The Metric with Details visualization is designed to monitor a single business KPI while providing quick access to the underlying data through filters and drill-down capabilities. Unlike charts that compare multiple categories or visualize trends, Metric with Details focuses on one key metric, making it ideal for monitoring performance at a glance while still allowing deeper analysis when needed.

The example above shows Sales w/Tax yesterday, comparing with the same day in previous year. It shows simply one total number and the changes in absolute and percentage.

You can also compare the selected metric against another time period, budget, or forecast to quickly evaluate business performance.

Metric with Details is the ideal visualization when you want to:

Typical use cases include:

Metric with Details supports all available business metrics in Zoined.

For example, you can monitor:

The selected metric can easily be changed at any time without recreating the report, allowing you to analyze different aspects of your business using the same report configuration.

Metric with Details supports all standard report filters, allowing you to focus on exactly the data you want to analyze.

Common filters include:

These filters can be combined to create highly specific analyses for different business scenarios.

Metric with Details makes it easy to compare the selected KPI with another period or target.

Available comparison options include:

This helps you quickly understand whether your business is performing above or below expectations.

Metric with Details supports drill-down analysis, allowing you to move from a high-level KPI to the underlying data.

For example, you can start by viewing total sales and then drill down into more detailed levels, such as:

The available drill-down levels depend on the selected grouping and report configuration.

Use Metric with Details when your goal is to monitor a single business KPI while maintaining the flexibility to filter, compare, and drill down into the underlying data.

It is particularly useful for:

Unlike Line Graphs, which emphasize trends, or Heat Maps, which reveal patterns across two dimensions, Metric with Details keeps the focus on one KPI while providing quick access to the detailed information behind it.

A Time series (with detail) is designed to compare values across time period, making it easy to identify the trend (increasing, decreasing, fluctuating) of one selected metric.

Unlike Line Graphs or Bar Graphs which is more flexible in the x-axis dimension selection, Time series focuses more on the fixed time-based x-axis.

The example above shows how Retail Sales (€) has changed from the beginning of the year (01.01.2026) to the current date (27.07.2026). The x-axis represents the timeline, grouped by Month, while the y-axis shows the Retail Sales (€) value. From the chart, we can see that Retail Sales have gradually decreased over the months, with sales in July being almost three times lower than in January. The chart also displays the corresponding values for the previous year, making it easy to compare seasonal patterns and performance over time. With this visualization, you can quickly identify trends in your selected metric and see the percentage change throughout the selected period.

Time Series (with Details) is the ideal visualization when you want to:

Typical use cases include:

Like other visualizations in Zoined, a Time series (with detail) supports standard report configuration options, including:

For example:

Sales (w/ Tax) with time period: Year to Date compared with Previous Year.

Use a Time series (with details) when your goal is to compare performance over time.

A Time series (with details) is particularly useful for:

A Metric summary is designed to get an overview of all of the selected metrics inside one table,to show the numeric changes in absolute, percentage or index format. It is the combination of multiple Metric (with details) in same table

A Metric Summary is the ideal visualization when you want to:

Typical use cases include:

A Metric Summary is particularly useful for management reports and dashboards where several important KPIs need to be reviewed together without opening a separate report for each metric.

A Data Table is designed to display detailed business data in a structured tabular format, making it easy to review, compare, sort, and analyze large amounts of information.

The Data Table above displays Sales w/Tax (€), Retail Sales (€), and Sales (pcs) grouping by Store for the Year to Date period, while comparing each metric with the Previous Year Corresponding Period. Each row represents a store, allowing you to compare multiple metrics and their performance side by side.

For example, from the table, we can see that London generated the highest Sales w/Tax (€) at €382,917, followed by New York and Tokyo. When comparing with the previous year, Los Angeles recorded the strongest sales growth, increasing Sales w/Tax (€) by 56.05%, while London, Helsinki, and Lagos also showed significant growth. In contrast, Beijing has no comparison values because there was no corresponding data for the previous year.

A Data Table is the ideal visualization when you want to:

Data Tables support sorting for the first left most metrics.

You can sort the results in:

Sorting makes it easy to focus on the information most relevant to your analysis.

