How do the simple concepts about data visualisation I outlined in Part 1 apply in the real world? The science of data visualisation is a broad and deep area of study, with optimal outcomes relying on many factors – such as the type of data, target industry and intended audience. Here I aim to review some fundamental considerations for effectively planning and designing data visualisations in B2B marketing.
The role of data in marketing is deep-rooted, with numerous objectives. These vary but have in common the need to reach and influence audiences of senior decision-makers. As with all presentations, it is important to customise your messaging and collateral (presentation deck, dashboards etc) with each audience in mind. This means understanding:
As a B2B marketer, your audience is likely to fall within two categories, external and internal:
External: Outside your organisation, your customers will be the main audience. The bread-and-butter of marketing is to influence them at every stage of the customer journey – from new prospects through to loyal clients.
Audience preferences naturally differ, but it’s a fair assumption that the kind of decisions most B2B marketers hope to influence must rest, ultimately, on solid justifications backed up by numbers. No doubt brand perceptions help, but few major spending decisions can be made without data that help justify the purchaser’s choice. Marketers need to get this data across in their marketing collateral in the most effective way possible.
With the B2B buyer journey longer than for B2C, and often involving multiple decision-makers, one approach B2B marketers can adopt is to address different considerations and purchasing influencers at each stage. This ensures you present them with relevant datapoints in a context they can relate to. They key here is simplicity of message: no chartjunk and a data-ink ratio as close to 1 as you can manage.
Internal: Marketers are also under greater pressure than ever to marshal data to impress internal audiences, using data to underpin strategy and prove ROI.
Business stakeholders, whether from finance, sales or general management, are most interested in understanding marketing’s impact on commercial KPIs. This maybe be in terms of sales revenue, customer acquisition or retention, all contributing to commercial success and demonstrating marketing ROI. With that in mind, I recommend you select only the most relevant datapoints that demonstrate simply – without the need for further manipulation – any correlations between marketing and commercial objectives. There is a reason the "dashboard" metaphor has stuck: it is a simple, easy-to-read array of KPIs that will get your points across quickly and memorably.
Conversely, senior stakeholders within the marketing function, given their familiarity and involvement in your marketing efforts, will likely require a more detailed view of how different markets, campaigns and channels perform, and the strategic implications, as a result of your presentation. Opting for an interactive report that allows you to easily manipulate, segment or filter your data is recommended, enabling you (or them) to navigate or drill-down into specific focus areas.
From tables to graphics
So you might know what story you need to tell for which audience, but how can you get from a row of data in a table to an effective visualisation? Data sources for marketers are so numerous, you’ll need to understand how to gather and optimise that data first – and see clearly whether it tells the story you’re aiming at. This has two stages:
Data preparation: Marketers typically deal with large amounts of data, from a variety of sources and channels. Depending on your organisation’s size and tech stack, your data will likely come in different formats and structures and will require unification (blending) and standardisation to ensure completeness and data integrity.
While most analytic and visualisation platforms offer varying solutions to these problems, thankfully the “MarTech” industry provides options for integrations and data connectors, allowing marketers to seamlessly export their data from multiple sources into a single destination to analyse or visualise with ease. These include platforms and solutions like those offered by Supermetrics or Adverity, to name just two.
Data interpretation: With your data ready for analysis, it is time to select the dimensions, trends, and metrics to visualise. While you may already have an idea of what your desired narrative is, analysing your data allows you to develop a deeper understanding of any relationships or trends. Some useful ways to do this include the following Excel fundamentals:
When concluding your analysis, I recommend you make sure your raw data remains complete, backed up and ready for future updates and (if needed) to share with your stakeholders/audience.
Now, assuming you’re clear on the point you want to make, you’re ready to turn your data into clear visuals.
Selecting and designing charts: Different graphical formats tell different narratives. How you visualise your data and your choice of formats will depend on two key factors. The first is data volume: the larger the data set, the more the need to focus on what is most relevant. Do you want to show a trend over time or a current snapshot, or both?
The second is data type: generally it can be classified as either qualitative or quantitative. Qualitative data is simply that which cannot be quantified, for example gender or nationality. Quantitative data, however, is countable and can be expressed in numerical form (and which itself can be subdivided in numerous ways).
You will likely work with a mix of both types, so it is important to select the most effective chart type. For a more detailed guide on which chart types to use when, check out this clear and informative article by Towards Data Science.
Some useful charting tools
1. Microsoft Excel: Perhaps the most widely used data tool ever, Microsoft Excel is the default tool for charting. Its wide adoption coupled with the ability to both analyse and chart data in the same application, makes it an easy option for users of all levels of experience. It currently offers over 17 different types of charts and graphs to use – though overuse and familiarity may blunt their impact, and fiddling with their design is often time-consuming.
One of Excel’s major drawbacks is its handling of real-time and unstructured data. While there are workarounds for this, they often require additional data add-ons and a lot of knowhow and expertise.
2. Microsoft PowerPoint: Another popular tool in the Microsoft Office suite, PowerPoint offers the same capabilities as MS Excel and is more suited for creating reports and presentation decks.
Edward Tufte (see Part 1) criticised PowerPoint specifically for several reasons – one being his claim that it is commonly used to reinforce the presenter’s case, rather than for “enlightening” the audience. I’m not so sure this criticism is relevant in terms of marketing (compared to academia), but his other points remain relevant: PPTs often make heavy use of overly simplistic and basic charts and graphs, usually presented in a hierarchical order, which limit the audience's ability to navigate and comprehend the data at their own pace. This is often coupled with poor typography and chart layouts – see Part 1 for more tips on what to avoid, here.
3. Google Data Studio: A reporting & dashboarding tool that is part of Google’s Marketing Platform (GMP). Data Studio is a free-to-use application that easily integrates with Google Analytics, Google Ads and the rest of the Google marketing suite. Data Studio allows you to integrate your marketing data from different sources to create visual and customisable dashboards.
With plenty of online tutorials and user guides, Data Studio may require prior knowledge of and experience with business intelligence (BI) tools. While premade templates are available, and a growing integration library, GDS will be easier to use if you are already using other Google Marketing Platform tools.
4. Tableau: A major player in the data visualisation sector, Tableau has become a popular choice as an enterprise solution for data analysis and visualisation. Combining the powers of a BI platform with a data charting application, Tableau is not strictly used for marketing data (unlike GDS). Its integration with different data sources, from typical CSV and MS Excel files to cloud servers like Amazon, Microsoft and others, opens its application to users from multiple functions as well as marketing. Tableau integrates with the major marketing analytics platforms such as Google Analytics and Adobe Analytics.
Tableau offers several products, starting with a free-of-charge version (with limitations), Tableau Public. But the high cost of a company-wide deployment remains one of its main drawbacks. Users with previous BI software experience will benefit the most of the platform’s advanced capabilities, while beginners will likely need to dedicate time to learn and upskill themselves.
Like all other skills, data visualisation requires regular practice and experimentation with different tools and technologies to improve your skills and output.
Although marketers may face initial barriers towards adopting a data-driven marketing approach – including costs, technological integration and deployment, and training and upskilling of the workforce – the results will eventually justify the investment. The ability to confidently analyse and visualise your data will empower you to better engage and communicate internally and with your customers – and get your narrative across with maximum power and persuasiveness.
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