Why Does Big Data in Marketing Need Visual Analytics?

Big data is becoming a primary factor in the marketing world, often used to make decisions and discover correlations between events. In this piece for RetailTechNews, digital branding and ad-tech writer Kayla Matthews explains that, despite this, using big data effectively still requires using visual analytics when interpreting the results.

It has become necessary to look for new ways of storing and indexing data, and at the same time, different methods of helping people interpret it. Using visual aids to bring context to data offers several advantages, including the following:

Emphasising valuable aspects

It’s typically difficult for people to look at numbers on a spreadsheet and understand their significance. However, visual analytics software can create bar graphs, pie charts, and other graphics-based representations of data that would otherwise be very difficult to comprehend.

For example, many marketers rely on big data when answering questions related to customers. They might want to gain a better understanding of how to generate leads, increase engagement, or urge people to keep giving companies their business.

Some marketing professionals also depend on big data to determine the specific aspects of a website that play the most significant roles in driving sales, or how long users usually spend browsing before they buy.

In all those cases, a simple strategy such as separating demographic groups with different colours on a graph makes it easier for viewers to keep the various aspects distinct and reduces confusion.

Saving time when delivering findings

Some people – particularly those at the executive level – may ask marketing teams to dive into big data and reach conclusions, then present them as efficiently as possible. That means they may not always have the patience to decipher intimidating amounts of data. Statistics indicate that people process visual data 60,000 times faster than text alone.

Moreover, they could request that marketing experts show them different representations of data on the fly, which is possible with visual analytics platforms, but not always when using other tools.

Spotting the outliers when looking for trends

When marketing professionals evaluate trends highlighted by big data, they often look for outliers – the things within a group that deviate from the expected norm. Focusing on the outliers and understanding their prevalence is especially important in A/B testing or when dealing with small sample sizes.

Fortunately, well-organised data can aid decision-making by letting marketers distinguish the outliers, sort through content quickly, and reach informed conclusions about how to accomplish goals. It may also become evident that the outliers represent a group of people with unmet needs that marketers can address.

Providing dynamic resources

There are particular cases where visual analytics are especially valuable, such as when looking at financial data or evaluating documents. Visual interpretations of data add worthiness when working with data that might often change, such as sales territory maps, complete with features showing where the most loyal customers reside.

Analysts point out that it’s necessary to think of visual analytics as being similar to software, meaning it’s easy to update. One of the reasons big data is so essential for marketers is because it profiles how things change over time.

Visual analytics should reflect those changes by being dynamic. If they do, the time it takes to make decisions often gets shorter, because people can access visuals that use updated information and interactivity to solidify comprehension.

Helping people absorb the facts

The insights big data delivers may become intimately familiar to the marketers who work with it every day. However, people who are seeing the data for the first time – such as audience members listening to a marketing presentation – may feel initially overwhelmed by the content they’re receiving. Also, some people naturally understand image-based data more than figures and statements.

In these cases, it’s critical to help individuals feel well-equipped for optimal understanding. Visual analytics can achieve that goal by not only increasing immediate and accurate comprehension but also by promoting long-term retention.

Visual analytics should not be overlooked

Regardless of the frequency with which marketers work with big data, and for what purpose, visual analytics make it more palatable. Therefore, they should prioritise incorporating it into their marketing research methods. This content was originally published in RetailTechNews.