Like other visualizations in Zoined, a Data Table supports standard report configuration options, including:

For example:

Sales (w/ Tax), Retail Sales and Sales (pcs) with time period: Year to Date compared with Previous corresponding period, grouping by Store.

The Data Table also provides several additional settings to customize how data is displayed.

Available settings include:

One of the main advantages of a Data Table is that it can easily be exported for further analysis or reporting after you created it.

This makes it particularly useful when working with detailed business data that needs to be shared or processed outside Zoined.

Use a Data Table when your goal is to analyze detailed business data and review exact values rather than summarized visualizations.

It is particularly useful for:

Unlike charts that emphasize trends or patterns, a Data Table provides the complete underlying dataset, making it the preferred visualization when accuracy and detailed analysis are the primary focus.

A Crosstab is an advanced version of the Data Table that provides greater flexibility for organizing and analyzing data in a tabular format. A crosstab (cross-tabulation or contingency table) is a data analysis tool that displays the frequency distribution and intersection of two or more categorical variables, allowing us to see the relationship between them and identify any patterns or trends.

Unlike a Data Table, where the table layout is fixed, a Crosstab allows you to customize how data is displayed by defining both the row and column layouts. This enables you to compare business dimensions side by side and create reports tailored to your analysis.

In addition to its flexible layout, Crosstab offers a wider range of table settings and formatting options

The example above displays Sales w/Tax (€) by Product Name and Code (rows) and Store (columns) for the Rolling 4 Full Weeks.

Each row represents a product, while each column represents a store. For every store, the table displays the current Sales w/Tax (€) alongside the corresponding value from the previous year, making it easy to compare both locations and time periods simultaneously.

For example, from the Crosstab, we can quickly identify which stores generate the highest sales for each product. For example, CHIC STYLES GOWNS 15 achieved its highest sales in London (€591), followed by Helsinki (€461) and Beijing (€443). We can also identify products that perform particularly well in specific stores. For example, HEGE SCARF generated €2,700 in Helsinki, while recording little or no sales in the other displayed stores.

A Crosstab is the ideal visualization when you want to:

Suppose you want to compare product sales across different stores.

A Data Table would typically display:

While this shows the total sales for each product, it doesn't tell you which stores contributed to those sales.

A Crosstab organizes the same data like this:

With this layout, apply with the show changes in absolute, percentage, index or proportion to the total, you can quickly answer questions such as:

Because products are displayed as rows and stores as columns, you can compare performance across both dimensions at the same time without switching between reports.

Crosstab offers the same operations including sorting, filter, comparing period and export data the same with Data Table.

The Crosstab provides several additional settings to customize how data is displayed comparing to Data Table.

In addition to the standard comparison modes available in the Data Table, the Crosstab introduces two additional proportion modes:

These modes help you understand the contribution of values within a column or a row, making it easier to identify patterns and compare performance.

Column % displays each value as a percentage of the column total.

Using the example below, where:

Column % answers the question:

"Within this store, what percentage of total sales comes from each product?"

For example, if a product shows 3.41% under Amsterdam, it means that product generated 3.41% of Amsterdam's total sales during the selected period.

This makes it easy to identify:

Row % displays each value as a percentage of the row total.

Using the same example:

Row % answers the question:

"For this product, what percentage of its total sales comes from each store?"

For example, if Amsterdam shows 19.03% for a product, it means 19.03% of that product's total sales were generated by the Amsterdam store.

This makes it easy to identify:

The Crosstab provides several additional settings to customize how data is displayed.

For this setting, you can:

Use a Crosstab when you need to compare two business dimensions simultaneously in a flexible, tabular format.

A Crosstab is particularly useful for:

Unlike a Data Table, which has a fixed layout, a Crosstab allows you to organize data by both rows and columns, making it easier to compare performance across multiple dimensions. Features such as Column % and Row % provide additional insights into how values contribute to the total, helping you identify top-performing products, stores, categories, or other business dimensions more effectively.

A Pie Chart is a circular visualization that displays how each grouping contributes to the total value of a selected metric. Each slice represents a grouping, and its size is proportional to its share of the whole.

