How AI is Improving Predictive Analytics

How AI is Improving Predictive Analytics

Artificial Intelligence (AI), machine learning, and predictive analytics are paving the way for intensive customer-centric data that can increase sales, generate leads, and enhance customer satisfaction.

Big data has become a key driver for enterprises to enhance their sustainability in a competitive business world. With more data being produced and stored than ever before, the need for more efficient, effective, and precise processes has grown too. Predictive analytics is one such powerful process.

Predictive analytics is the process of using data mining, statistics, and modelling to make predictions. The software mines and analyses historical data patterns to predict future outcomes by extracting information from data sets to determine patterns and trends

Simply put, it is used on a set of data that defines a range of parameters such as the previous order history of a customer, their interests, pages they view most, products that can benefit them, and products they might need along with their existing order. It can bring you insights and accelerate customer understanding.

According to Gartner, predictive analytics describes any approach to data mining with four attributes:

  1. Emphasis on prediction (rather than description, classification, or clustering)
  2. Rapid analysis measured in hours or days (rather than the stereotypical months of traditional data mining)
  3. An emphasis on the business relevance of the resulting insights (no ivory tower analysis)
  4. A focus on ease of use, thus making the tools accessible to business users.

Also Read: Real-time Analytics, What Marketers Should Know

How Does AI Impact Predictive Analytics?

Predictive analytics, when paired with computational power, allows businesses to identify their potential customers or probable responses by using personalised data collected over time.

As humans, many of our decisions are not based on logic. Emotions, trust, intuition, communication skills, inner satisfaction and culture all play a crucial role in persuading us to buy a certain product or make a particular decision.

Artificial intelligence algorithms are increasingly integrating the ability to identify these key emotions and produce insights that make prospecting more effective for potential buyers. For example, you have sales data on thousands of customers and the different items they have bought.

Without the help of AI algorithms, all you will see is a bunch of complex data, rows after rows, mentioning product codes or names which will not only lead you anywhere but are also highly complex to understand.

For example, a popular combination of products bought together by consumers is product A and product B, and 75% of people who bought this combination also purchased a product C along with it. Now you can easily analyse the remaining 25% of the customers and suggest them the product C. This way, you will be recommending your customers a valuable product that has been found useful and effective by other similar minded buyers as well.

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You can create a pattern by using AI algorithms that will segregate and form data sets based on multiple factors. It can be on the number of people who bought a certain product, a particular item that sold more in a season, a specific combination that is often preferred by consumers, their date of purchase, feedback, ratings, cost of the orders, and shipping preferences

Behavioural Patterns Offer Insights

Companies have started to scale the power of the ultimate duo of AI and predictive analytics. Humans tend to make choices based on a set behavioural pattern and not always logic. We frequently purchase the same items, prefer a selective range of brands, behave in similar ways, and act on similar intuitions.

Predictive analytics has propelled the AI market by bringing customer intelligence the ability to go beyond the understanding of the historical data. It is producing useful insights that delve into what happened and suggest what could be done to improve a certain scenario. Leading solutions like SAS Advanced Analytics software is infused with cutting-edge, innovative algorithms that can solve most intractable problems and make the best decisions possible.

Anomaly Detection

Anomaly detection analyses the data, and pinpoints towards anything out of the usual operations or expectations. It could help brands predict whether a certain campaign succeeds beyond its goals if a video will go viral or spur interest in the audience if the audience was engaging with the content.

By using anomaly detection, one can deduce what worked in their favour and what did not. It can help you identify features that lead a prospective client to become a customer or those features that caused them to walk away. This helps bridge the gap between customers and organisations where the latter can understand their customers’ behavioural patterns and eliminate hurdles that push them away.

Also Read: How is Conversational AI Powering Marketing and Sales

PayPal collaborated with Rapidminer to gauge the intentions of top customers and monitor their complaints. To gain a better understanding of what drives product experience improvement, PayPal needed to analyse customer feedback.

Boston-based Rapidminer builds software platforms for data science teams within enterprises that can assist in data cleaning/preparation, machine learning, and provide predictive analytics services such as churn prevention, demand forecasting, and fraud detection.

Rapidminer worked along with AI and data science engineers at PayPal to develop a system that could perform sentiment analysis for customer comments in over 150,000 text-based forms in several different languages, including 50,000 tweets and Facebook posts.

Paypal learned that the login issues seemed to spike during November and December (holiday season) when users were more actively making purchases and instances of forgotten passwords were high. Within the first few weeks of integrating RapidMiner into their system, PayPal customers succeeded in recovering their passwords 50% more often than before the integration.

AI Takes Predictive Analytics to the Next Level

Predictive analytics is not confined to a particular niche; it can be used in a wide array of industries and verticals. Here are some of the major industries that are honing the skills of AI technology combined with predictive analysis to fuel their growth and enhance customer experience.

Social Media Analysis

Digital transformation has produced a fundamental shift in how information is being produced, processed and stored. Companies can now easily track user comments on social media, which enables them to gain immediate feedback and understand their customers’ perspective about their brand.

It allows brands to innovate, create and communicate their product in a much more effective manner. Also, customer satisfaction is one of the key drivers to amp up sales and generate leads, so if a happy customer leaves a good review on your brand’s social media platforms, it makes more people believe in your organisation and gives extra credibility. Not only do you reduce customer churn but also decrease customer acquisition costs.

Weather Forecasting

Weather forecasting has improved thanks to the advanced predictive analytics models. Today’s five-day weather forecast is as accurate as a one-day forecast in the 1980s. Governments and agencies have been able to warn citizens and take necessary steps in case of hurricanes, floods and natural calamities by using predictive analytics.

Satellite monitoring is used to collect data about the land and atmosphere. This data is then fed into weather forecasting models that predict the weather changes in the coming days. Forecast as long as 9-10 days is easily possible now. One of the recent examples of how predictive analytics helped in weather forecasting is the extreme polar vortex that reduced temperatures in Minnesota and Winsconsin up to -50 degrees Fahrenheit. The weather was predicted well in advance, which helped tackle the situation in a much stronger and effective manner.


Google was one of the first companies to step foot in the healthcare domain using predictive analytics. The Google Flu Trends (GFT) analysed anonymous, aggregated internet search activity to provide real-time estimates of influenza activity for a corresponding region to predict flu patterns. Though useful, the GFT provided overstated numbers, which led to less than ideal information.

Nonetheless, many organisations began carving their niche into the healthcare industry using predictive analytics. Online pharmacies are using AI combined with predictive analytics to understand and analyse their customers’ health issues, prescriptions, dosage, the amount of time before they need to repurchase their medicine, the brands they are used to, brands they never buy, etc.

Also Read: Immersive Technology Makes for Richer Customer Experience

Get Started With Predictive Analytics

You need to create a strong analytical platform that is capable of carrying the necessary volume of data along with handling the diversity of each data set. The most successful companies use multiple data sources to collect information including structured, unstructured, text-based, machine or IoT (Internet of Things) data.

The more data brands collect, the more they can analyse and process quickly, which means marketers are more likely to get actionable insights in a faster and efficient way. Marketers must look for a platform which allows you to easily store, modify, update and process all kinds of large datasets that they might require in the future.