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Why cannabis datasets will give you a bad (data) trip

by Cory Munchbach

September 24, 2021

Marketers are going through an identity crisis. Ever-changing consumer privacy regulations and the deprecation of third-party cookies are driving companies to look for alternative data sources for targeting and personalisation.

Yet not all of these sources are created equal, particularly if they are based on third-party data that is inaccurate or incomplete. In fact, I recently resisted the urge to roll my eyes when reading about a new partnership offering marketers the ability to reach recreational and cannabidiol (CBD) product users with greater ease.

Touted as a way to “tip the scales by making targeting possible for a growing and likely valuable cohort,” the offering is really nothing more than another example of shiny new object syndrome, where marketers are encouraged to target at a superficial level. Taking this route is likely to leave companies using poor data and getting underwhelming results.

Remember the obsession with marketing for millennials? Targeting a group of people based on a single attribute has always been (and will always be) a flawed strategy, whatever homogenised group you’re picking. But let’s start with why cannabis datasets are not the answer since that’s the most recent example.

The promise of a new untapped audience might sound appealing, but like millennials, cannabis users are equally – near infinitely – diverse. Simply how a person uses cannabis varies: some take it for recreation while others use it for medicinal purposes. As a result, different cannabis users are likely to have very different tastes and interests. And the current regulatory environment for cannabis also makes it risky based on geography. Since marketers aspire to deliver the right message to the right customer at the right time, targeting individuals based on cannabis or CBD use alone is inherently contradictory to what they are trying to achieve.

More problematic is that taking such a one-dimensional and imprecise approach puts brands at risk from both a reputational and monetary perspective. For instance, in 2016, the Federal Communications Commission (FCC) in the US, in the Broadband Privacy Notice of Proposed Rulemaking, adopted a broader definition of harm than simply monetary, describing it as, “a concept that can be broadly construed to encompass 'financial, physical, and emotional harm.'”

For example, let’s say a woman has a miscarriage, but is continuously served ads for baby-related items. This could now be classified as harm. In the case of cannabis, not all users are looking for a recreational high. What about people who are using it to treat severe anxiety, post-traumatic stress disorder, or other emotional challenges?

By turning third-party audience segments over to brands, the providers of the cannabis data are essentially abdicating themselves of responsibility, while brands are opening themselves up to potential penalties in the form of brand damage and fines from the FCC. Before investing in these kinds of third-party datasets, companies need to ask themselves, “Is it worth it?” A question, by the way, that should be asked even without the threat of a fine or penalty.

Solving the looming identity crisis means shifting away from using third-party data in favor of first-party data that is grounded in trust and transparency. That means only collecting data that consumers have consented to, limiting that data collection to what you actually need in order to accomplish your goals, and clearly demonstrating how that data is used to create value.

When the consented first-party data that’s collected is unified into an actionable and accessible single customer view, marketers and other growth-focused teams can create multi-dimensional segments based on any combination of attributes, and send those segments to their activation channels to deliver highly relevant, personalised experiences.

Put in the context of cannabis users, companies could combine this known attribute along with their other interests, behaviors, preferences, demographics, psychographics, and more to create a more meaningful and relevant dialogue, and get ever closer to the holy grail of delivering the right message to the right person at the right time. Now what a great trip that would be.

Tags

First-party data
customer data platform