Most of us have used Amazon for shopping. When we re-open the app and take a look around, we see that most of the screen space is devoted to a “recommended products” section. For instance, if one had been looking for smartphones, they see a section with the trending smartphones that may catch their fancy.
We also see a listed “top categories” section, which may include electronics, health, and personal care, grocery, and gourmet, etc. – based on the categories of products the user mostly shops for. Also, there is a “More items to consider” section that is populated based on the products the user has bought or looked for in the past. How exactly does Amazon make the experience so much relevant and unique to each user? The answer is predictive analytics! Amazon uses algorithms that analyze the user’s shopping trends, search patterns, frequently bought items, etc. to predict their future behavior.
Predictive analytics is a branch of advanced analytics that uses techniques ranging from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about the future. Organizations are looking at predictive analytics to find new opportunities and solve difficult problems.
Predictive analytics enables organizations to function more efficiently. Many companies use predictive models to forecast inventory and manage resources. Airlines do it all the time to set ticket rates. Hotels try to predict the number of guests per day to ensure that they have enough staff and resources, maximize occupancy and increase revenue.
Organizations combine multiple analytics methods to improve pattern detection and prevent criminal behavior. All the activities on the network are monitored real-time to spot anomalies that indicate fraud or vulnerability, which are then examined for suspicious patterns to make predictions.
Optimizing marketing campaigns:
Predictive analytics helps companies identify customers that are likely to abandon their product or services - they can either be offered incentives to stay or the company can recalibrate its marketing strategy. This also works the other way.
Consider a business that has a $5000 budget for an upsell marketing campaign and has 3 million customers, in which case a 10% discount cannot be extended to everyone. Predictive analytics and business intelligence can help forecast the customers who have the highest probability of buying the product, then send the coupon to only those people to optimize revenue.
Credit scores – a number generated by a predictive model that gives insight into a person’s creditworthiness – are a well-known example of predictive analytics. They are used to assess a buyer’s likelihood of default for purchases and finds application in loan disbursement, insurance claims, and collections, etc.
Augmented analytics tools backed with predictive analytics like Phrazor by vPhrase have taken the market by storm. What makes Phrazor stand out is the fact that apart from predicting behavior, it also presents analysis in the form of easy-to-understand narratives which in turn speed up decision making. To prove the point, consider the following snapshot of a client portfolio statement for a leading investment advisory house generated by Phrazor.
Personalized Portfolio Analysis Report based on Predictive Analysis
As we see, the report is comprehensible and easily digestible even to a layman. These personalized portfolio reports are provided post thorough analysis of the market, the client’s risk appetite, and investment history. Reports like these lead to happy, satisfied customers – which in turn is increased revenue and brand recognition for the firm! Additionally, Phrazor can be configured as required and can seamlessly integrate with the organization’s existing architecture.
To learn more about predictive analytics and how to embed it in your application, request a demo of Phrazor.