CM sister pub Event Marketer conducted a survey of AI in events during the month of March, and 86 percent of respondents said they were not using AI intentionally right now. However, 75 percent do believe that AI will change the course of events. Explore these insights and others in EM‘s latest Pulse Survey.

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The marketing industry and culture at large is adjusting to the sudden influx of large language model generative AI tools, such as ChapGPT, that could potentially alter the way we work, perform and do business. But whether those changes are perceived as positive or negative depends on who you ask.

For a group of more than 1,400 leaders in technology, academics and AI research, who signed an open letter on March 22 that called for a pause in AI development until better safety standards and oversight are in place, artificial intelligence can pose “profound risks to society and humanity.”

We’re talking more than college essay writing, of course. Warnings about unhinged AI experimentation have long been echoed by some of the letter’s more well-known signatories, including Elon Musk. But what of AI’s practical applications, and those for marketing purposes in particular?

Peter Prodromou, president of AI-focused marketing agency Boathouse Group, takes a more moderate point of view and supports practical uses for AI while simultaneously developing guidelines to follow, according to an AdExchanger podcast. He dishes on the difference between AI and machine learning, AI’s applications for social learning, and tips for avoiding companies that are merely attempting to capitalize on the trend.

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For the past two and a half years, Levi’s has been integrating artificial intelligence applications into the brand’s data strategy by investing in targeting consumers with relevant marketing messages. The focus is threefold: targeted email messages; the online experience across its app and website; and the buying experience. AdExchanger spoke with Levi’s AI chief Katia Walsh about applying AI to marketing processes, transforming the 170-year-old company into a digitally-advanced brand, combating bias in algorithms, and more.

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The Weather Company has access to a trove of first-party data, which gives it a distinct targeting advantage in a post-cookie world. But it’s also investing in AI technology—through IBM Watson Advertising—to create quality content; focusing on a subscription model; and deprioritizing revenue gained from short-term advertising within its apps in order to create a richer experience for consumers. Here’s a look at the strategy behind the brand’s recent decision to reduce its in-app ad footprint by 42 percent, according to a piece in AdExchanger, plus how the company is using AI to improve its subscription offering.

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Olay is holding its own AI functions accountable in a new initiative to combat hidden bias in beauty algorithms. Following is what the brand’s #DecodetheBias campaign entails and how its effort to unearth bias in data sets supports more inclusive coding practices, according to an article in AdExchanger.

Olay’s campaign, which coincides with National Coding Week, has the goal of sending at least 1,000 women of color to code camp through a partnership with Black Girls CODE. The idea is to change who is doing the coding—and therefore influence beauty algorithms that Olay has revealed are perpetuating monolithic beauty standards. The campaign includes national TV spots and print ads and also encourages other brands to examine bias within their own data sets.

The brand analyzed its own technology, a web-based tool that relies on selfies to recommend skin care products, to uncover bias—and, importantly, rectify it. After partnering with ORCAA, an organization that audits algorithm risk, the company found that its Olay Skin Advisor tool was less precise depending on age and less accurate for people with darker skin tones.

A key part of the campaign is supporting a more diverse talent pool within businesses. Because “as more women and women of color become coders,” Headley told AdExchanger, “we’ll get better code and more inclusive code.”

For more detail on Olay’s #DecodetheBias campaign, read on in AdExchanger.

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“We had to quickly pivot to provide support in forms beyond product,” explains Pam Griffin, Associate Director, Cleaning Division for the Clorox Co. “My focus was on providing resources to consumers in a time of heightened anxiety, especially when misinformation was prevalent, and reaching consumers in environments where we could offer meaningful and reliable information in lieu of available product.”

