Trial and error is the nature of analysis, and mobile app analytics is no exception to this rule. When we’re combing data in search of the elusive answer, we’re bound to err at times. But what if you get things consistently wrong with your analytics. Nothing can be more frustrating than spending a whole lot of time trying to analyze your marketing data, only to realize that your approach is flawed. In this blog, we will talk about two must have requirements for all app businesses to get their mobile analytics right.
Pick KPIs that Matters
For most businesses, analytics is abunch of confusing numbers on a screen the bulk of which tend to become useless. What businesses actually need from analytics is the right information, at the right time to get the right insights. To ensure this, they need to focus on measuring things that matter, which essentially means picking the right KPIs.
The L’Oreal Group’s approach to customer engagement highlights the importance of right KPIs. The beauty wanted to bridge the communication gap between the cosmetic giant and its customers, as it catered to its customers only through retailers. The company was looking for feedback to leverage loyalty and augment brand awareness. The group tapped into both web and mobile analytics to get the right insights. Using CRM technology L’Oreal has started analyzing social media content product reviews and news stories. Based on the comments or stories, posts are routed internally to dedicated employees in the command center to engage with the writer.
In yet another instance, Westpac Banking Corp. leveraged analytics tocapture and centralize customer activity and understand their transaction behavior. Based on behavioral analysis, the Australian bank matched customers with new offerings.
The moral of the story is that before you plunge into app analytics, make sure you have a clear idea of what you’re hoping to achieve.
Get a Segmented View
A scattergun approach to analytics can only lead to confusing insights. Most companies unwittingly adopt such an approach because they fail to target specific groups of users. What companies need instead, is a segmented view of user behavior can help make analysis more accurate.
Here’s an example of how segmentation helps CVS Health Corp improve operational efficiency. In order to improve employee-customer interactions, the company leverages linguistic analytics to understand the caller’s personality and behavioral preferences. It then automatically routes the call to like-minded agents whose personality were pre-screened. With this move, the drugstore chain managed to reduce call time duration and improve call performance significantly. Similarly, Navy Federal Credit Union, one of the 50 largest financial institutions in the United States, uses sophisticated segmentation of its 3.2 million members to match them for specific service offerings.
While segmentation is the first step, it should ideally be followed by creation of homogenous sub groups of differently valued customers. This would facilitate cluster analysis and make it possible to target and report against sub segments.
With these two requirements you will have a holistic appraisal of what is actually required. In the process, you will be truly tracking what counts.