How Predictive Analytics Helps You Understand What Your Customers Really Want

How Predictive Analytics Helps You Understand What Your Customers Really Want

In the present competitive business sense, it is important to understand customer preferences and forecast their future behavior. This is a key factor in staying ahead of the curve. Predictive analytics is a form of data analytics that uses statistical algorithms and machine learning techniques in order to identify the likelihood of future outcomes based on the past. Both predictive analytics and model forecast have an important to play in understanding and anticipating customer behavior. When predictive analytics are deployed at your place of business, you have a meaningful insight into customer behavior, needs and wants. Using predictive analytics makes it easier to customize what you have to offer.

What is Predictive Analytics?

Predictive analytics is the process of looking at past and current data to anticipate future happenings or behaviors. Predictive data analytics utilizes multiple data sources to predict future customer behaviors. These data sources can include elements like customer purchase history, web browsing activity, social media interactions, etc. Recognizing patterns and trends in the data allows businesses to accurately anticipate customer behaviors. If customer A continuously buys a specific product or continuously searches out a certain service, predictive models can accurately project what might be next for that individual.

Analyzing Consumer Behavior

Being able to understand consumer behavior is basically the heart of predictive analytics. The company that can best forecast what its customers are likely to want in the future is better able to deliver the right products and services to meet their needs. Customers may be swayed to buy a required product or service because of past purchases, current trends, or preferences at that moment.

Predictive analytics allows companies to mine large amounts of data effectively to gain some insights about customers’ behavior. For example, e-commerce companies can look at a customer’s purchasing habits and browsing mode in a pool of information, right down to predicting which product the customer may buy next. Retail is able to observe how often a customer buys a particular item and suggest products based on a purchase history for those who have a higher propensity to purchase the product.

Personalization and Customer Experience

One of the main advantages of predictive analytics is its ability to provide personalized customer experiences. Using a customer’s past interactions, businesses can provide personalized experiences by understanding their actual preferences. Take Netflix for example. The service creates a personalized account for each user which provides them suggestions based on the viewing history of the customer. This turns into repeated viewing, requiring the customer to pay the subscription fee on continual basis.

Similar to Netflix, online retailers such as Amazon and eBay, predictive models are utilized to show customers products they are most likely interested in. Not only does this help boost the bottom line, but it is aesthetically pleasing for the customer that the retailers know “their tastes.” When businesses can prognosticate a customer’s wants and desires before the customer knows, it generates trust and loyalty.

Utilizing predictive analytics can also help fine-tune the various marketing strategies and initiatives. By assessing data points from customers, businesses can determine marketing pegs to target key customer segments. The generated targeted marketing campaigns can assist businesses in developing personalized email marketing campaigns for their customers based on previous email interactions, that provided higher open rates and increased conversion.

Inventory and Supply Chain Management

Predictive analytics is not only about understanding customers’ preferences but also making sure that businesses can satisfy those preferences in an efficient manner. Typically, predictive analytics relies on sales forecasts to be able to better manage inventory. For example, when a fashion retailer uses predictive analytics to forecast what styles, colors and sizes customers will want in the next season, they will be better able to stock the appropriate amount of inventory.

Having sufficient inventory can also minimize the threat of stockouts resulting in lost sales, or of overstock resulting in ‘black box’ costs. Predictive models can help businesses avoid stockouts and overstock, and help ensure product availability at the right time.

Predicting Customer Churn

Predictive analytics can also be used to anticipate customers’ potential churn. Understanding the timing and explanation of customers’ behavioral antecedents can allow businesses to take advantage of the opportunity to retain customers. Historical data can provide insight into when customers are usually at risk of churning (e.g., decreased engagement or lower frequency of purchases). Businesses can analyze their historical data to identify the antecedents of risky customers.

Once risky customers are identified and the triggers analyzed and understood, businesses can allocate time and resources for customer retention. Incentives could include personalized or bespoke rewards or loyalty builds (New reward, % off, freebie of brand.). Predictive analytics allows an opportunity to intervene before customers decide to walk away.

Development of products

Predictive analytics can also enhance product development by reviewing customer opinions, needs, and behavior. Businesses are able to find market gaps, determine desired qualities by customers and forecast what products are most likely to be accepted.

For instance, a manufacturer of smartphones can use predictive models to map a customer’s opinions of earlier products and therefore make a forecast as to which qualities customers are likely to value in the next product. The manufacturer is then able to design a product that is representative of the customers needs leading them to eventually sell more product to increase brand loyalty.

Competitive Advantage

In today’s fast-paced global business environment, companies that can accurately forecast their customers’ needs can gain a real edge over their industry rivals. Predictive analytics allows a company to take advantage of proactive decision-making,as opposed to merely reacting to customer needs. By anticipating the need for customer-desired solutions, businesses have an opportunity to offer satisfaction and build long-term relationships.

Further, companies that utilize predictive analytics gain the ability to manage resources, efficiency levels, and ultimately monetary profit. This affords a stronger market position, higher retention levels, and a better chance of success.

Final Conclusion

Predictive analytics is a robust mechanism that enables businesses to develop a deeper understanding of their customers as well as their activity. With data-driven insights and increased knowledge, organizations can provide better customer experiences, enhance marketing, some inventory decisions, and customer attrition. In addition, predictive analytics empower agility in business response, proactive action to market demand changes and improved overall efficiency. As businesses adapt to use predictive models over time, the ability to “guess” what customers need moves from guessing to decisions based on informed and data-driven efforts that ultimately lead to success.