Less than half of all CMOs use quantitative data to support their strategy and decision making. By leveraging artificial intelligence, pr ictive analytics, and machine learning along with data, algorithms, and models, we (as marketers) can more effectively communicate with our audiences. Through pr ictive analytics and business intelligence, CMOs can better understand consumer behavior, optimize their resources and investments, and retain customers – so why is it that more than half of companies today are underutilizing this resource? They may still not fully understand the capabilities and resources associat with pr ictive data analytics.
Some examples of how pr ictive analytics can be implement include:
- Audience segmentation through cluster modeling
- Use recognition models to acquire new customers
- Scoring leads with propensity modeling and pr ictive scoring
- Use collaborative filtering to recommend ads and content
- Use enhanc segmentation to personalize the experience
Netflix is a great example of a marketing team that uses its data to generate analytics that pr ict what users want to see and how they will search for it. Netflix collects data from search keywords, ratings, viewing dates, preferr genres, and even when users hit pause. Through data collection and sophisticat analytics, models can discover human behavior and buyer patterns, to the point where Netflix bases 75% of viewer activity on personaliz recommendations. At over 151 million subscribers, this is a massive data set. However, Netflix knows which shows to update and which shows to recommend to users bas on past behavior and expect behavior patterns. This strategy has consistently work out well for Netflix, and it’s no surprise to their executive team.
Use artificial intelligence to pr ict your wins
Business intelligence enables marketers to gain comprehensive no matter what your business type consumer insights that can shape their marketing and communication strategies with target consumers. However, many US and UK marketers believe that their companies lack the resources to analyze large amounts of data. How can these aub directory companies use business intelligence to make smart decisions? Choosing the right tools is not an easy task.
