Data can help businesses better understand their customers, improve their advertising campaigns, personalize their content and improve their bottom lines.
However, you will need the right data analytics tools and processes before reaping the many benefits of having this information at your disposal. Raw data can have tons of potential, but you’ll need data analytics to unlock the power to grow your business. Without analytical technologies, your business cannot make the most informed and advantageous decisions to supercharge its future.
We are breaking down everything you need to know about data analytics and how to use it to optimize and streamline your operations.
What is analytical data? These datasets help draw conclusions through data analytics. With this technique, you can take raw data and discover any patterns to then gain useful insights.
Data analytics processes often involve using special software with capabilities like machine learning algorithms and automation.
Data Scientists and Analysts use data analytics technology and techniques in their research, and businesses also use it to inform their decisions. Data analysis can help companies learn more about their customers, evaluate their ad campaigns, personalize content, create content strategies and develop products. Ultimately, businesses can use data analytics to boost business performance and improve their bottom line.
Businesses can acquire data in a few different ways. Some utilize historical data or collect information from a specific initiative. They can also source their data directly from their customers or website visitors. Many companies choose to purchase data from professional organizations, too.
There are three main types of data that businesses can utilize to optimize their operations, including the following:
The use of data analytics may include information about an audience’s demographics, interests, behaviors and more.
Utilizing different types of data analytics allows businesses to paint the full picture of their operations, using essential information from the past, the present and accurate projections for the future.
Each type of analytics is useful in its own way, and they are even more advantageous for your business when they work together. Explore the primary kinds of data analytics and how they can positively impact your business:
The primary question descriptive analytics answers for your operation is what has happened historically in your data environment. The purpose of this type is to describe the past in easily digestible visualizations, such as:
The information gathered via descriptive analytics can also take the form of a narrative to describe what has occurred. Having a snapshot of your data history and what is happening right now allows companies to identify patterns and trends among their customers and processes.
Descriptive analytics helps highlight cause-and-effect relationships across your operation using data aggregation and mining. Ultimately, this type offers your business a historic overview by condensing large data sets into simple breakdowns.
While descriptive analytics demonstrates what has happened, diagnostic analytics aims to answer why something has occurred. Its primary purpose is to identify and explain why certain anomalies or situations happen.
The techniques used for diagnostic analytics include:
All of these processes help analyze raw data and determine the reasons behind past events and performance to inform the future.
Diagnostic analytics helps pinpoint where and why problems are happening, making it easier to refine your processes and address customer concerns. It plays a key role in decision-making and how your business refines its operations to serve consumers better and boost your bottom line.
Predictive analytics utilizes historical data to anticipate future trends with accuracy. It aims to answer the questions of what is likely to occur down the road and why, based on what has happened in the past.
The most common techniques used to inform predictive analytics include:
Automated tools comb through the data sets to identify cause and effect to project how the future is likely to unfold. Predictive analytics helps businesses minimize the guesswork in their operations so they can make better, more informed decisions about everything from anticipating staffing needs to optimizing inventory management.
Finally, prescriptive analytics aims to supercharge your predictive data by taking it one step further. It helps your business anticipate likely future outcomes while also suggesting how to respond to optimize your processes.
Prescriptive analytics analyzes how various choices can impact your company, and it recommends a specific course of action to help your company thrive. It uses machine learning, advanced algorithms and simulations to deliver a road map for your future business decisions.
Working with this type of data analytics can give your operation an incredible competitive edge. The combination of these analytics can help elevate nearly every aspect of your business.
Data has the potential to provide a lot of value to businesses. 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 enhance 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 understands how to implement it. Some uses of data analytics include:
Companies can use the insights they gain from data analytics to inform their decision-making and harness better results.
Analytics eliminates much of the guesswork from the following processes:
These tools offer a comprehensive view of your customer base, meaning your business will be able to adequately meet and satisfy their needs. Today’s technology is also beneficial in collecting and analyzing brand-new data in real time, giving your company the opportunity to always be in the know when conditions are changing.
Effectively marketing to your target audience is much easier when you have a 360-degree view of their behaviors.
Data analytics offers powerful insights regarding the performance of marketing campaigns, allowing your team to assess what is and is not working to achieve the best outcomes.
With Lotame’s next-generation data platform, Spherical, your business has valuable insights into your most-engaged audience segments. Determine how well a campaign will convert and adjust targeting criteria — either using automation or manual processes — to personalize content for different segments.
Your business can save time, money and energy by improving your targeting and boosting conversion rates.
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.
Data analytics has many advantages, including:
With a greater understanding of who your target audience is and what they want, your company can spend less time creating campaigns and content that do not align with your market’s interests.
