7 types of Predictive Analysis for Customer Experience
A lot of apps where you purchase your goods, often recommend you things which are exactly what you were looking for or needed. Nowadays most of the sites have implemented Artificial Intelligence software on their site for this kind of predictive analysis to help improve their customer experience. Even the call centers help a lot in this kind of customer analysis as a customer can call them and they can recommend the client on what they need to buy or solve their query on an item they are interested in.
One can even use Artificial Intelligence software to see what the customer has purchased before and what they might be interested in the future. Which makes the job of the call center much easier. These would lower the cost of operation of the call center as well as the website.
One should even analyze the data that they receive from the call center to understand what is required to improve and what is not working in the business, hence monitoring live calls of the contact center are an important part of predictive analysis for a better customer experience.
Primarily there are 7 types of predictive analysis for customer experience which we have listed below—
Predict Customer Needs:
Prediction of a customer’s needs are one of the most important things which a business can thrive on, if a business knows what a customer needs it can sell them exactly that and make a profit out of it and at the same time it would make the customer overjoyed because they found exactly what they need without even asking or looking for it.
With the data, a website, or a business gathers from a customer’s previous purchase history, it can predict when the customer would need the product again plus when the customer would be looking for something needed. With the help of such data, a business or website can also personalize their page according to the customer’s requirement so that it meets the customer’s needs.
Real-time Customer Feedback:
Predictive analysis can also help tailor customer recommendations. With every feedback, a customer puts on a website, the algorithms of the website can decide what the customer is looking for and hence create a new recommendation for the customer as per the data they put as feedback to their site.
Customer’s want can change quickly and it is upon the business plus their website to catch up with it, with predictive analysis of the data the business can quickly identify the changing customer need with the help of artificial intelligence and hence provide the customer with the items or the things that they are looking for.
A company like Amazon, Spotify, and Netflix use this to identify what the individual customer wants or is in the mood for. Hence, recommending exactly the type of movie they want to watch or exactly the type of song they want to hear.
Predictive analysis of the browsing data from a website can help in managing their workforce a lot better. With the data received from the number of customers visiting the website of a business can determine if they need more workforce or less workforce.
If the traffic on their website is low then they can reduce the workforce as they can predict there wouldn’t be too many calls coming through from the customers and if more customers are visiting the website, the business can predict there will be many calls coming through so they need to increase the workforce in their call center for a better customer experience.
Optimizing Price Model:
This model is mainly used in the insurance sectors. Through predictive analysis, these insurance companies can predict the life cycle of their customers and due to this, they can increase their profits. This predictive analysis can determine when a person would need a new house or a new car or when their baby would grow up and need a driver’s license.
Through this strategy, the insurance company can predict everything and can come up with a new plan customized for your needs at the right time when you need it, which in terms would lead to an increase in the company’s revenue. Along with that they even can predict and give better prices for the age of the person and their needs.
Taking the example of car insurance, the company can predict how often a person drives with the help of the in-car sensors, if the person drives less, he would be given a better price compared to a person who drives more since he can get into more accidents than a person who doesn’t drive the car so often.
Real-time Agent Analysis through Speech Analytics:
By monitoring agents in real-time a company could create more opportunities compared to unmonitored calls. A company could help its agents to address a customer more effectively and hence leaving the customer satisfied.
Through speech analytics, the agents would always be able to judge how the customer’s mood is and would always have the right things to say and make the customer happy with the business which would keep them returning to their website. Real-time speech analysis is gaining high popularity in the current market due to reliability.
Predicting Market Bets:
This type of marketing bet is largely popular in the capital market industry. One could analyze the data received from the market and create offers tailored to what the customer needs so that they get hooked to the company and invest more in it. Such marketing can save a lot of money for the business and as well as make a lot of revenue without spending way too much.
These predictive marketing bots have been researched on and seen that one should offer exactly what the customer needs to stay in and not more for this kind of marketing bets to be successful in business and hence creating a higher revenue than other businesses.
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