“Helping hands” by Cat Burston is licensed under CC BY 2.0
Machine learning holds a lot of promise. But if you forget the people behind the data, your initiatives will fail PHOTO: Cat Burston

The future of machine learning technology in marketing sounds promising.

Machines will not only fulfill ordinary, everyday tasks but accomplish things humans cannot even conceive of by deriving predictive models with powerful, automated technology. Customers stand to benefit too, as machine learning will apply advanced algorithms to offer spot-on, personalized recommendations, improving customer experiences and brand interactions.

And while it’s easy to get caught up in the hype and promise, remember what really matters in growing a business — the people who buy and use the products.

Rather than view customers solely through the lens of data and number crunching, marketers must prioritize building trust through real, meaningful interaction, with customer empathy at the core. 

Customer empathy will continue to be a key attribute startups must master in order to compete with, and outperform the giants in their industry. 

Startups that value customer empathy’s role in marketing and tackle real customer relationships alongside machine learning will succeed. Those who don’t will fail.

A Future Vision of Machine Learning and Marketing

In the past year, we have seen machine learning inching its way into the B2B space, although it has yet to gain widespread adoption. 

That will change in the year ahead. Machine learning will become a common tool for marketers to meet business goals and objectives.

When done right, the technology will offer marketers the ability to track consumer behavior and determine and replicate successful marketing campaigns. It also holds tremendous potential for adapting personalized campaigns at scale, instantaneously altering how an individual customer is targeted.

B2B marketers can look to examples of how machine learning has already evolved customer experiences for B2C audiences, including:

Upselling Customers to Understand Customers’ Evolving NeedsSeveral European banks have incorporated machine learning techniques to develop new recommendation engines for clients, resulting in 10 percent increases in sales of new products.

Building Loyal, Lifetime Customers by Proactively Addressing Concerns Directed to the Customer Service Team: Salesforce’s Service Cloud Einstein is an example of this, as it’s designed to improve operational efficiencies of customer call centers.

Designing Personalized Experiences, At Scale: According to Forbes, top auto executives anticipate the arrival of smart cars on the road by 2025. Vehicles will not only integrate into IoT, but also adjust to the owner’s personalized preferences (temperature, audio, seat position, etc.) and environmental factors such as traffic and road conditions.

While these represent a snapshot of the exciting potential of machine learning, it’s clear that the technology alone cannot tackle all aspects of marketing. 

We need a human touch.

A Vision of a Human-Machine Marketing Team

A harmonious picture of human and machine at work might look something like this: a machine learning algorithm for website personalization analyzes data in real-time and based on visitor characteristics, determines the best website version to deliver to a visitor. 

Meanwhile, behind the scenes, the marketers are hard at work doing what they do best: designing experiences to serve up to their customers based on their deep understanding of buyer personas, sales goals and business priorities, to provide the algorithm with the right selection of options from which to choose.

With the data and insights provided by the machine learning algorithm, a marketer can think more holistically about the brand experience and determine the right metrics for which the algorithm optimizes. 

A deep understanding of the customer and business is critical to getting this selection right, and that requires an in-depth knowledge beyond what the data alone can provide. This is where customer empathy comes in.

Tips for Leaders to Build a Culture of Customer Empathy

From my years at Salesforce and now at Optimizely, I’ve sewed a common thread through my team’s work: spending time with customers to understand their pain points. 

In both B2B and B2C marketing, it’s critical to understand exactly which processes or workflows can and should be automated and which pieces are best left to a human. This requires we speak directly with customers and feel their challenges.

Machine learning holds incredible potential to change the way we do business and interact with customers. Companies should keep these three things in mind as the technology makes its way into every aspect of our lives:

  1. Emphasize customers first, no matter how automated the process becomes.
  2. Remember the importance of in-person interactions and building trust and loyalty through gathering feedback and engaging in one-on-one conversations.
  3. Build a culture focused on customer empathy by hiring and training management and team members who share this perspective.

Machine learning cannot take the place of engaging with customers in real life, nor in gaining a deep understanding of their interests, motivations, fears and desires. Companies must adapt their culture to keep pace with machine learning without losing sight of what grew their business in the first place: people.