If you saw Netflix’s The Social Dilemma, you understand why consumers and marketers alike were deleting social accounts and setting limits on their scrolling. Machine learning is a powerful tool but also a potentially destructive and hazardous one. With about three-quarters of consumers distrustful of (and afraid of) machine learning and AI, what can marketers do to earn trust and tread lightly? We’ve put together how you can use this tool for good.
What is machine learning?
We admit this is a bit of a loaded question. Machine learning is a big industry that’s constantly in flux. And, sometimes, the definition gets hazy. You can think of it like this: machine learning takes data (like a ton of data) and makes sense of it. This isn’t just numbers on a spreadsheet, either. This data consists of clicks, images, numbers, or words––anything that can be digitally stored.
Think about it another way. Whenever you’re on your phone, scrolling through Instagram Reels and interacting with posts, online shopping for your partner or self, or in any way inputting info into the Internet of Things, your data is collected. This data is then fed into algorithms that use—you guessed it—machine learning to find patterns about you for more hyper-targeted content. As a marketer in this digitally-centric world, you’ve probably used machine learning to your advantage for everything from social campaigns to audience search trends.
And, as a subset of artificial intelligence, you can bet that machine learning isn’t going anywhere. So we say embrace the change. But do it sustainably and with your consumer’s best interests in mind.
Social dilemmas for marketers: how to overcome them
So, let’s talk about the statistic we threw out at the beginning of this article. Only about 25 percent of consumers trust AI and machine learning. More than this, “half of consumers do not fully grasp the impact of AI.” With this overwhelming lack of understanding, mixed with worries about privacy, it’s no wonder there’s a lack of trust.
For marketers to use machine learning for good, they have to understand these pain points and address them with consumers before there’s an issue. You can start by being intentional with the following dilemmas.
Your consumers don’t know much about AI:
The answer here is simple: education. As marketers, it’s our responsibility to be open with consumers and help them understand what information we need from them and why. Maybe this is a blog post about machine learning’s benefits to the consumer. Or maybe it’s simply outlining how your brand uses a customer’s data on your site. A positive cause-effect relationship can go a long way.
There’s distrust or worry about privacy:
On that note, protect your consumers. Yes, machine learning is helpful when it comes to understanding customer patterns: what they like, where they search, and current life changes (like pregnancy) that might influence shopping. But, if used wrong, this can feel like a major invasion of privacy. So, again, be transparent.
There’s a concern of bias:
Bias is pretty much a given because even machines are programmed by people who view the world differently. But there’s a difference between “intentional, necessary bias” and “bad bias.” Stay away from the bad bias––like those systems that skew racist, sexist, or other biased outputs.
It also helps to look at which types of AI/machine learning customers trust the most. The list starts with entertainment recommendations and automated sales and goes down to automated financial planning and hiring processes. If the type of machine learning your brand uses differs from these, go back to the above education and transparency points.
How marketers can use machine learning for good
Brands today have a responsibility to their customers. And marketers are often in charge of carrying out that responsibility––to be transparent, use data for good, and provide value to customers throughout the learning and buying journeys. Regardless of a brand’s industry, most use AI in some capacity.
Here’s how you can take your social dilemmas and turn them into solutions.
Make it a personal journey
Part of machine learning’s beauty is its ability to find patterns in the chaos of data. Which then leads to hyper-personalized data about a brand’s customers. Marketers should use this data to create a personalized journey that feels authentic and valuable.
Let’s say your client has a clothing brand. With machine learning, we can find out where their target audience shops, their basic demographics, and other things of interest. We also know that they want an experience that’s fully customized. You can then use this data to create a quiz on your site that generates outfit and accessory recommendations, maybe followed up by an email campaign that offers recommendations each season based on their initial quiz answers.
Brand example: Just think about ordering your favorite coffee from Starbucks and then getting a recommendation based on that selection. Or binge-watching on Netflix? Those “movies you might enjoy next” emails are the epitome of personalization from data.
Use more accurate targeting as a benefit
Another way to use this AI for good is to find activity patterns and meet your audience where they are, without interruption. This requires not simply relying on the algorithms but adding a human approach to your marketing strategies. Your audience may be mostly on Instagram, but pay attention to what parts of Instagram (Reels, Stories, who they follow, etc.). Not only will more accurate targeting benefit your client’s brands, but it also means that you’re not wasting your time and money targeting people who will never be interested in what you’re selling.
Let’s go back to the clothing brand example now. If the data leads you to TikTok and Instagram, find out what avenues within those channels your audience is frequenting so you can authentically join the conversation and spark interest.
Brand example: Elf Cosmetics’ “Eyes, Lips, Face” campaign on TikTok is one of the most successful for a reason: it “was deliberately designed to feel native to TikTok” and to its target audience. And with four billion views and three million interaction videos, the accurate targeting paid off.
Provide value to the consumer
Whether provided knowingly or collected behind the scenes, customer data should be used with respect in a marketing campaign. You don’t want the consumer to feel like Big Brother is watching them, but you want them to feel like your brand understands their needs. That’s where precise content comes in. This could mean using the data to find out what your customers need and creating hyper-specific content to answer those needs. Or, it could be using tools like conversational AI (chatbots) for customer service or using a tool for “a customer question-driven content strategy.” Precise content, like accurate targeting, means that you’re respecting the consumer’s data, their time, and their needs.
The takeaway? Aim for a marketing strategy that builds on your consumer’s needs in such a way that they never feel taken advantage of. You should never be the brand that feels like a “necessary evil.”
Brand example: Machine learning can do just that––promote learning. Duolingo uses an AI-powered chatbot “for users to practice their conversational language skills, judgement-free.” This precise content ticks off the personalization part of the checklist as well.
Is your marketing strategy ready for the future?
Machine learning is relatively new, but it’s most definitely the future of marketing. The question is, how will you address consumers’ pain points and use this AI without creating a social dilemma? We think it’s possible to have both hyper-personalized data and consumer trust. And, at Savy, we’re all about big data making a big, positive impact.