Websites and apps typically gather a huge flow of user behaviour data, from tools such as Google Analytics and Adobe Analytics, with which to better target their marketing and product development. The company assumes that either:
- Having a smart web analyst or online marketer skim through the reports daily will enable management to keep tabs on what is going well and what aspects are not
- Recruiting a ‘data science’ team, and giving them access to the raw user event data, will surface one-off insights into what types of customers can be targeted with which promotions
Having worked in a dozen such companies, I think both assumptions are flawed. Humans are not good at spotting interesting trends, yet for all but the highest scale web businesses, the problem is not really a ‘big data’ challenge.
For a mid-sized business, the problem is best framed as, how do you extract regular, easy-to-absorb knowledge from an incomplete online behavioural data set, and how do you present / visualise the insight in such a way that digital managers can act on that insight?
- Data: the raw inputs; e.g. the individual signals that user A clicked on button B at a certain time when visiting from a certain IP address
- Information: provides answers to “who”, “what”, “where”, and “when” questions
- Knowledge: the selection and synthesis of information to answer “how” questions
- Wisdom: the extrapolation or interpretation of this knowledge to answer “why” questions
Information is what Google Analytics excels at providing an endless variety of charts and tables to query on mass the individual events. Yet in the traditional company process, it needs a human analyst to sift through those reports to spot problems or trends and yield genuine knowledge. And this role requires huge tolerance for processing boring, insignificant data – and massive analytical rigour to spot the few, often tiny, changes.
Guess what? Computers are much better at the information processing part when given the right questions to ask – questions which are pretty standard in the web analytics domain. So Littledata is extending the machine capability up the pyramid, allowing human analysts to focus on wisdom and creativity – which artificial intelligence is still far from replicating.
In the case of some simpler insights, such as bounce rates for email traffic, our existing software is already capable of reporting back a plain-English fact. Here’s the ‘information’ as presented by Google Analytics (GA).
And here is the one statistically significant result you might draw from that information:
Yet for more subtle or diverse changes, we need to generate new ways to visualise the information to make it actionable. Here are two examples of charts in GA which are notoriously difficult to interpret. Both are trying to answer interesting questions:
1. How do users typically flow through my website?
2. How does my marketing channel mix contribute to purchasing?
Neither yields an answer to the “how” question easily!
Beyond that, we think there is huge scope to link business strategy more closely to web analytics. A visualisation which could combine a business’ sales targets with the current web conversion data, and with benchmarks of how users on similar sites behave, would give managers real-time feedback on how likely they were to outperform.
That all adds up to a greater value than even the best data scientist in the world could bring.