By summer 2020, Clorox decided to experiment with artificial intelligence, in particular IBM Watson Advertising Conversations. Implementing the tool required collaboration not just with functional groups within Clorox but also with its PR and media agencies (Ketchum and OMD, respectively) and, of course, IBM. Griffin and team developed a list of nearly 100 anticipated consumer questions. Using machine learning and natural language processing (NLP), the technology was able to not only answer the questions but also understand user intent so that its recommendations were tailored specifically to the consumer.

Executional work began in late September, and the chatbot went live on December 21. It was incorporated into ads running on the Weather Channel’s website and targeted paid media. Clorox also placed the chatbot in a pop-up on its own website’s COVID resources hub—the first brand outside of IBM itself to integrate the tool into its site. To reach consumers offline, Clorox featured a QR code directing people to the site’s chatbot in its January 2021 national free-standing inserts.

As well as inviting consumers to ask questions, the media assets offered a menu of topics that visitors could explore. Common questions focused on product availability, cleaning advice, which Clorox products kill COVID-19 and tips for caregivers and essential workers.

“We were pleasantly surprised to find that we didn’t encounter a significant number of queries that fell outside the scope of what we built,” Griffin notes. “We did make minor content adjustments midstream to ensure we had the most up-to-date product information as our brands continued to receive EPA claim approval for killing the COVID-19 virus, and we will be making more significant updates to the chatbot in the coming quarter to incorporate content that will pivot our focus to helping address anxiety and confusion associated with being outside the home and on the go as the country begins to reopen at scale.”

In terms of effectiveness, Clorox’s AI implementation has exceeded IBM’s activation and engagement benchmarks, according to Griffin. The average user went three “conversations” deep per visit, and 63 percent of those surveyed said they were satisfied or very satisfied with their experience. “That’s kind of remarkable when you remember that in many cases we were talking to consumers who could not access product, and who you might therefore expect to be dissatisfied,” Griffin adds. IBM considered the program to be so successful that it honored Griffin as one of its Women Leaders in AI for 2021.

Griffin offers two pieces of advice for those considering adding AI and chatbots to their marketing arsenal. First, “it’s really important to put in the initial work and engage your functional experts to make sure the content you develop is as thorough, accurate and useful as it possibly can be and that you are in compliance with all legal and data-related regulations.” Second, be sure to think not only as a marketer but also as a consumer when crafting the user experience. “Many of our best ideas came from taking a moment to consider our own perspective, concerns and needs living through these unprecedented times.”

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Typically, there are three different stages or types of an email campaign: the initial campaign, which may include product recommendations in the form of wish lists; last-minute deals that leverage FOMO to help sell a product; and the post-purchase campaign that offers thank yous and further discounts. Through analyzing your data with AI, marketers can tailor campaigns to customers depending on each stage.

AI software can assist with personalizing email messages. Using the data from past campaigns and predictive analytics, marketers can use AI to create the smallest possible segments. Machine learning software allows personalization of discounts and optimization of emails overall, from the content, offers and even the copy, ultimately increasing conversion rates.

Determining the subject lines that work best in an email campaign is another use of AI technology. Analyze your past email marketing campaigns to determine optimal subject lines, from the number of words to the choice of specific phrases. For more ways in which AI can assist marketers’ email campaigns, including retargeting, read on in Multichannel Merchant.

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AI Personalization

AI-powered recommendations, writes Jan Soerensen, General Manager, North America at Nosto, will become more transparent to consumers by providing clearer explanations for why certain products are suggested to shoppers. For instance, the consumer might be told that the item is in their size or it has a similar color to a previous purchase. Such explanations will serve to make AI recommendations less intimidating and ultimately more successful.

Image Recognition

Shoppers will soon be able to search for products within images and videos, and online personalization engines will be able to recognize specific colors and patterns. While this “shop the look” capability exists now to some degree, the capability will gain more traction, particularly in verticals like fashion and interiors.

Psychographic Personalization

Demographics that are commonly used in ecommerce personalization include age, gender, geography and online behavior. But new types of personalization, such as personality types (i.e. trusting, confident, adventurous) could be applied to the user experience in the future and inform product recommendations.

