Digital Analytics: The Good, the Bad & the Ugly
Consumers are researching, purchasing and comparing software side-by-side online now more than ever. As we move into a new year it’s a good time to reflect on how digital success is measured, what works and what doesn’t. There is a theme throughout the digital environment of a paralysis of choice: a completely overwhelming landscape of metrics and comparisons that often leads decision-makers in circles. Below we break out varying types of approaches when considering online analytics implementations to help combat this paralysis.
A company knows their goals. They know the actions within their web space that generate the most meaning to their business strategy. Take those key performance metrics and build a holistic strategy around garnering the data points. Keep the focus small, the objective clear and construct an analytics implementation that reflects those decisions. This strategy allows for higher focus, maintaining accuracy within the metrics. A marketing strategy is easier to develop and optimizing the strategy is more precise with a pinpoint focus on generating conversions. The scope of maintaining data integrity is less daunting and more impactful when the ecosystem contains 3-5 metrics opposed to 10 or more.
Tracking lots of data across websites is not a bad thing. However there is a pitfall when a strategy morphs into tracking just to track. This can cause a snowballing effect of greater data volume, poorer integrity and less actionable understanding of the story the data points are telling. More is not necessarily better. A poor analytics strategy is usually a derivative of unclear business objectives. As data accrues and more and more variables are tracked, their importance becomes diluted. It becomes easier to focus on the metrics that look favorable even if they are weak barometers for the success of the business. Keep it focused and keep the data collection insightful.
A key to making clear decisions from digital data is context. Often, time is a major contextual factor. With accurate long-term data a company can see normalized trends that tell them, yes we are doing better than last year and here is why. Unfortunately when overbearing analytics strategies are implemented there are often inconsistencies that corrode the benefiting factor of long-term data collection. Dropping tracking tags, website functionality changes, altering data point focus and volatile marketing strategies all disrupt the ability to make recommendations from normalized data sets. Many of these ugly scenarios can be avoided by keeping a keen eye on the actions that matter and ensuring persistent accuracy of these over time.
As we look ahead in 2014, remember that data collection recommendations and insights are only as good as a company’s initial strategy. A volatile set of objectives can reset the clock on cohesive digital data collection. Focused goals with definable metrics are the seeds that harvest the greatest insight into the true performance of a digital property.
[This post originally appeared on Asking Smarter Questions and is republished with permission.]
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