Pie Charts are ideal for understanding the distribution of a metric and quickly identifying the largest and smallest contributors.

The example below shows Sales (w/Tax €) by Store for the Rolling 4 Full Weeks period. The chart displays the top-performing stores together with their percentage contribution to total sales, while the remaining stores are grouped into Other for a cleaner and more readable visualization.

A Pie Chart is the ideal visualization when you want to:

Typical use cases include:

Pie Charts work best when you want to emphasize proportions rather than exact values. They provide a clear overview of how each category contributes to the total, making it easy to identify dominant categories and compare their relative shares at a glance.

Like other visualizations in Zoined, a Pie Chart supports standard report configuration options, including:

In addition, you can customize how the chart is displayed by configuring the following settings:

These settings allow you to tailor the Pie Chart to your analysis while keeping the visualization clear and easy to interpret.

Use a Pie Chart when you want to understand how a total is distributed across different categories. It provides a simple and intuitive way to visualize the proportion each grouping contributes to a selected metric, making it easy to identify the largest and smallest contributors at a glance.

A Pie Chart is particularly useful for:

With support for Grouping, Filters, Comparison Period, customizable Label Display, and options to show Top or Selected Groups, the Pie Chart can be tailored to your reporting needs while remaining clean and easy to interpret. When many categories exist, the remaining values are automatically grouped into Other, helping keep the visualization focused and readable.

A Bar Chart is designed to compare values across different categories, making it easy to identify the highest, lowest, and relative performance of each group.

Unlike Line Graphs, which emphasize trends over time, Bar Charts focus on comparing categories at a specific point in time. This makes them ideal for comparing stores, suppliers, product categories, sales areas, or other business dimensions.

The example Bar Chart above displays Sales w/Tax (€) by Payment Type for the Year to Date period. The categorical variable, Payment Type, is plotted on the horizontal axis, while the height of each bar corresponds to the total Sales w/Tax (€). The blue bars represent the selected period, and the grey bars represent the Previous Year Corresponding Period. From the chart, we can see that Debit Card generated the highest sales during the selected period, followed closely by Cash and Credit Card. All three payment types show higher sales than during the corresponding period in the previous year, making it easy to compare both the contribution of each payment method and how sales have changed over time.

A Bar Chart is the ideal visualization when you want to:

Typical use cases include:

Like other visualizations in Zoined, a Bar Chart supports standard report configuration options, including:

In addition, you can customize how the chart is displayed by configuring the following settings:

These settings allow you to tailor the Bar Chart to your analysis while keeping the visualization clear and easy to interpret.

A Dual Axis Bar Chart extends the standard Bar Chart by displaying two related business metrics within the same visualization.

Instead of showing only one metric, you can combine a Primary Metric with a Secondary Metric, allowing you to compare related KPIs side by side.

For example:

Primary Metric

Secondary Metric

This makes it easy to compare revenue and sales volume in a single report.

Other common combinations include:

Use a Bar Chart when your goal is to compare performance across different business categories.

A Bar Chart is particularly useful for:

When you need to compare two related KPIs in the same visualization, a Dual Axis Bar Chart provides an effective way to analyze both metrics side by side. Unlike Line Graphs, which focus on trends over time, Bar Charts emphasize the differences between categories, making them ideal for performance comparisons.

A Chart (with Weather) is a specialized variation of the Bar Chart that combines weather data with business metrics for each store. Weather information is automatically retrieved based on the geographical location of each store, allowing you to analyze how weather conditions may influence business performance.

By displaying business metrics alongside weather data, this visualization makes it easier to identify patterns and understand how factors including temperature and weather condition (sunny, cloudy, rainy) correlate with sales and other key performance indicators. One example is showing below.

A Chart (with Weather) is the ideal visualization when you want to:

Like other visualizations in Zoined, a Chart (with weather) supports standard report configuration options, including:

One important note: To visualize weather information:

Similar to Bar Chart, you can also select the secondary metric and showing the dual bar chart with weather.

Use a Chart (with Weather) when you want to understand how weather conditions may influence business performance. By combining weather data with business metrics in a single visualization, it becomes easier to identify trends and compare performance under different weather conditions.

