Taking Inspiration from Amazon and Netflix

offer a highly visual and intuitive experience with capabilities that guide reps through the analytic process—leveraging recommendation engines, greater interactivity, and easy access to all types of data sources to cull new and more relevant insights that improve each customer interaction.

Eliminate the hard work of analytics

Analyzing large stores of data is hard work. To put marketing and sales reps in charge of their own success, as briefly mentioned earlier, shouldn't require them to rely on IT to do the work for them.

This is where consumer applications excel; they hide all the complexity involved in data science. For example, Amazon's recommendations engine involves complex algorithms, but it doesn't take a data scientist or IT professional to operate. Building that same level of simplicity into a business analytics application is critical to make it accessible for reps. Hiding the complexity of data analysis by embedding data science into the application lets meaningful work such as data profiling, modeling, acquisition, and blending be done for the user automatically.

It's important to empower business users to interact with data, filter out the noise, and bring to the front what matters. Borrowed from all those recommendation engines in popular consumer applications like Amazon and Netflix, this concept brings ease of use to business applications.

Replicate Social Norms

In order to help reps take advantage of customer data, organizations should replicate the way reps communicate in the "real world" to foster a more collaborative approach to analysis. An analytics application is more than just data science. Visualizations, and the ability to distribute that information throughout a work group, department, or company, are critical aspects of an app. Looking at distributing information, it's logical to model a business application off social media—and while this isn't a new strategy per se, it is a dynamic change in business intelligence.

In traditional BI and analytics applications, sharing typically happens via a static, top-down dashboard—presenting users with a quick view of the organization's key metrics. This dashboard may never lead to the discovery of new insights or metrics that are not already being tracked, nor is it interactive or collaborative. Conversely, social sharing app Pinterest gives all users a bottoms-up, collaborative way to share images. It does this by employing concepts such as following, tagging visuals with metadata, and annotating content. Those same concepts also make sense when applied to business intelligence—users who can discover their own insights outside of top-down BI should be able to share them without creating static reports. By doing so, they'll be able to effortlessly distribute sales figures to their team the way they would pin wedding photos or recipes with friends.

At the end of the day, whether they're conducting a lead gen campaign or having a follow-up call, marketers and sales reps are at a disadvantage in the competitive landscape today if they're unable to quickly and easily find pertinent customer insights. To put them in charge of their success, organizations should consider a new consumer-driven approach to BI and analytics and ensure these three measures are in place so reps can be more independent about getting the information they need, and close more deals.

David Abramson is the director of product management at Logi Analytics, where he shapes the delivery of business intelligence and analytics software products. Prior to joining Logi Analytics in 2003, he led database application development teams for federal government agencies, including the Department of Transportation, Department of Defense, and the United States Postal Service.

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