Three steps to maximising Big Data analytics

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Despite its much-hyped potential to revolutionize marketing and customer engagement, Big Data has become a double-edged sword for many organizations. As it turns out, more data does not necessarily equate to good data and extracting clear, actionable insights is much more difficult than any of us imagined.

As a result, these massive stockpiles of data have become nothing more than vanity metrics: superficial numbers that make us feel good, but in reality do little to indicate real success. Sometimes, big numbers simply don’t tell the whole story.

For example, skyrocketing site visits might give the appearance that business is booming or that the latest marketing campaign was an overwhelming success. But what if all those new site users are merely “drive-by” visitors who spend just a few seconds before moving on? Or worse, what if they’re immediately turned off by of a critical site error, the navigation is too confusing or any number of other issues? Judging merely by the number of visits, you might be patting yourself on the back, but the reality could be a disaster.

Conventional Big Data analytics have failed to deliver a way to dive deeper into the data, to measure true engagement and to access the most critical behavioral indicators that can help drive smart decision-making. Fortunately, there are a few steps you can take to maximize the value of Big Data and get past the vanity numbers to derive real, actionable insights.

Step 1: Look beyond average

Simple mathematics dictate that what appears to be average is highly influenced by weighted data at opposite ends of the spectrum. When it comes to determining what resonates most with your ideal high-value users, basing this assumption on average user data can be highly inaccurate. For example, one image/stock art e-tailer calculated an average of 29 image views per user per week. However, calculating the median revealed that the typical user actually only views 12 images. In reality, power users were viewing proportionally more images, completely throwing off the perception of usage. Accessing this insight often requires looking beyond average or other conventional measurement tactics to understand salient user behavior.

Step 2: Examine outliers

Sometimes the most critical insight can be found in unexpected places. Analyzing user behaviors and activities that fall outside the expected norm can help identify hidden issues and unusual behaviors—both good and bad. For instance, one photo sharing site discovered an issue with its user interface for renaming photo albums when a large number of users kept the default name “New Gallery” instead of giving each album a relevant and searchable name. The product team adjusted the interface design and added help content to show users how to change the album name. As a result, more users are now creating specific gallery names, enhancing searchability.

Step 3: Follow the right path

Logging relevant customer activity is important, but understanding exactly what prompted certain actions or events can be even more valuable. For example, perhaps a large number of users are experiencing the same site error, but on the surface, everything appears to be fine and it seems impossible to duplicate. Delving deeper into the data to understand what happened just before the error can help you identify the root cause and solve the problem. At the same time, motivating desired behaviors often requires taking appropriate action three, four or more steps prior to the target action step. Analyzing the path of users who took the desired action and replicating these triggers for all users can maximize desired results.

While big numbers and massive data sets might seem impressive, the reality is that without the tools and techniques to gain real, actionable insight from Big Data, it’s virtually useless, leaving marketers struggling to find the proverbial needle in a haystack. This new paradigm requires that we move beyond vanity metrics and the “more is better” approach to drive real results. By diving deeper into the data, and knowing the right questions to ask, we can look past the superficial statistics to see the true character underneath.

Read more about Big Data in Business Review Australia



About the author

Christopher Gooley, CEO and co-founder of Preact, has architected, developed and launched multiple B2B and B2C online services. As co-founder of Preact, Christopher is building data-driven tools to help companies better understand their customer’s actions to enable support and account management teams to proactively solve problems, increase engagement and multiply per customer revenue. 


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