- Data storytelling is the process of combining graphics and narratives to help audiences understand complex data
- There are eight types of graphs and charts that marketers can use to tell data stories
- This guide will help you understand why data storytelling is important and what best practices you should follow
We’re seeing the growing importance of storytelling with data in 2021—primarily because of the amount of data being shared with audiences over the past year.
But data needs to make sense to people if it’s to lead to better engagement and increased conversions. That’s where visualization comes in.
According to Venngage’s recent study, data storytelling has become a popular tool in an organization’s arsenal, with 48 percent of marketers creating data visualizations weekly.
In this article, we will share why businesses are turning to data storytelling to tell their brand stories and to capture the imagination of their customers.
Why is storytelling with data important?
Data-driven storytelling combines data and graphics to tell a compelling story. It also gives the data more context so audiences can understand it better.
The visual representation of data lies can show readers patterns and connections they may not have deduced on their own.
That’s what makes them such a necessary tool in a small business’ arsenal—data graphics can help businesses track their performance and set goals.
Kinds of data visualizations for storytelling
There are numerous visual tools available to render data—they highlight why data visualization is important.
Some of the kinds of data visualizations for storytelling include:
- Bar Graphs
- Bubble Charts
- Line Charts
- Pie Charts
- Scatter Plots
Each visualization technique serves a purpose. Bar graphs and charts are ideal for creating comparisons, whereas line charts show linear relationships.
Maps show geographical data, like this example about the languages of the world.
Pie charts share data according to set categories, while scatter plots show relationships between multiple variables.
To understand which charts and graphs to use to tell your data story, you can refer to the below infographic.
Five advantages of data storytelling
What advantages can businesses expect when storytelling through data?
These are the questions that marketers and designers ask themselves before undertaking such a design-heavy project.
But there are several uses for data graphics that make them worth investing time and effort into.
1. Provides deeper analysis into information
If you look at the types of visualizations described above, you can see how they provide greater insight into information.
A text post or report can do the same work but will require much more labor from the reader—increasing the chances of them leaving your page for shorter content.
A graphic, on the other hand, tells the reader the same information in a much shorter time. This improves engagement rates and conversions.
Visuals can also convey patterns easily allowing the reader to analyze information quickly by connecting the dots themselves.
2. Promotes problem-solving
Data stories are succinct materials that boost the problem-solving process and improve productivity.
This is because decision-makers don’t have to read reams of text or sift through information on their own—the graphics do the work for them and speed up problem-solving.
3. Engages internal and external audiences
Content marketing is geared toward engagement—and that’s why strong visuals that catch the eye are so important.
Visuals are more attractive than blocks of text—and data graphics that are well-made even more so than others.
This is because a data story is compelling in itself—numbers, percentages, relationships, and connections are all reasons for a reader to stop what they’re doing and look at your graphic.
As a result, you increase traffic and views to your content and your website, all while promoting a favorable impression of your brand.
4. Improves reporting abilities
Reports are part and parcel of business life. A great data story is key to a memorable and powerful analysis, like this simple but elegant finance infographic template.
There is so much data involved in creating reports—if they are articulated through numbers and tables, your audience will be lost, and worse, bored.
That is why great data storytelling is so important in report-making, not just to keep people interested but to tell a good story.
5. Wide reach
Graphics can be repurposed in multiple ways and for a variety of channels. Social media platforms like Twitter, which are chockful of information, require a strong visual to get attention.
That attention can be generated through data storytelling. Bite-sized visuals arrest the viewer as they’re scrolling through their feed—they’re also easy to absorb and more shareable.
Visualized data makes for great content whether for social channels, newsletters, blog posts, or website landing pages.
A great graphic has the potential to go viral, widening the reach of your content and influence.
Data storytelling best practices
Paying heed to the importance of visualizing data means following a few best practices. You can’t create visuals without having a goal.
You also need to understand the subject matter and the needs of your audience so your data tells the story you want it to and engages your readers.
Here are the six best practices for creating visualizations that will boost customer retention.
