Gain Quality Leads That Won’t Break Your Budget With Artificial Intelligence

Cost per lead (CPL) has long been considered the gold standard in marketing – it’s hardly surprising, as 63 percent of businesses cite generating traffic and leads as their biggest marketing challenge.

However, while CPL can be a reliable metric, it does have one rather large omission: it doesn’t consider the quality of the leads. A low CPL is desirable, but it shouldn’t come at the cost of high-quality leads – this will result in a low conversion rate, and fewer sales for the brand. No one wants that.

The good news is that a low CPL doesn’t have to mean low-quality leads. By harnessing the power of artificial intelligence (AI), marketers can find high-quality leads with a high conversion rate, and at the same time bring down the CPL.

Pick Out the High Performers: Identifying and Targeting Lookalike Audiences

An AI-enabled platform can identify which devices customers use, and build a behavior profile based on their cross-screen activities. Once you know who your highest performing audience groups are (i.e. those most likely to convert browsing into purchases) by demographics and interests, the platform can identify other users who share those attributes. This is called finding lookalike audiences.

Brands can therefore identify and buy access to the best audience for their campaigns, expanding their reach beyond the traditional domain.

Maybe your best audience is the one who viewed 10 products over the course of three days, as opposed to those that viewed five products in a single day, for example. The traditional marketing approach would be to target each of these groups in turn and hope for the best. However, AI removes the guesswork by allowing you to focus on those highest performing customers for maximum return.

Remarketing to reduce delays in sales

Artificial intelligence also helps with remarketing (reaching potential customers who have expressed interest in a product but not yet converted). By reaching the user on every screen they use, you will shorten the time between the initial visit and the actual purchase.

For example, if a user browses a product on his laptop and later on his smartphone, an advanced system can remarket to him via both platforms, through email for the laptop and by an app notification for the smartphone, say. This makes it much easier for the consumer to purchase, as it only involves a couple of clicks, rather than them switching devices and/or from an email program to a web browser.

A Person, Not a Device: The Importance of a Single Customer View

An AI-enabled platform lets you tailor recommendations and advertising creative to each user’s unique cross-screen behavior and browsing history (aka the Single Customer View). Because it recognizes these activities as those of an individual, and not just a device, it builds a picture of them as a person, with certain interests, behaviors and habits. Not only does this increase the chances of a higher conversion rate, it also makes it easier to find lookalike audiences, because you know more specifically what you are looking for.

For instance, you are interested in the cross-screen web browsing history of a male consumer named A. Using traditional marketing techniques, you would only see that content being consumed on different screens is about sports, technology, finance and travel. Multiple touchpoints make it hard to tell how many consumers are using these devices, and these topics are also too vague to really build a picture of what he is interested in.

By analyzing the cross-screen behaviors and the keywords within the online content A consumes, AI can link all the devices owned by him, and determine that he is searching for basketball with virtual reality, bitcoin, budget bed and breakfasts while travelling, and so on. Immediately you have a much more vivid picture of who A is and where his interests lie, which enables you to tailor your marketing materials to him as an individual. Crucially, you can start a dialogue with A – and with those who share his interests – which will build a relationship between him and your brand.

Put a Limit on It: Frequency Capping and the Power of Saying No

Of course, with all this power at your fingertips, it’s tempting to bombard the consumer with marketing messages, but that risks overloading them, which will only serve to alienate them from your campaign. It will also cost you more.

Instead, smart AI algorithms employ frequency capping to ensure you don’t overwhelm your customers. It also means you spend your budget efficiently and limit wasted impressions. As it works across every screen your user owns, it knows they are the same person instead of assuming a new individual for each device. That way, you won’t send them mixed marketing messages.

Capping is available per day or per action during the life cycle of a campaign. AI can find the best rule depending on your preferences. You can also limit it to a total number of impressions or clicks.

Taking global beauty and skincare brand Estée Lauder as an example, it employed the above techniques to huge success. It used Appier’s CrossX Lookalike feature to identify new, high-value, young audiences from the profiles in the CrossX database with data collected from over 3,000 campaigns run by Appier. Estée Lauder increased its number of leads by 167 percent, while reducing its CPL by 63 percent. It also shortened the time to conversion among valuable users who were interested in the brand by using CrossX’s remarketing and frequency capping tools.