A Chart (with Weather) is particularly useful for:

Like a standard Bar Chart, it supports Grouping, Filters, Comparison Periods, and Dual Axis Charts, while automatically enriching your data with weather information based on each store's location. This makes it a valuable visualization for businesses where customer behavior and sales are influenced by changing weather conditions.

The stacked chart is the extension of the standard bar chart where we can look not only at numeric values across one grouping variable but two. Each bar in a standard bar chart is divided into a number of sub-bars stacked end to end, each one corresponding to a level of the second grouping variable.

When there is only one bar to be plotted, a pie chart might be considered as an alternative to the stacked bar chart. However, when considering multiple pie charts, multiple stacked bar charts will tend to take up less space, allowing for an easier view of the full data.

The stacked bar chart above displays Sales (w/Tax €) for the Rolling 4 Full Weeks, grouped by Store and subdivided by Product Category. The primary grouping is Store, allowing you to compare total sales across locations. We can see that London generated the highest sales during the selected period, followed by Tokyo and New York, while eCommerce recorded the lowest sales. Each bar is stacked by Product Category, making it easy to compare the contribution of each category within a store. Across most stores, Children Indoor contributes the largest share of sales, followed by Children Special and Childrens Clothing. Smaller categories such as Children Outdoor, Women Shoes, and Other account for a relatively small proportion of sales in most locations. This visualization makes it easy to compare both overall store performance and the sales composition of each store at the same time.

A Stacked Bar Chart is the ideal visualization when you want to:

Typical use cases include:

Unlike a standard Bar Chart, which compares only the total value for each group, a Stacked Bar Chart also shows how that total is made up of different subgroups. Each bar is divided into colored segments representing a secondary grouping, making it easy to see both the overall performance and the contribution of each subgroup at the same time.

This makes the Stacked Bar Chart particularly useful when you want to compare totals while also understanding the composition behind those totals.

Like other visualizations in Zoined, a Stacked Chart supports standard report configuration options, including:

In addition, you can customize how the chart is displayed by configuring the following settings:

These settings allow you to tailor the Bar Chart to your analysis while keeping the visualization clear and easy to interpret.

Stacked bar charts can be set to show percentage, or relative frequency. Here, each primary bar is scaled to have the same height, so that each sub-bar becomes a percentage contribution to the whole at each primary grouping level. This removes our ability to compare the primary category levels’ totals, but allows us to perform a better analysis of the secondary groups’ relative distributions. The fixing of the heights of each primary bar to be the same also creates another baseline at the top of the chart where a second subgroup can be tracked across primary bars.

A Stacked Bar Chart uses two groupings:

Choosing the right order of these groupings is important, as it determines what questions the visualization answers.

Using the example below:

This layout allows you to compare the total sales of each store while also seeing how much each Product Category contributes to those sales. For example, you can quickly identify that London generated the highest sales and that Children Indoor contributes the largest share of sales in most stores.

If you reverse the groupings:

the focus changes. Instead of comparing stores, you compare the total sales of each Product Category, while seeing how each store contributes to that category's sales.

As a general guideline, choose the grouping that represents your main business question as the Primary Grouping, and use the Secondary Grouping to explain how each total is composed. This makes the visualization easier to read and helps highlight the insights that matter most.

There are some limitations to keep in mind when interpreting the chart.

It is easy to compare the total height of each bar, making it simple to identify which primary group has the highest or lowest value.

However, comparing the size of an individual segment across multiple bars is more difficult. Only the segment positioned at the bottom of the stack shares a common baseline. The remaining segments start at different positions, making it harder to judge whether they increase or decrease between groups.

While a Stacked Bar Chart clearly shows how a total is composed, it is not always easy to compare similarly sized segments within the same bar. Small differences between categories can be difficult to distinguish, especially when many segments are displayed.

Use a Stacked Bar Chart when your goal is to understand the composition of a total and the relative contribution of each subgroup. If your primary goal is to compare the values of individual subgroups across categories, consider using a Bar Chart, Grouped Bar Chart, or Line Chart instead. These visualizations make it easier to compare individual values with greater accuracy.

