In 2019, most marketing decisions are not based on analytics. That’s despite a 39 percent increase in analytics-influenced marketing decisions from 2013 to 2019, according to a CMO report. Investment in marketing analytics has increased by 20% over the same period[i].
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Regardless of how analytics results are used, marketers continue to spend a lot of money on them. According to Gartner’s latest CMO spending survey, marketers are spending a lot of money on marketing analytics—16 percent of their budgets. That’s more than is spent on activities like content creation, marketing operations, or branding. This raises a big question: Are enough good ideas coming out of that money? (
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To answer this question, I often recommend that marketing leaders divide their thinking into three categories. The figure below summarizes these three categories of concepts and the analytical techniques that can generate them.
Does your marketing plan communicate all three concepts? Do you think your marketing insights need more reach? Does your understanding indicate where more investment is worthwhile? I have two suggestions to improve your knowledge:
If you use prescriptive analytics to inform your marketing decisions, I’d love to hear from you. Whether your first attempt at acceptance analysis failed, or you’re an optimization veteran with jaw-dropping payoffs, I want you to share your story. This is the easiest way to send me a message.
[i] Analysis based on data found here. Note that this study was chosen for its longitudinal nature, but it included more small companies than most studies I’ve used.
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Webinar Gartner Genius Brands of the Year: Digital Marketing Achievements Discover 45 Brands Gartner Leads Your Way…Watch Now Data analytics is the science of analyzing raw data to draw conclusions from that data. Many data analysis techniques and processes have evolved into automated mechanical processes and algorithms that operate on raw data consumed by humans.
Data analysis is a broad term that encompasses many different types of data analysis. Any data can employ data analysis techniques to gain insights that can be used to improve things. Data analysis techniques can reveal trends and indicators that would otherwise be lost in large volumes of data. This information can then be used to optimize processes to increase the overall efficiency of the business or system.
For example, manufacturing companies often log the uptime, idle time, and work shifts of various machines, and then analyze the data to better plan workloads to keep machines running at near maximum capacity.
Data analysis goes beyond identifying production bottlenecks. Game companies use data analysis to set reward tables for players to keep most players active in the game. Content companies use the same data analytics to click on, view or reorder content for another view or click.
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Data analytics is important because it helps businesses optimize performance. Incorporating it into their business models means companies can help reduce costs by identifying more efficient ways of doing business and storing large amounts of data. Companies can also use data analytics to make better business decisions and analyze customer trends and satisfaction, which can lead to new and better products and services.
Parts of modern data analysis can be traced back to SQL. Created in 1979, this computing language makes it easier to query relational databases and analyze resulting datasets. SQL is still widely used today.
Data analysis forms the basis of many quality control systems in the financial world, including the increasingly popular Six Sigma program. If you measure something wrong—whether it’s weight or defects per million on a production line—it’s nearly impossible to optimize.
Industries that employ data analytics include travel and hospitality, where turnover can be fast. The industry has the ability to collect customer data and understand where, if any, problems are and how to fix them.
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Healthcare uses a combination of large volumes of structured and unstructured data and uses data analytics to make quick decisions. Likewise, the retail industry uses vast amounts of data to meet the changing needs of customers. Data collected and analyzed by retailers can help identify trends, recommend products and increase profits.
In December 2021, the median total earnings of data analysts in the US was just over $93,000.
Data analysts can use many different analytical methods and techniques to process data and extract information. Some of the most popular methods are listed below.
In addition to the various mathematical and statistical methods used for number crunching, data analysis has also developed rapidly in terms of technical capabilities. Today, data analysts have a variety of software tools to acquire data, store data, process data, and report results.
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Data analysis has always been loosely associated with spreadsheets and Microsoft Excel. Today, data analysts often interact with native programming languages to transform and manipulate databases. Open source languages such as Python are frequently used. More specialized data analysis tools, such as R, can be used for statistical analysis or graphical modeling.
Data analysts also assist in reporting or communicating results. Tableau and Power BI are both data visualization and analysis tools that you can use to collect data, perform data analysis, and share results through dashboards and reports.
Other tools are emerging to help data analysts. SAS is an analytics platform that can aid in data mining, while Apache Spark is an open source platform that can be used to process large datasets. Data analysts have a wide range of technical options that can add value to their companies.
Data analytics is important because it helps businesses optimize performance. Building this into their business models means companies can help reduce costs by identifying more efficient ways of doing business. Companies can also use data analytics to make better business decisions and analyze customer trends and satisfaction, which can lead to new and better products and services.
Data & Analytics
There are four main types of data analysis. Descriptive analytics describe what happened during a specific period of time. Diagnostic analysis focuses more on why things happen. Predictive analytics is geared towards things that will happen in the near future. Ultimately, prescriptive analysis suggests a course of action.
Data analytics has been adopted by many industries, such as the fast-turnover travel and hospitality industries. The industry has the ability to collect customer data and understand where, if any, problems are and how to fix them. Healthcare is another industry that uses a combination of large amounts of structured and unstructured data, and data analytics can help make quick decisions. Likewise, the retail industry uses vast amounts of data to meet the changing needs of customers.
In a world increasingly dependent on data and statistics, data analytics can help individuals and organizations gain confidence in their data. Using a variety of tools and techniques, a raw set of numbers can be transformed into informative, educational insights that aid in decision-making and informed management.
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The sudden drop in sales has prompted businesses across industries to recalibrate sales processes through analytics strategies to improve sales efficiency. Consistent with this shift, businesses around the world have been forced to generate higher profits with fewer sales resources. It should be noted that the traditional sales model leads to increased procurement costs. However, businesses are now realizing the benefits of analyzing sales performance and the need to update sales strategies to drive growth and reduce costs.
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