Companies that operate in the digital market today have access to comprehensive data about their products, making data analytics a key to business success. As a result, the market for data analytics solutions became very competitive with multiple vendors that offer tools with more and more features and great user experience.
It seems simple. Implement a data analytics tool, visualize your data and make data-driven decisions. Does it work in practice? Do most companies manage to convert data to actions?
Data analytics platforms generate beautiful daily reports containing a lot of substantial information. Is anything done with these reports after the first several weeks? Usually, based on my experience, the answer is no. As Paul Sartori of Station 10, says, “If it (the report) has no action, it’s not worth spending time on”.
The Common Practice
Product managers and analysts usually focus on defining an event table that contains all possible events (better safe than sorry – you do not want to discover that important data was not recorded). Then, they build a dashboard that displays the main parameters (new users, retention, conversion rates, etc.). That should work, right?
Not exactly. The result of this common practice is that we have too much information, which limits our ability to focus on the important data. In many cases, you find out that it is very difficult (if not impossible) to obtain the piece of information you really need. You are working hard to drill down and get the right answers, instead of finding the right questions.
In the case your analytics reports indicate that your product is not doing well, the obvoius next step is to investigate why it happens and what needs to be done. As Paul Sartori adds: “Why not do that in the first place instead of wasting time on pretty but pointless dashboards?”
The problem with the common practice is that it is a bottom-up approach. We gather all the details and end up with a huge pile of hay of data, trying to find our needles. Instead, we need to use a top-down approach, to anticipate where the needles will fall. Focusing on the important, actionable data.
'Right-side-up' Analytics Methodology
To be data-driven means to use data to the right decisions that serve your strategy. Therefore, we should select the most relevant metrics that will enable us to get answers to the critical questions that will enable us to execute our strategy.
The strategic steps for right-side-up data analytics implementation are:
● Define your goals
● Select the metric / KPIs that really matter
● Ask strategic questions to explain cause
For each goal, we should define what can affect it and the potential causes. The deeper we dig before defining the metrics the better, since once we start defining metrics we will tend to forget the questions and focus on more metrics.
The next steps are tactical:
● Choose the investigation methods to answer the questions
● Create an event table / tracking plan
Choosing an effective investigation method is supercritical. Data analysis can be very time-consuming if a more complicated method is used. I made this mistake too many times to learn that over exploring usually causes more harm than good. Optimize your investigation methods – look for the shortest path to the answer, thus reduce the chance for data overload and errors.
The last step is where the common practice starts – defining the events table. At this point, we have all the logic – we just need to define the required event and make sure we do not forget anything. It is important to:
- Use the minimal number of events possible
- Truly utilize the power of event properties to indicate context and causation
- Be very specific and detailed with explaining each event’s triggers and properties syntax.
About the author:
Yoav Yechiam is the Chief Analytics and Product Strategy consultant at Y-Perspective. Yoav teaches Analytics courses and workshops, lectures on Product Strategy and Analytics and have published a few articles on that subjects.
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