Create visual hierarchies
Hierarchies are necessary for people to read and interpret your data. Visual hierarchies are a key component of data storytelling because they help readers create context and patterns.
Since you don’t want to write too much text to explain your graphic, hierarchies are the best way to convey context. Here are the best ways to build visual hierarchies and context:
- Placement of elements from top to bottom
- Grouped elements
- Varying colors
- Varied visual styles
- Increasing font sizes
Users will be able to deduce the relationship between data and elements using the above methods.
Build trust into data visuals
The benefits of visualization are completely lost if you can’t elicit trust in the people viewing your information.
When we put statistics together for studies at Venngage, we survey hundreds, if not thousands, of respondents before beginning the design process.
This is necessary to avoid cherry-picking data, which can be misleading, as this graph shows, and accidentally designing bad infographics.
It is always best to compile data from trusted sources that are unbiased. Verify that data with at least two other sources so you know that the data is representative of the information.
Only then should you move into the design phase. When creating your visuals, avoid distortion as much as possible by following these methods:
- Choose charts and graphs that suit your data
- Your visual should include a scale to give context to the data
- Baselines for data should always start at zero
- Both axes should appear in the graphic and be equal in size
- Use all relevant data in the visual; don’t leave important data out
Size plays a major factor in trust-building—use similar-sized visual elements, like icons, that can be scaled on a graph.
Show changes in data through size and space but both should be equal between all visual elements.
Keep visualizations simple
Pulling together data requires a great deal of time and effort. It can be tempting to design visuals that express as much information as possible.
But that mindset can negate the effectiveness of visually representing data, and overwhelm your audience.
Visualizations should be simple and easy to understand—not only is this a brand design trend in 2021, but it keeps readers more engaged, like this chart we created.
While a complex visualization may look sophisticated and interesting, if your audience spends too much time trying to understand it, they’re going to eventually give up and move on.
A badly-designed graphic, like the one below, will also give readers a negative impression of your brand and product, losing you more potential customers.
Data graphics should be simple enough to understand at a glance—that’s all the time you have to get users’ attention.
Don’t overuse text
If your data story needs more text to understand it, the visual isn’t well-designed. While there needs to be some text in the graphic, it shouldn’t dominate the image.
You can always write a blog or social media post around your findings, but your readers shouldn’t be lost without the context.
The benefits of data-driven storytelling lie in the fact that your information can be communicated through the visual medium.
If you’re relying on text to do all the talking, your graphic is lacking. Use graphic elements like icons and shapes, and break your data down into bite-sized portions so it’s easy to convey.
Use colors wisely in visualizations
Colors have a lot to do with the importance of data visualization storytelling—they can be used to highlight key information in a graphic and augment the data story you are trying to tell.
But that doesn’t mean you use all the colors in the palette in your graphic. Again, too many colors, like too much information, can overwhelm the audience.
On the flip side, by using too few colors, you can mistakenly create connections between data that aren’t correct.
Use your brand colors in your visualizations, and augment them with two or three colors. Try not to exceed five colors or five hues of a single color.
If you’re wondering what kinds of colors work together, you can use this list to choose color combinations.
Use muted colors in your graphics, instead of bold ones, as that is what is on-trend at the moment and will make your visuals more relevant to audiences.
Highlight data in visuals
As much as you want users to understand the data as you present it to them in a visual, you aim to capture their attention as quickly as possible.
Even the simplest visuals need some highlights to draw the eye and it’s a great way to maintain the integrity of your data story.
Use a highlight color to make relevant data stand out or increase the font size or icon size to do the same.
By spotlighting the most important information, you will be more successful in attracting attention to your visual and telling your data story.
Businesses can leverage the importance of data storytelling
We’ve highlighted how data storytelling can make a difference in business growth in 2021.
Graphics share insights and correlations that audiences may have overlooked, while still being compelling tools that engage and convert customers.
The post Unlocking the secrets of data storytelling in 2021 appeared first on Search Engine Watch.
Beginning in 2011, search marketers began to lose visibility over the organic keywords that consumers were using to find their websites, as Google gradually switched all of its searches over to secure search using HTTPS.
