Predictive analytics in marketing is the use of statistical data and algorithms based on machine learning to speculate customer behavior and market conditions. The exponential growth of data generated through multiple points has presented difficulty in making sense of it.
Thus, predictive analytics can help marketing professionals harness the value of their past data to improve outcomes in the future. But what are the various points of application of predictive analytics in marketing?
Through technological developments, the applications of predictive analytics in marketing have skyrocketed.
The concept of predictive analytics is not a new technique. Humans have been trying to predict the future based on past data points for a long time. But recent technological advancements have allowed firms to gather meaningful insights from a broad range of data sources at faster speeds.
Segregate Customers Based on Profile
Different customers have different needs. The statement is obvious, but it has only recently become noticeable. By using predictive analytics, consumers can be segmented based on their demographic profile. Through historical data, the customers’ purchasing patterns can be analyzed, and accordingly, they can be grouped with others who have made similar purchases. Grouping buyers gives insights into which types of products are purchased by which types of consumers.
Target Campaigns for the Right Customers
Once data has been gathered and consumers have been segmented, marketing campaigns that are tailored to match the client requirements can be displayed to them. Not all customers will respond positively to a message or an offer, but the ones that will are already identified through predictive. So, sending client specific marketing will receive a better response than a one-campaign-fits-all strategy.
Predict the Churn Rate and Develop Retention Measures
If a customer is not happy with the product or service, they may discontinue use. This is the churn rate, which represents the amount of potential revenue lost. Predictive analytics in marketing can help create retention mechanisms through personalized intervention. A customer who feels heard and is privately catered for is less likely to leave.
Set Dynamic Pricing
In the eyes of the customer, you are competing with other firms on the most important factor: price. Optimizing pricing to maximize revenue while retaining enough customers is difficult. But predictive analytics can help analyze factors such as the pricing of competitors, changes in trends, supply-side components, and more to aid in setting and changing prices accordingly.
Predictive Analytics in Marketing Through Customer Service
Many businesses overlook the role of customer service in the firm’s marketing strategy. A customer may have had a negative experience with the firm, but solid client support can regain their trust in the business for future purchases. By using predictive analytics to forecast customer concerns, appropriate service measures can be established to increase customer happiness.
Conclusion
Overall, predictive analytics has come a long way due to the exponential increase in data and technological advancements. To make data-driven decisions, predictive analytics in marketing can help firms looking to cater to customers on a personal level, increase retention rates, make pricing dynamic, and improve customer service. This enables marketers to have an edge over competitors in the business landscape.
