For CMOs: Take Your Company’s Data to a New Level for Marketing Insights

This guest post from Phyllis Zimbler Miller, ‎Digital Marketer, comments on uses of predictive analytics for marketing insights that could benefit from in-database scalability and ease of production deployment with Oracle R Enterprise.

Does your company have tons of data, such as for how many seconds people watch each short video on your site before clicking away, and you are not yet leveraging this data to benefit your company’s bottom line?

Missed opportunities can be overcome by utilizing predictive analytics

Predictive analytics uses statistical and machine learning techniques that analyze current and historical facts to make predictions about events. For example, your company could take data you’ve already collected and, utilizing statistical analysis software, gain insights into the behavior of your target audiences.

Previously, running the software to analyze this data could take many hours or even days. Today, with advanced software and hardware options, this analysis can take minutes.

Customer segmentation and customer satisfaction based on data analysis

Using predictive analytics you could, for example, begin to evaluate which prospective customers in which part of the country tend to watch which videos on your site longer than the other videos on your site. This evaluation can then be used by your marketing people to craft regional messages that can better resonate with people in those regions.

In terms of data analysis for customer satisfaction, imagine an online entertainment streaming service using data analysis to determine at what point people stop watching a particular film or TV episode. Presumably this information could then be used, among other things, to improve the individual recommendations for site members.

Or imagine an online game company using data analysis of player actions for customer satisfaction insights. Although certain actions may not be against the rules, these actions might artificially increase a player’s ranking against other players, which would interfere with the game satisfaction of others. The company could use data analysis to look for players “gaming” the system and take appropriate action.

Customer retention opportunities from data analysis

Perhaps one of the most important opportunities for analysis of data your company may already have is for customer retention efforts. Let’s say you have a subscription model business. You perform data analysis and discover that your biggest drop-offs are at the 3-month and 6-month points.

First, your marketing department comes up with incentives offered to customers right before those drop-off points – incentives that require extending the customer’s subscription.

Then you use data analysis to evaluate whether there is a statistical difference in the drop-offs after the incentives have been instituted.

Next you try different incentives for those drop-off points and analyze that data. Which incentives seem to better improve customer retention?

Companies with large volume data

Your company may already be using Oracle Database. If your company’s database has a huge amount of data, Oracle has an enterprise solution to improve the efficiency and scalability of running the R statistical programming language, which can be effectively used in many cases for this type of predictive analytics.

Oracle R Enterprise offers scalability, performance, and ease of production deployment. Using Oracle R Enterprise, your company’s data analysis procedures can overcome R memory constraints and, utilizing parallel distributed algorithms, considerably reduce execution time.

Regardless of the amount of data your company has, you still need to consider how to get your advanced analytics into production quickly and easily. The ability to integrate R scripts with production database applications using SQL eliminates delays in moving from development to production use.

And the quicker and easier you can analyze your data, the sooner you can benefit from valuable insights into customer segmentation, satisfaction, and retention in addition to many other customer/marketing applications.

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