AI is an immensely powerful tool for marketers looking to increase the quality of their leads, while decreasing their cost per lead. It shows that when it comes to marketing leads, low cost can mean high quality.

Supercharge Your Remarketing Strategy With Artificial Intelligence

Did you know that the average conversion rate on e-commerce sites globally is around 2.6 percent? That says a lot about the immense potential to grow online sales.

One common practice to achieve that growth is through remarketing. Now with advances in artificial intelligence (AI), you can take advantage of methods such as machine learning (ML) to ensure that your remarketing is more effective than ever.  

Mike browses on your site and adds a pair of shoes to the shopping cart. He then abandons his cart without actually purchasing anything. As Mike continues his online activity, he is shown an ad promoting the very shoes that he was evaluating, pushing him to go back to your website and complete the purchase. The ad may offer a discount as an incentive. This is classic remarketing.

Remarketing aims to reach interested prospects who have not converted, and retarget them with relevant marketing messages that will entice them to purchase from you. It offers higher return on investment (ROI) because it engages people who have already shown an interest in your product and are hence more likely to convert.

Advertising is not the sole remarketing channel. Brands can re-engage with shoppers through eDMs and in-app notifications as well. The more targeted and relevant your messaging, the more effective your remarketing strategy.

And this is where AI can help.  

Boost Retargeting Performance With AI-based Segmentation

Traditional segmentation marketing, on which remarketing is based, has its own drawbacks – it’s difficult to link the right person with the right product, especially after shoppers log out, and even more complex to sift through data on different interests and groups to discern true interest and needs. Campaigns rely heavily on experimentation to see what works, and this can be time-consuming and expensive.

In this context, AI can facilitate better user segmentation.

AI helps optimize remarketing strategies by using ML to interpret data around users’ purchase history and identify patterns that can help predict future purchase behavior.

For instance, an AI-powered audience buying platform like Appier’s CrossX Programmatic Platform uses deep learning algorithms to analyze dozens of user behaviors in real time and predict which users have a higher chance of converting. Marketers can then prioritize retargeting their most valuable users.

Trigger Conversions With Personalized Recommendations

ML can also combine user behavior analysis and product information to generate personalized recommendations, further improving chances of conversion.

Here are some benefits of optimizing remarketing strategy using AI:

  • Retarget real shoppers for better ROI

ML can distinguish between shopping – actual conversion – and searching (window shopping) behavior, allowing you to retarget users with a higher likelihood of converting. Would you rather remarket to Elle who spends a lot of time browsing online but rarely buys or Patricia who visits an online site when she wants something and buys it?

  • Capture fast-changing behaviors using deep learning models

ML can identify patterns and predict changes in shopping behavior to make accurate recommendations. For example, Daisy has been browsing coats for a few days, but a sudden change in weather means a warm weekend, and so she searches for a T-shirt on Friday night. AI will give you insights around the short-term change in Daisy’s shopping behavior and recommend T-shirts for her to buy, increasing the possibility of an immediate purchase.

  • Retarget shoppers with the items they actually want to buy

AI can help you distinguish between serious intent to purchase from a user’s longtime browsing pattern versus something that they just checked out online but are not really interested in.

Offer Value Across the Customer’s Entire Shopping Journey

Remarketing offers value through the customer’s entire purchase journey online. Take a user who books a ticket to Tokyo. Traditional remarketing would target them with flight promotions to different destinations. This offers low value at best and is inaccurate targeting at worst. Once the user has booked their trip to Tokyo, they will next shop around for hotels, tour bookings, etc. And this what remarketing should address.

AI is enabling smarter marketing by making relevant recommendations based on previous purchasing behavior and data across ecommerce sites, expanding the number of products the consumer is exposed to. Marketers can use valuable insights on users’ intentions to retarget them with products that are similar in function, design, etc.

When AI is used to remarket cross screens, this proposition is further strengthened. Cross-screen remarketing allows you to reach your user on every screen they own and shorten the time between initial visit and purchase. And AI-powered platforms like CrossX help reduce the complexity of cross-screen remarketing by giving you insights on who to reach, on what screen, with what message and when.