How AI Can Shape the Future of Direct Mail Marketing

The following is republished with the permission of the Association of National Advertisers. Find this and similar articles on ANA Newsstand.

By Christopher Karpenko

As artificial intelligence (AI) continues to gain traction across various industries, marketers have been taking notice. While this technology is often discussed in relation to digital tactics, such as chatbots on company websites, it can also be incorporated into direct mail marketing efforts.

Many marketers already have omnichannel campaigns in place, coordinating direct mail and digital marketing efforts to engage customers on multiple channels while helping to keep their brand top of mind.

With AI, this combination can be even more powerful.

In an unreleased 2019 study conducted by SIS International Research and commissioned by the U.S. Postal Service, retail marketers were interviewed about their views on artificial intelligence. Of all the respondents already utilizing AI, 82 percent said they use it for more than one channel. On average, they utilize it across two to three different channels.

However, many marketers are unsure of how to get started with artificial intelligence. In fact, that same study revealed that only 30 percent of all the marketers surveyed had fully established the use of AI marketing tools for their campaigns.

Below are a few of the most popular AI tools, with hypothetical use cases that illustrate how these innovations can be incorporated into direct mail.

AI for Visual Listening

Of all the brand-owned images online, including a company’s logo, up to 80 percent do not actually mention the brand by name in the associated text, notes Christophe Folschette, co-founder and global sales director at U.K.-based social media listening and analytics platform Talkwalker, in a 2016 interview published on his company’s corporate blog. Visual listening, though, can provide marketers with the insight they need to speak to consumers’ specific interests and needs. This allows companies to gain insight into how, where, and when consumers are interacting with a brand.

By analyzing images online, artificial intelligence can pick out photos on social media that include brand logos and brand text — even without accompanying captions that mention the company by name.

Considering that images featuring company logos are shared at a rate of about 3,000 per day, according to a report from Talkwalker, “Decoding Consumer Behavior: The Not-So-Secret World of the Social Consumer,” visual monitoring can provide marketers with many valuable insights. These can then be used to inform campaigns and overall brand messaging.

Hypothetical Use Case:

Visual listening informs direct mail messaging.

A granola bar company uses visual listening to scan customers’ social media posts. From previous surveys, the brand already knows that many customers bring these bars on hikes and outdoor adventures, so the company has created campaigns around this theme.

However, the visual listening AI tool shows that many consumers are bringing the granola bars to work and on commutes, enjoying them in fast-paced urban environments.

The company decides to tap into this consumer segment using direct mail, creating a campaign targeting young professionals in city settings, highlighting the convenience and nutritional value of the on-the-go snacks. The mailpiece includes a QR code leading to an online quiz: “Which granola bar flavor is right for you?” At the end of the experience, users would be offered an exclusive promo code.

By utilizing the AI findings in a way that targets these consumers on multiple channels, the company stands to increase engagement.

AI for Advanced Analytics

Advanced analytics refers to the autonomous or semi-autonomous process of examining data through high-level tools and methods to project future consumer trends.

This involves a few different methods: big data, predictive data analytics, and data mining. Big data involves searching for existing insights and connecting data points and sets, as well as cleaning data. Predictive analytics refers to the actual predicting of future trends, events, and consumer behaviors based on the clean data and existing insights. Data mining provides the raw data to be utilized in big data and predictive analytics.

Predictive analytics is used across a wide range of industries and applications. According to IndustryWeek, consulting firm McKinsey even predicts that AI combined with machine learning (which allows computers to actually learn the intricacies of a supply chain) will be able to reduce supply chain forecasting errors by 50 percent and supply chain administration costs by 25 to 40 percent.

Hypothetical Use Case:

Advanced analytics improves mail segmentation.

A wildlife-conservation nonprofit works with a third-party AI provider that builds customized analytic models for the organization. The nonprofit is aiming to improve consumer segmentation for its house mailing list, which it has been using to help secure donations.

From the data gathered through AI, the organization finds that while many people want to be able to donate quickly and easily through digital channels, most people age 60 and older still want to be able to mail checks.

