The industry has been talking about machine learning for years, but what’s different today is that marketers have begun to implement machine learning tools and tactics to advance their marketing efforts towards advanced personalization strategies. Machine learning is taking over personalization by disrupting content management, how marketer’s approach analytics and one’s ability to reach customers across channel at scale. So how can marketers achieve this level of personalization through machine learning? By taking a collect, detect and act approach.
Find the right customer: At the beginning of machine learning efforts, marketers often want to use all of their data (at once) from each and every source. To effectively power machine learning, data needs to be staged. In the collect phase, focus on building a true 360-degree marketing database to both manage and analyze data sets by making sure that data is aggregated from all available channels and integrated across channels for a single customer view. On the technology front, machine learning plays a critical role in the development of content. For example, machine learning can define what colors and pictures are most successful.
Understand your customers: Next, you have to apply customer analytics and insights to understand your customers along with their interactions with your brand. Leveraging analytical tools for performance reporting, dashboards and visualization are essential. Also, don’t forget to test. Creating a test lab will allow you to review data ‘with a fine tooth comb’ and discover anomalies in the data encouraging you to adjust your approach and take the right action.
Engage with your customers: Now, you’re ready to leverage machine learning to engage with your customers at scale. Building consumer segmentation, nurture and campaign design programs with integrated marketing omnichannel technologies will allow you to actively reach your customers. These programs should be fueled by a content creation strategy that you’re able to manage and deliver on in real-time. For example, machine learning can define what colors and pictures are most successful and match these creative elements to the preferences of consumers yielding higher digital interaction metrics.
For example, eBay is starting down the path of an enhanced customer experience by using machine learning to drive better results. Their holistic approach includes beginning with the ‘collect’ phase to utilize large data pools for decisioning. Then, as they are ‘detecting’ (getting to understand/know their customers), analytics are applied to create real-time interactions and provide product recommendations. Additionally, channel preferences are monitored on a 24/7 basis to learn what is working and what is not. And then comes the ‘act’ (the time to engage with customers). Through advanced segmentation and campaign design, no two emails, product recommendations or curated purchasing paths are the same.
The technological advancements of machine learning continue to develop, while the need for humans is essential. Because machine learning is automating the process of improvement, it allows humans (marketers, product managers, etc.) to better focus on strategy versus managing a campaign. There’s a dynamic shift with what people are doing in marketing. As marketers, we need to let the machines take over the process and don’t for a moment dismiss the importance of the human component.