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Optimising marketing strategy effectiveness in an increasingly data-driven world: A guide

inauspiciously Marketing teams have not traditionally been at the forefront of conversations around company data efforts. However, as we enter 2021, consumers are expecting more, and they’re expecting it faster. Consumer attitudes have been so altered by digitalisation – particularly accelerated by the pandemic – that to stay agile, companies have to make swift and well-informed decisions. To accurately answer customer needs, business decisions need to be based on what the data is indicating.

Despite the fact it’s becoming increasingly clear that data analytics has the power to reimagine entire organisations, Gartner has found that marketing leaders are using only 58% of their marketing stack’s data potential, a figure that has stayed the same since 2019. This can largely be attributed to the key data challenges that are preventing data analysts from unlocking the full value of their marketing intelligence.

Data analysts are central to deriving value from marketing data and driving well-informed marketing decisions. To demonstrate their widely-regarded importance, a survey conducted by Dimensional Research found that 71% of companies planned to hire more data analysts. However, the same research found that simply hiring more analysts may not help businesses extract more value from their data, as this vital resource is being critically underutilised. According to the survey – conducted in 2020 and surveying approximately 500 data professionals across five continents – data analysts only spent 50% of their time actually analysing data.

Key data challenges

Analysts reported, for example, spending as much as a third of each working day attempting to access the data they need. A lack of access to data causes delays in leveraging insights. These delays risk data analysts working with out-of-date data. Producing reports using old data is often of little use, especially during fluctuating economic landscapes where insights leveraged from irrelevant data quickly become unactionable. At worst, making decisions based on these insights can have a misleading impact on business direction. It is worrying therefore, that 41% of data analysts said they had used data that was two months old or older. By the time intelligence is derived from such data, the actions indicated may no longer be the appropriate course to take.

Ultimately, data flows into a company from many disparate sources. The modern marketing data stack itself consists of numerous sources including advertising, email, social media and so on. And the bigger the company, the bigger the data stack. This means that it can be extremely difficult for larger enterprises to manually connect the dots between marketing efforts and the key drivers of success. It’s at this point that many organisations choose to introduce data pipelines to help manage and centralise data for more efficient analysis.

However, 60% of data analysts surveyed by Dimensional Research also reported that they waste time waiting for engineering resources. Time spent managing data pipelines is possibly one of the main reasons marketing teams are struggling to unlock the full potential of their data and derive the most useful business intelligence. Waiting for engineering resources means analysts often spend time performing processes extraneous to their job description in order to keep projects moving. Often this means resorting to creating reports via Excel, because they cannot access data via a dedicated dashboard. The large amount of time needed to manage these data pipelines makes it an extra cost that could just as easily be outsourced. Plus, as a company continues to grow, the amount of incoming data grows too, and it can be extremely difficult to manually manage and scale data pipelines correspondingly.

Removing data hurdles to stay ahead of the curve

In order to effectively optimise their marketing strategies, enterprise marketing teams need to ensure data analysts spend more time analysing and less time ‘finding’ the data. Enterprises can solve key marketing data challenges by centralising data across the entire modern marketing stack using a data pipeline tool that automatically centralises all sources and meets their specific use case needs.

Using technology to automate this process not only helps marketing leaders figure out how effective individual campaigns are and what specifically is generating the most leads, but also means teams can spend more time focusing on building new campaign strategies rather than constantly waiting on reports. The companies finding success right now are those empowering marketing teams to lead data efforts and push their organisation ahead of the curve.

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