Consumers are bombarded with a blizzard of advertising messages every day, both online and offline. The fact that more and more people are using adblocking clearly shows that from much of this advertising is unwelcome. This is due to the fact that most of the advertising we encounter is of limited or no relevance. Here’s how marketers can increase the relevance of their ads by using Deep Learning.
In this article you will learn:
- What Deep Learning is.
- Why and how Deep Learning is used in the marketing.
- How can the usage of Deep Learning algorithms boost your marketing strategy.
- Other benefits of Deep Learning in business.
Table of Contents:
- What is Deep Learning?
- Why should Deep Learning algorithms be used in digital marketing?
- Advantages of Deep Learning in marketing
- What can you expect in the future?
What is Deep Learning?
Deep Learning is a subcategory of a much broader concept—Machine Learning. It involves the construction and use of neural networks in such a way that they mimic the human brain, i.e., they learn with each specific task, constantly optimize, and as a result generate even more impressive results.
The advantage of computers over the human mind is undoubtedly the speed and precision of operation. Deep Learning allows for huge volumes of data to be processed in a very short time and proper decisions to be made on their basis. This opens up extremely wide possibilities in many business areas, including marketing. Already today, artificial intelligence in various forms supports decision-making engines of many programs and applications, thanks to which the implementation of a marketing strategy is largely automated, and its results are usually much more satisfying.
How does Deep Learning in business use modern tools and programs that support marketing activities? Artificial intelligence is used today, for example, in:
- Automatic creation of a wide variety of content.
- Bidding for favorable advertising rates.
- Personalizing marketing activities.
- Accurately analyzing user behavior online.
- Creating advanced recommendation systems.
- Designing chatbots.
- Speech recognition and natural language processing (NLP).
The cited examples of Deep Learning in business prove that, with the help of technology, it is possible today to take marketing activities and marketing strategy to a whole new level. Artificial intelligence makes it possible to control data collected by an organization, process it in an extremely short period of time, and draw the right conclusions on its basis.
This is an opportunity not only to achieve better results but also to significantly reduce costs. Today, technology is able to replace marketers in many of their daily duties and perform their previous work much faster and more precisely. Freed from repetitive tasks, professionals can thus focus on more creative and demanding aspects of their work to make the most of the opportunities that modern technology brings.
Good to know: The Evolution of AI Applications
Why should Deep Learning algorithms be used in digital marketing?
Digital marketing has reached a point where the purpose of advertising often has the opposite desired effect on the recipient—a concept known as ad fatigue. A clear indication of this is that the use of adblocking has increased in recent years, mainly on mobile devices. At the same time, individualization has become a growing trend. Consumers want customized offers that fit their preferences. It is all about getting the right message to the right person at the right time. With the help of Deep Learning in business, your marketing strategy can improve the efficiency with which you generate customer interest and establish contact with your potential customers.
Advantages of Deep Learning in marketing
Harnessing modern technology and its use in the area of marketing brings a number of significant benefits, which translate directly into the effectiveness of the marketing strategy and, consequently, into financial results.
What is the reason for the high effectiveness of Deep Learning in marketing?
Deep Learning and real-time bidding
More than half of all marketing budgets are allocated to purchasing ad placements programmatically. This is in the context of rising ad spending in a growing ecommerce landscape. Marketing decision-makers are tasked with making their budgets go further.
The mechanism that makes all of this possible is known as real-time bidding. It’s a near-instantaneous process and happens in less than the blink of an eye between a given user landing on a particular site and an ad appearing on-screen. In the space of mere milliseconds, there’s a flurry of frenzied activity fed by advanced AI systems. These systems are weighing up how much to bid for the placement against the statistical likelihood of user engagement. The more sophisticated the technology, the better able it is to accurately assess the value of showing an ad to a specific person at a given time.
Deep Learning not only excels at this task, it also wins with its ability to personalize ads to match the user. It’s a statistical powerhouse that learns and optimizes, and it outperforms standard Machine Learning approaches.
Because of the transition from physical stores to omni-channel business models and online shopping in recent years, the ability to anticipate customer behavior in ecommerce has grown in importance. Because if you can more accurately predict customer behavior, you can sell products and services more effectively.
71% of consumers now expect a personalized experience from brands. There are benefits for consumers and brands alike—a more personalized experience provides a more comfortable and convenient purchase journey for a user. That use is likely to respond with an elevated perception of a brand and loyalty.
When Deep Learning is deployed in ecommerce, be it in retargeting, branding, or prospecting campaigns, it learns with each passing second, resulting in a faster and more precise analysis of buying potential, and can communicate with a higher degree of addressability, i.e., personalization. According to our analysis, the effectiveness of recommendations can increase by up to 41% compared to campaigns that do not use Deep Learning.
