As a business, wouldn't it be great to know your customer’s next decision? Or, reacting in real time to customers looking for products or services like yours?
If both answers are “yes,” you’ll love this post.
In this guide, we’re talking about five ways how using AI predictive analytics helps to understand the next moves of customers:
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Flexible pricing strategy
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Personalized product recommendations and promotions
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More effective online advertising campaigns
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Strategies to predict and reduce customer churn
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Customer behavior research on social media.
AI Customer Predictive Analytics 101
Imagine yourself sitting on the sofa with some snacks, browsing Netflix for some new movie or documentary to enjoy. The platform always has tons of recommendations - and they’re very nicely personalized and relevant in most cases.
Little do Netflix users know that an AI predictive analytics algorithm is generating all those awesome recommendations. It processes:
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Popularity metrics
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User ratings
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Stream-related data like duration
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Metadata (actors, genre, etc.)
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Search data
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Additional features like language, culture, and demographics.
By consistently analyzing user behavior, Netflix’s algorithm makes excellent predictions. The result is happy users enjoying relevant content suggestions.
The same strategy also applies to online sales.
Predictive analytics are already involved in recommendation engines, flexible product price optimization, customer service, ad campaign targeting, and even web security. Let’s talk about these use cases in more detail next.
Related: The Basics of Artificial Intelligence
AI and Predictive Analytics: 5 Use Cases
These are the ways in which businesses can use AI-powered predictive analytics algorithms to understand their customers better.
1. Flexible Pricing Strategy
Technology: AI-enabled dynamic pricing
These days, two people looking at the same product or hotel room booking can get different prices. How? AI algorithms analyze their purchasing history, browsing behavior, and other data to determine who has more interest in that specific product or service.
This strategy is called dynamic pricing. It means changing prices in response to real-time supply, demand, and specific customers. For example, the peak pricing tactic allows to benefit from demand fluctuations and increase prices when demand is higher than usual.
Amazon is a pioneer of dynamic pricing - on average, the eCommerce giant changes prices every eight minutes. This way, the company sells more products by taking advantage of all opportunities.
Related: How Machine Learning is Disrupting C-Commerce
2. Personalized Promotions and Recommendations
Technology: AI-powered product recommendation engine
A business can use behavioral analytics to analyze customer behavior and understand what products and services they might buy. AI takes customer browsing data, process it, and define those preferred items.
Using AI sales personalization strategy to provide relevant recommendations can help online businesses to resolve a major issue. This issue is a poor shopping experience caused by irrelevant suggestions.
Since 38 percent of customers stop buying from sellers that give poor recommendations, solving this issue can get you more sales. In fact, Amazon’s recommendation engine generates about 35 percent of the company’s revenues.
3. Better Online Ads
Technology: AI ad creation, testing, and customer churn prediction
Knowing more about customers helps to make more relevant and personalized online ad campaigns. A machine learning-powered tool can create different versions of ads for specific users based on your data. So, you can find the best-performing ads faster.
Major ad providers are already working on including AI in online advertising platforms. Google, for example, has just released a new version of Google Ads, which includes AI-enabled predictive analytics features.
Thanks to machine learning, the new Google Analytics now lets users know about potential trends such as rising demand for their products or services. Also, the tool uses AI to predict customer churn and revenue from campaigns.
4. Reduce and Prevent Customer Churn
Technology: machine learning analysis of customer activity
Online businesses have to be perfect with customer service: 17 percent of customers leave after one bad experience and 59 percent - after several. In most cases, predicting and understanding customer churn is difficult, so it remains a major point for many businesses.
AI is slowly changing how businesses forecast and mitigate churn. Predictive analytics algorithms that analyze tons of customer data define customers who might be likely to leave. They do so by learning the historical churn data.
Data scientists use various math tactics to identify customers who might leave and when. For example, regression analysis formulates churn as a regression task and considers its relationships with other values.
This graph shows the correlation between the number of customer support calls and churn probability.
Credit: Towards Data Science
5. Do Customer Research on Social Media
Technology: social media sentiment research and content analysis
Social media is a huge source of customer behavior data that businesses can use to improve the ROI of marketing. Getting the insights from that data is now possible thanks to AI-powered analytics apps.
Here are some ways in which AI helps with customer research:
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Track comments to get feedback and understand people’s perspectives on a brand, product, or service
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Analyze social media posts to differentiate loyal customers, detractors, recommenders, and potential customers.
The data on post engagement, content, reach, and other insights can help brands to:
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Create more relevant and personalized social media marketing campaigns
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Make more effective brand communication strategies
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Analyze and compare brand reach and perceptions with other companies.
Ultimately, AI gives marketers and businesses more actionable insights to communicate with social media users. When properly used, those insights will help to find new audiences, manage brand reputation, increase content engagement, and discover emerging trends.
What Customer Behaviors To Focus On?
You’ll need a lot of data to understand your customers’ mindsets and their next moves. To get all answers and apply analytics in the five use cases, pay attention to:
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Website data (browsing data, shopping history, etc.)
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Social media usage data
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Customer loyalty program data
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Google search keyword data
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Conversion path data
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Device data.
An AI algorithm using this data will give you useful insights into customer preferences and goals. This is exactly you need to understand and predict their next move—even months before they make it.