Use a Stacked Bar Chart when you want to compare overall performance while also understanding how each total is composed. By combining two groupings in a single visualization, it allows you to see both the total value for each primary group and the contribution of each subgroup at the same time.

A Stacked Bar Chart is particularly useful for:

To get the most meaningful insights, choose the grouping that represents your main business question as the Primary Grouping, and use the Secondary Grouping to show how each total is made up. While Stacked Bar Charts are excellent for understanding composition, they are less suitable for comparing individual subgroup values across groups. In those cases, a Bar Chart, Grouped Bar Chart, or Line Chart may provide a clearer comparison.

A bubble chart is an extension of the scatter plot used to look at relationships between three grouping variables. Each dot in a bubble chart corresponds with a single data point, and the variables’ values for each point are indicated by horizontal position, vertical position, and dot size.

The Bubble Chart above displays Sales w/Tax (€), Sales (pcs), and No. of Orders (excl. Returns) for stores during the Rolling 4 Full Weeks. Each bubble represents the performance of a single store. A bubble's horizontal position shows the total Sales w/Tax (€), while its vertical position shows the total Sales (pcs). The size of each bubble indicates the Number of Orders (excl. Returns), with larger bubbles representing stores that processed more customer orders.

From the chart, we can see a clear positive relationship between Sales w/Tax (€) and Sales (pcs)—stores that generate higher revenue also tend to sell more products. The third variable, Number of Orders, adds further insight by showing which stores achieved their results through a larger volume of customer transactions. For example, stores in the upper-right area of the chart combine high revenue, high sales volume, and a high number of orders, while stores in the lower-left represent lower overall sales performance. This makes it easy to identify top-performing stores, spot outlier

A Bubble Chart is ideal when you want to explore the relationship between three numeric metrics in a single visualization. Unlike a Scatter Plot, which compares two metrics, a Bubble Chart adds a third dimension by using the size of each bubble to represent another metric.

This allows you to analyze how three business measures relate to one another at the same time, making it easier to identify trends, clusters, and outliers.

Typical use cases include:

A Bubble Chart is particularly useful when you want to answer questions such as:

By visualizing three metrics in a single chart, a Bubble Chart provides insights that would otherwise require multiple Scatter Plots to uncover. It makes it easier to understand not only the relationship between each pair of metrics, but also how all three interact simultaneously, helping you identify patterns that may not be obvious when viewing the metrics separately.

Like other visualizations in Zoined, a Bubble Chart supports standard report configuration options, including:

A Bubble Chart is most effective when the relationship between all three metrics helps answer a clear business question. Before creating the chart, choose metrics that complement each other and provide meaningful insights when viewed together.

The horizontal and vertical axes should represent the two metrics that are most important to compare directly. The bubble size should add valuable context rather than simply repeating the same story.

Example

A Bubble Chart showing Sales w/Tax (€), Sales (pcs), and Number of Orders allows you to understand the relationship between revenue, sales volume, and customer transactions. Using Sales (€), Sales (pcs), and Retail Sales (€) together, however, would provide less additional insight because the metrics are closely related.

Bubble Charts work best when they display a manageable number of data points. Too many bubbles can overlap, making the chart difficult to read and interpret.

If your analysis focuses on comparing the exact values of a single metric across many groups, a Bar Chart or Data Table may provide a clearer visualization.

Bubble size represents the magnitude of a metric and is therefore most suitable for positive values. Metrics that frequently contain negative values are generally better displayed on the horizontal or vertical axis, or visualized using another chart type.

A Bubble Chart is a powerful visualization for exploring the relationship between three numeric metrics in a single view. By combining the horizontal axis, vertical axis, and bubble size, it helps you identify trends, correlations, clusters, and outliers that may be difficult to spot when analyzing each metric separately.

Bubble Charts are particularly useful when comparing the performance of stores, products, suppliers, or other business dimensions across multiple key performance indicators. By visualizing three related metrics simultaneously, they provide valuable insights into how different aspects of your business interact.

To create an effective Bubble Chart, choose metrics that complement one another, keep the number of data points manageable, and use bubble size to represent a meaningful third metric. When used appropriately, a Bubble Chart offers a clear and intuitive way to uncover relationships between business metrics and support better data-driven decisions.

