The job of marketers is to get the right product in front of the right consumer at the moment that they’re most likely to buy that product. The ability for marketers to do just that and to drill down to more and more specific niches of customers has grown exponentially over time. AI technology is now being used to help marketers get even more specific with predictive targeting and personalization.
How Machine Learning Can Improve Advertising
The biggest capability of machine learning and AI is that it can process huge amounts of data to make predictions. This can be used in advertising and marketing to detect signals and get the right ad in front of the right person, at the right time, even without the need of personally identifiable information. With the help and support of machine learning models and AI technology, ads are getting hyper-relevant and can deliver the ROI marketers expect out of digital campaigns.
Predictive Targeting and Testing
Predictive targeting is a marketing technique that uses AI and machine learning to predict future customer decisions based on behavior patterns and historical data. The data is used to predict the probability or likelihood that a certain person will take an action, such as making a purchase, engaging with products, or converting in some other way.
Predictive targeting tools can also help brands to create better customer personas, helping them to decide who to target with which campaigns, making sure that potential customers are getting the most relevant ads possible.
AI Product Recommendations and Hyper-Relevance
One of the best and most efficient ways to move a customer through the buyer’s journey is with a product recommendation ad. But, it all comes down to how relevant your ads can be to a person at any given time. This is where an AI-powered recommendation model can help take the guesswork out of the process.
Recommendation models are usually built on known customer attributes and habits. It allows the model to then recommend products to a new customer based on the information the model knows about them. You’ll see recommendation models in action on Netflix and Amazon for shows to watch and products to buy, as well as in popular search engines and social networks that rely on ads for their revenue streams.
Advanced Recommendation Models
One of the major updates to recommendation models in the last few years is that they’re moving from using explicit feedback to implicit feedback. The earliest forms of recommendation models used explicit feedback from customers, information that customers supplied, such as their preferred product categories. Newer models are using more implicit feedback to make recommendations, looking for behavioral signals to understand intent.
Advanced recommendation models are also getting more granular and specific. Instead of just using product categories to make recommendations, they’re now using SKU numbers and getting specific as individual products.
By getting this specific with recommendations, the landscape of advertising is changing. It’s no longer about the products and product categories, it’s all about the customers and their path to purchase. Advertisers are now guessing what customers will want to buy even before a customer might know themselves.
And, recommendation models aren’t stopping here. The future of AI and machine learning models for advertising will not only use historical user data but will also incorporate a user’s reaction to the ad. Recommendations will be updated, essentially, in real-time. As you can imagine, working with this volume and diversity of data requires a data partner that understands the challenge deeply, like Appen.
Personalized Ad Targeting
People and organizations change their preferences all the time, and it’s more important than ever for advertisers to be able to detect and adapt to these changes in real time. What’s more, people are more likely to purchase if the ad is personalized to their journey, so.this means that personalized ads are no longer a luxury, they’re a requirement.
Ads can be personalized through a number of different tacts and lenses, such as:
- Individual traits
- Previous purchases
Better Brand Safety and Alignment
While the overarching goal of ad personalization and predictive targeting is to get the right advertisement in front of the right customer, that’s not the only benefit.
Using AI and machine learning for product recommendations to customers can also help your brand to build a better customer relationship and to manage brand safety and alignment. Where your ad is placed reflects on your company. Brand trust and favorability can drop if your ad is seen next to biased, negative, or non-factual content.
The context surrounding your ad is a critical component of your brand identity and alignment. You can use AI tools and machine learning algorithms to make sure your ads are only showing up in places that align with your brand and that are safe.
Make Better Advertising Decisions
One of the biggest benefits that AI and machine learning can have on advertising is the ability to make better brand and advertising decisions. With AI, your brand is making advertising decisions based on data.
Instead of guessing who will react to which ads and when you’ve got the data to back you up. You’ve also got machine learning helping you to decide where to place ads and ensure that you’re maintaining a safe and consistent brand image. We all carry biases with us that can affect our analysis of a certain situation. Good machine learning models don’t have this problem. Your decisions will be based on data, not hunches.
Machine Learning and Predictive Targeting in Action
Hearing about the benefits of machine learning on advertising is one thing. Then, there’s reading about how a real-life company has implemented these tools to solve problems and improve their business. Zefr and Gumgum are two businesses that have done just that.
Zefr is a platform company that helps other companies to place their ads in contextual, relevant environments. Zefr helps other companies keep their advertising and brand safe. They help companies to place their ads in contextually relevant spaces that will also delight customers.
The challenge: Zefr needed to be able to scale their data labeling and didn’t have enough internal resources. Appen enabled Zefr to accurately and efficiently label a high volume of data with its crowdsourcing solution. At first, Zefr worked with Appen to get more data so they could help their customers place ads in brand-safe spaces. But, as access to large amounts of data become more and more available to them, Zefr expanded, giving their customers the ability to place ads in customer-specific, nuanced spaces.
With the help and support of the Appen Data Annotation Platform, Zefr is able to provide their customers with quantitative information on where to place their ads and provide a consistent experience without sacrificing quality.
GumGum is a contextual-first advertising technology company that captures people’s digital attention, without the use of personal data. GumGum provides the platform that delivers the next generation of contextual intelligence, industry leading dynamic ad creatives, and the ability to measure and optimize advertising campaigns to better understand a consumer’s mindset that captures attention and drive action and outcomes.
The challenge: To keep up with labeling the increasing volume of training and testing data, GumGum needed to partner with a vendor that could quickly turn around annotation projects and scale up when needed.
GumGum’s internal team needed a faster way to label data, giving each image and text a classification. GumGum works with Appen to speed up their labeling process. By partnering with Appen, GumGum can more efficiently create high-quality data sets and train its machine learning algorithms. GumGum can rely on Appen to provide large amounts of labeled data to continuously improve existing models and create new sophisticated models which leads to growing their business and providing the best product possible to their customers.
Your AI Advertising is Only As Good As Your Data
When it’s used correctly, machine learning in advertising benefits everyone. Customers get ads that are relevant to them and advertisers get to sell their products. One caveat to always keep in mind when working with machine learning and AI: your output will only be as good as your data.
When you’re working with high volumes of data and new technology, it’s easy to let low-quality data and biases slip through the cracks. If you’re going to implement machine learning models and predictive targeting into your advertising plan, you must seek out high-quality training data sets to make sure you get the most out of your investment.
What Can We Do to Help
Our data annotation experience spans over 25 years, providing our expertise in training data for countless projects on a global scale. By combining our human-assisted approach with state-of-the art technology platforms, we give you the high-quality training data you need.
Our text annotation, image annotation, audio annotation, and video annotation will give you the confidence to deploy your AI and ML models at scale.
Interested in learning more about our data annotation services for advertising use cases? Contact us today and one of our team members will reach out to you as soon as possible.