Ditch the guesswork, predictive AI knows your customers
Ditch the guesswork, predictive AI knows your customers
Read to know how predictive AI can unlock better business decisions and stronger customer relationships.
Key takeaways
AI-powered predictive analytics unlocks significant business value, from boosting sales to improving customer experiences.
Companies must prioritize ethical AI practices, particularly in regulated industries like banking and healthcare, to build customer trust and ensure compliance.
Generic AI solutions may not meet strict privacy and regulatory standards in sensitive industries; sector-specific tools are often essential.
Start with pilot projects to demonstrate the value of AI within your organization before scaling up.
AI isn't magic – it thrives on clean, well-structured data, making thorough data preparation a crucial prerequisite for success.
Last year, I started Multimodal, a Generative AI company that helps organizations automate complex, knowledge-based workflows using AI Agents. Check it out here.
I have binge-watched many shows just because Netflix's recommendation engine knows what I want a little too well. That's some world-class AI at work. Those ads that pop up on your feed, just a bit too close to something you've been thinking about? Again, that's AI doing its thing. For the most part, we're getting used to this kind of personalized experience.
Predictive AI can absolutely benefit both businesses and their customers. But it's not automatic. It takes thoughtful planning and a commitment to using data strategically.
This article is your guide to how businesses can harness predictive AI to understand their customers. The goal? Making smarter decisions and building stronger relationships – without making them feel like they're being tricked into clicking "buy now."
How predictive AI works
We need at least a little bit of theory before we dive in. If you want to go deeper, there are many great in-depth, technical articles out there, like this one.
But let me give you a brief rundown first.
Predictive AI relies on mathematical models that discover patterns within historical data.
These models learn to map input features to their associated outcomes or labels. This allows the prediction of outcomes for new, unseen data points. The model then takes a dataset of examples with known outcomes. It also tracks historical and current data to find relevant patterns and then modifies and learns from them. It adjusts its internal parameters to minimize the difference between its predictions and the true outcomes.
In simpler words, predictive AI is like a data detective. It examines past cases to learn the tell-tale signs (patterns) that lead to a specific conclusion (outcome). Then, it uses that knowledge to solve new cases (predict outcomes for unseen data).
Why predictive models help and where artificial intelligence fits in
Trying to guess what customers want is a never-ending game for businesses. You do some surveys, and maybe get people in a room for a focus group.
You may also analyze the data you collect online – but, overall, your process feels like a guessing game.
Not only that, but it’s time-consuming and laborious too. It probably involves analysts and business operators charting Excel sheets and preparing manual reports, which then go through multiple people before the recommendations are implemented. Even so, it’s highly likely that the recommendations are mistargeted.
AI, however, takes predictive analysis to the next level, especially thanks to its ability to quickly sift through mountains of data and spot patterns that humans may miss.
Handling the data explosion: We live in the age of Big Data. It's great, but traditional predictive analytics models choke on it. AI thrives on massive, complex datasets, uncovering difficult-to-spot patterns.
Knowing the big picture: Predictive analytics is all about taking raw data points and figuring out patterns that make them relevant. AI is excellent at doing this. Think of it like being handed a messy pile of puzzle pieces – traditional methods try to brute force loads of data points together, while predictive AI models figure out the picture first, making the assembly way faster.
Beyond number crunching: AI can do excellent numerical data analysis, but it can also deftly handle other forms of data. Depending on the applications, it can analyze images (e.g., for early signs of equipment failure), text (e.g., to understand customer sentiment hidden in reviews), and even audio and video. This opens up new possibilities and can give businesses even deeper forecasting insights.
The self-learning factor: AI isn’t static; it improves with more real-time data, adapting to new trends and refining its predictions on the fly. Traditional data analytics tools? They need a human to manually update them. Even then, they’re slow and prone to errors.
Give me some real numbers
Predictive AI facilitates hyper-personalization, making marketers and salespeople more productive. This, in turn, saves costs and generates revenue.