A Heat Map is designed to visualize data using color intensity, making it easy to identify trends, patterns, peaks, and low-performing areas at a glance.

Instead of comparing the length of bars or the position of points on a chart, a Heat Map uses color to represent the relative value of each data point. This allows you to quickly identify where values are highest or lowest without comparing individual numbers.

The example Heat Map above displays the Number of Orders (excl. Returns) over the Last 7 Days, grouped by Date on the horizontal axis and Hour of the Day on the vertical axis. Each cell represents the number of orders placed during a specific hour on a specific day. Like a standard data table, each cell displays the numeric value, but it is also color-coded, with higher values shown in warmer colors and lower values in cooler colors. From the Heat Map, we can quickly identify the busiest and quietest periods. We can see that order activity generally peaks between 12:00 and 16:00, with the highest activity occurring around 14:00 on 24 July and 25 July, where 31 orders were recorded. In contrast, the late evening hours consistently show lower order volumes. This makes it easy to identify daily patterns, peak business hours, and periods of lower customer activity at a glance.

A Heat Map is the ideal visualization when you want to:

Typical use cases include:

Like other Zoined visualizations, Heat Maps are highly configurable.

You can choose:

For example:

Metric

X-axis

Y-axis

In addition, you can customize how the chart is displayed by configuring the following settings:

These settings allow you to tailor the Bar Chart to your analysis while keeping the visualization clear and easy to interpret.

Each cell in the Heat Map represents the value for a specific combination of the X-axis and Y-axis.

For example:

Category × Calendar Month

or

Store × Day of Week

The color intensity represents the relative value of that combination.

This allows you to quickly identify peaks, low-performing areas, and recurring patterns without comparing individual values across the chart.

One of the biggest advantages of a Heat Map is its ability to reveal patterns that might be difficult to spot in other visualizations.

For example, you can quickly identify:

Rather than focusing on individual values, Heat Maps help you understand the overall distribution of your data.

Use a Heat Map when your goal is to quickly identify patterns, trends, and variations across two business dimensions.

A Heat Map is particularly useful for:

Unlike Bar Charts, which compare values directly, or Line Graphs, which emphasize trends over time, Heat Maps use color intensity to make patterns and anomalies immediately visible. This makes them an excellent choice when you want to quickly understand the overall distribution of your data rather than focus on individual values.

A Metric Trend visualization compares the value of a selected metric between two time periods and highlights which groups have experienced the largest increases and decreases. Metric Trend will show two horizontal bar graph, one for increases change and one for decreases change.

Instead of focusing only on the current values, the visualization emphasizes how much each group has changed, making it easy to identify top performers, declining performers, and significant changes.

The visualization supports both Absolute Change and Percentage Change, allowing you to analyze changes from different perspectives.

The Metric Trend above displays Sales w/Tax (€) by Store, comparing the Rolling 4 Full Weeks with the Previous Year Corresponding Period.

Each row represents a store. The grey bar shows the value during the comparison period, while the colored bar highlights the change between the two periods.

In the first example, the stores are sorted by largest positive change. Helsinki shows the strongest improvement, increasing sales by 22%, followed by Amsterdam (10%) and Lagos (7%). Beijing shows no percentage change despite having the highest sales value.

The second example displays stores with the largest declines. New York experienced the greatest decrease at -34%, followed by Los Angeles (-20%) and London (-17%). Sorting the visualization by the magnitude of change makes it easy to identify which stores require further investigation.

A Metric Trend is ideal when you want to understand how performance has changed between two time periods.

It helps you quickly identify which groups have improved the most, which have declined, and where additional analysis may be required.

Typical use cases include:

Unlike a standard Bar Chart, which focuses on the current values, a Metric Trend highlights the change itself, making it easier to spot significant increases and decreases.

Like other visualizations in Zoined, a Metric Trend supports standard report configuration options, including:

Additional visualization settings include:

These settings allow you to focus on either the largest numerical changes or the largest relative changes.

Metric Trend allows you to analyze change in two different ways.

Displays the actual numeric difference between the selected period and the comparison period.