For more ways in which personalization could evolve in 2021, including recognizing long-term consumption habits and pinpointing the most profitable visitor segments to target, read more in Multichannel Merchant.

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More and more marketers are adding artificial intelligence (AI) to their toolbox of late, and with good reason. AI promises significant automation of workflows and intellectual processes, and to a great extent, it delivers on that promise. However, it’s crucial that marketers not lose sight of the fact that AI is not magical or omnipotent.

The algorithms behind AI are beholden to the integrity—or lack thereof—in the measurements and datasets used to train them. If a model is trained to predict future states of the market, the data it is built with must be representative of that market. Unfortunately, this is often not the case. As a result, algorithms risk becoming as biased as their constituent data sets. Left unaddressed, such errors and inconsistencies can cascade through an entire marketing strategy and severely hobble its performance. Among the numerous potential pitfalls to monitor, AI bias often remains the most undetectable, and therefore, dangerous. The best way to mitigate this bias is by maintaining the right level of human interaction throughout the marketing chain while applying healthy scrutiny to data sources.

The Danger of Relying on Internal Data

Bias issues increase exponentially when marketers rely primarily on their own data to train their algorithms. This is increasingly common across many industries (due at least in part to growing privacy restrictions) and is particularly evident in the movie industry.

Organizations that assume their own data is somehow representative of the market as a whole are making a big mistake. Their AI algorithms are heavily biased toward what they have done in the past. Ironically, young companies are at the greatest disadvantage, as they have the smallest data pools, despite the best of data-driven intentions.

The hard truth is, algorithms are unable to imagine a future that is different from the past. Nowhere is that more clearly evident than in box-office prediction models. While there is technically no limit to their variety, there are three approaches that are often taken when developing such forecasting algorithms. For each, accuracy depends on the quality of the data made available to them.

Historical Box Office Models

Marketers don’t have access to paid engagement data or tracking data (surveys measuring awareness and intent) prior to eight weeks out from the film’s release. As a result, early-phase box office models rely largely on historical databases such as IMDB and Box Office Mojo. The algorithm’s understanding of the film’s prospective viewers is based entirely on how similar releases have fared in the past. It matches metadata points like cast, director, rating and synopsis to historical analogs, crunches the numbers, and projects how well the new release will do. Not surprisingly, sequels and franchise installments tend to get glowing projections.

The issue is that, historically, Hollywood has been biased toward white, male films and not very representative of minorities. That built-in bias prevents early-phase algorithms from recognizing the tremendous value in unprecedented films such as “Get Out,” Jordan Peele’s directorial debut, which , which turned out to be the 10^th-most-profitable film of 2017.

More broadly, if studios are relying on these algorithms to greenlight films—before even getting into the marketing and distribution—AI becomes a blocker to new and different kinds of content being produced. Producers can say their decisions to greenlight sequels and franchises are data-based, and they are, but the data is biased by historical box office performance.

Paid Media Engagement Models

As a film’s release date becomes visible on the horizon, marketers begin serving media on various paid digital platforms, typically beginning with a trailer drop and a few unique spots. User engagement with said media then begins to generate measurable data, which accrues to indicate absolute brand health as well as relative interest levels between different audiences. A media engagement-driven algorithm uses an understanding of how paid engagement patterns correlated with box office performance for past films, and it identifies similar patterns in the upcoming release. Bias is an issue here because different audiences engage with platforms in different ways.

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If your algorithm uses a metric biased toward one audience to predict the box office, you will be blind to signals from other audiences that might engage in different ways. It will provide false signals. Similarly, certain ages, genders, or ethnicities that don’t use (or overuse) social media won’t be taken into account, despite the fact that they may be hand-raising elsewhere. The model will often over-predict for movies that skew toward younger demographics and underpredict ones that skew older.

