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14 Best Types of Charts and Graphs for Data Visualization [+ Guide]

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14 Best Types of Charts and Graphs for Data Visualization [+ Guide]

There are more types of charts and graphs than ever before because there’s more data. In fact, the volume of data in 2025 will be almost double the data we create, capture, copy, and consume today.

This makes data visualization essential for businesses. Different types of graphs and charts can help you:

  • Motivate your team to take action
  • Impress stakeholders with goal progress
  • Show your audience what you value as a business

Data visualization builds trust and can organize diverse teams around new initiatives. Let’s talk about the types of graphs and charts that you can use to grow your business.

Channels like social media or blogs have multiple sources of data and when you manage these complex content assets it can get overwhelming. What should you be tracking? What matters most? How do you visualize and analyze the data so you can extract insights and actionable information?

1. Identify your goals for presenting the data.

Do you want to convince or clarify a point? Are you trying to visualize data that helped you solve a problem, or are you trying to communicate a change that’s happening?

A chart or graph can help you compare different values, understand how different parts impact the whole, or analyze trends. Charts and graphs can also be useful for recognizing data that veers away from what you’re used to or help you see relationships between groups.

Clarify your goals, then use them to guide your chart selection.

2. Figure out what data you need to achieve your goal.

Different types of charts and graphs use different kinds of data. Graphs usually represent numerical data, while charts are a visual representation of data that may or may not use numbers.

So, while all graphs are a type of chart, not all charts are graphs. If you don’t already have the kind of data you need, you might need to spend some time putting your data together before building your chart.

3. Gather your data.

Most businesses collect numerical data regularly, but you may need to put in some extra time to collect the right data for your chart. Besides quantitative data tools that measure traffic, revenue, and other user data, you might need some qualitative data.

These are some other ways you can gather data for your data visualization:

  • Interviews
  • Quizzes and surveys
  • Customer reviews
  • Reviewing customer documents and records
  • Community boards

4. Select the right type of graph or chart.

Choosing the wrong visual aid or defaulting to the most common type of data visualization could cause confusion for your viewer or lead to mistaken data interpretation.

But a chart is only useful to you and your business if it communicates your point clearly and effectively.

To help find the right chart or graph type, ask yourself the questions below.

Then, take a look at 14 types of charts and graphs you can use to visualize your data and create your chart or graph.

Download the Excel templates mentioned in the video here.

5 Questions to Ask When Deciding Which Type of Chart to Use

1. Do you want to compare values?

Charts and graphs are perfect for comparing one or many value sets, and they can easily show the low and high values in the data sets. To create a comparison chart, use these types of graphs:

2. Do you want to show the composition of something?

Use this type of chart to show how individual parts make up the whole of something, like the device type used for mobile visitors to your website or total sales broken down by sales rep.

To show composition, use these charts:

3. Do you want to understand the distribution of your data?

Distribution charts help you to understand outliers, the normal tendency, and the range of information in your values.

Use these charts to show distribution:

4. Are you interested in analyzing trends in your data set?

If you want to know more information about how a data set performed during a specific time period, there are specific chart types that do extremely well.

You should choose a:

5. Do you want to better understand the relationship between value sets?

Relationship charts can show how one variable relates to one or many different variables. You could use this to show how something positively affects, has no effect, or negatively affects another variable.

When trying to establish the relationship between things, use these charts:

Featured Resource: The Marketer’s Guide to Data Visualization

Screen Shot 2020-04-09 at 3.09.44 PMDownload this free data visualization guide to learn which graphs to use in your marketing, presentations, or project — and how to use them effectively.

Different Types of Graphs and Charts for Presenting Data

To better understand each chart and graph type and how you can use them, here’s an overview of graph and chart types.

1. Bar Graph

A bar graph should be used to avoid clutter when one data label is long or if you have more than 10 items to compare.

Types of charts and graphs example: Bar chart - customers by role

Best Use Cases for These Types of Graphs:

Bar graphs can help you compare data between different groups or to track changes over time. Bar graphs are most useful when there are big changes or to show how one group compares against other groups.

The example above compares the number of customers by business role. It makes it easy to see that there is more than twice the number of customers per role for individual contributors than any other group.

A bar graph also makes it easy to see which group of data is highest or most common.

For example, at the start of the pandemic, online businesses saw a big jump in traffic. So, if you want to look at monthly traffic for an online business, a bar graph would make it easy to see that jump.

