How Analytics can help you Prevent Customer Problems Before they arise

Foreseeing a complex situation before it arises and acting on the problem to prevent losses is fundamental to any business’s success. With predictive data now available, organizations can work to address the issues and minimize consumer churn and complications.


Establishing consumer loyalty is necessary for any savvy business. But with many options available to today’s consumers, getting them to continue working with you and not switch to your competitor is becoming increasingly difficult. But how can analytics help you ensure customer loyalty? How can you prevent customer problems before they arise?

A major cause of customers’ disagreeable actions is the smartphone industry, which often witnesses a lot of product-return within the “free-return” window. Though most of these customers claim that their reason for the product return is due to a glitch, studies have shown otherwise. It is most likely due to a lack of knowledge in operating the device or a failure to understand such a product’s specific utilization.

Preparing for the Unforeseen with Data Analytics

What is the intent of the consumer? Why will a consumer visit a particular outlet or store? What will they likely purchase? Many would say you shouldn’t count the chickens before they hatch; you can do that with predictive data analytics.

Predictive data analytics utilizes the patterns and trends of a consumer’s regular visits or purchases. In this digital space, retailers can use data predictions to have a pre-conceived idea of a buyer’s demographic and use these details to meet the future demands of a dedicated number of consumers. A case in point is the use of price optimizer software. There are now lots of price optimizer software in the digital space – and with these predictive data tools, companies can better determine how customers respond to product pricing through different platforms.

The different effective predictive models include:

Visit Data

With data on the number of visits, duration of stay visited locations, and days since the last stop, a retailer can fetch better predictions concerning future visits and purchasing decisions. This gives retailers an analytical edge, allowing them to know and classify visitors – and maintain the regularity and loyalty of individual visitors.

Particular Predicting

Predictive analytics makes location and online/offline data available to retail marketers. With the knowledge of who your customers are and having relevant data impressions, retail marketers can successfully predict a buyer’s visit within a period.

Establishing Certainty

Retailers can use predictive analysis to personalize their services to an individual or a group of consumers. Every business must have marketing schemes and strategies for every group of consumers. As far as purchasing decisions are concerned, certain traits are shown by consumers. When these traits are identified and used smartly, a retailer will better identify and segment different consumers according to their needs.

Several companies leverage predictive analytics to enhance their bottom line, grow their business, and effectively address customer problems before they arise. With price optimizer software, these companies can navigate through business complexities and achieve better control over outcomes and pricing models to grow actionable intelligence to ensure consistent development of pricing strategies over time.