Technology has a history of making organizations adapt. Marketers are continuously sharpening their skill sets—from the early days of the web to the proliferation of mobile, and now with AI and machine learning. With the current pace of digital innovation, companies large and small are constantly rethinking the ways they operate.
Consulting firm Bain & Company has been helping brands with business transformation for more than 40 years. I sat down with their partner and global head of digital, Elizabeth Spaulding, to discuss how brands are evolving to meet consumers’ rising expectations for relevant and assistive experiences, and the challenges and opportunities they’re finding along the way.
Matt Lawson: Machine learning as a concept is decades old. Why is machine learning in marketing more important now than ever before?
Elizabeth Spaulding: Now more than ever, importance is being placed on precision in marketing—reaching the right user in the right moment with the right message. But this level of precision can’t be accomplished by humans alone. Machine learning technology is enabling smarter marketing by allowing marketers to drive customer intimacy at scale. Now, marketers can learn what customers want and react to their changing preferences in real time. It’s new technologies and analytics techniques that are making this possible.
For instance, companies are starting to use machine learning to understand the next best action (NBA) to take with a customer. A few years ago, a call center agent was encouraged to upsell customers based on their previous purchases. NBAs take this to the next level: if the system predicts that a customer is at risk of churning, the agent will focus on mitigation rather than selling to drive higher customer satisfaction and lifetime value.
Data-driven businesses that use machine learning to serve more relevant experiences for their customers are better positioned to take share away from their competitors.
Data-driven businesses that use machine learning to serve more relevant experiences for their customers are better positioned to take share away from their competitors.
How do leading organizations use data as a competitive advantage?
Successful organizations start with a business objective that’s supported with a robust data strategy. We’re seeing some of the best organizations groom data science teams who can apply big data, including machine learning, to business problems. In fact, Bain research shows that leading companies are 3.2X more likely to have the right analytics talent embedded in marketing.
Wayfair, for example, has different marketing technology objectives supported by different analytics teams. Both its direct response team, that focuses on acquisition and remarketing, and its brand team have dedicated analytics and data science resources, as well as marketing engineering support. This allows them to quickly experiment and shift focus to new projects.
Advancements in technology usually mean new mindsets and skill sets. How are companies planning for this?
We’re seeing successful companies assess how technology will redefine current roles and the skill sets their teams will need to drive future growth. The most effective organizations are comprised of small, nimble teams that are agile in their approach. They combine the cross-functional talents and expertise that they need to win in today's competitive environment, taking ownership to make things happen quickly.
Domino’s is a leading example within the restaurant category that has started to think of themselves as a technology company. They’ve grown their workforce to include data scientists and engineers who collaborate with marketing to push the envelope in customer experiences. In addition to customer-facing improvements in online ordering, digital marketing, and loyalty, we’ve also seen these companies successfully use data and technology to quickly capture insights from frontline locations about customer preferences in order to design new menu items.
The role of the marketer is also starting to evolve. How are you seeing machine learning technology transform the work that marketers do?
As consumers navigate across multiple channels, platforms, and media, they’re leaving behind millions of signals about their intent, context, and identity. The problems marketers aim to solve and the data they are dealing with are complex. Machine learning is an effective approach to process all of that complexity at scale and surface the insights that matter to marketers.
Machine learning is an effective way to process complex data and surface insights that matter.
Most often we see incremental adoption of artificial intelligence and machine learning for high-value use cases. For example, there are a number of startups (Kuaizi Tech, Vizual.ai) that use AI and machine learning to select the best image or video to maximize click-through rates in a campaign, and drive higher return on ad spend and customer intimacy. These companies benefit from machine learning because the technology frees up marketing from debating which creative is best so they can launch more campaigns faster, or reinvest in new areas of marketing.
Also, it enables an organization that is still experimenting with AI and machine learning to take a step on this journey in a low-risk way as they build up their appetite for bigger initiatives. Giving the marketing team the runway to build new muscle in the organization is critical and requires playing the “long game” versus the natural bias to short-term results.