For example, AI-powered predictive maintenance in manufacturing can reduce downtime by 30-50% and increase machine life by 20-40%. That translates into major cost savings and a smoother-running operation.
Don't think of AI-powered predictive analytics as an upgrade. Because it saves money, makes your team more productive, and increases bottom-line profits, it's a whole new system.
Why do businesses need AI-powered predictive analytics capabilities?
To move from guesswork to data-driven decisions.
Instead of just looking at historical data, it's about using this data to forecast what could happen next very quickly, accurately, and way in advance of the next revenue cycle.
Think of it like this:
Traditional analytics: "Last quarter, these were our best-selling products"
Predictive analytics: "Based on buying patterns, we predict these products will be hot next quarter, and these customers are most likely to buy them"
With AI and predictive analytics, companies can go from reactive (dealing with what's already happened) to proactive action. Imagine knowing in advance which customers are at high risk of leaving your service. Armed with that info, you can try to save those relationships before it's too late. That's not just good customer service; it’s protecting your bottom line.
But where will you see the impact?
The ways predictive analytics can transform a business are impressive. Here's a quick list:
Pump up your sales conversions: Knowing who's most likely to click "buy" lets you recommend the perfect offer right when they're ready. This data is usually a factor of consumer profiles that AI can draw very accurately
Pricing power: Analyze key data points on what customers are willing to pay and price your products for maximum profit. These again will be based on buying patterns and competitors’ performance.
Smart inventory moves: No more warehouses stuffed with unsold items. Predict demand accurately so you stock just what you need. Predictive AI uses sales numbers and trends to determine which products sell the fastest and when.
Laser-focused marketing: Target the right audience with the right message, saving money and getting better results.
Streamlined operations: Use AI to smooth out the kinks that cost you time and money. Generate accurate predictions for staffing needs or forecast equipment failure. This is based on historical hiring and maintenance patterns, plus a general overview of the industry norms.
Examples of businesses using predictive AI
At Multimodal, we work a lot in insurance, where predictive AI can be super useful. For example, it can help insurers assess risk more accurately. This could lead to personalized insurance policies with dynamic premiums that adjust based on real-time factors – like driving habits (tracked with sensors) or wellness data (provided by wearables).
Another key example in this space is Tesla. The company’s autonomous car technology relies on using radar, sonar, and neural networks to predict human behavior. Their massive data sets could also potentially make them a great car insurance provider, with plans catered to each driver based on their specific driving habits.
Banks like JPMorgan Chase, Wells Fargo, and Bank of America are actively using predictive analytics to personalize customer experiences, optimize risk assessment, and even streamline their internal operations.
In the healthcare arena, companies like Optum, UnitedHealth Group, and Humana rely heavily on AI-powered insights for everything from analyzing insurance claims to developing targeted wellness programs for their members.
This isn't just about pie-in-the-sky potential. We're talking about boosting sales, cutting costs, and improving business operations.
The double-edged sword of AI in business
AI is a game-changer, but it's not without its challenges – especially when it comes to how businesses interact with their customers. There's a fine line between helpful personalization and feeling creepily monitored. It's a balancing act you need to get right to reap the future outcomes of predictive AI.
When done right, AI-powered personalization can be a win-win for both businesses and customers.
One of the most visible ways AI is transforming business-customer interaction is through hyper-personalized sales and marketing. Customers love a good, super-relevant recommendation engine. Take e-commerce websites for example. Based on browsing and buying patterns, AI engines can suggest relevant products on each user’s homepage, helping them find the right product that fits their needs.
Here's the flip side: No one likes feeling like a lab rat in some company's data experiment. The line between personalization and intrusion is often blurry and different for everyone. Have you ever been shown an ad for something you just talked about near your phone? It gives some people the chills, even if there's a logical explanation.
The bottom line is that trust matters. If your customers feel like AI is making decisions without their knowledge or that their data is insecure, even the most well-intentioned company faces a backlash.