Valuable information is also uncovered regarding people’s preferred communication frequency; on average, people prefer to receive direct mail from the nonprofit once a month. Additionally, the wildlife organization finds that many people are specifically interested in receiving communications about sea life.

The nonprofit uses this information to further segment its mailing list, creating different messaging for different segments based on age, wildlife interests, and communication frequency.

From there, the organization creates mailpieces focusing more on marine life and begins enclosing pre-stamped return envelopes for the older segments to make it easier for them to mail donation checks.

Finally, the nonprofit creates a simple online donation portal and sends a mailpiece promoting it to younger, digital-savvy consumer segments.

AI for Hyper-Targeted Advertising

Hyper-targeted advertising involves creating messaging and content that is personalized to specific consumers. This allows marketers to serve up customized ads through multiple channels, tapping into customers’ specific needs based on their interactions with the company’s digital media and print efforts.

Marketers using hyper-targeted ads are very satisfied with the results, according to the SIS/USPS study. While only 24 percent of marketers are currently using hyper-targeted advertising powered by AI, 100 percent of them said they would continue using this in their campaigns.

Hypothetical Use Case:

Hyper-targeted mail speaks to specific customer needs.

An ecofriendly women’s apparel brand uses AI to analyze customer behavior on the company’s digital channels. The brand looks at the types of blog posts consumers view, whether they leave items in their carts, and what products get the most views.

The company finds that one customer segment is spending a lot of time reading blog posts about the company’s sustainable practices, so it decides to send consumers direct mail that highlights the environmentally friendly practices utilized in the creation of the clothing, with a promo code included.

For the customers abandoning products in their carts, the brand sends out personalized direct mail reminding these consumers of the items left behind and highlighting some of their unique features. The mailpiece also includes a QR code that customers can scan to receive free shipping.

AI for Content Creation

AI content creation helps marketers create relevant, engaging content that speaks to consumers’ specific wants and interests. By analyzing social posts, news articles, blog posts, and other types of digital content, AI algorithms can pinpoint trending topics and keywords. This allows marketers to shape their messaging around subjects already known to be relevant and useful to consumers.

According to the SIS/USPS study, only 12 percent of respondents already using AI are currently making use of content creation, meaning there is ample opportunity for companies to get ahead of the curve in utilizing this technology.

Hypothetical Use Case:

Content creation makes it easy to shape mail messaging.

A ride-sharing provider uses AI tools to analyze competitors’ messaging and see what kind of language, tone, and phrasing they’re using to reach consumers. Through this AI analysis, the company is also able to gain insight into any trends shaping current campaigns within the industry.

Blog posts, news articles, social conversations, online forums, and other digital content are also scanned, allowing the ride-sharing business to gather even more insight into current trends, the general sentiment surrounding the field as a whole, and the types of conversations taking place.

Finally, the business uses AI to glean information on competitors’ direct mail content templates — how these companies lay out messaging on actual mailpieces and how much content is included.

From there, the ride-sharing provider crafts direct mail campaigns that speak to consumers’ existing needs and interests, shaping the messaging around current trends affecting the industry. Specifically, the business finds that safety is a top concern, so it creates mailpieces highlighting its stringent driver-safety training program and its commitment to the safety and security of every passenger.

The company also saves time on the design, copywriting, and overall creative development of its mailpiece, as it was able to gather inspiration and ideas from competitors’ existing mail templates and copy.

The Future of AI for Direct Mail Marketing

These are just a few examples of how artificial intelligence can be used for direct mail efforts. As these technologies and tactics continue to evolve, marketers will be able to push AI-backed direct mail even further, creating sophisticated campaigns that drive customers to act.

By leveraging AI for direct mail, marketers can create highly relevant, engaging messaging that speaks to specific consumer interests and behavior, keeps their brands top-of-mind, increases overall engagement, and, ultimately, boosts the bottom line.

Christopher Karpenko is the executive director of brand marketing at the United States Postal Service, a partner in the ANA Thought Leadership Program.