Neural Networks in AI: Contextual Personalization for Enhanced Business Value

Neurons

Artificial intelligence grows incredibly more sophisticated every year, and one of the most interesting developments is Neural Network Intelligence. If you thought AI has already turned a corner on mimicking human intelligence, Artificial Neural Networks (or ANN’s) might soon make this happen faster. The point is to imitate more rational thinking and deductive reasoning capabilities.

Up until recently, trying to replicate complex functions of a human brain wasn’t possible in AI programs. Things have started to advance quickly, and it’s time to learn about what this means in AI to enhance your business’s value.

In many cases, it can lead to more personalized experiences for your customers to help them and help you make smarter business decisions.

Overall, this progresses your ability to streamline your business’s commerce structure, hence leading to improved business aspects that bring higher revenue.

Before you get there, though, you need to learn about the technological and scientific aspects behind Neural Network Intelligence. It look more and more like a human, including learning through continued user interactions. In this case, it works similar to machine learning where it builds up superior intelligence over time.

Let’s look at how neural networks work and how you can apply this to bring more contextual personalization to customer experiences.

The Science Behind Artificial Neural Networks

When you delve into the science behind “ANNs”, the intention is to recreate brain connections using silicon and wires. Thanks to new advancements, AI recently transformed enough to build something resembling neurons and dendrites.

This occurs by creating multiple nodes, mimicking how neurons work. Just like neuron links, nodes have output as well, otherwise known as node values. Each of these connections have a weight, or an integer number controlling the signal. The weight in each connection can become adjusted based on the node output’s quality.

The topology behind this fall into two basic categories: FreeForward and FeedBack. For the former, it’s used strictly for pattern generation, recognition, or classification. In the latter, you’re creating feedback loops. In other words, it’s where you get into neural patterns to help you make better business decisions.

Since you’re bringing machine learning into this concept, you’ll frequently see neural networks use several different learning strategies. Supervised learning is more for pattern recognition, but unsupervised learning uses clusters to help find hidden patterns.

Reinforcement learning goes on observation, and that’s where Neural Networks truly shine to change how you create personalized experiences.

Processing Information in Real-Time

You’ve likely read a lot about real-time tools and how incredibly useful they are to make faster business decisions. AI now plays a major part in this thanks to Neural Networks. The latter uses human brain functions to learn through processing information in real-time so it becomes “smarter” with more user interaction.

This continues to improve and adjusts to any changes based on what a user prefers. For instance, if a user has specific preferences, the AI program is going to alter itself to suit a customer’s buying habits and whims. Any volatile behavior allows adjustments based on sudden changes in customer preferences.

What this does is bring recommendations on how you should approach communication with your customers. In metaphorical terms, it’s AI acting as an all-thinking oracle giving real-time results on how to personalize the marketing and buying experience.

In all, this replicates the feel of customers interacting with a well-trained sales associate. Instead, it’s done entirely online to give a customer the ultimate buying experience tailored just for them.

The problem is, many companies continue to use outdated forms of AI that don’t completely look at the customer as an individual.

AI Platforms Looking at Population and Probability

To show how fast AI changes, many businesses still use an older version of AI using recommendations via study of populations and probability. A couple of years ago, this was the best choice to personalize customer relationships. While better than no personalization at all, it still didn’t dig deep enough into analyzing individual buyers.

The focus was more on past behaviors as a whole, which was a good introduction for what AI could do for businesses. Also going by probability, it only gave a partial picture of what a customer might or might not do.

Having AI think like a human brain allows it to think more abstractly and fully understand consumer complexity. No one person is alike, and each customer is going to have their own pain points to integrate into your personal approaches.

Another weakness of older AI is it didn’t effectively accommodate new product lines in your business. Cutting this out of the recommendation schema created mass blind spots to product catalog performance. The only solution was to add it manually, and this led to downtime and lost revenue.

The Business Value Impact of Neural Networks in AI

You’ll find significant evidence showing personal one-on-one experiences are a vital part of today’s commerce structure. Regardless, many marketing analysts note that personalizing experiences can backfire if you don’t make it relevant to a customer’s life.

This is where Neural Network AI is going to help bring major business value by further understanding customer likes, dislikes, and intentions.

In the end, you’ll be able to increase more sales per customer, increase customer retention, create more loyalty, help your shopping cart conversions, and improve customer retention value.

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