Artificial Intelligence (AI) along with machine learning and predictive analytics has become the way for intensive customer-centric data that can increase sales, generate leads, and enhance customer satisfaction.
Big data, as the name suggests has become a key driver for enterprises to enhance their sustainability in a competitive business world. Every day the data is multiplied to a bigger number. Hence more data is stored every day than ever before, the need for more efficient, effective, and precise processes has grown too. Having this huge data is useful for businesses to have predictive analytics. AI helps in segregating unstructured data; Predictive analytics is one such powerful process.
Predictive analytics is the process of using data mining, statistics, and modeling to make predictions. The software mines and analyses historical data patterns to predict future outcomes by extracting information from data sets.
Set of data that defines a range of parameters like the previous order history of a buyer, their interests, pages they view most, products that can benefit them, and products they might need along with their existing order the same as e-commerce sites do. It brings you insights and accelerates customer understanding.
According to Gartner Inc., predictive analytics describes any approach to data mining with four attributes:
1. Emphasis on prediction
2. The rapid analysis measured in hours or days
3. An emphasis on the business relevance of the resulting insights
4. A focus on ease of use, thus making the tools accessible to business users.
How Does AI &Predictive Analytics work?
When Predictive analytics is paired with computational power, helps businesses to identify their potential customers or probable responses by using personalized data collected over time.
Like we humans, many of the decisions are not based on logic. Emotions, trust, intuition, communication skills, inner satisfaction and culture play a crucial role in coaxing us to buy a certain product or make a particular decision.
Artificial intelligence algorithms are experts in integrating these key emotions and produce insights that make prospecting more effective for potential buyers.
Without the help of AI algorithms, all you will see is a cluster 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 X and product Y, and 65% of people who bought this combination also purchased a product Z along with it. Now you can easily analyze the remaining 35% of the customers and suggest product Z. 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. Isn’t it a magical marketing idea?
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.
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 excelling through AI technology combined with predictive analysis to fuel their growth and enhance the customer experience.
1. Social Media Analysis
Global Digital transformation has produced a fundamental change in how information is being produced, processed, stored, and used. Companies and marketers can now easily track user comments on social media, which enables them to gain immediate feedback and understand their customer’s perspectives about their brand. Twitter, LinkedIn, Facebook, Instagram, and Pinterest are some of the famous names.
It allows brands to innovate, create, and communicate their product in a much more effective manner. Also, one of the key drivers that is customer satisfaction has ramped 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 organization and gives extra credibility. It is also one of the less expensive media to reach maximum customers.
2. Weather Forecasting
Weather forecasting has improved greatly 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.
Google was one of the first companies to step foot in the healthcare domain using predictive analytics. The Google Flu Trends (GFT) analyzed 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 organizations began carving their niche into the healthcare industry using predictive analytics. Online pharmacies are using AI combined with predictive analytics to understand and analyze their customers’ health issues, prescriptions, dosage, the amount of time before they need to repurchase their medicine, etc.
To get the best results 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 analyses 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 that allows you to easily store, modify, update, and process all kinds of large datasets that they might require in the future.