Operational Use Cases for Analytics

This question originally appeared on Quora: What are some great use cases for analytics?

In our previous blog post titled The Difference Between Reporting and Analytics, we defined the nuances between reporting and analytics and how each influences the business.

As a refresher, reporting means translating raw data into information. Whereas analytics means to translate information into business insights, ultimately empowering decision makers to make better decisions, faster.

With those definitions in place, we’re going to expand on the importance of business analytics and drill down into two use cases that you can implement at your organization. So, let’s start transforming your petabytes of data into actionable insights.

Predictive Analytics

To be clear, we’re not talking about the Predictive Analytics market that’s aimed to be worth $9.2 billion by 2020. In this context, we’re talking about using Business Intelligence (BI) to perform predictive (synonyms: forecast, extrapolation) analysis while leveraging your business data.

A BI solution can tell you a lot of things: what’s happening with your business in real-time, visualize how your customers are interacting with your product and give you historical information about last quarter’s sales goals. With all this real-time and historical data at its fingertips, it’s only logical that a BI solution would be able to forecast future projections about your business.

BI solutions have all the necessary capabilities of providing companies with predictive analytics: recognizing patterns, date sliders and extrapolation functionality. In connecting multiple data sources like Amazon Redshift (a data warehouse for historical data) and Salesforce (for real-time customer activity), you’re then able to blend the data and create a predictive view into your business via your BI solution.

A few business examples of predictive analytics include:

• Tracking sales pipeline per quarter

• Merchandise sales projections on a daily basis

• Subscription signups per week

• Expected marketing leads per quarter

Historically, BI has been used to look for trends at the macro level, then drill down to further investigate questions or problems. But, once you use the extrapolation function and think of BI as a predictive analytics solution as well—these types of analyses will give your company a data-driven goal to work towards.

Customer Analytics

Understanding your customer is a vital task. With today’s customer being more empowered and connected than ever before, it’s imperative to track how customers behave with your product, so you can react accordingly.

Drawing from the previous section, a BI solution can help companies focus on customer-based behavior data and leverage it on a daily basis. In connecting CRM data with in-app data (from your production database), you can get a holistic view on how your customer interacts with your product. The deeper you understand your customers’ habits, preferences and where they drop off, the more accurate your conversations and predictions for future opportunities.

Here are a handful of ways to blend your data sources in a BI solution to get a pulse on your customer base:

• Customer Churn: Through using your customer data (from your production database), you can track and analyze in-app activities completed and time spent to gauge usage. Then, combine that data with interactions across channels like email, support tickets and social to scope customer sentiment and therefore the likelihood of churn.

• Opinion Analysis: If you’re a word-based platform (think Reddit, Twitter, tumblr) you can run a sentiment analysis and see how your customer base is reacting to topical trends, brands, etc. You can perform this analysis by blending your data warehouse (consisting of historical data) with Cornell’s sentiment analysis.

• Recommendations: Using the above theory of predictive analytics, you can use customer data to perform recommendation analysis - ultimately driving sales up. Some examples of recommendations are best next offer, similar products, etc. Use the data you have to identify and predict the products or services that customers will most likely be interested in next.

Conclusion

Through a BI solution, companies can move beyond pure reporting and perform analysis based on their data. With this operational efficiency, your company can then increase throughput while remaining at the same budget level because you have access to data that allows you to uncover trends and prevent inefficiencies.