We’ve entered an era in which marketers are being bombarded by volumes of data about consumer preferences. In theory, all of this information should make grouping users and creating relevant content easier, but that’s not always the case. Generally, the more data added to a marketer’s workflow, the more time required to make sense of the information and take action.
Machine learning is a subset of artificial intelligence. The technology equips computers with the capacity to analyze and interpret data to proffer accurate predictions without the need for explicit programming. As more data is fed into the algorithm, the more the algorithm learns, in theory, to be more accurate and perform better. If marketers expect to create more meaningful campaigns with target audiences and boost engagement, integrating machine learning can be the tool to unveil hidden patterns and actionable tactics tucked away in those heaping amounts of big data.
It’s important not to overlook the real cost-efficiency of such intelligent marketing campaigns. For the past few years, cosmetics retail giant Sephora has boasted a formidable email marketing strategy, embracing predictive modeling to send customized stream of mails with product recommendations based on purchase patterns from this inner circle [of loyal consumers. Predictive modeling is the process of creating, testing, and validating a model to best predict an outcome’s likelihood. The data-centric tactic led to a productivity increase of 70 percent for Sephora, as well as a fivefold reduction in campaign analysis time — alongside no measurable increase in spending.
As the influx of data continues growing uncontrollably, the implementation of machine learning in marketing campaigns will become even more relevant when it comes to striking up engaging conversations with consumers. Indeed, it could be so integral that spending as a whole on cognitive and artificial intelligence systems could reach a whopping $77.6 billion by 2022, according to the International Data Corporation. Companies have already recognized the positive impact that machine learning can have on their brands, including higher engagement rates and increased ROI. Other marketers will likely soon be following their lead.