Deep Learning algorithms are not only able to ascertain which product to display to a user at a given time but also decide what kind of ad creative will best serve the purpose of generating engagement. To fully leverage this, your brand should find a provider with a wide portfolio of great-looking creatives that precisely align with your branding aesthetic.
Predicting consumer behavior
Beyond its expertise in predicting user engagement in discrete instances, Deep Learning in business can better predict sales levels for whole promo periods, seasons, and even years.
With a deeper analysis of user behavior, Deep Learning can tell brands which products are likely to be in demand in any upcoming period. This enables brands to have more efficient warehouse management—shipping high-demand products to warehouses in advance and clearing low-demand products, for example, through discount or cross-selling channels.
This is, of course, of great benefit to ecommerce brands with a high through-flow of products where warehousing space is at a premium. Think no further than sizable brands with massive product inventories, such as those found in Marketplaces, Home & Garden, and Fashion.
Advanced AI also allows brands more streamlined supply chain management—a more accurate prediction of consumer trends informs better efficiency to the entire length and breadth of the supply chain. With Deep Learning, companies can push predictive analytics to new levels, detecting potential issues before they arise. Overall, supply chain visibility can also be improved, allowing for better customer relationships and quicker delivery fulfillment, with optimized routes for cost reduction and enhanced productivity.
Exposing the hidden
Deep Learning in retargeting has not only made it possible to analyze basic user behaviors, such as which products or product categories are visited, but also other hidden data. With Deep Learning in marketing, it is possible to analyze the visit time on products and the sequence of visited subpages in a store. Using data, machines interpret exactly what users did at the store and thereby predict their actual purchase intentions. It is possible to determine which products users are most interested in and, thus, send them customized offers with which they are most likely to engage.
With all this data, the next step is deciding how to present an offer in an ad and in what order. Deep Learning algorithms analyze offers and how attractive they are from a user’s perspective. Deep Learning is much more sophisticated than classic retargeting, as the products displayed on an ad are more personalized. This approach makes it possible to implement a rule where there is otherwise no clear pattern for a particular group of users. The algorithms understand each user on a deeper level; they look for the best deals and the order in which the offers should appear on the ads for the users.
Our behavior profiles are constantly changing. Deep Learning in marketing can build a real-time behavioral profile and adjust what is presented on a banner every time an ad is displayed. Algorithms determine what should be shown on each banner, adjusting the contents based on a user’s responses to previous offers. Thanks to powerful algorithms and constant analysis, Deep Learning can rebuild user behavior profiles in real time.
What can you expect in the future?
The unceasing and dynamic development of technology, as well as great interest shown in the subject of artificial intelligence among the world’s largest corporations, allow us to assume that the possibilities of using Deep Learning in everyday marketing activities will grow from year to year. Just think of the game-changing potential of ChatGPT, which is already being put to work in marketing in areas such as sharpening messaging and gathering insights. There are also untold possibilities outside of Deep Learning in marketing in spheres such as medicine—designing new remedies and treatments—and in developing measures to limit environmental damage and counteract climate change.
Deep Learning is a wellspring of potential, being flexible enough to adjust to any change or challenge, and there’s no bigger change on the horizon for marketing than cookieless.
Deep Learning in business works perfectly wherever decisions are made on the basis of data. Therefore it is expected that the algorithms will help marketers better plan and deliver their campaigns. This is especially important in the context of the decision to retire third-party cookies by Google.
Can Deep Learning still be a superpower with cookieless?
The demise of third-party cookies will mean greater data privacy for billions of internet users, but it represents a seismic change for online advertisers. Without cookies that track user behavior across sites, the personalization of messages becomes more challenging. However, it’s a challenge that Deep Learning is uniquely able to meet and still display highly personalized ads to individuals without collecting personal data. It will remain the most powerful tool in the cookieless era, helping brands to scale campaigns with highly relevant messaging.
It can do this with a winning combination of cookieless tools that are already in place, such as contextual targeting and Google’s Protected Audience API. And the time is nigh as the deadline for cookieless depreciation—the end of 2024—looms large.
Brands should immediately seek out early adopters and testers of cookieless solutions that have a significant head start on companies that continue to rely on outdated methods.
Deep Learning in business is transforming the ecommerce landscape and is also transforming what we thought impossible. But it’s also about finding a partner who has a proven track record of demonstrating expertise in leveraging this incredible tool.
If you have any questions, comments or issues, or you’re interested in meeting with us, please get in touch.