Other use cases for bar graphs include:

  • Product comparisons
  • Product usage
  • Category comparisons
  • Marketing traffic by month or year
  • Marketing conversions

Design Best Practices for Bar Graphs:

  • Use consistent colors throughout the chart, selecting accent colors to highlight meaningful data points or changes over time.
  • Use horizontal labels to improve readability.
  • Start the y-axis at 0 to appropriately reflect the values in your graph.

2. Column Chart

Use a column chart to show a comparison among different items, or to show a comparison of items over time. You could use this format to see the revenue per landing page or customers by close date.

Types of charts and graphs example: Column chart - customers by close date

Best Use Cases for This Type of Chart:

While column charts show information vertically, and bar graphs show data horizontally. While you can use both to display changes in data, column charts are best for negative data.

For example, warehouses often track the number of accidents that happen on the shop floor. When the number of incidents falls below the monthly average, a column chart can make that change easier to see in a presentation.

In the example above, this column chart measures the number of customers by close date. Column charts make it easy to see data changes over a period of time. This means that they have many use cases, including:

  • Customer survey data, like showing how many customers prefer a specific product or how much a customer uses a product each day.
  • Sales volume, like showing which services are the top sellers each month or the number of sales per week.
  • Profit and loss, showing where business investments are growing or falling.

Design Best Practices for Column Charts:

  • Use consistent colors throughout the chart, selecting accent colors to highlight meaningful data points or changes over time.
  • Use horizontal labels to improve readability.
  • Start the y-axis at 0 to appropriately reflect the values in your graph.

3. Line Graph

A line graph reveals trends or progress over time and you can use it to show many different categories of data. You should use it when you chart a continuous data set.

Types of graphs example: Line chart - avg days to close

Best Use Cases for These Types of Graphs:

Line graphs help users track changes over short and long periods of time. Because of this, these types of graphs are good for seeing small changes.

Line graphs can help you compare changes for more than one group over the same period. They’re also helpful for measuring how different groups relate to each other.

A business might use this type of graph to compare sales rates for different products or services over time.

These charts are also helpful for measuring service channel performance. For example, a line graph that tracks how many chats or emails your team responds to per month.

Design Best Practices for Line Graphs:

  • Use solid lines only.
  • Don’t plot more than four lines to avoid visual distractions.
  • Use the right height so the lines take up roughly 2/3 of the y-axis’ height.

4. Dual Axis Chart

A dual-axis chart allows you to plot data using two y-axes and a shared x-axis. It has three data sets. One is a continuous set of data and the other is better suited to grouping by category. Use this chart to visualize a correlation or the lack thereof between these three data sets.

Types of charts and graphs example: Dual axis chart - revenue by new customers

Best Use Cases for This Type of Chart:

A dual-axis chart makes it easy to see relationships between different data sets. They can also help with comparing trends.

For example, the chart above shows how many new customers this company brings in each month. It also shows how much revenue those customers are bringing the company.

This makes it simple to see the connection between the number of customers and increased revenue.

You can use dual-axis charts to compare:

  • Price and volume of your products
  • Revenue and units sold
  • Sales and profit margin
  • Individual sales performance

Design Best Practices for Dual Axis Charts:

  • Use the y-axis on the left side for the primary variable because brains are naturally inclined to look left first.
  • Use different graphing styles to illustrate the two data sets, as illustrated above.
  • Choose contrasting colors for the two data sets.

5. Area Chart

An area chart is basically a line chart, but the space between the x-axis and the line is filled with a color or pattern. It is useful for showing part-to-whole relations, like showing individual sales reps’ contributions to total sales for a year. It helps you analyze both overall and individual trend information.

Types of charts and graphs example: Area chart - users by lifecycle stage

Best Use Cases for These Types of Charts:

Area charts help show changes over time. They work best for big differences between data sets and also help visualize big trends.

For example, the chart above shows users by creation date and life cycle stage.

A line chart could show that there are more subscribers than marketing qualified leads. But this area chart emphasizes how much bigger the number of subscribers is than any other group.

These types of charts and graphs make the size of a group and how groups relate to each other more visually important than data changes over time.

Area graphs can help your business to:

  • Visualize which product categories or products within a category are most popular
  • Show key performance indicator (KPI) goals vs. outcomes
  • Spot and analyze industry trends

Design Best Practices for Area Charts:

  • Use transparent colors so information isn’t obscured in the background.
  • Don’t display more than four categories to avoid clutter.
  • Organize highly variable data at the top of the chart to make it easy to read.

