What is Time-Series Data?: Patterns, Uses and Examples.

Summary/Overview

Time-series data is data that’s collected over a set period to track changes and help predict trends. Popular amongst marketers, time-series data can show patterns in customer behaviour across varying periods, whether that’s across a year, a month or even a day.

Tracking and analysing information this way can provide insight for teams in content planning, marketing analysis and business strategy. Adobe Express can be used to convert this data into easy-to-understand charts and graphs.

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What is time-series data?

Time-series data, also known as temporal data, is the process of regularly capturing information from a set data point. For example, you might record the number of visitors on your site at 12 pm every day for a month. The number of visitors would naturally fluctuate day to day. However, by collecting this information over a longer period, it’s easier to identify anomalies.

The interval at which you record your data is up to you and will be determined by the information you’re trying to capture. The important thing is consistency, because it makes spotting patterns simpler.

The ability to identify patterns makes time-series data popular among those who want to measure the impact of business decisions. This could be marketers who want to understand how well sales perform in the days after an email is sent out – something you might be asked to present to stakeholders. Another example could be business strategists analysing daily traffic to determine opening hours for a physical store.

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Types of time-series data.

There are various types of time-series data, and you may encounter a mix of alternatives in your role. Defined by the pattern or cycle on which you capture the data, some will work better than others depending on what you want to do.

• Trend data. Looks at the overall direction of the data over a specific period rather than the peaks and troughs you might experience.
• Seasonal data. This includes regular occurrences over a shorter period in the year, for example, a spike in toy sales leading up to the festive period.
• Cyclical data. This type of data fluctuates in a repeating pattern that isn’t tied to a season, but isn’t over a long enough period to be a trend.
• Irregular or random data. As the name might suggest, irregular data shows unpredictable changes that can’t be accounted for.
• Stationary vs. non-stationary data. Stationary data has statistics that don’t change over time, for example the hours in a day. Meanwhile, non-stationary data could change – e.g. the number of parcels delivered in a day.

Time-series data examples.

Time-series data has a broad range of applications, and the information can be used by a variety of businesses to make decisions and uncover trends.

• Sales data. Time-series data can be used to identify periods when sales peak but also slump and can help inform strategic decisions for teams.
• Inventory data. Keep track of your businesses stock and predict when new shipments will be needed.
• Marketing engagement data. Track the impact of your marketing efforts, whether it’s click-throughs, purchases or movement along the funnel.
• Web and App analytics. A common example of time-series data can be found in analytics tools, where they’re used to provide information on how users interact with sites and apps.
• Weather data. This can be used to identify anomalies in recent weather, predict future weather and more.
• Stock data. Track how the stock market is performing to try and predict the best times to invest your money.
• Heart rate monitoring. Whether it’s for medical or fitness reasons, time-series data can provide insight into any unusual fluctuations in your bpm, and help you track changes over a long period.
• Social media activity. If you’re a marketer, you can use time-series data to track the performance of your social media platforms, checking for click-through rates, mentions and more.
• Energy consumption. Useful for businesses big and small, those looking for greener alternatives or improving the output of their products, it’s possible to track your energy consumption as time-series data.

Why businesses use time-series data analysis.

For almost all businesses, across all industries, data can be used as a tool to provide important insights. Explore the various ways businesses use time-series data.

Sales and Revenue Forecasting.

Better Forecasting

• Make informed predictions about sales, demand, or customer behaviour.

Improved Timing and Scheduling

• Launch campaigns or offers at the most effective times based on historical data.

Cost Efficiency

• Avoid over-ordering or under-stocking by understanding seasonal demand patterns.

Anomaly Detection

• Spot sudden drops or spikes that may indicate technical issues, fraud, or external impacts.

Marketing and Campaign Performance.

Trend identification

• Time-series data is useful for identifying long-term trends in your marketing metrics.

Performance tracking

• Understand how your marketing efforts are affecting your performance with before and after comparisons.

Predictive analytics

• Time-series data is essential in forecasting future performance, something that is crucial for planning campaigns.

Optimised timing for marketing

• Learn when audiences are most responsive, when it’s best to launch new campaigns, and how to optimise your marketing outputs.

Website and App Traffic Monitoring.

Performance benchmarks

• Historical data can be used to establish benchmarks, which can help identify the impact of changes to your site.

Understand customer behaviour

• Monitor trends in your chosen metric, whether that’s bounce rate, session duration or pages per session.

Improve UX

• Use time-series data to understand how design or functional changes are impacting customer behaviour.

Device and geographic trends over time

• Understand the changes in how people access your site, and any shifts in the regional or geographic locations for your customers.

Employee productivity and resource allocation.

Track productivity trends over time

• You can use time-sensitive data to identify peak productivity periods and improvement (or declines) in productivity.

Plan resources in a strategic way

• If you’re able to identify business spikes, for example, during the Christmas period or over summer, then you can carefully plan your resources.

Detect burnout or overloads

• Being able to see dips in productivity and spikes in workload can help you identify and prevent burnout amongst staff.

Improve team communication

• Time-series data can show your teams how well they’re performing, opening conversations around what can be done better.

What is the difference between time series and cross-sectional data?

Cross-sectional data is data that is collected at a single point in time but spread across multiple factors, such as products, regions, or people. This information serves to give you a snapshot of what’s happening right now, as opposed to what’s happening over a period of time. For example, a marketing team might survey 100 customers in a single day to assess their satisfaction with a recent campaign.

Time-series data, on the other hand, collects its information from the same set of sources, but over a chosen period – whether that’s an hour, day, week, month or year. The aim of this type of data is to show changes and reveal patterns or cycles.

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Useful things to know.

What is a time series data set?

A time-series data set is the collection of data at regular intervals over a give period. How regular and how long the period, is up to you and will likely be determined by what you’re trying to collate. The aim of a time-series data set is to understand trends or changes over time.

What are the four components of time series data?

• Trend. This is the long-term direction of data over an extended period.
• Seasonality. The regular repeating of patterns at a fixed interval (a year, month, day, etc)
• Cyclical patterns. Fluctuations that happen over a long period but aren’t seasonal. Typically, they will occur due to economic or business changes.
• Random or irregular variations. Short-term fluctuations that can be caused by unusual or one-off events.

How do I store time-series data?

How you choose to store time-series data will depend on your set-up and situation. Many companies purpose-build databases to store time-series data, especially if data storage is a continuous process. Smaller operations might prefer to use SQLs, or NoSQL databases.

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