Example

A store increases sales from €40,000 to €50,000.

Absolute Change = +€10,000

This mode is useful when you want to understand the real business impact.

Displays the relative increase or decrease compared to the previous value.

Example

A store increases sales from €40,000 to €50,000.

Percentage Change = +25%

This mode is useful when comparing groups of different sizes.

Use Absolute Change when the size of the increase or decrease is most important.

Use Percentage Change when comparing groups with very different volumes.

Example

A €5,000 increase may be significant for a small store but relatively minor for a flagship location.

Sorting the visualization helps surface the most important changes immediately.

For example, sorting by Percentage Change highlights the fastest-growing or fastest-declining stores, while sorting by Absolute Change highlights the largest business impact.

Large increases or decreases often indicate opportunities or problems worth investigating.

Examples include:

Metric Trend helps you identify these outliers quickly before drilling down into the underlying data.

A Metric Trend helps you understand how performance changes over time by comparing a metric across two periods. By highlighting both positive and negative changes, it enables you to quickly identify top performers, declining groups, and unusual trends.

Whether you analyze Absolute Change or Percentage Change, Metric Trend provides a clear overview of where performance is improving or deteriorating, helping you focus your analysis on the areas that matter most.

A Line Graph is designed to visualize how your business metrics change over time, making it one of the best visualizations for identifying trends, comparing performance across different periods, and monitoring business development.

In a typical example, you might visualize Year to Date Sales, grouped by Month and split by Sales Channel, allowing you to compare how your physical stores and e-commerce have performed throughout the year.

The example Line Chart above displays Sales w/Tax (€) by Month for the Year to Date period. The horizontal axis represents the months from January to July, while the vertical axis shows the Sales w/Tax (€) value. Two lines are displayed for each business area: the darker lines represent the selected period (2026), and the lighter lines represent the Previous Year Corresponding Period (2025). From the chart, we can see that Stores generated significantly higher sales than eCommerce throughout the selected period. Sales for Stores peaked in January, remained relatively strong through March, and then declined steadily towards July. In contrast, eCommerce maintained a much lower but relatively stable level of sales, with only minor fluctuations throughout the period. By displaying both periods together, the Line Chart makes it easy to identify overall trends, seasonal patterns, and changes in performance over time.

A Line Graph is the ideal visualization when you want to:

Typical use cases include:

The most common use of a Line Graph is to display a business metric over a selected time period.

For example:

Time Period

Grouping

Split by

The visualization then displays separate lines for each sales channel, making it easy to compare how each channel has developed throughout the selected period.

Common time groupings include:

Although Line Graphs are most commonly used to visualize data over time, they can also be used to compare data across other business dimensions.

The horizontal axis does not have to represent a time period. Instead, you can group the data by different dimension available in Zoined, allowing you to visualize trends and compare performance in different ways.

Some common grouping options include:

For example, instead of displaying Sales by Month, you could create a Line Graph that shows Sales by Category. This provides a simple way to compare the performance of different product categories while still benefiting from the familiar Line Graph visualization.

Tip: While Line Graphs can be used with non-time groupings, they are generally most effective when the data has a natural order, such as dates, months, weekdays, or ordered business categories. For comparing unordered categories, a Bar Chart may often provide a clearer visualization.

One of the strengths of the Line Graph is its interactive legend.

Each line in the chart can be temporarily hidden or displayed without modifying the report.

For example, if the chart contains:

you can simply click E-commerce in the legend to hide that line and focus only on the stores. Clicking it again will display it once more.

This allows you to quickly focus on the data that is most relevant to your analysis.

Hovering over a data point displays additional comparison information, including:

For example:

This makes it easy to understand not only how values have changed, but also the magnitude of that change.

If you prefer to see all values at once, you can enable Data Labels, which display the value for every point directly in the chart.

Like many other Zoined visualizations, Line Graphs support drill-down analysis.

For example:

By selecting a month, you can drill down to view the daily performance for that specific month.

After drilling down, you can continue interacting with the chart, including hiding or showing individual lines, just as in the original report.

You can return to the previous view at any time using the Back button.

Line Graphs support several comparison options, making it easy to evaluate performance against historical or planned data.

