Understanding the Differences Between Web and Business Analytics


Understanding the Differences Between Web and Business Analytics

Analytics is the systematic analytical study of statistics or data. It’s used for the exploration, interpretation, and creation of meaningful patterns from large amounts of unstructured data. It also involves applying statistical techniques towards efficient decision making. Analytics is a term that encompasses many fields and subfields. It covers a wide range of approaches and techniques, including mathematically, statistically, qualitatively, and monetarily.

Analytics paves the way to explore new ideas, provide insights into problems, and reveal patterns and trends that can be used to enhance and improve performance and service delivery. By developing a series of analytic techniques and methods, data scientists can build more-detailed and better-quality inferences. A data scientist may specialize in one or more of these four areas: Information systems, operation research, marketing research, or information systems architecture. Data science experts use mathematical and statistical techniques to extract and organize the inherent information in large sets of real or historical data, which they then use to support any business goal. These techniques are often categorized into two main categories: descriptive and predictive analytics.

descriptive analytics focuses on the interpretation and creation of patterns from large amounts of unstructured data. It may involve the use of sophisticated statistical methods and software to uncover patterns from publicly available information. This form of analytics makes the most of the statistical tools and data analysis platforms available. The primary goal of a descriptive analytics approach is to provide insights into phenomena and relationships by studying the data in a context that is not directly associated to the entity or subject of interest. One major benefit of using descriptive data analysis in the context of business is that it can provide insights into the business models that support the product, service, or industry.

Predictive analytics on the other hand, studies phenomena and relationships that are directly related to the entities or subjects of interest. When applied to marketing, for example, a predictive analytics approach would focus on the relationships between customer demographics, purchasing habits, product attributes, sales cycles and product features. Proper analytics requires that an analyst is able to understand the meaning behind all of the relevant data, including measurements, data sources, aggregates, and other characteristics of those relationships. Although this type of analytics has a relatively shorter history, it too has provided countless benefits and opportunities for those who have mastered it.

In the past decade, two new approaches to statistical analysis have emerged and become integral elements of an effective marketing strategy: real-time analytics and web analytics. Real-time analytics refers to the ability to analyze marketing and promotional efforts in the context of their historical data, as opposed to the results of the action itself. For example, a recent study demonstrated that text message marketing was three times more effective at driving leads than traditional cold calling. The reason behind this finding was found when the researchers studied the effect of sending out one hundred randomly chosen text messages, versus the same number of unsolicited cold calls. While the number of calls resulted in three times more leads, the true impact was realized only after the marketers had analyzed the data collected over eighteen weeks.

Web analytics, on the other hand, is often considered a subcategory of real-time analytics, but there are significant differences between the two. Web analytics is focused on gathering and organizing data in order to provide insights that can ultimately help marketers improve their business models. Whereas, real-time analytics requires the use of statistical methods in order to gain insights that can ultimately be used to optimize marketing campaigns and programs.

If you’re interested in leveraging analytics to drive business intelligence and insight into your business models, it’s important to understand the difference between data mining and predictive analytics. Data mining deals with obtaining non-public information in order to support strategic decisions. Typically, the information is gathered from publicly available databases and sources and is then used to support tactical business moves. These decisions include whether to launch a particular product or service, what marketing efforts to pursue, when to scale up current efforts, or what types of sales activities are most likely to result in profit. As an example, if a company is interested in launching a television advertising campaign, they might use publicly available data from a cable company to determine the likely demographics of the audience, the timing of the broadcast, how many people tuned in and so on to determine the effectiveness of the campaign.

In contrast, a more appropriate form of analytics is predictive analytics that utilizes internal knowledge to give businesses an insight into their strategic business moves. Rather than being interested in knowing how many people watched a particular program, this type of market analysis seeks to find patterns and relationships that prove relevant to future actions. One example might be if a business believes that a particular television advertisement was effective but the data shows otherwise. This could be evidence that the ad was watched by fewer people than was expected, or that a particular demographic watched the commercial more than another. Proper analytics helps to build a foundation for marketers that can ultimately lead to successful marketing strategies.