6. Stacked Bar Chart

Use this chart to compare many different items and show the composition of each item you’re comparing.

Types of charts and graphs example: Stacked bar chart - mqls to sqls

Best Use Cases for These Types of Graphs:

These graphs are helpful when a group starts in one column and moves to another over time.

For example, the difference between a marketing qualified lead (MQL) and a sales qualified lead (SQL) is sometimes hard to see. The chart above helps stakeholders see these two lead types from a single point of view– when a lead changes from MQL to SQL.

Stacked bar charts are excellent for marketing. They make it simple to add a lot of data on a single chart or to make a point with limited space.

These types of graphs can show multiple takeaways, so they’re also super for quarterly meetings when you have a lot to say, but not always a lot of time to say it.

Stacked bar charts are also a smart option for planning or strategy meetings. This is because these charts can show a lot of information at once, but they also make it easy to focus on one stack at a time or move data as needed.

You can also use these charts to:

  • Show the frequency of survey responses
  • Identify outliers in historical data
  • Compare a part of a strategy to its performance as a whole

Design Best Practices for Stacked Bar Graphs:

  • Best used to illustrate part-to-whole relationships.
  • Use contrasting colors for greater clarity.
  • Make the chart scale large enough to view group sizes in relation to one another.

7. Mekko Chart

Also known as a Marimekko chart, this type of graph can compare values, measure each one’s composition, and show data distribution across each one.

It’s similar to a stacked bar, except the Mekko’s x-axis can capture another dimension of your values— instead of time progression, like column charts often do. In the graphic below, the x-axis compares each city to one another.

Types of charts and graphs example: Mekko chart - world's largest asset managers

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Best Use Cases for This Type of Chart:

You can use a Mekko chart to show growth, market share, or competitor analysis.

For example, the Mekko chart above shows the market share of asset managers grouped by location and the value of their assets. This chart makes it clear which firms manage the most assets in different areas.

It’s also easy to see which asset managers are largest and how they relate to each other.

Mekko charts can seem more complex than other types of charts and graphs. So, it’s best to use these in situations where you want to emphasize scale or differences between groups of data.

Other use cases for Mekko charts include:

  • Detailed profit and loss statements
  • Revenue by brand and region
  • Product profitability
  • Share of voice by industry or niche

Design Best Practices for Mekko Charts:

  • Vary your bar heights if the portion size is an important point of comparison.
  • Don’t include too many composite values within each bar. You might want to reevaluate your presentation if you have a lot of data.
  • Order your bars from left to right in such a way that exposes a relevant trend or message.

8. Pie Chart

A pie chart shows a static number and how categories represent part of a whole — the composition of something. A pie chart represents numbers in percentages, and the total sum of all segments needs to equal 100%.

Types of charts and graphs example: Pie chart - customers by role

Best Use Cases for This Type of Chart:

The image above shows another example of customers by role in the company.

The bar graph example shows you that there are more individual contributors than any other role. But this pie chart makes it clear that they make up over 50% of customer roles.

Pie charts make it easy to see a section in relation to the whole, so they are good for showing:

  • Customer personas in relation to all customers
  • Revenue from your most popular products or product types in relation to all product sales
  • Percent of total profit from different store locations

Design Best Practices for Pie Charts:

  • Don’t illustrate too many categories to ensure differentiation between slices.
  • Ensure that the slice values add up to 100%.
  • Order slices according to their size.

9. Scatter Plot Chart

A scatter plot or scattergram chart will show the relationship between two different variables or reveals distribution trends. Use this chart when there are many different data points, and you want to highlight similarities in the data set. This is useful when looking for outliers or for understanding the distribution of your data.

Types of charts and graphs example: Scatter plot chart - customer happiness by response time

Best Use Cases for These Types of Charts:

Scatter plots are helpful in situations where you have too much data to quickly see a pattern. They are best when you use them to show relationships between two large data sets.

In the example above, this chart shows how customer happiness relates to the time it takes for them to get a response.

Great use cases for this type of graph make it easy to see the comparison of two data sets. This might include:

  • Employment and manufacturing output
  • Retail sales and inflation
  • Visitor numbers and outdoor temperature
  • Sales growth and tax laws

Try to choose two data sets that already have a positive or negative relationship. That said, this type of graph can also make it easier to see data that falls outside of normal patterns.

