- Bid optimization, targeting, segmentation, automation, and audience extension in advertising are enabled by AI.
- The task of AI is to process a tremendous amount of information and interpret it in a digestible way for an ad stack to act upon.
- Obstacles of AI implementation are related to the lack of expertise and high implementation costs.
- Thanks to AI in programmatic, ad purchasing has become faster, cheaper, and more efficient.
- CEO of SmartyAds shares insights and details on AI advertising, its pros, and how businesses can overcome personalization challenges.
Take a look around – we have uber-precise AI-based advertising mechanisms in place: smart playlists, content recommendation tools on YouTube and Netflix, chatbots instead of consultants, and megastores without cashiers. We are already living in the era of AI. Still, for the most part, the penetration of AI in advertising is so delicate that it goes mostly unnoticed.
For almost a decade, AI advertising has been helping marketers deal with daily grinds, like segmentation, automation, and interpretation of the big data into the customer’s intent.
Today, AI technology in advertising is wrapped around automation, personalization, segmentation, and other functions that ad stacks are impossible to imagine without.
So, why do ad stacks need these functions in the first place? The answer is because of the data.
If in the era of manual ad placements, professionals complained about severe data scarcity, today the amounts of data are so overwhelming they barely can be processed without technology.
When the ad tech market is oversaturated, data-driven AI solutions appear every now and then, so it is important to understand how important this technology can be for your stack in order to make the right decisions and justified investments.
The importance of AI in ad tech stack and how it’s different from other technologies
Using AI in advertising helps to identify and recognize behavioral patterns by analyzing huge arrays of big data points gathered over a long period of time.
Almost all sources of data employed for obtaining customer insights (directly delivered personal info, social media, online, and offline buying habits) can be used in order to predict future behavior and purchasing inclinations.
This way, ad stacks create predictive models in order to determine the patterns of user behavior. These patterns, in turn, make it possible to deliver product recommendations suitable for a specific person, time, and context.
In simple words, the greatest ability of AI in ad tech stack is that it can look at tremendous amounts of detached user information and interpret it in a human-like way.
Unlike traditional computing systems, AI’s cognitive function brings in an understanding of who is the target audience, what they like or may not like, what purchasing choice they are most likely to make, and what device they will most probably use for this.
This way AI can perform a lot of tasks in ad stacks, from workflow automation to targeting the personalization of advertising messages and their delivery.
What’s the role of other technologies like machine learning, neural networks, and deep learning then?
1. Machine learning
Machine learning is also a branch of AI. ML as a rule works side-by-side with AI and performs the function of experiential learning.
It gathers data, analyzes it, and learns over time to recognize new patterns in order to be able to instruct the system on how to optimize ad campaigns in the future.
This way, for instance, ML can analyze the bidding patterns in a variety of auction types and conditions and rely on this information in order to develop the best bidding strategy.
2. Neural networks
Neural networks are built upon mathematical models that reproduce the work of the human brain in order to replicate artificial intelligence.
Their algorithms are based on tightly interrelated nodes that work somehow similarly to human neurons which are oriented mostly at pattern recognition.
In ad stacks, the primary goal of neural networks is to process as much data as possible to get precise and most valuable outputs from gathered data.
3. Deep learning
Deep learning is a subset of machine learning which applies data processing capacities of neural networks to better analyze data in different contexts, recognize patterns, and make these patterns applicable to categories normally used for classification.
Advertising giants like Google and Facebook are known for their deep learning implementation for predictive modeling.
There are major opportunities that these algorithms create for ad stacks when it comes to particular functions: geo analysis, segmentation, insights into bid floors and timeouts optimization, audience extension, and the list goes on.
These technologies are closely interrelated and help the advertising stack to function smoothly and effectively.
Three pros that make marketers invest in AI advertising stacks
In the near future, advertising will be completely redefined. The situation when a user sees an abundance of banners with completely irrelevant products will become extinct.
Personalized marketing messages in digital advertising give a client a sense of value and fosters user loyalty, which translates not only in increased CTR and conversions but also in better engagement and user retention.
The following statistics can fully demonstrate this trend:
- 88% of marketers in the U.S. claim that personalization has a measurable impact on their advertising outcomes
- 40% of company executives in ecommerce report that personalization directly affects their sales and company revenues
- With personalized marketing, brands normally see at least a 20% uplift in sales
- 80% of users report being more inclined to make a purchase when the ad is personalized
Marketing and advertising professionals that implement AI achieve much better ad campaign outcomes, which, in turn, translates into higher incomes that ad stack generates during the period. The following three reasons give a short explanation of how AI contributes to this:
1. Increasing conversions
CRM systems with in-built AI, for instance, can automatically determine the likelihood of a conversion, suggest which type of service or product the client will buy and what type of the message will be most relevant for the client.
