Marketing departments have staffed up their data analytics teams who spend more time wrangling data than building insights.
In 2016, 69% of marketing leaders expected the majority of their decisions to be driven by data by 2018. With marketing analytics accounting for 9.2% of the marketing budget in 2017-18 — the largest share of any category — it’s time to wonder: Are we there yet?
Analysts don’t have the time, tools or processes to execute on their vision
Gartner surveyed over 500 companies in the 2018 Marketing Analytics Survey to uncover what’s working and where marketers have progressed on their journey of using data and analytics to power modern marketing. Gartner research director Lizzy Foo Kune offered a sneak peek at survey results during the Gartner Digital Marketing Conference 2018 in San Diego. The data tells a story striking in both the progress made and the work to be done.
Bigger teams are (still) wrangling data
According to the 2018 survey, average team size grew from a couple of people a few years ago to 45 full-time employees (FTEs). Yet when asked which activities marketing analysts spend the majority of their time on, data wrangling topped the list along with data integration and formatting. The good news is that teams spend less time on ad hoc reports compared with 2016, indicating that process improvements are underway.
Expensive talent is misaligned
It also turns out that companies’ top analytics talent may be underutilized. Nearly half of the leaders surveyed said some of their most expensive and experienced analysts spend their time preparing data to be analyzed rather than analyzing the data. When asked which activities their company’s data scientists or advanced analytics staff performed for the marketing analytics team, more than 45% of respondents said their data scientists perform foundational activities such as data visualization or preparing data for analysis.
Some of the most skilled analytics talent spends their time doing work that is necessary but not necessarily the work that will drive competitive differentiation and breakthrough insights. Analysts don’t have the time, tools or processes to execute on their vision.
Privacy impacts practices
Nearly three-quarters of respondents say consumer privacy concerns will create barriers to marketing analytics practices. The foundation upon which modern, data-driven marketing is built — individual, user-level data — is under threat from new regulations and consumer privacy concerns. Marketers must not only be experts connecting and modelling customer data but also must be experts in responsible customer data stewardship. The activation of the European Union’s General Data Protection Regulation (GDPR) adds urgency to an already complex challenge.
With 78% of survey respondents having aspirations to be level 4 or 5 on the Gartner Data-Driven Marketing Maturity Model by 2023, what steps can marketing leaders take today to be a data-driven organization tomorrow?
- Rethink your allocation of resources. Given the rapid growth of both the average size of analytics teams and percentage of budget allocated to marketing analytics, leaders need to question if they need so many dedicated in-house analytics staff. Strategic outsourcing and automation start to make sense when a significant volume of foundational tasks are currently handled by talented, expensive (and coveted) data scientists.
- Become a (personal) data steward. With privacy concerns set to burrow deeper and deeper into the marketing mentality, addressing these concerns will be a foundational practice for any company that wants to advance its data-driven marketing maturity. Consent management will continue to be a priority area.
- Let good enough be good enough. Marketing analysts can spend what feels like, and may actually be, 20 to 30 hours of their work week shepherding, wrangling and corralling their data. For example, connecting regional sales data across ten big box stores with impressions and media market data from a digital campaign can seem like a full-time job. In the world of marketing analytics, perfection is getting in the way of analysis and proving to be the sand in the gears of the entire analytics engine.