It is not a new concept for marketers to use data to derive strategy. However, in 2022, the process for capturing this data, as well as the volume and variety of data available to marketers, will redefine data-driven marketing.
Data silos created by apps, teams, and legacy systems have resulted in resources that are incomplete or incongruent. The marketer’s reach has increased thanks to access to all company data, including customer data, sales data, and research. Moreover, the sheer volume of data has made the process of turning data into useful insights more complicated and difficult.
When it comes to maximizing spend and performance, brand managers can use advanced marketing analytics to improve collaboration, real-time decision-making, and income. This begins with lowering the perceived hurdles to dealing with data.
Here are four assumptions that marketing leaders should avoid in order to maximize the value of marketing data:
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To employ AI, marketers should become mathematicians
According to a study conducted by Demandbase and Wakefield Research, 80% of B2B marketers believe AI would “revolutionize” marketing in the next five years. Only 26% of respondents said they were confident in their own understanding of AI’s applicability. Furthermore, more than half of those surveyed said that integrating technology and staff training were barriers to entry.
Machine learning does not require marketers to be experts in complex mathematics or algorithms, contrary to popular opinion. The best marketing intelligence platforms, or the applications and infrastructure that enable access to and analysis of marketing data, will seamlessly integrate AI technology through visualization, reporting, and insight generation, giving marketers more time to uncover trends and growth opportunities.
Data stifles creativity
Data-driven insights and creative thinking go hand in hand in effective marketing. The be-all and end-all of strategic output are not data-in, data-out. Marketing will always be a creative sector, but data-driven creativity will give marketers the upper hand.
Integrating audience, brand, and sales data to forecast consumer behavior may tell marketers what they need to convey, but it doesn’t tell them how to personalize that communication. As a result, data science complements rather than replaces a marketer’s intuition.
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The data will be handled by someone else on the team
Due to evolving roles within each business, a few organizations reported major difficulties in brand-agency coordination. Data management is certainly a loss of this dynamic. Collaboration between brand and agency teams is critical, yet data appears to be moved from one team to the next.
Closing the data literacy gap between brand and agency teams is critical if marketers want to enhance collaboration and, as a result, increase the value of marketing performance. Faster, timelier decision-making requires proximity to data. There is a lot of room for growth when marketers put the right tools and processes in place for collaborative data practices.
Businesses have infused too much into data management to obstruct this system
Despite having access to multi-channel data, marketers’ strategies still force everything via the same antiquated, siloed processes.
Obtuse data management and reporting systems have the wretched advantage of depriving marketers valuable insights that could affect revenue, client retention, and media spend. As data accessibility grows, marketers will be able to weigh the costs of maintaining outdated systems.
Fortunately, when the goals are clear, disruption is easier to handle. Marketers should search for a solution that delivers a single view of all data in near-real time for a speedier path from data to insights.
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