Design Best Practices for Scatter Plots:

  • Include more variables, like different sizes, to incorporate more data.
  • Start the y-axis at 0 to represent data accurately.
  • If you use trend lines, only use a maximum of two to make your plot easy to understand.

10. Bubble Chart

A bubble chart is similar to a scatter plot in that it can show distribution or relationship. There is a third data set shown by the size of the bubble or circle.

Types of charts and graphs example: Bubble chart - hours spent online by age and gender

Best Use Cases for This Type of Chart:

In the example above, the number of hours spent online isn’t just compared to the age of the user, as it would be on a scatter plot chart.

Instead, you can also see how the gender of the user impacts time spent online.

This makes bubble charts useful for seeing the rise or fall of trends over time. It also lets you add another option when you’re trying to understand relationships between different segments or categories.

For example, if you want to launch a new product, this chart could help you quickly see the cost, risk, and value of your new product. This can help you focus your energies on a new product that is low risk with a high potential return.

You can also use bubble charts for:

  • Top sales by month and location
  • Customer satisfaction surveys
  • Store performance tracking
  • Marketing campaign reviews

Design Best Practices for Bubble Charts:

  • Scale bubbles according to area, not diameter.
  • Make sure labels are clear and visible.
  • Use circular shapes only.

11. Waterfall Chart

Use a waterfall chart to show how an initial value changes with intermediate values — either positive or negative — and results in a final value.

Use this chart to reveal the composition of a number. An example of this would be to showcase how overall company revenue is influenced by different departments and leads to a specific profit number.

Types of charts and graphs example: Waterfall chart

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Best Use Cases for This Type of Chart:

These types of charts and graphs make it easier to understand how internal and external factors impact a product or campaign as a whole.

In the example above the chart moves from the starting balance on the far left to the ending balance on the far right. Factors in the center include deposits, transfers in and out, and bank fees.

A waterfall chart offers a quick visual that makes complex processes and outcomes easier to see and troubleshoot. For example, SaaS companies often measure customer churn. This format can help visualize changes in new, current, and free trial users, or changes by user segment.

You may also want to try a waterfall chart to show:

  • Changes in revenue or profit over time
  • Inventory audits
  • Employee staffing reviews

Design Best Practices for Waterfall Charts:

  • Use contrasting colors to highlight differences in data sets.
  • Choose warm colors to indicate increases and cool colors to indicate decreases.

12. Funnel Chart

A funnel chart shows a series of steps and the completion rate for each step. Use this type of chart to track the sales process or the conversion rate across a series of pages or steps.

Types of charts and graphs example: Funnel chart - marketing funnel process

Best Use Cases for These Types of Charts:

The most common use case for a funnel chart is the marketing or sales funnel. But there are many other ways to use this versatile chart.

If you have at least four stages of sequential data, this chart can help you easily see what inputs or outputs impact the final results.

For example, a funnel chart can help you see how to improve your buyer journey or shopping cart workflow. This is because it can help pinpoint major drop-off points.

Other stellar options for these types of charts include:

  • Deal pipelines
  • Conversion and retention analysis
  • Bottlenecks in manufacturing and other multi-step processes
  • Marketing campaign performance
  • Website conversion tracking

Design Best Practices for Funnel Charts:

  • Scale the size of each section to accurately reflect the size of the data set.
  • Use contrasting colors or one color in gradated hues, from darkest to lightest as the size of the funnel decreases.

13. Bullet Graph

A bullet graph reveals progress toward a goal, compares this to another measure, and provides context in the form of a rating or performance.

Types of charts and graphs example: Bullet graph - new customers

Best Use Cases for These Types of Graphs:

In the example above, this bullet graph shows the number of new customers against a set customer goal. Bullet graphs are great for comparing performance against goals like this.

These types of graphs can also help teams assess possible roadblocks because you can analyze data in a tight visual display.

For example, you could create a series of bullet graphs measuring performance against benchmarks or use a single bullet graph to visualize these KPIs against their goals:

  • Revenue
  • Profit
  • Customer satisfaction
  • Average order size
  • New customers

Seeing this data at a glance and alongside each other can help teams make quick decisions.

