AI-Powered Ad Targeting and Personalization

Artificial intelligence (AI) is transforming the digital advertising landscape, taking the complexity and scope of marketing campaigns to new levels. The term ‘AI’ covers a variety of techniques such as machine learning, deep learning, and natural language processing. These techniques are becoming increasingly commonplace in marketing teams to scale advertising efforts. With AI’s role in data-driven decision-making growing, here are the core ways it can enhance ad targeting and personalization.

Five ways that AI is improving marketing effectiveness

Until recently, digital advertising largely based its targeting efforts on customer demographics, interests, and behavior. However, as focus on data privacy continues to grow, behavioral tracking and targeting are slowly but surely on the way out. Stepping into its place is artificial intelligence, which has grown in sophistication and scope and can transform the capabilities of digital ad targeting and personalization.

1. Consider a wider range of factors

Successful return on ad spend (ROAS) is all about delivering the right message at the right time on the right platform – and this is different for each individual. While the use of customer demographics and interests remains important to achieve high ROAS, AI is able to consider a much broader range of factors.

AI can help increase the relevancy of ads by considering the context – dynamically matching the ad content based on the webpage the customer is viewing. It can factor in possible intent to make advertising more proactive – offering product or service solutions that customers may not know about. And it can even respond to mood cues – adapting ads depending on the customer’s emotional state. Optimizing ad relevancy is key to increasing ROAS.

2. Uncover greater insight from less (high-quality) data

One of the key challenges that many brands face when delivering data-driven marketing is the data itself. Just like humans, AI can’t make the right decisions if it doesn’t have the right data to work with. And if you feed it poor-quality data, you will end up with poor-quality decision-making which can be actively harmful to marketing efforts.

AI and machine learning can help by working with smaller data sets, using techniques such as synthetic data, to great effect. Historically, if a brand didn’t have enough first-party data, it would have to rely on purchasing poor-quality third-party data sets to bolster their marketing strategy. As third-party data increasingly becomes a thing of the past, the sophistication of AI can leverage greater insight from smaller, high-quality data.

3. Predictive modeling

AI-assisted predictive modeling can hugely improve customer acquisition efforts. Traditional interest targeting can be quite exclusionary as people will only be targeted if they match all the set criteria. AI can not only consider a wider range of factors but also include those that match most, if not all, the interest targeting. It can also match the creative with the person depending on the strongest interests.

The other key way that predictive analytics is benefiting marketing teams is by predicting campaign outcomes at the concept stage. Using historical data, advertising teams can identify opportunities they may have otherwise missed and essentially ‘test’ the effectiveness of campaigns before they are published.

4. Improve forecasting capabilities

Forecasting is the process of predicting future customer journey steps based on past buying behavior. To give a basic example: if a customer has just booked flights to Paris, they may now be interested in looking at Parisian hotels. Brands can then work on cross-selling product or service opportunities that are relevant and that customers will welcome. AI can expand on this by analyzing and predicting the next steps in the customer journey that are not as obviously linked.

Having this knowledge helps marketers proactively add value for the customer by optimizing individual customer journeys and supporting their next actions. It’s also possible to make better sales forecasts to improve budget allocation and marketing return on investment (ROI).

5. Scale conversational marketing

Conversational marketing is an important element of personalization, providing dialogue-driven, interactive experiences that engage customers. This is traditionally a labor-intensive process but with the recent advancements in conversational AI, brands can rapidly scale the benefits of this one-to-one style of marketing.

Machine learning chatbots and virtual assistants now use natural language processing to converse with clients. While a human touch will always be required along the way, this is an effective method of engaging customers directly to understand their needs and offer personalized solutions quickly. As the complexity of AI’s capabilities continues to progress at pace, it’s increasingly possible to gain unique customer insights and add value.

The Round-Up: Harness the opportunities in AI

As artificial intelligence advances, marketers will be able to access more refined targeting models and greater personalization abilities. If advertisers harness AI successfully, they will quickly find improved outcomes for both marketing teams and customers. Marketers will be able to find and engage more of their highest value customers. Customers will receive increasingly personalized experiences on their terms – a mutually beneficial value exchange.