As it did so, the organic keyword data available to marketers in Google Analytics, and other analytics platforms, slowly became replaced by “(not provided)”. By 2014, the (not provided) issue was estimated to impact 80-90% of organic traffic, representing a massive loss in visibility for search marketers and website owners.
Marketers have gradually adjusted to the situation, and most have developed rough workarounds or ways of guessing what searches are bringing customers to their site. Even so, there’s no denying that having complete visibility over organic keyword data once more would have a massive impact on the search industry – as well as benefits for SEO.
One company believes that it has found the key to unlocking “(not provided)” keyword data. We spoke to Daniel Schmeh, MD and CTO at Keyword Hero, a start-up which has set out to solve the issue of “(not provided)”, and ‘Wizard of Moz’ Rand Fishkin, about how “(not provided)” is still impacting the search industry in 2017, and what a world without it might look like.
Content produced in association with Keyword Hero.
“(not provided)” in Google Analytics: How does it impact SEO?
“The “(not provided)” keyword data issue is caused by Google the search engine, so that no analytics program, Google Analytics included, can get the data directly,” explains Rand Fishkin, founder and former CEO of Moz.
“Google used to pass a referrer string when you performed a web search with them that would tell you – ‘This person searched for “red shoes” and then they clicked on your website’. Then you would know that when people searched for “red shoes”, here’s the behavior they showed on your website, and you could buy ads against that, or choose how to serve them better, maybe by highlighting the red shoes on the page better when they land there – all sorts of things.”
“You could also do analytics to understand whether visitors for that search were converting on your website, or whether they were having a good experience – those kinds of things.
“But Google began to take that away around 2011, and their reasoning behind it was to protect user privacy. That was quickly debunked, however, by folks in the industry, because Google provides that data with great accuracy if you choose to buy ads with them. So there’s obviously a huge conflict of interest there.
“I think the assumption at this point is that it’s just Google throwing their weight around and being the behemoth that they can be, and saying, ‘We don’t want to provide this data because it’s too valuable and useful to potential competitors, and people who have the potential to own a lot of the search ranking real estate and have too good of an idea of what patterns are going on.
“I think Google is worried about the quality and quantity of data that could be received through organic search – they’d prefer that marketers spend money on advertising with Google if they want that information.”
Where Google goes, its closest competitors are sure to follow, and Bing and Yandex soon followed suit. By 2013, the search industry was experiencing a near-total eclipse of visibility over organic keyword data, and found itself having to simply deal with the consequences.
“At this point, most SEOs use the data of which page received the visit from Google, and then try to reverse-engineer it: what keywords does that page rank for? Based on those two points, you can sort of triangulate the value you’re getting from visitors from those keywords to this page,” says Fishkin.
However, data analysis and processing have come a long way since 2011, or even 2013. One start-up believes that it has found the key to unlocking “(not provided)” keyword data and giving marketers back visibility over their organic keywords.
How to unlock “(not provided)” keywords in Google Analytics
“I started out as a SEO, first in a publishing company and later in ecommerce companies,” says Daniel Schmeh, MD and CTO of SEO and search marketing tool Keyword Hero, which aims to provide a solution to “(not provided)” in Google Analytics. “I then got into PPC marketing, building self-learning bid management tools, before finally moving into data science.
“So I have a pretty broad understanding of the industry and ecosystem, and was always aware of the “(not provided)” problem.
“When we then started buying billions of data points from browser extensions for another project that I was working on, I thought that this must be solvable – more as an interesting problem to work on than a product that we wanted to sell.”
Essentially, Schmeh explains, solving the problem of “(not provided)” is a matter of getting access to the data and engineering around it. Keyword Hero uses a wide range of data sources to deduce the organic keywords hidden behind the screen of “(not provided)”.
“In the first step, the Hero fetches all our users’ URLs,” says Schmeh. “We then use rank monitoring services – mainly other SEO tools and crawlers – as well as what we call “cognitive services” – among them Google Trends, Bing Cognitive Services, Wikipedia’s API – and Google’s search console, to compute a long list of possible keywords per URL, and a first estimate of their likelihood.
