Consumers Expect Personalization. Only Machine Learning-based Marketing Can Deliver It at Scale.

It used to be that personalization was a luxury or a value add, something a brand did to go above and beyond the call of duty to really wow customers and prospects. Today, it’s increasingly seen as expected. 

Consumers might not understand what exactly machine learning-based customer relationship management platforms and big data analytics are. But they know some brands have them and are able to customize experiences and outreach to better serve them — and those are the brands they are defecting to from less technologically sophisticated competitors.

It’s a change with serious implications for bottom lines as 80-percent of consumers say they are more likely to purchase from a company that offers a tailored experience, and 66-percent say they expect brands to understand their individual needs, according to Startup Bonsai

Furthermore, the impact of personalization extends beyond acquiring customers and making sales; 70-percent of consumers surveyed by Salesforce said that how well a company understands their individual needs impacts their loyalty.

When you only have a handful of customers to serve, personalization isn’t a heavy lift, but few businesses serve such a niche market and most are actively endeavoring to continually grow their audience, customer base, and roster of brand loyalists. That’s a lot of people to cater to. Doing it the old fashioned way, purely manually, is logistically fraught (if it’s possible at all).

Self Taught… and Getting Smarter All the Time

Enter machine learning (ML), digital technologies that collect, organize, and interpret huge swaths of customer and market data and make predictions based upon it — predictions that feed into a process that gradually self-teaches the system to improve its predictions with each new iteration.

Everything from spam filtering to network intrusion detection and computer vision is getting the machine learning treatment today, and so are the tools that marketers rely on, particularly to deliver personalized experiences at scale.

66-percent of consumers say they expect brands to understand their individual needs.

For brands targeting younger demographics like generation Y and Z, machine learning is especially important because those growing customer segments are notoriously jaded about traditional marketing approaches. They tune out banner ads, rarely fall for hard sales pitches, and roll their eyes at inauthentic attempts to court them.

Only the right content at the right time stands a chance of getting past their defenses. One of the latest counterintuitive innovations in ML-based marketing to these groups has been content approaches that learn to be slightly flawed. Research has shown that Gen Z is skeptical of content that is too perfect. They know reality is messy and want brands to be real with them. Showing a spotless countertop in an ad is the first giveaway that something artificial is happening.

Paradoxically as it may sound, it was the inhumanly accurate algorithms of machine learning platforms that discovered that adding into a few unfiltered and unedited touches here and there in marketing outreach works better than anodyne and spotless executions. Leave it to a robot to realize humans are driven by their emotions and crave organic experiences.

How Can Brands Take Advantage of ML

If you’re sold on the power and usefulness of machine learning in marketing, the next question is probably: where can I best take advantage of it? One of the easiest ways to start leveraging ML technologies for your marketing is through systems like Mailchimp’s Content Optimizer, an automated system that improves the performance of email campaigns.

Consumers Expect Personalization. Only Machine Learning-based Marketing Can Deliver It at Scale.

There are competing technologies but they all work similarly. Marketers develop email campaigns, execute them, and then the ML system analyzes the results to discover common patterns. It looks at every functional aspect, including:

  • Tone
  • Call to Action
  • Design
  • Layout
  • Colors
  • Imagery
  • Length
  • Timing
  • Segmentation 

After seeing which emails generated the highest conversions (or whichever key performance indicator you’ve prioritized), it makes helpful suggestions to optimize future campaigns. It makes traditional marketing methodologies look positively archaic by comparison.

…replacing hunches and blind trial and error with data-informed decision-making.

“The creative process was completely dominated by opinions with little data,” said John Wolf, Mailchimp’s product manager of smart content. “It was very manual, very labor-intensive, lots of cycles to get the creative right, and I was thinking there just has to be another way.”

That better way is replacing hunches and blind trial and error with data-informed decision-making. Content Optimizer can understand how skimmable an email is, how visually appealing it is predicted to be for a given audience, even how likely the recipient is to follow through on a call to action.

And small changes can add up to significant progress. “If we improve campaign performance across our user base by just 10%, that will create 190 million incremental online visits to our customers’ businesses,” Wolf said.

Don’t Lose the Human Touch

In the mad scramble to take advantage of machine learning technologies it’s important not to forget that human beings are still on both ends of this chain of communications. ML systems, as advanced as they are, still require oversight to ensure that creative directions, overarching brand strategies, and individual preferences are being considered.

It’s equally important to remain transparent with your audience about where and how these tools are used. Consumers have shown a willingness to trade some of their privacy and hand over substantial amounts of personal information in exchange for personalization and better customer experiences, but they want to know they are entrusting the right brands with their sensitive data.

Lastly, machine learning systems are only as good as the humans that manage them. If data is trapped in silos and not available through a customer relationship management platform, personalizing at scale will always be out of reach. 

So, collect data at every brand touchpoint along the customer journey, transparently disclose your data practices, centralize it, and give your machine learning technologies every opportunity to optimize outreach that delights your customers with personalized experiences for everyone.

 

Hanlon works at the cutting edge of digital marketing, helping brands leverage the most powerful automation and machine learning technologies available. Contact us today to discuss how we can optimize your outreach.