Survey Tracking Models

As a film’s release draws near, analysts can gain access to industry-standard survey panel reports that track audience awareness and intent. This tracking information offers data scientists a rich data set with which to train algorithms for predicting box office success.

Once again, bias can be a stumbling block. If the composition of the panel or the survey being used values certain demographics over others, it may reach faulty conclusions. The sample for surveys is not always representative of the general market audience, even in the best circumstances. For instance, the very fact that surveys are often delivered online means that the sample is skewed toward people with spare time to fill out surveys, or those in search of compensation for their time and opinions.

Similarly, algorithms must make judgments about the relevant value of different measured attributes, i.e., aided awareness, interest, etc., which may not hold true equally across all audiences. For example, if word-of-mouth promotion disproportionately drives audience turnout for certain audiences, their lifetime value (LTV) may not be taken into account by the model.

Human Involvement is Always Needed

Marketers can reduce their vulnerability to AI bias somewhat by relying on as many different, complementary models as possible. Combining multiple inputs results in much richer insights. In addition, it’s important for companies to be conscious of where they are sourcing their data from, selecting against misrepresentative or inapplicable data sets.

The single most important thing marketing organizations can do to mitigate the negative impact of AI bias is to constantly maintain human involvement. That may sound self-evident, but it’s lacking in a surprisingly large number of cases. The output of the model is not the end-all and be-all, and it should not serve as the foundation on which deterministic decision-making processes are built. AI is first and foremost artificial, and its value begins and ends with the input of both human creativity and critique.

Alex Nunnelly is senior director of analytics at Panoramic.

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The question marketers are failing to ask, however, is “Are consumers satisfied with the buying experience?”

The paradox of today’s data-driven, AI-driven marketing is that despite a large number of channels that provide information and customer service (Hello, chatbots!), consumers are craving more human experiences.

In a report titled Are You Listening? The Truth About What Customers Want in a Digital World, Calabrio found that three out of four consumers in the US and UK are more loyal to businesses that give them the option to interact with a human as opposed to only chatbots or digital channels. That’s not all—a full 37% question the legitimacy of the company itself, if not given the option!

So how does a brand reap the benefits of marketing automation, and become more “human” at the same time? The answer lies in organizational empathy.

Does Empathy Matter in Marketing?

In marketing, we talk so much about customer journey and experience. Antonio Damasio, eminent neuroscientist and Professor of Psychology, Philosophy, and Neurology, at the University of Southern California, said: “We are not thinking machines that feel, we are feeling machines that think.”

This is underscores the importance of empathy in marketing and customer experience, especially where AI is involved.

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In an article on Clickz, Kevin Lindsay, director of product marketing at Adobe, explained how AI has the potential to beat human marketers at analyzing visitor and customer attributes, processing the most relevant ones, personalizing touchpoints in real-time, and delivering the right solution, all at scale. However, using AI to know how their customers are feeling at the moment, and humanizing their experience, is still a “brand fantasy.”

Because as Mary Beech, CMO and VP, Kate Spade, so eloquently puts it, “The best marketing isn’t about the brand; it’s about the customer.”

The Limits of AI, Automation, and Martech

Even though AI has pervaded every aspect of modern life and artificial empathy is now a thing, technology still has some way to go beyond “Alexa, what should I cook for dinner?”

While we’ve been debating the ethical complexities and utopian avatars of AI for long, the deepest of deep minds still remains a few marbles short of the average human marketer. Here are some inadequacies that might limit the level of automation you can achieve:

• Complexity of implementation: “Algorithms” and “models” are terms full of limitless possibilities, but they are guzzlers of resources, time, effort and money. They need time and expertise to research, set up, test, and implement. After that, there is a steep learning curve for the end users if they are to make the most of it. Finally, there is an intensive (and expensive) maintenance process that involves constant verification of data and results.
• Robotic customer service: Chatbots can seem human-like to most customers most of the time. Until of course an issue that the machine hasn’t learned how to deal with crops up. The lack of emotional intelligence or knowledge of nuances in language can lead to bots being anything from unhelpful to offensive to spooky at times.
• Uncertainties: The absence of human discretion leads to the system making some not-so-tactful or ineffective decisions. Do you really need to send an email for every action a user takes within your app? Why do you need them to log in with their Facebook account? Should you wish them on their anniversary when you can’t be sure they’re divorced?

