The need for data visualisation within marketing

The importance of data visualisation continues to grow with the ever-increasing scale of data signals, data sets and data availability for both brands and marketers.

Through data visualisation in a digital advertising ecosystem, we’re able to articulate a visual representation and summary that allows specific trends or analysis to be easier to interpret, action and compare. Marketers are empowered to understand trends, patterns and insights that can link back to actionable business areas.

In a technical landscape as complex as digital and specifically programmatic, information design is more important than ever. This is due to the way it has the power to make complex ideas not only accessible, but also relatable by optimising the visuals into viral content. This has been an increasing trend recently, i.e. snackable bits of content that can live on social media, allowing quick, but valuable sources of understanding.

As data becomes more accessible and democratised however, what changes can we expect to see and how can the marketing industry utilise data visualisation?

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Primed for opportunity

The digital advertising industry has been practising the art of data-led planning processes for many years now, and even more so recently when exploring cross-channel and omni-channel execution. Data sits at the heart of the digital ecosystem, especially when exploring ideas around innovation and the changes we expect to see in the future.

For brands specifically, a global centralised data strategy execution and alignment becomes key for success in digital advertising. Sophisticated data strategies start to create less market, regional and siloed democratised data approaches. This begins to enable consistency and more robust data informed analysis, and importantly predictions when thinking about media and digital.

Predictive analytics when utilising historical and real-time data, continues to be the holy grail for fledgling brands through to established global blue-chip organisations. We will continue to see the hurdles through legislation and governance playing a role in achieving this execution as brands and marketers continue to innovate and utilise first party data and consumer consumption.

The of role of data scientists

Data visualisation is a part and parcel of a data scientist’s life and one of the most important tools in their arsenal. Data visualisation plays two key roles, firstly by communicating results clearly to a general audience. and secondly organising a view of data that helps in defining the next step in a project.

The process of data visualisation only amplifies the role and need for data scientists and analysts in the corporate world. Many leading global brands already use data science resources outside of logistical divisions in their businesses, with a renewed and recent focus on media and advertising data science modelling to enhance performance and profitability of digital marketing campaigns.

The smartest brands are leaning on data science teams and expertise to go one step beyond data visualisation and model the future data from the historical. Data science teams can make previous data actionable for future advertising and media operations. Modelling data for the predictive, not just from historic is one key pillar for success and advantage against the competition.

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Overcoming potential pitfalls

One clear issue is implementing data visualisation with the creation of assumptions, in some cases dangerously directing future planning and strategies. Data visualisation, whilst extremely beneficial, can also be subjective on face value analysis. Interpretation is key, along with interrogating the story and details behind any assumptions, as there are many factors that can easily sway data visualisation. An easily forgotten bank holiday in one region or geography can impact multi-market and channel planning, insights and trends.

Data visualisation can also create disjointed storytelling, if data is not articulated or interpreted in the right fashion. Like a SWOT analysis, there’s always crossover, repetition and rationale behind the findings or results. Ultimately the rationale and story need to be associated with the findings – allowing the experts to bring these to life is important.

This raises the differences between a data visualisation and an infographic — two different things that not many are aware of. An infographic lies on one side of the spectrum where it often is a visual which is ‘declaring something’ and is often more conceptual, simplified and concise. Data visualisation, on the other hand, is more exploratory, with no definitive answer or outcome. These are often where you’ll see the ‘art’ side of information design come in, as they are designed to be viewed as a visual journey, whereas an infographic will be aiming to supply you with quick, hard facts.

Not actioning the data insights proactively or quickly enough can make the workflow and effort redundant. Yesterday’s results don’t necessarily impact tomorrow when it comes to data inference, some marginal gains might not hang around for long – so capitalising in a timely fashion is important. Not missing the boat to action against the data insights inside the visualisation is paramount for marketers and brands in a digital world.

Data visualisation and specific custom-built dashboards to showcase digital and media metrics aren’t just creating engaging visuals and colourful graphics – it’s an imperative tool at the disposal of marketers to interrogate historical performance and results to use in the future and shape strategic direction.

Written by Mike Gray, director of client partnerships EMEA at MiQ

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