With this, sales managers can stay much more focused regarding their efforts and where to apply them.
2. Finding the best channels of customer communication
AI may also help marketers to determine the relationship among the number interactions and channels (email, call, push message) in order to define the probability of a transaction for different segments of clients.
3. Enabling advertising personalization
Advertising and AI applied together can easily boost personalization outcomes. This way, retailers can breathe new life into loyalty programs, which for some reason have become ineffective.
Since such programs are primarily based on discounts and hot offers, they can get a second life; e.g. with relevant location-based geomarketing or geofencing ad campaigns.
This is why for the marketers, brands, and advertisers investing money in AI-based stacks appears to be the number one commercial opportunity.
In fact, a survey from Deloitte reveals that over 82% of medium to big-sized companies in the UK are embracing AI opportunities.
At the same time, barely 15% of these companies know how to actually master the full scope of AI capabilities (24% in the U.S.,22% in Germany, 19% in Canada, and 17% in France).
Since advertising technologies are heavily data-reliant, investing in comprehensive AI-based ad stacks could be another opportunity looming ahead.
While Facebook and Google own most of the user data, they are probably the only companies that use AI capacities to the fullest.
Now businesses that invest in AI-based ad stacks will be able to create personalized products and services which easily hook new customers in with individual offers.
The cons of using AI in advertising and how to overcome them
If the benefits of AI-based advertising come with workflow automation, segmentation, and message personalization capabilities, the cons of using AI in advertising may not be so obvious.
1. Lack of experience
Inertia and a lack of technical expertise are the main reasons why AI advertising stack is a far-fetched dream for many companies.
In regards to this, it is imperative to employ the right people at the stage of ad stack development, in the majority of cases this constitutes inviting the data scientists, data, and software engineers in the organization.
2. Higher cost
AI-based advertising has always been more expensive compared to options that don’t have ad targeting, segmentation, and AI automation capabilities on board. Naturally, greater technological capabilities always entail an increase in cost.
Choosing between an AI advertising platform and non-AI, remember that the first one will most likely be more advanced and thus, more expensive.
The good news is that as technology matures and gains a wider market adoption, its price tends to decrease.
A good example is programmatic advertising. Based on algorithmic buying, programmatic first came on the scene with Google DoubleClick in 1996.
Later on, it turned into a fast-growing ad tech industry with plenty of solutions affordable for businesses and independent advertisers alike.
Programmatic AI-based advertising platforms make it easy to segment audiences, personalize messages, use flexible settings to configure campaign parameters, and optimize ad campaigns on the go.
Such platforms are complex AI-powered tools that take into account myriads of criteria in order to make sure that a purchased ad is right for both the targeted user and the advertiser.
Such platforms automatically buy impressions on websites on behalf of advertisers and channel them to the target audiences at the right device and time.
How programmatic AI tools drive deeper personalization
1. Dynamic creative optimization
Technology that adapts ads according to the design, color, and layout for each individual user in accordance with their taste and preferences and in real-time.
As a result, companies can develop unique design solutions that combine branding, performance, and personalization in one creative.
2. Predictive bidding
Predictive bidding helps the system to aptly evaluate the data arrays and offer the right bid at the right moment during the programmatic auction.
This trick helps to ultimately reduce the cost per ad impression. The algorithm analyzes the user’s purchasing history, along with behavioral patterns, and accurately determines which offer will most likely lead to conversion.
3. Product recommendations
This AI advertising algorithm adds recommended products to the advertising that the user sees on the page.
To determine purchasing intent, the product recommendations function takes into account user purchasing history, most popular products, and previous actions, as well as the actions of other customers who share similar characteristics.
After blending these factors, technology recommends those products that can be potentially purchased by the customer.
To sum up
The advertising industry is, to the core, built around data. That’s why there’s no better mission for AI in ad stacks than to automate processes, simplify routine tasks, reduce advertising budgets, and personalize user experience.
At the same time, implementation of small algorithms into the ad stack should not be costly or require the installation of dozens of third-party applications that only silo the advertising tools that marketers have to manage.
By investing in smart programmatic AI advertising platforms every company can maximize the potential of data with comprehensive campaign personalization, automation, and ongoing campaign optimization.
Ivan Guzenko is CEO of SmartyAds.