With access to more data than ever, we now have the tools to harness it to optimize strategy. From acquiring new customer relationships, to tailored messaging, to increased customer retention, predictive insights help businesses make data-driven decisions.
What is predictive analytics?
What if you could use data like a secret superpower to help you predict future outcomes, tap into the minds of customers, and as a result, make your customers happy?
Armed with big data and predictive analytics, you can connect with customers in a whole new way and, in turn, maximize profit.
How does this look? One part of the equation is using the data to answer key business questions:
What is the lifetime value of a customer that made a purchase for the first time today?
Are there hidden factors that impact cost and margins?
How do we reduce churn and retain the most valuable customers?
Where does all the data come from?
It can be pulled from more sources than you think, and the data you add will give you important insights into the customer experience.
Interactions – email and chat transcripts, call center notes, web click-streams, and in-person dialogues
Attitudes – opinions, preferences, needs, and desires gathered through survey results and social media
Descriptions – attributes, characteristics, self-declared information, addresses and other location information, and demographics
Existing business data – transactions, purchases, phone logs, store visits, web page visits, mobile interactions, response to past promotions, lifetime value, tenure with the company, payment history, and usage history
Predictive insights in action
How are companies applying predictive analytics to increase business value?
Improving retention with enhanced customer service based on behavioral segmentation.
A large mobile service provider wanted to reduce attrition by targeting which customers were likely to cancel service soon, so they could take preemptive action to retain their business. By developing predictive models based on specific behaviors for different people, they were able to craft offers that would likely appeal to certain segments. They fed this information into the call center, so service reps could provide specific offers to high-risk segments to improve customer satisfaction.
Reducing employee turnover and hiring for success using predictive insights.
A leading financial institution used advanced analytics to increase the long-term effectiveness of hiring choices. By analyzing patterns in their HR data, they were able to identify which skills, experience, and behaviors were predictive of success on the job for frontline employees. They could use a similar approach to reduce employee attrition, finding solutions by analyzing a broad scope of data including hiring information, Voice of the Employee feedback, intranet browsing patterns, and more.
While traditional business intelligence reports on what’s happened in the past, predictive analytics gives you the power to create business impact on an individual level, and to do that for millions of individuals.
By mining and analyzing all your available big data, you can develop reliable, repeatable models for predicting outcomes and drive more profitable decision-making.
About the Author
Steven Ramirez, CEO at Beyond the Arc, Inc., a leading business management consultant with expertise in customer experience strategy, communications, brand strategy, social media, data science, and PR & media relations. He is a strategic advisor with over two decades of experience in working with entertainment, technology, fintech, and financial services companies. He has strong strategy development, marketing, PR, and negotiation skills and has significant experience in managing mergers and acquisitions.
Apart from extraordinary entrepreneur skills, Steven is a member of several national independent film producing organizations and is the former President and Chairman of the Board of Directors of the Film Arts Foundation. He has taught The Business of Film course at UC Berkeley and is frequently invited as a guest lecturer at the Haas School of Business. He earned both his Bachelor and MBA degrees from Berkeley.