper the silos tend to run. The social media team, for example, tends to myopically obsess over social media success metrics without having a deeper understanding of how their efforts fit into the overall customer journey from initial discovery to repeat purchase behavior.
This is why so many companies authentically believe they are data-driven, yet most are just beginning to fully comprehend what data is truly meaningful, how to navigate through all of it and, most importantly, how quickly they can act upon the insights once they are discovered.
Understand the digital analytics value chain.
A deeper understanding of your customer behavior is usually the most important insight. As business leaders, we tend to focus on metrics such as bounce rates and conversion metrics, but how do these metrics actually translate into truly understanding our customers? Over the past decade, we’ve gotten really good at reporting the actions our customers take, but by and large, we’re still pretty far from truly understanding our customers and their needs.
The goal of any analytics solution is not to track customers. The goal of data analytics is to understand the needs of our ideal customers and to begin applying our learning as we deepen our relationships with them.
State your hypothesis and allow the data to prove or disprove it.
Truly data-driven companies begin by analyzing the data they have and then come up with a well-defined hypothesis that they can prove or disprove with the available data. This is why speed to insights must drive your data analytics in 2018. The faster you can prove or disprove your hypothesis with data, the sooner you will be confident in your understanding of your customers’ needs. Having a clear picture of your customers allows you to deliver more relevant marketing campaigns because you can deliver the right message at the right time on the right media platform. The act of experimentation allows you to better understand the needs of your customers and having the right data will support your hypothesis about their needs — or challenge you to dig deeper.
This approach also has the added benefit of helping you to understand the motivations of your customers. By shifting away from an obsession with clicks and bounce rates, you can begin to see the bigger picture of what’s really going on with your customers and how you can continue to improve in order to serve them better.
I’m on a mission to help more companies become data confident as we all go through our own digital analytics transformation journey. Do you know how many analytics tags you currently have on your website? If so, do you know what each one of them does (and if they are even necessary anymore)? Do you have a solid picture of where you are and where you are headed in your company’s digital analytics transformation journey? And are you aware of your company’s data blind spots and how to ensure the data you are analyzing is, in fact, the whole story?
My hope is that these tips will help you find the answers to these and many more questions as you play a pivotal role and lead your company through its own digital analytics transformation journey.