I always advise brands to stay transparent so their AI use doesn’t feel predatory. And like in any relationship, communication is key with your customers if you’re using AI to analyze their behavior and forecast future outcomes.
Collecting data for predictive model training
You have to be careful with your customers’ data. But where can you get it and how do you make your machine learning models work with it?
First, you likely already have a lot of it.
Think of all those existing data systems you use every day:
CRM (Customer Relationship Management): CRMs can be a bit of a love-hate thing, but buried in those records are insights into what your customers buy, what makes them happy, and where you’re making mistakes.
Sales figures: Don't just look at the overall numbers (though those are important). AI can find patterns, like sales exploding every spring, certain products that always seem to be bought together, or which type of customers open a savings versus a fixed deposit account.
Website analytics: AI can accurately and quickly analyze website numbers, spotting clues about what's working on your site and what's making people bounce.
Social media engagement: Social media is a window into how people feel about your brand. AI can even detect if customer sentiment is starting to turn negative.
Customer interactions: Every complaint logged, every question answered, and every claim settled is a goldmine for figuring out how to retain existing customers and acquire new ones.
The dirty work (but, super necessary): data preparation
Data isn't magically ready for AI the moment it's collected. Here's the not-so-fun part:
Fixing errors: Bad formatting, typos, and any other minor mistakes can mess up your results. Think of this as data flossing – not glamorous, but prevents major headaches down the road.
Combining datasets: Data comes from multiple systems, and in many formats. You’ll need to streamline and combine all datasets before they become good fodder for an AI engine.
Preparing data: This can get technical, but the idea is making all your data something AI can actually "digest." Here’s how that goes:
Handling Missing Values: Empty cells or missing data can throw off AI models. You’d need outlier treatment, data normalization and scaling, and standardization.
Feature Selection: Identifying the most important features/variables that have the most predictive power. Techniques include filter methods, wrapper methods, embedded methods, and feature extraction.
Data Encoding: Many machine learning algorithms need numerical data. This step converts categorical data (text, labels) into numbers.
Data Splitting: Divide data into sets for training, validation, and testing.
Beyond current data – the power of enrichment
Using only your own, proprietary data can lead to seeing only one piece of the puzzle. That's where third-party data comes in:
Open data sources: Governments, researchers and consulting companies have free research which is very useful.
Market trend analysis: AI can track news in real time, giving you insight into what direction your industry is moving in.
Competitor monitoring: What they're doing well can inspire you, what they're doing poorly could be your competitive advantage. AI can pick out these pieces and connect them to your business seamlessly.
Start with what you've got, but don't feel limited. The right mix of internal and external data is like rocket fuel for your AI predictions.
How does AI actually learn about your customers?
The answer lies in the digital trail your customers leave behind. AI isn't some all-seeing, all-knowing entity that understands us better than we understand ourselves. At its core, AI systems are just really, really good at finding patterns in huge amounts of data. The more raw data they have, the better they learn.
Here's a breakdown of how AI can analyze some types of data to predict our preferences:
Browsing history: The websites you visit, the time you spend on them, and similar metrics all reveal your interests.
Purchasing patterns: Past buys strongly indicate what you'll want in the future. It's why those "You Might Also Like" suggestions sometimes feel eerily accurate.
Demographic information: Age, gender, income bracket, and similar data can help build a broad profile that companies use for targeted advertising. I’ve found that demographic information can be especially helpful in industries like insurance and banking.
Your business likely already has access to all this data. When you start using AI to analyze it, your predictive metrics will look better. Consequently, your recommendation engines and offers will become more tailored to each customer very quickly, and change together with your customers’ preferences. To make sure your customers don’t feel weirded out by this, being transparent is key.
No, your customers won’t run away from AI
Businesses that build loyalty understand that customers respond to a clear explanation of how AI is being used in a way that benefits them. It's a balancing act. I personally love Spotify’s approach to AI.