Bullet graphs are one of the best ways to display year-over-year data analysis. You can also use bullet graphs to visualize:

  • Customer satisfaction scores
  • Product usage
  • Customer shopping habits
  • Social media usage by platform

Design Best Practices for Bullet Graphs:

  • Use contrasting colors to highlight how the data is progressing.
  • Use one color in different shades to gauge progress.

14. Heat Map

A heat map shows the relationship between two items and provides rating information, such as high to low or poor to excellent. This chart displays the rating information using varying colors or saturation.

Types of charts and graphs example: Heat map chart - highest degree vs. class identification

Best Use Cases for Heat Maps:

In the example above, the darker the shade of green shows where the majority of people agree.

With enough data, heat maps can make a viewpoint that might seem subjective more concrete. This makes it easier for a business to act on customer sentiment.

There are many uses for these types of charts and graphs. In fact, many tech companies use heat map tools to gauge user experience for apps, online tools, and website design.

Another common use for heat map graphs is location assessment. If you’re trying to find the right location for your new store, these maps can give you an idea of what the area is like in ways that a visit can’t communicate.

Heat maps can also help with spotting patterns, so they’re good for analyzing trends that change quickly, like ad conversions. They can also help with:

  • Competitor research
  • Customer sentiment
  • Sales outreach
  • Campaign impact
  • Customer demographics

Design Best Practices for Heat Map:

  • Use a basic and clear map outline to avoid distracting from the data.
  • Use a single color in varying shades to show changes in data.
  • Avoid using multiple patterns.

Put These New Types of Charts and Graphs Into Action

Now that you’ve chosen the best graph or chart for your project, try a data visualization resource that makes your point clear and visual.

Data visualization is just one part of great communication. To show your customers, employees, leadership, and investors that they’re important, keep making time to learn.

Editor’s note: This post was originally published in November 2020 and has been updated for comprehensiveness.

Blog - Data Visualization [List-Based]

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Is Twitter Still a Thing for Content Marketers in 2023?

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Is Twitter Still a Thing for Content Marketers in 2023?

The world survived the first three months of Elon Musk’s Twitter takeover.

But what are marketers doing now? Did your brand follow the shift Dennis Shiao made for his personal brand? As he recently shared, he switched his primary platform from Twitter to LinkedIn after the 2022 ownership change. (He still uses Twitter but posts less frequently.)

Are those brands that altered their strategy after the new ownership maintaining that plan? What impact do Twitter’s service changes (think Twitter Blue subscriptions) have?

We took those questions to the marketing community. No big surprise? Most still use Twitter. But from there, their responses vary from doing nothing to moving away from the platform.

Lowest points

At the beginning of the Elon era, more than 500 big-name advertisers stopped buying from the platform. Some (like Amazon and Apple) resumed their buys before the end of 2022. Brand accounts’ organic activity seems similar.

In November, Emplifi research found a 26% dip in organic posting behavior by U.S. and Canadian brands the week following a significant spike in the negative sentiment of an Elon tweet. But that drop in posting wasn’t a one-time thing.

Kyle Wong, chief strategy officer at Emplifi, shares a longer analysis of well-known fast-food brands. When comparing December 2021 to December 2022 activity, the brands posted 74% less, and December was the least active month of 2022.

Fast-food brands posted 74% less on @Twitter in December 2022 than they did in December 2021, according to @emplifi_io analysis via @AnnGynn @CMIContent. Click To Tweet

When Emplifi analyzed brand accounts across industries (2,330 from U.S. and Canada and 6,991 elsewhere in the world), their weekly Twitter activity also fell to low points in November and December. But by the end of the year, their activity was inching up.

“While the percentage of brands posting weekly is on the rise once again, the number is still lower than the consistent posting seen in earlier months,” Kyle says.

Quiet-quitting Twitter

Lacey Reichwald, marketing manager at Aha Media Group, says the company has been quiet-quitting Twitter for two months, simply monitoring and posting the occasional link. “It seems like the turmoil has settled down, but the overall impact of Twitter for brands has not recovered,” she says.

@ahamediagroup quietly quit @Twitter for two months and saw their follower count go up, says Lacey Reichwald via @AnnGynn @CMIContent. Click To Tweet

She points to their firm’s experience as a potential explanation. Though they haven’t been posting, their follower count has gone up, and many of those new follower accounts don’t seem relevant to their topic or botty. At the same time, Aha Media saw engagement and follows from active accounts in the customer segment drop.