Available comparison options include:

When a comparison period is selected:

A Line Graph can display both a Primary Metric and a Secondary Metric in the same visualization.

For example:

Primary Metric

Secondary Metric

When a secondary metric is added:

This allows you to compare two related business metrics within a single visualization.

Other useful combinations include:

When a Line Graph contains many groups, such as a large number of stores, the visualization can become difficult to read.

To simplify the chart, you can limit the visualization to the Top N results.

For example:

The remaining stores are automatically combined into an Other category using Top + Other.

This option can be set by using Show dropdown and select suitable Entries to show.

This provides a cleaner visualization while still including the performance of all remaining groups.

You can also temporarily hide individual stores directly from the legend, making it easy to focus on only the stores you want to analyze.

Line Graphs can be added to dashboards just like any other visualization component.

Dashboard components can be resized to better fit your dashboard layout, making it easy to combine multiple Line Graphs with other visualizations.

For example, a dashboard could contain:

This provides a quick overview of multiple business trends in a single dashboard.

Use a Line Graph when your primary goal is to understand how data changes over time or to compare trends between multiple groups.

A Line Graph is particularly useful for:

Although Line Graphs are most commonly used with time-based data, they can also be used with non-time groupings such as Category, Supplier, or Sales Area, providing a flexible way to compare business performance across many different dimensions.

Comparison Analysis is designed to help you understand what has changed between two time periods and identify the business dimensions responsible for those changes.

Unlike traditional charts that focus on displaying current values, Comparison Analysis highlights the differences between two datasets, making it easier to investigate increases, decreases, and the factors driving those changes.

The visualization combines a stacked chart with change analysis, allowing you to compare both the distribution of values and the magnitude of change in a single report.

The Comparison Analysis visualization uses stacked bars to show both the total value and the contribution of each grouping for every category.

In the example above, the metric is Sales w/Tax (€), grouped by Day of Week and stacked by Store. Each stacked bar represents the total sales for a particular day, while each colored segment represents one store's contribution to that day's total sales.

For example, on Thursday, the total sales amount to €28,920 during the selected period. The stacked bar is divided into colored segments representing Beijing, New York, eCommerce, Amsterdam, London, and Other. The size of each colored segment shows how much that store contributed to the total sales for Thursday. We can immediately see that Other contributes the largest share of sales, followed by London, while eCommerce contributes only a small proportion.

For every day, the visualization compares the selected period with the Previous Year Corresponding Period. Above each pair of bars, the report displays:

For example, Thursday generated €28,920 in sales during the selected period, compared with €27,353 during the previous year, representing an increase of 5.7%. In contrast, Sunday generated €7,411, a decrease of 10.7% compared to the previous year.

By combining stacked bars with comparison values and percentage changes, Comparison Analysis makes it easy to understand how overall performance has changed, which groups contribute most to the total, and which groups are driving positive or negative changes over time.

Comparison Analysis is the ideal visualization when you want to:

Typical use cases include:

Unlike a standard stacked chart, Comparison Analysis focuses on the Top Changers rather than simply the highest-performing categories.

For example, when displaying the top five entries:

This allows you to immediately focus on the business areas that have changed the most instead of only seeing the largest contributors.

Comparison Analysis provides several ways to evaluate changes between periods.

You can choose to display:

Shows the numerical difference between the selected periods.

For example:

Shows the percentage difference between the selected periods.

This makes it easier to compare relative growth across categories of different sizes.

The Change Index combines both the absolute change and the percentage change to highlight the most significant business changes.

This helps prioritize the categories that have the greatest overall impact instead of focusing only on large values or large percentages individually.

One of the most powerful features of Comparison Analysis is the Change view.

Instead of displaying total sales, the visualization displays the actual change between the selected periods:

This makes it much easier to identify what is driving changes in your business.

Use Comparison Analysis when your goal is to understand how business performance has changed between two periods and identify the factors driving those changes.

It is particularly useful for:

Unlike Bar Charts, which compare current values, or Line Graphs, which focus on trends over time, Comparison Analysis is specifically designed to explain what changed and help you quickly identify the underlying causes of those changes.