In addition to wasting less money, you can enjoy the benefits of better campaign outcomes and more strategic content. Your business can harness analytics to:
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 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.
Data analytics is nothing new. Today, though, the growing volume of data and the advanced analytics technologies available mean you can get much deeper data insights more quickly. The insights that big data and modern technologies make possible are more accurate and more detailed. In addition to using data to inform future decisions, you can also use current data to make immediate decisions.
Some of the technologies that make modern data analytics so powerful are:
Artificial intelligence (AI) is the field of developing and using computer systems that can simulate human intelligence to complete tasks. Machine learning (ML) is a subset of AI that is significant for data analytics and involves algorithms that can learn on their own. ML enables applications to take in data and analyze it to predict outcomes without someone explicitly programming the system to reach that conclusion. You can train a machine learning algorithm on a small sample of data, and the system will continue to learn as it gathers more data, becoming more accurate as time goes on.
Having ML tools is more important than ever, especially because experts anticipate a 37.3% annual growth rate for artificial intelligence technologies from 2023 to 2030.
Before you can analyze data, you need to have procedures in place for managing the flow of data in and out of your systems and keeping your data organized. You also need to ensure that your data is high-quality and that you collect it in a central data management platform (DMP) where it’s available for use when needed. Establishing a data management program can help ensure that your organization is on the same page regarding how to organize and handle data.
The term data mining refers to the process of sorting through large amounts of data to identify patterns and discover relationships between data points. It enables you to sift through large datasets and figure out what’s relevant. You can then use this information to conduct analyses and inform your decisions. Today’s data mining technologies allow you to complete these tasks exceptionally quickly.
Again, predictive analytics technology helps you analyze historical data to predict future outcomes and the likelihood of various outcomes occurring. These technologies typically use statistical algorithms and machine learning. More accurate predictions mean businesses can make better decisions moving forward and position themselves to succeed. It allows them to anticipate their customer’s needs and concerns, predict future trends and stay ahead of the competition.
Some of the key advantages of having predictive analytics tools include:
Let’s look at a few quick examples of data analytics and how you might collect data and analyze it to help improve outcomes for your business.
Let’s say you are a marketer who’s running an online ad campaign to promote a new smartphone. You might start by targeting the ad to people who bought the previous version of the phone in question. As your campaign runs, you use data analytics techniques to sift through the data generated when people clicked on the ad.
By examining data about these users’ interests, perhaps you discover many of them are interested in photography. This may be because your new phone has a better camera than the previous model. Using this information, you could fine-tune your ad to focus on users who bought the previous phone and like photography. You could also find new audiences of people who didn’t buy the older phone but are interested in taking photos.
As another data analytics example, let’s say you publish a site that features videos about sports. As people visit your site, you could collect data about which videos different visitors watch as well as how highly they rate the videos, which ones they comment on and more. You could also gather information about the demographics of each user. You can use data analytics tools to determine which audience segments are most likely to watch certain videos. You can then suggest videos to people based on the segments they fit into best. For example, you might find that older men are most likely to be interested in golf, while younger men are most likely to be interested in basketball.
For some real-life examples of how Lotame’s data analytics tools have helped business drive improved results, check out our case studies.
While data analytics can provide many benefits to the companies that use it, it’s not without its challenges. Working with the right partners and using the right tools can help businesses to overcome these difficulties.
One of the biggest challenges related to data analytics is collecting the data. There’s a lot of data that businesses could potentially collect, and they need to determine what to prioritize. Collecting data requires tools that can gather data from website visits, ad clicks and other interactions and deliver it in a usable format.
Once you collect your data, you need somewhere to store itn. This can take up a considerable amount of space and contain many different types of information. You have to integrate both structured and unstructured data from online, offline, internal and external sources.
You also need to ensure data quality so your results are accurate. In addition, your data needs to be accessible and not siloed so everyone throughout your organization has the same repository. Lastly, you need to have the knowledge and tools necessary to make sense of the data and provide insights.
At Lotame, we help marketers and media companies use data to engage existing customers and attract their next best. We create smarter, faster, and easier ways to drive growth and derive value from customer data — whether you have it or need it.
Our platform, Spherical, offers customer intelligence capabilities to help examine data and unlock valuable insights. Gain a 360-degree view of your audiences by analyzing data signals across user attributes and behaviors. These insights can help you to improve your targeting, campaign performance and content personalization.
You’ll also gain insights into the behaviors that make up campaign interactions and how those attributes impact the path to conversion. Whatever actions you’re tracking, Spherical can help you improve campaign targeting and find new audiences. These analytics processes help you understand your audience better and boost the performance of your campaigns.
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Want to learn more about how we can help you turn your data into actionable insights you can use to improve your campaigns, personalize your content and reach your business goals? Fill out the form below and contact us today!