Blue bonus

One change at Twitter has piqued some brands’ interest in the platform, says Dan Gray, CEO of Vendry, a platform for helping companies find agency partners to help them scale.

“Now that getting a blue checkmark is as easy as paying a monthly fee, brands are seeing this as an opportunity to build thought leadership quickly,” he says.

Though it remains to be seen if that strategy is viable in the long term, some companies, particularly those in the SaaS and tech space, are reallocating resources to energize their previously dormant accounts.

Automatic verification for @TwitterBlue subscribers led some brands to renew their interest in the platform, says Dan Gray of Vendry via @AnnGynn @CMIContent. Click To Tweet

These reenergized accounts also are seeing an increase in followers, though Dan says it’s difficult to tell if it’s an effect of the blue checkmark or their renewed emphasis on content. “Engagement is definitely up, and clients and agencies have both noted the algorithm seems to be favoring their content more,” he says.

New horizon

Faizan Fahim, marketing manager at Breeze, is focused on the future. They’re producing videos for small screens as part of their Twitter strategy. “We are guessing soon Elon Musk is going to turn Twitter into TikTok/YouTube to create more buzz,” he says. “We would get the first moving advantage in our niche.”

He’s not the only one who thinks video is Twitter’s next bet. Bradley Thompson, director of marketing at DigiHype Media and marketing professor at Conestoga College, thinks video content will be the next big thing. Until then, text remains king.

“The approach is the same, which is a focus on creating and sharing high-quality content relevant to the industry,” Bradley says. “Until Twitter comes out with drastically new features, then marketing and managing brands on Twitter will remain the same.

James Coulter, digital marketing director at Sole Strategies, says, “Twitter definitely still has a space in the game. The question is can they keep it, or will they be phased out in favor of a more reliable platform.”

Interestingly given the thoughts of Faizan and Bradley, James sees businesses turning to video as they limit their reliance on Twitter and diversify their social media platforms. They are now willing to invest in the resource-intensive format given the exploding popularity of TikTok, Instagram Reels, and other short-form video content.

“We’ve seen a really big push on getting vendors to help curate video content with the help of staff. Requesting so much media requires building a new (social media) infrastructure, but once the expectations and deliverables are in place, it quickly becomes engrained in the weekly workflow,” James says.

What now

“We are waiting to see what happens before making any strong decisions,” says Baruch Labunski, CEO at Rank Secure. But they aren’t sitting idly by. “We’ve moved a lot of our social media efforts to other platforms while some of these things iron themselves out.”

What is your brand doing with Twitter? Are you stepping up, stepping out, or standing still? I’d love to know. Please share in the comments.

Want more content marketing tips, insights, and examples? Subscribe to workday or weekly emails from CMI.

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Cover image by Joseph Kalinowski/Content Marketing Institute



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45 Free Content Writing Tools to Love [for Writing, Editing & Content Creation]

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45 Free Content Writing Tools to Love [for Writing, Editing & Content Creation]

Creating content isn’t always a walk in the park. (In fact, it can sometimes feel more like trying to swim against the current.)

While other parts of business and marketing are becoming increasingly automated, content creation is still a very manual job. (more…)

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MARKETING

How data clean rooms might help keep the internet open

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How data clean rooms might help keep the internet open

Are data clean rooms the solution to what IAB CEO David Cohen has called the “slow-motion train wreck” of addressability? Voices at the IAB will tell you that they have a big role to play.

“The issue with addressability is that once cookies go away, and with the loss of identifiers, about 80% of the addressable market will become unknown audiences which is why there is a need for privacy-centric consent and a better consent-value exchange,” said Jeffrey Bustos, VP, measurement, addressability and data at the IAB.

“Everyone’s talking about first-party data, and it is very valuable,” he explained, “but most publishers who don’t have sign-on, they have about 3 to 10% of their readership’s first-party data.” First-party data, from the perspective of advertisers who want to reach relevant and audiences, and publishers who want to offer valuable inventory, just isn’t enough.

Why we care. Two years ago, who was talking about data clean rooms? The surge of interest is recent and significant, according to the IAB. DCRs have the potential, at least, to keep brands in touch with their audiences on the open internet; to maintain viability for publishers’ inventories; and to provide sophisticated measurement capabilities.

