Artificial Intelligence (AI) has been in the limelight lately as many companies and brands Zara and H&M incorporate AI in their business model. As a marketer, you may wonder whether this is a cause for concern. Is AI going to take our jobs? In fact, AI can actually make marketing easier and more efficient for marketers through deep learning technology.
But what is deep learning? How does this work? And how can it be applied to marketing and sales in your company? Here’s everything marketers need to know about deep learning and its helpful role in the marketing industry.
What is Deep Learning in Artificial Intelligence?
machine learning vs deep learning
Example of Deep Learning in Marketing and Advertising
How Marketers Can Use Deep Learning
Adopting Deep Learning in Marketing
What is Deep Learning in Artificial Intelligence?
Deep learning is a subset of machine learning and a discipline within AI that uses algorithms that mimic the human brain. Deep learning algorithms use neural networks to learn a specific task. Neural networks consist of interconnected neurons that process data in both the human brain and the computer.
Similar to how humans learn from experience, deep learning algorithms perform a task repeatedly, making adjustments each time to improve the results. “Deep learning” refers to the massive (deep) layers of neural networks that enable learning.
machine learning vs deep learning
Deep learning is a type of machine learning. Machine learning means that computers learn from data by using algorithms to think and act without being programmed – in other words, without human intervention. As mentioned earlier, deep learning is about computers learning to think using structures modeled after the human brain.
Machine learning also involves less computing power, while deep learning requires less human intervention.
Example of Deep Learning in Marketing and Advertising
Let’s say we’re an online car dealership, and we want to use real-time bidding (RTB) to buy ad space for our product on other websites for retargeting purposes.
RTB is an automated process that takes place in less than 100 milliseconds. When a user visits a website, the advertiser is alerted, and a series of actions determine whether the advertiser bids for an ad display.
At RTB, we use software to decide whether we want to bid for a particular ad – the software will decide by estimating how likely a website visitor is to buy one of our products. We call this the “propensity to buy”.
In this example, we will use deep learning to make this prediction. This means that our RTB software will use neural networks to predict the propensity to buy.
Neural networks inside our RTB software consist of neurons and the connections between them. The neural network in the above image only has a handful of neurons.
In this scenario, we want to find out whether a certain website visitor is likely to buy a car and whether we should pay for any advertising to target the visitor. The result will depend on the interests and actions of the website visitor.
To predict purchase propensity, we first select several “features” that are important to define this person’s digital behavior. Those features would include which of the following four web pages were visited:
- Price determination.
- car configurator.
- Specifications.
- financing.
Those features will affect the output of our neural network and our inference. That output can have one of two values:
- The website visitor is interested in the product or “ready to buy”. Conclusion: we should display an ad.
- The website visitor is not interested in the product or is “not ready.” Conclusion: don’t show any ads.
For each input, we use either a “0” or a “1”.
“1” means the user visited the webpage. The neurons in the middle will combine the values of their connected neurons using weights – which means they define the importance of each visited webpage.
This process continues from left to right until we reach the “output” neurons – “Ready to buy” or “Not ready” as per our earlier list.
The higher the value of the output, the more likely this output is to be correct. —or more accurately predict network user behavior.
In this example, a website visitor viewed the pricing and car configurator pages, but skipped the specifications and financing. Using the numerical system above, we get a “Score” of 0.7, which means there is a 70% chance that this user is “Ready to Buy” our product.
So, if we look at our original formula, that score indicates the conclusion that we should buy RTB ad placements.
neural network training
Training a neural network means feeding the network the data it needs to generate results. The challenge is to develop the right “weighting” factors for all the connections inside the neural network, which is why it has to undergo training.
In our car dealership example, we will feed the neural network data from many website visitors. The data will include visitor characteristics such as which web pages users have visited. The data will also include indicators of their final purchase decisions by us, labeled as “yes” or “no”.
The neural network processes all of this data, adjusting the weights of each neuron until the neural network makes an appropriate calculation for each individual within the training data. Once that step is complete, the weights are fixed, and the neural network can more accurately predict results for new website visitors.
How Marketers Can Use Deep Learning
“Machine learning can be used for efficiency or optimization gains,” says Jim Lesinskico-author of The AI Marketing Canvas: A Five-Step Roadmap for Implementing Artificial Intelligence in Marketingin an interview with Kellogg Insight.
“So, for example, any rote reporting can be automated and done more efficiently. Those full-time employees can then be redeployed and redeployed to other strategic growth projects.” can go,” he said.
But more importantly, says Lesinski, AI and deep learning have the potential to accelerate growth.
“More and more, CEOs, boards and marketing departments are looking to marketing as the main growth engine, charged with making informed-by-data forecasts or projections to find the optimal combination of the right product at the right price.” Promoted in the right way to the right people through the right medium,” he said.
“Big data plus machine learning can, in many cases, make those predictions and drive development better than humans without data or humans assisted only by data,” explained Lesinski.
Here are some ways marketers can use deep learning to drive growth.
Division
Deep learning models are able to find patterns in data making them excellent for advanced segmentation. This allows marketers to easily and quickly identify a target audience for a campaign while machines use past behaviors to predict potential leads.
The machines can also use neural networks and data to identify which customers are on the verge of leaving – allowing marketers to act quickly. Ultimately, AI takes the guesswork out of segmentation, allowing marketers to focus their efforts elsewhere.
Our HubSpot AI, for example, makes segmentation easy through our automated email data capture feature. This feature allows users to automatically capture important contact information such as names, job titles, phone numbers and addresses from leads and prospects. This feature makes segmentation, routing and reporting quick and easy for marketers.
over privatization
A recent McKinsey study shows that 71% of consumers expect companies to have personal interactions, and 76% are disappointed when they don’t. While personalization is important for customer experience, it is difficult to execute when there is so much information to analyze.
However, deep learning can be used to develop personalization engines that can help marketers streamline the process of delivering hyper-personalized content. Examples of hyper-personalized content include websites that display content depending on who is browsing or push notifications for customers who leave without making a purchase.
Hyper-personalization may also extend to communication features such as live chat, and deep learning may make it easier to collect information from these live chats. Our live chat name recognition ai, For example, it can collect valuable contact information (such as names) and update it without integrating anything into HubSpot CRM.
predicting consumer behavior
Deep learning also helps marketers predict what they will do next by tracking how customers move through your website and how often they make purchases. In doing so, AI can tell companies which products and services are in demand and should be the focus of upcoming campaigns.
Adopting Deep Learning in Marketing
Although deep learning and AI may sound intimidating, it is actually another tool that marketers can take advantage of to streamline processes and fuel their company’s growth. Marketers can integrate deep learning and AI into many aspects of digital marketing and sales automation. So, don’t fear the machine – embrace it!
