Channel organizations today are placing their bets on the power of data to market and sell smarter, faster and more effectively. But data alone isn’t going to cut it. There’s a science to unlocking data's full potential, and when you master that science, you're able to increase service revenue, strengthen ties with customers and build a better overall service renewals business.

What Exactly Is Data Science?

Data science encompasses ideas such as machine learning, neural networks and predictive modeling, with an emphasis on statistics and math to build models of future activities and customer trends based on historic business data. This can indicate how customers will behave and ultimately buy new services as well as accessories, new products and more. 

For example, knowing the best time to send a service renewal email campaign can make an enormous difference in the number of renewed contracts a company can achieve, leading to a sizeable increase in service revenues. This piece of knowledge can be fed into your data warehouse for business intelligence (BI) tools to consume, then sent to a marketing or sales automation platform, where the campaign parameters would be defined. Automation of service sales campaigns is considered best practice and is fast becoming the only answer to addressing the daunting scale of contracts and contract renewals that most sales teams are dealing with today. But automation is difficult, if not impossible, without the right data in place.

What Is The Difference Between Data Science And Business Intelligence?

BI is a component of data science. The intent of BI is to help end users create reports and dashboards easily, including a level of interactivity that raw data or traditional reports do not provide. A good BI experience relies on the visualization of data and ease and flexibility with which it can be manipulated and explored, such as on a dashboard. In most cases, a typical BI analysis might be limited to historical data, or it might be severely limited because of very poor data quality. 

In service sales, a BI analysis might offer visualization of expiring service contracts or products sold without service agreements. If your company has high quality data, this information can be turned into actionable business intelligence to drive service sales growth across product and service lifecycles. Going one step further, BI coupled with data science and predictive analytics can achieve complex forecasting, such as projecting buyer behavior and identifying upsell and cross-sell opportunities. The bottom line is that your BI should give you insights that allow you to adapt your business accordingly, scaling or retracting as needed.

Gain Precision With Predictive Modeling

Predictive modeling and analysis is another key aspect of data science. By drawing upon industry-specific expertise– whether gained from external or internal resources-- and then adding components of data mining, you can find correlations that might represent valid predictors of a specific metric, such as projecting buyer behavior and the potential for upsell and cross-sell opportunities. This will allow you to build an initial predictive model. The next step after determining the data you want to predict is to train the model, making the results more granular. With each step taken in this direction, and the resulting data fed back into the model, each successive campaign will become more personalized and therefore increasingly accurate. The findings can then be used to suggest campaign and service quote parameters based on the analysis.

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