Predictive Analytics is the Secret for Businesses to Survive Digital DarwinismWe live in a real-time world, yet many companies still struggle to understand who their customers are, let alone how they’re changing today, and tomorrow. This is problematic as consumer-facing technologies are only further influencing the continued evolution of customer behaviours, expectations and preferences. This means that how companies understand customers and what customers value and why are increasingly dividing. This, unfortunately, sets the stage for disruption, which is everywhere these days, across every industry. I call this “digital Darwinism.” Technology, society and cultural norms evolve, and as a result, every business must adapt or brave a path of inevitable obsolescence.
It all comes down to the same problem. The divide that separates businesses and people, and their goals and aspirations, is only expanding. And, without change and without intentional investments in new, strategic and technological frontiers, that gap will only widen further - in fact, the widening effect will accelerate. To survive and thrive, businesses will have to not only catch up with times, tastes and trends but also get in front of these changes to predict the future.
The good news is that we live in an era of readily accessible machine learning and automation technologies, all of which are available now.
Machine learning becomes the engine to close the engagement divide and help organisations get in front of digital DarwinismEvery day, consumers use their smartphones, social media and websites to discover and explore, ask questions, seek information, make decisions, and more. As they do, they don’t just leave behind digital footprints, they also broadcast signals of intent that reveal what they really want.
One of the most immediate areas of positive impact on machine learning is predictive marketing and customer experience. The irony and promise of this, of course, is that machine learning can help marketers and executives understand the massively growing volume of mobile/digital signals to predict customer intent and deliver more human, meaningful and personalised engagement. Without machine learning, brands are locked into a never-ending treadmill of trying to catch connected customers as they rapidly evolve. Said another way, marketing is perpetually reacting to, and not leading, customer behaviour and as such, not delivering real-time, useful and value-added experiences.
I recently had the opportunity to interview David Baekholm, senior VP of growth marketing at HomeAway. His work is dedicated to this very topic. In an article he contributed to Think With Google, he shared a three-step process to stop chasing customers and instead predict what they want and when. In one of his steps, he recommended aggregating data across silos to feed a more holistic view of customers into one intelligent system that benefits everyone.
“By identifying customer behaviour signals (through your website, app, or other channels) you can start to get a clear understanding of what kind of behaviours correlate with likeliness to convert, either now or in the future,” David wrote. “At HomeAway, all our segmentation is now based on real-time behaviours. If someone comes to our site, for example, we can say, ‘that person behaves exactly like someone who is going to convert in two weeks’ time.’ We then know what types of content and messaging might be useful for this person so we can keep them engaged until they’re ready to buy,” he continued.
David also said that this work, these predictive capabilities, would be impossible to effectively segment and reach audiences without these advanced but attainable technologies.
I also had a chance to listen to Unilever CMO Chief Marketing and Communications Officer Keith Weed during Advertising Week in New York.
Weed believes that the best thing brands and marketers can do, is to become consumer-centric. He joked that it sounded so much simpler than it really is. But as Weed described it, “it’s a radical change and that radical change still hasn’t yet sufficiently landed amongst marketers and advertisers.” But, at the heart of customer-centricity is real-time customer data, across the entire journey, that reveals what’s happening now and what’s likely to happen in the future.
According to Weed, companies need to gather the necessary data points and organize around them. He revealed a very interesting example using machine learning in its data partnership with Google. In this instance, Unilever found that in the hair care industry it could predict the next hair trend with 90% accuracy six months ahead. The point is that it’s intentional and as such, it can create destination sites, how-to videos, engage influencers, and so on, to drive engagement and boost brand resonance by delivering value.
With machine learning, marketing becomes a growth engine for the business and a strategic ally in the race against digital DarwinismMarketing and customer experience (CX) can take the lead in making sense of these new signals and be charting distinctive paths to purchase as consumers move seamlessly across devices, channels, and needs. With a little help from machine learning, those signals translate into actionable strategies for brands to anticipate future intent and better assist customers with getting things done. Marketing and CX now have a real capability to serve as a powerful growth driver for every business. By tying together customer intent/needs (digital signals), machine learning and business objectives, marketing and CX teams can deliver value to customers and stakeholders across the board at every stage. To do so, organizations have to evolve the roles of marketing and CX beyond their purviews today. The goal is to translate real-time data into actionable insights to influence and collaborate with other stakeholders to predict and deliver more holistic customer experiences:
- Set up a cross-functional data team.
- Leverage search behaviour to zero-in on exact signals that reveal what people want.
- Use those insights to predict intent across media and across channels.
- Align that signal with growth goals you want to achieve.
- Create a growth engine with automation and machine learning.
- Measure. Learn. Grow.
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