How data clean rooms can help. DCRs are a type of privacy-enhancing technology that allows data owners (including brands and publishers) to share customer first-party data in a privacy-compliant way. Clean rooms are secure spaces where first-party data from a number of sources can be resolved to the same customer’s profile while that profile remains anonymized.

In other words, a DCR is a kind of Switzerland — a space where a truce is called on competition while first-party data is enriched without compromising privacy.

“The value of a data clean room is that a publisher is able to collaborate with a brand across both their data sources and the brand is able to understand audience behavior,” said Bestos. For example, a brand selling eye-glasses might know nothing about their customers except basic transactional data — and that they wear glasses. Matching profiles with a publisher’s behavioral data provides enrichment.

“If you’re able to understand behavioral context, you’re able to understand what your customers are reading, what they’re interested in, what their hobbies are,” said Bustos. Armed with those insights, a brand has a better idea of what kind of content they want to advertise against.

The publisher does need to have a certain level of first-party data for the matching to take place, even if it doesn’t have a universal requirement for sign-ins like The New York Times. A publisher may be able to match only a small percentage of the eye-glass vendor’s customers, but if they like reading the sports and arts sections, at least that gives some directional guidance as to what audience the vendor should target.

Dig deeper: Why we care about data clean rooms

What counts as good matching? In its “State of Data 2023” report, which focuses almost exclusively on data clean rooms, concern is expressed that DCR efficacy might be threatened by poor match rates. Average match rates hover around 50% (less for some types of DCR).

Bustos is keen to put this into context. “When you are matching data from a cookie perspective, match rates are usually about 70-ish percent,” he said, so 50% isn’t terrible, although there’s room for improvement.

One obstacle is a persistent lack of interoperability between identity solutions — although it does exist; LiveRamp’s RampID is interoperable, for example, with The Trade Desk’s UID2.

Nevertheless, said Bustos, “it’s incredibly difficult for publishers. They have a bunch of identity pixels firing for all these different things. You don’t know which identity provider to use. Definitely a long road ahead to make sure there’s interoperability.”

Maintaining an open internet. If DCRs can contribute to solving the addressability problem they will also contribute to the challenge of keeping the internet open. Walled gardens like Facebook do have rich troves of first-party and behavioral data; brands can access those audiences, but with very limited visibility into them.

“The reason CTV is a really valuable proposition for advertisers is that you are able to identify the user 1:1 which is really powerful,” Bustos said. “Your standard news or editorial publisher doesn’t have that. I mean, the New York Times has moved to that and it’s been incredibly successful for them.” In order to compete with the walled gardens and streaming services, publishers need to offer some degree of addressability — and without relying on cookies.

But DCRs are a heavy lift. Data maturity is an important qualification for getting the most out of a DCR. The IAB report shows that, of the brands evaluating or using DCRs, over 70% have other data-related technologies like CDPs and DMPs.

“If you want a data clean room,” Bustos explained, “there are a lot of other technological solutions you have to have in place before. You need to make sure you have strong data assets.” He also recommends starting out by asking what you want to achieve, not what technology would be nice to have. “The first question is, what do you want to accomplish? You may not need a DCR. ‘I want to do this,’ then see what tools would get you to that.”

Understand also that implementation is going to require talent. “It is a demanding project in terms of the set-up,” said Bustos, “and there’s been significant growth in consulting companies and agencies helping set up these data clean rooms. You do need a lot of people, so it’s more efficient to hire outside help for the set up, and then just have a maintenance crew in-house.”

Underuse of measurement capabilities. One key finding in the IAB’s research is that DCR users are exploiting the audience matching capabilities much more than realizing the potential for measurement and attribution. “You need very strong data scientists and engineers to build advanced models,” Bustos said.

“A lot of brands that look into this say, ‘I want to be able to do a predictive analysis of my high lifetime value customers that are going to buy in the next 90 days.’ Or ‘I want to be able to measure which channels are driving the most incremental lift.’ It’s very complex analyses they want to do; but they don’t really have a reason as to why. What is the point? Understand your outcome and develop a sequential data strategy.”

Trying to understand incremental lift from your marketing can take a long time, he warned. “But you can easily do a reach and frequency and overlap analysis.” That will identify wasted investment in channels and as a by-product suggest where incremental lift is occurring. “There’s a need for companies to know what they want, identify what the outcome is, and then there are steps that are going to get you there. That’s also going to help to prove out ROI.”

Dig deeper: Failure to get the most out of data clean rooms is costing marketers money


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