When businesses use technology such as AI and automation to boost efficiencies, the outcomes will scale quickly. Managing the consequences calls for not just empathy, but alignment of “purpose” between the brand and its consumers. But while humans survive on meaning and a sense of fulfillment, machines thrive on clear instructions.

Kate O’Neill, who calls herself a “Tech Humanist” and is the author of a book of the same name, explains that this is why businesses that transform themselves digitally need to do so in a human-centric way and communicate their purpose to their customers.

By clarifying their strategic purpose, organizations can not only provide better customer experiences, but also increase brand loyalty, build a community, as well as foster a meaningful and productive work culture.

Empathy is the Missing Link between AI and Humans

“Empathetic marketing” connects companies, brands, employees and customers in a harmonious, productive and win-win way. You might be forgiven for thinking that ROI and the bottom line is all that matters to companies. While authoring my first book The Content Formula, I stumbled on the counter-intuitive secret to selling: Don’t talk about the stuff you sell.

“Then what should we talk about?” I hear you asking. Show, don’t talk. Show empathy towards your customers. Help, don’t sell. Help them solve a problem.

When marketers and ad execs walk into their office every day, a strange thing happens. They relish the idea of using data to “target” their customers with branded messaging. They begin to see “people” as users, leads, personas, prospects, audience, cohorts or whatever label is the flavor of the day. They forget that people don’t want to “consume” an AI-chosen ad 15 times during the course of an hour-long show or watch a 20-second pre-roll before a one-minute video.

Noah Fenn, global lead, strategic partnerships at Google, calls this phenomenon collective amnesia of marketers.

And the only antidote on offer is empathy. Put yourself in the shoes—or more accurately, behind the screens—of consumers. Listen to them and deliver the experiences that they want, not the ones you’d like them to have.

Consider the Cleveland Clinic. They produced a video titled “Empathy: The Human Connection to Patient Care” to encourage their 40,000+ employees to understand and imbibe the brand’s core value of empathy and premise of treating every patient (their customer) as they should be treated.

The fragments show the pain, struggle, and victories that unfold in a hospital setting. Amanda Todorovich, Cleveland Clinic’s director of content, decided to go social with the video. The tear-jerker touched the hearts of over four million viewers, earned Amanda the Content Marketer of the Year award from CMI, and played a central role in taking Cleveland Clinic’s blog from 0 to 67 million visitors in a span of six years. It generates enough revenue to cover the costs of their content expenses, reaffirming the proposition that empathy is the counterintuitive secret to success.

Amber Osborne, CMO at Doghead, reinforces the notion that it’s people who make a product. “No matter what you are trying to build, if you know in your heart it’s something valuable, keep pushing, keep building, keep networking. Our community members, our customers and the amazing success stories of our product keep us going every day,” she affirmed.

Be Human, Do Human

“We don’t focus on our customers,” said no one ever. And yet, marketers fail miserably at empathizing with customers. In order to fix the brand-customer empathy gap, you need to ask (and honestly answer) yourself:

• Do you understand the core emotional motivators of your customers? Does your messaging resonate with these motivators?
• Do you build a connection before you attempt a conversion?
• Do you test your assumptions and biases for every marketing campaign?
• Does your AI-driven revenue model incorporate the nuances of empathetic marketing?

Once you base your marketing, sales, and business growth on empathy, you’ll start gleaning insights that scale up your revenues while building lasting relationships with your customers.

Michael Brenner is the author of “Mean People Suck.”

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