“All these results are then tested against real, hard data that we buy from browser extensions.
“This info will be looped back to the initial deep learning algorithm, using a variety of mathematical concepts.”
Ultimately, the process used by Keyword Hero to obtain organic keyword data is still guesswork, but very advanced guesswork.
“All in all, the results are pretty good: in 50 – 60% of all sessions, we attribute keywords with 100% certainty,” says Schmeh.
“For the remainder, at least 83% certainty is needed, otherwise they’ll stay (not provided). For most of our customers, 94% of all sessions are matched, though in some cases we need a few weeks to get to this matching rate.”
If the issue of “(not provided)” organic keywords has been around since 2011, why has it taken us this long to find a solution that works? Schmeh believes that Keyword Hero has two key advantages: One, they take a scientific approach to search, and two, they have much greater data processing powers compared with six years ago.
“We have a very scientific approach to SEO,” he says.
“We have a small team of world-class experts, mostly from Fraunhofer Institute of Technology, that know how to make sense of large amounts of data. Our background in SEO and the fact that we have access to vast amounts of data points from browser extensions allowed us to think about this as more of a data science problem, which it ultimately is.
“Processing the information – the algorithm and its functionalities – would have worked back in 2011, too, but the limiting factor is our capability to work with these extremely large amounts of data. Just uploading the information back into our customers’ accounts would take 13 hours on AWS [Amazon Web Services] largest instance, the X1 – something we could never afford.
“So we had to find other cloud solutions – ending up with things that didn’t exist even a year ago.”
A world without “(not provided)”: How could unlocking organic keyword data transform SEO?
If marketers and website owners could regain visibility over their organic keywords, this would obviously be a huge help to their efforts in optimizing for search and planning a commercial strategy.
But Rand Fishkin also believes it would have two much more wide-reaching benefits: it would help to prove the worth of organic SEO, and would ultimately lead to a better user experience and a better web.
“Because SEO has such a difficult time proving attribution, it doesn’t get counted and therefore businesses don’t invest in it the way they would if they could show that direct connection to revenue,” says Fishkin. “So it would help prove the value, which means that SEO could get budget.
“I think the thing Google is most afraid of is that some people would see that they rank organically well enough for some keywords they’re bidding on in AdWords, and ultimately decide not to bid anymore.
“This would cause Google to lose revenue – but of course, many of these websites would save a lot of money.”
And in this utopian world of keyword visibility, marketers could channel that revenue into better targeting the consumers whose behavior they would now have much higher-quality insights into.
“I think you would see more personalization and customization on websites – so for example, earlier I mentioned a search for ‘red shoes’ – if I’m an ecommerce website, and I see that someone has searched for ‘red shoes’, I might actually highlight that text on the page, or I might dynamically change the navigation so that I had shades of red inside my product range that I helped people discover.
“If businesses could personalize their content based on the search, it could create an improved user experience and user performance: longer time on site, lower bounce rate, higher engagement, higher conversion rate. It would absolutely be better for users.
“The other thing I think you’d see people doing is optimizing their content efforts around keywords that bring valuable visitors. As more and more websites optimized for their unique search audience, you would generally get a better web – some people are going to do a great job for ‘red shoes’, others for ‘scarlet sandals’, and others for ‘burgundy sneakers’. And as a result, we would have everyone building toward what their unique value proposition is.”
Daniel Schmeh adds that unlocking “(not provided)” keyword data has the ability to make SEO less about guesswork and more substantiated in numbers and hard facts.
“Just seeing simple things, like how users convert that use your brand name in their search phrase versus those who don’t, has huge impact on our customers,” he says. “We’ve had multiple people telling us that they have based important business decisions on the data.
“Seeing thousands of keywords again is very powerful for the more sophisticated, data-driven user, who is able to derive meaningful insights; but we’d really like the Keyword Hero to become a standard tool. So we’re working hard to make this keyword data accessible and actionable for all of our users, and will soon be offering features like keyword clustering – all through their Google Analytics interface.”
To find out more about how to unlock your “(not provided)” keywords in Google Analytics, visit the Keyword Hero website.