The stats are in: 15.9% of companies have increased their marketing budgets in the second quarter of 2024, up from 9.4% in the first quarter. This marks the highest level of marketing budget growth in a decade.
So, what’s fueling this shift?
One game-changing technology leading the charge is deep learning.
So, this begs the question: What is deep learning, and how can it supercharge your business’s conversion rate optimization (CRO)?
In this beginner’s guide, we’ll break down the basics of deep learning, show you how it works, and reveal how companies like Quin AI use it to unlock extraordinary results.
Ready to dive in? Let's go!
In a nutshell, deep learning is a subset of machine learning. This helps computers learn in a way that's similar to the workings of the human brain.
It’s like teaching a computer to think by giving it lots of information to look at to find patterns and improve over time.
It uses a system of artificial neural networks - layers of tiny, connected "neurons" that work together to make sense of information and learn from it, just like we do when we learn from experience.
Think of it like this: when you see a cat for the first time, your brain recognizes the features (whiskers, fur, four legs, the way it looks like it might pounce and attack your ankles, etc.) and categorizes it as a cat.
Similarly, deep learning allows computers to automatically identify patterns in data, whether it's text, images, or user behavior on a website.
Needless to say, this enables businesses to unlock unprecedented insights from data, which can be crucial for conversion rate optimization.
How artificial neural networks work
In simple terms, an artificial neural network consists of:
1. Artificial neurons: These are like tiny units that each make a decision about the input they receive, such as recognizing a pattern or feature.
2. Layers: Neurons are organized into layers - input, hidden, and output layers. Information flows through these layers, with each learning increasingly complex patterns.
a. Input layer: These are the first neurons to receive the raw information (like an image or text).
b. Hidden layer (like recurrent and/or convolutional layers): These are the neurons in the middle. They don't see the whole picture but work behind the scenes to figure out patterns or details from the information they get.
c. Output layer: This final team of neurons puts everything together and gives the final answer or prediction (for example, recognizing an object in a picture).
3. Training: Neural networks gain insights through a procedure known as training, where they adjust their internal parameters based on examples and feedback. Over time, they get better at making more accurate predictions or decisions.
By learning from vast amounts of data, these networks can perform tasks that were once impossible for machines, like predicting customer behavior or personalizing user experiences. This leads us nicely to our next point...
The role of deep learning in personalization and customer experience
Personalized customer experiences are no longer a bonus; they're expected:
- 71% expect companies to deliver personalized interactions
- 76% of customers get frustrated when they don't receive a personalized experience
- 66% of shoppers expect companies to understand their unique needs and expectations
- 52% of customers expect all their offers to be personalized
Fortunately, this is where deep learning shines.
Deep learning empowers businesses to create hyper-personalized customer experiences by understanding each customer's preferences and behavior in real-time.
This means the products you see, the content you read, and even the offers you get can all be specifically tailored to resonate with each customer.
At Quin AI, our platform uses innovative technology to make this happen. By analyzing, learning from, and predicting each customer's behavior in real time, we help companies pinpoint what their customers want.
This allows enterprises to leverage Quin AI's deep learning models to personalize user experiences on the fly. Our models continuously learn and adapt to changing behaviors, allowing us to make real-time updates to recommendations, offers, and messaging, boosting conversion rates.
At this point, it's also worth noting that by analyzing vast amounts of data from your websites and apps, Quin AI can identify emerging trends, equipping enterprises to stay ahead of the curve and make strategic decisions that drive revenue.
Overcoming challenges with Quin AI
While deep learning is powerful, some businesses may hesitate to adopt it. So, below, we've listed a few of the common challenges companies have when leveraging deep learning and how Quin AI helps overcome these hurdles. Let's take a look:
1. Complexity and expertise
Complexity is a significant concern for product and technology leaders. In fact, one study shows that the training aspect of deep learning sometimes requires hundreds or even thousands of special computers called GPUs working together for weeks to get the job done. That's a lot of computing power!
As such, building and training deep learning models can require specialized knowledge and resources that many companies simply lack.
The solution: Quin AI simplifies this by offering a no-code AI platform, meaning businesses don’t need a team of data scientists or special computers to leverage the power of deep learning. Quin AI is explicitly designed to be user-friendly, allowing companies to easily integrate deep learning into their CRO strategies. All they have to do is copy and paste a simple line of code to hit the ground running!
2. Model interpretability
Another issue for many is that deep learning models can sometimes act like "black boxes," where it's unclear how decisions are made. In industries like healthcare, finance, and law, where transparency is vital, this lack of interpretability can be a major drawback.
The solution: Quin AI addresses this by providing explainable AI tools that allow users to see how and why decisions are made. Our platform ensures the logic behind our recommendations and predictions is clear, giving businesses the transparency they need to remain compliant.
3. Data privacy
Managing large datasets, especially those containing personal or sensitive information, raises significant concerns about data privacy. This is particularly relevant given the increasingly strict regulations surrounding this domain.
Unfortunately, we live in a world where data breaches and the misuse of personal information are all too common, making these concerns all the more pressing.
The solution: Quin AI is built with privacy in mind, relying only on first-party data to drive insights. This means we don't use identifiable data. Instead, we focus on the shopper's actions - where they click, how they scroll, what website elements they interact with, etc. This empowers businesses to unlock the power of deep learning without compromising customer privacy or regulatory compliance.
Example: How Quin AI achieves success
To better illustrate Quin AI’s deep learning algorithms in action, here’s a quick example of how our platform works in real-life:
Akbank leveraged Quin AI to predict their website visitors' interest in their credit cards and engage visitors who were likely to abandon the site by reminding them of the benefits of Akbanks's cards. This proactive approach doubled the average number of form credit card applications, with over 45% converting into credit card sales.
Unlock the power of deep learning for your business
Deep learning is no longer reserved for tech giants. With Quin AI’s intuitive platform, enterprises can easily leverage the power of deep learning to get granular insights into their customers and automatically provide shoppers with personalized experiences.
The result? A boost in engagement and overall revenue, as demonstrated by the above example, where Akbank increased its sales by 14%.
Ready to transform your conversion rate optimization? Schedule a call with Quin AI’s expert team today and discover how our cutting-edge solutions can give your business a competitive edge!
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