Spotify is pretty upfront about using AI to track my listening patterns and suggest songs I’d like – and that builds trust. I am not concerned about my privacy because the company is not trying to hide its AI use from me as a consumer, and that’s exactly what you should strive to do as well.
AI isn't just about companies squeezing more money out of customers. When done thoughtfully, it can lead to scenarios where businesses and their customers both benefit.
Better products, better prices
Sometimes, products hit store shelves and totally flop. Why? Because companies didn't quite understand what people truly wanted, or misjudged how much they'd be willing to pay. AI can change this by:
Spotting trends early: By analyzing social media buzz, search patterns, and even niche forum discussions, AI can spot emerging trends quickly. This lets companies develop products that are a hit from day one.
Understanding what customers value: A great product isn't just about features, it's about solving real problems. AI can analyze customer reviews and support tickets to pinpoint common pain points that could be addressed with new product development.
Additionally, predictive AI can help companies set fairer but competitive prices. By analyzing competitor prices, production costs, and market demand, AI models can help companies price products in a way that's both profitable for them and reasonable for consumers.
Again, think of Tesla here. With the amount of data they have, they could develop personalized pricing that beats most major insurers if they decide to offer insurance solutions.
Making the online world a smoother ride
Have you ever landed on a website that was so confusing that you just gave up? That's a missed sale for the business and a bad experience for you. Predictive models can help improve website UX, too.
AI-powered website optimization: Tools like Hotjar use AI to analyze how visitors interact with websites. This reveals where people get stuck, what they click on first, and how the design could be improved for a more seamless experience.
Predicting customer needs: Some companies use predictive insights to subtly personalize their website for different visitors. Imagine a healthcare provider whose homepage changes based on whether you're a new patient or someone needing help with a specific condition. It saves you time and clicking!
I’ve seen companies like Netflix gaining exponentially more success once they integrated predictive AI fully into their business. Not every company is prioritizing these things. But in my experience, the ones that do, understand that investing in AI for customer benefit is also a long-term play for their success.
Predictive AI out in the wild
Amazon uses AI to analyze individual browsing and purchase history. This lets them tailor offers, product recommendations, and even website content uniquely for each shopper. The result? It’s constantly growing in terms of retail revenue and customer retention rates.
Another example I often give to our banking customers comes from JPMorgan Chase. They use machine learning algorithms to analyze massive amounts of transaction data for unusual patterns and detect fraud early on. They work in a highly regulated industry, so they don’t use a generic predictive AI tool. We’ll get to this bit later, though.
I also appreciate Optum, a major healthcare provider, in this aspect. They use AI to analyze patient records and identify individuals at high risk of developing chronic conditions. This lets them intervene with preventative care plans, improving health outcomes and reducing long-term costs.
Competitive advantage isn't just a buzzword
Predictive AI makes it real. Here’s how:
Better decisions, faster: Traditional analytics rely heavily on looking backward. AI lets you make proactive moves based on where the market is headed. In a world obsessed with speed, this is a major edge.
Efficiency = Higher profits: AI streamlines processes across the board. Think about JP Morgan’s AI fraud detection system flagging suspicious activity, saving millions in losses. That's money directly back into their bottom line, giving them more to invest in innovation.
Happier customers = loyal customers: When AI delivers personalized experiences and solves customer problems before they escalate, it builds a kind of trust that's hard to beat. That's the kind of thing your competitors can't copy-paste.
Here’s your predictive AI roadmap
First, you don’t have to be a huge tech company with unlimited resources. The good news is, that there's a wide range of AI tools and solutions out there. There are also external AI partners you can work with so you don’t have to spend thousands building your team of engineers.
Start with pilot projects: Don't try to boil the ocean. Define a specific, measurable problem that AI could help solve. Is it churn prediction in your insurance business? Optimizing loan approval processes at your bank? Pick one area, implement a focused AI solution, and track the results closely. This is how you prove the value of AI and get buy-in for bigger initiatives down the road.
