Consumer privacy is a growing concern across industries and something with which marketers, in particular, are struggling. With numerous global privacy regulations, pressure to adhere to external “Do Not Track” signals, and increased consumer expectations for transparency, marketing organizations now face a sea change.
Does this mean that companies should abandon their data-driven marketing efforts? Not necessarily. However, there’s no question that marketing teams will have to adapt their strategies and embrace new avenues for data-driven marketing in our privacy-conscious environment.
The shift away from third-party cookies is one significant challenge that brands have begun to face. Both Firefox and Safari have already stopped supporting third-party cookies, and Google recently announced its plan to phase out support by 2022. But, by availing themselves of the right technologies, companies can turn the post-cookie era to their advantage by using predictive analytics instead to build deeper customer relationships.
Predictive analytics can help organizations improve outbound marketing efforts and improve lead conversions. Of course, most marketing teams are already deploying predictive analytics to some extent; the consumer privacy sea change offers a chance to take these deployments to the next level. For example, better data capture capabilities provide a foundation for utilizing machine learning and modeling future events. From there, companies can better quantify growth opportunities, model scenarios, and predict campaign effectiveness.
And because the move away from cookies forces brands to start anew, they have an opportunity to operate more transparently and show customers how they can benefit from sharing their data. This can help assuage concerns surrounding data sharing, as consumers will have a greater level of trust and understand the value exchange for providing personal data.
So with that in mind, the following are a few predictive analytics best practices to remember as you adapt your business to a more privacy-conscious world:
- Understand Your Goals. Chances are, your marketing team has numerous goals for any predictive analytics investment. It’s important to understand the nuances of each, as that will determine the types of data you need to collect. Another consideration is the right analytics platform. While there are a variety of predictive analytics tools on the market, companies need an underlying platform that can integrate data from multiple sources and assess its quality and reliability to maximize campaign impact.
- Make Data Actionable. Marketing departments have a wealth of data coming from a wide range of sources. A crucial part of making this information actionable is identifying what matters most and further segmenting this data into key buckets that offer timely insights into trends, strengths, and weaknesses.
- Personalize the Customer Experience. As mentioned above, the current marketing tech environment provides an opportunity to nurture customer relationships. By applying predictive analytics to customer interactions in the post-cookie era, companies can work to continually refine and personalize their digital experience. This, in turn, will engender greater consumer trust and ultimately result in more opportunities to strengthen customer relationships and increase sales.
For more on how marketers can tap predictive analytics to drive value, check out these previous APEX of Innovation posts.