What is Data Analytics?

Data analytics is the science of analyzing raw data in order to make conclusions about that information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.

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Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize processes to increase the overall efficiency of a business or system.

Understanding Data Analytics

Data analytics is a broad term that encompasses many diverse types of data analysis. Any type of information can be subjected to data analytics techniques to get insight that can be used to improve things.


For example, manufacturing companies often record the runtime, downtime, and work queue for various machines and then analyze the data to better plan the workloads so the machines operate closer to peak capacity.


Data analytics can do much more than point out bottlenecks in production. Gaming companies use data analytics to set reward schedules for players that keep the majority of players active in the game. Content companies use many of the same data analytics to keep you clicking, watching, or re-organizing content to get another view or another click.


The process involved in data analysis involves several different steps: 

  1. The first step is to determine the data requirements or how the data is grouped. Data may be separated by age, demographic, income, or gender. Data values may be numerical or be divided by category.
  2. The second step in data analytics is the process of collecting it. This can be done through a variety of sources such as computers, online sources, cameras, environmental sources, or through personnel.
  3. Once the data is collected, it must be organized so it can be analyzed. Organization may take place on a spreadsheet or other form of software that can take statistical data.
  4. The data is then cleaned up before analysis. This means it is scrubbed and checked to ensure there is no duplication or error, and that it is not incomplete. This step helps correct any errors before it goes on to a data analyst to be analyzed.

Key Takeaways

  • Data analytics is the science of analyzing raw data in order to make conclusions about that information.
  • The techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.
  • Data analytics help a business optimize its performance.


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4 Ways to Use Data Analytics

Data has the potential to provide a lot of value to businesses, but to unlock that value, you need the analytics component. Analysis techniques give businesses access to insights that can help them to improve their performance. It can help you improve your knowledge of your customers, ad campaigns, budget and more.

As the importance of data analytics in the business world increases, it becomes more critical that your company understand how to implement it. Some  benefits of data analytics include:

1. Improved Decision Making

Companies can use the insights they gain from data analytics to inform their decisions, leading to better outcomes.

Data analytics eliminates much of the guesswork from planning marketing campaigns, choosing what content to create, developing products and more. It gives you a 360-degree view of your customers, which means you understand them more fully, enabling you to better meet their needs. Plus, with modern data analytics technology, you can continuously collect and analyze new data to update your understanding as conditions change.

2. More Effective Marketing

When you understand your audience better, you can market to them more effectively. Data analytics also gives you useful insights into how your campaigns are performing so that you can fine-tune them for optimal outcomes.

Using the Lotame Campaign Analytics tool, you can gain insights into which audience segments are most likely to interact with a campaign and convert. You can use this information to adjust your targeting criteria either manually or through automation, or use it to develop different messaging and creative for different segments. Improving your targeting results in more conversions and less ad waste.

3. Better Customer Service

Data analytics provide you with more insights into your customers, allowing you to tailor customer service to their needs, provide more personalization and build stronger relationships with them. 

Your data can reveal information about your customers’ communications preferences, their interests, their concerns and more. Having a central location for this data also ensures that your whole customer service team, as well as your sales and marketing teams, are on the same page.

4. More Efficient Operations

Data analytics can help you streamline your processes, save money and boost your bottom line. When you have an improved understanding of what your audience wants, you waste less time on creating ads and content that don’t match your audience’s interests.

This means less money wasted as well as improved results from your campaigns and content strategies. In addition to reducing your costs, analytics can also boost your revenue through increased conversions, ad revenue or subscriptions.


What Insights Can You Gain From Data Analytics?

By collecting various kinds of data from numerous sources, you can gain insights into your audiences and campaigns that help you improve your targeting and better predict future customer behavior.

One valuable type of data is information about customer behaviors. This refers to data about specific actions that a user takes. They might, for instance, click on an ad, make a purchase, comment on a news article or like a social media post.

This and other types of data can reveal information about customer affinities — expressed or suggested interest in activities, products, brands and topics. A customer may express interest in your brand by signing up for your email list. They may also indirectly express interest in a topic by reading about it on your website. They may express interest in a product by clicking on one of your ads for it. Some other potential sources of customer affinity data include survey responses, social media likes and video views.

By combining this data with information about your current customers’ demographics, you can gain insights into the customer segments that are most likely to be interested in your brand, content or products. Demographic information includes information about customers’ ages, genders, income, marital status and various other characteristics. For example, you might find, through data analytics, that people between the ages of 18 and 35 are the most likely to purchase your product. You might also find that people who are married make up most of your website’s audience. By targeting multiple characteristics, you can create more specific audiences who are highly likely to convert.

You can then use this information to predict the behaviors of various types of users and target your ads and content more effectively. 


Types of Data Analytics

Data analytics is broken down into four basic types. 

  1. Descriptive analytics describes what has happened over a given period of time. Have the number of views gone up? Are sales stronger this month than last?
  2. Diagnostic analytics focuses more on why something happened. This involves more diverse data inputs and a bit of hypothesizing. Did the weather affect beer sales? Did that latest marketing campaign impact sales?
  3. Predictive analytics moves to what is likely going to happen in the near term. What happened to sales the last time we had a hot summer? How many weather models predict a hot summer this year?
  4. Prescriptive analytics suggests a course of action. If the likelihood of a hot summer is measured as an average of these five weather models is above 58%, we should add an evening shift to the brewery and rent an additional tank to increase output.

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