Successful AI projects almost always involve a team effort. Your sales and marketing teams understand the customer journey. They know where the pain points are and where opportunities get missed. Your IT department knows the ins and outs of your data systems. They'll be crucial in ensuring data is clean, formatted correctly, and accessible to your AI models.
Data scientists or external partners will be the folks who build the models, but their work needs to be guided by the goals and insights of your broader team.
Honest data assessment: Is your data accurate, complete, and stored in a somewhat accessible way? (AI can't fix a hot mess of data, but it can help you clean it up).
Vendor research: Explore AI platforms tailored to your industry. This is highly important because what you can and should do with AI will vastly differ from what Amazon, Spotify, or Netflix do.
It's an investment, not just another bill to pay
Let's talk about the elephant in the room: cost. In our AI ROI article, we talked about how AI can unlock value for businesses, and how much it can cost to get real ROI. But the way to think about it is as an investment that fuels future growth, not just another expense to grumble about.
Think about it like this:
The cost of doing nothing: Staying stuck with outdated methods means wasted opportunities. It means falling behind competitors who are using AI to work smarter, not harder.
Small wins add up: Even an AI pilot project focused on one specific area (like better customer retention) can yield significant ROI. That early success builds momentum for wider adoption.
The future is data-driven: Companies that make embracing AI part of their core strategy are the ones who will thrive in the years to come. Those who resist? Well, they risk getting left behind.
A word of caution (because we need to be real)
I wrote about Tiktok’s manipulative AI engine a couple of years ago. Meta has also faced backlash multiple times for mishandling customer data or being overly manipulative. But when we're talking about sensitive data in highly-regulated industries like banking and healthcare, compromising on quality and ethics can have drastic consequences.
The problem with generic AI powerhouses
Companies like IBM and Amazon have built impressive AI platforms. But when it comes to industries like healthcare and finance, those generic solutions are – in most cases – simply unfit.
Data sensitivity: We work with extremely sensitive customer information when building AI agents for our finance and healthcare clients. Throwing it into a generic AI platform that's also used by who-knows-who raises major questions about privacy and compliance. Does that platform meet the strict standards your industry needs?
The nuances of regulation: Banking and healthcare shouldn’t just protect data; they must also follow complex protocols around making decisions, mitigating biases, and similar processes. Generic AI tools often lack the fine-tuning needed to make sure your models are explainable and comply with ever-changing regulations.
It's not just about tech: IBM has great data scientists, but do they really understand the intricacies of loan risk assessment or healthcare reimbursement models? Industry-specific (or, better yet, company-specific) AI solutions are built by people who live and breathe your sector, ensuring the models are rooted in your unique challenges and opportunities.
The 'Black Box' problem: Explainability is key in regulated industries. With some generic AI platforms, you feed data in, and predictions come out. But what's happening in between is a mystery. This is a big red flag in regulated industries. For things like denying a loan or flagging a medical claim as potentially fraudulent, you might need to provide authorities with the system’s reasoning behind that decision. Our customers are always concerned about being able to show why their AI model made a certain decision, both to protect their company and ensure fairness for customers. Again, here’s when I emphasize transparency and explainability.
Investing in industry- or company-specific AI solutions offers a level of control, transparency, and compliance that generic tools simply can't compete with.
I also host an AI podcast and content series called “Pioneers.” This series takes you on an enthralling journey into the minds of AI visionaries, founders, and CEOs who are at the forefront of innovation through AI in their organizations.
To learn more, please visit Pioneers on Beehiiv.
Dive right in
The potential for AI to improve business operations and create better customer experiences is huge. But we have to get this right. That means demanding transparency from companies and insisting on ethical AI practices at every step.
The best way to start is by exploring solutions specifically tailored to your industry. There are tools out there to help your business make smarter decisions, build stronger customer relationships, and gain that competitive edge.
I’ll be back soon to discuss how AI can solve industry-specific issues. Until then, reach out if you want to chat more. See you in two weeks,
Ankur A. Patel.