Big data and analytics can help a business predict consumer behavior, improve decision-making across the board and determine the ROI of its marketing efforts. By addressing these aspects adequately, the business would not only be able to protect its market share, but also expand into new territories. Below is a detailed look at this topic.
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The Growing Popularity of Digital Marketing
As of 2015, the global digital-advertising industry was worth a whopping $154 billion. What’s more, experts expect the industry to hit $260 billion by 2020, translating to a 69 percent growth during that period. In the US alone, digital advertising revenue stood at $50.5 billion in 2015. Additionally, American companies, on average, spent 6.5 percent of their marketing dollars on digital marketing in 2016. It is worth noting that digital marketing comprises mobile internet, display internet, paid-search internet and classified internet. With that in mind, more and more businesses across America are embracing the role of big data in business. In fact, a recent study done in the US shows that 38 percent of organizations place big data among the top five business issues, 26 percent say it is an important challenge and 21 percent believe it is probably the single most effective way to gain a competitive advantage. Additionally, 72 percent of the marketers who took part in this study said digital media skills are vital, whereas 78 percent said such skills have a direct impact in their line of work. All the marketers who took part in the study anticipate that the role of big data in marketing will grow even bigger in the future.
Leveraging Big Data to Grow Sales and Revenues
Chief marketing officers (CMOs) across the country are increasingly incorporating big data into their decision-making process. For instance, a recent study has revealed that 42 percent of CMOs make marketing decisions based on customer-acquisition numbers, 40.5 percent based on customer insight, 39.1 percent prioritize digital marketing when making such decisions, 35 percent place greater emphasis on customer retention, and 34.5 percent make marketing decisions based on branding. It is worth noting that 46 percent of the polled marketers said that they would use various analytics strategies to gain consumer insight in 2017. Examples of such strategies include location-based targeting, personalization, and an increase in mobile and real-time reporting.
Sources of Business Data
Internal and external sources generate 54 percent and 25 percent of business data respectively. The remaining 21 percent of data comes from a combination of the first two sources. The top four ways business leaders source business data are sales and financial transactions (56 percent), leads and sales contacts from customer databases (21 percent), email correspondence (39 percent), and productivity applications (39 percent). Overall, big data boosts a business’s performance, improves customer segmentation and enhances the decision-making process. More specifically, 29 percent of marketers in the US say that marketing analytics has helped them grow their organization’s sales revenues by as much as 26 percent. Additionally, 54 percent of companies using customer analytics have seen their profits grow considerably.
The Three Levels of Analytics
The three levels of analytics, according to tech authority Gartner, are descriptive analytics, predictive analytics and prescriptive analytics. Descriptive analysis entails examining data and content manually with the aim of understanding what happened. Some of the techniques that a business can employ to do this include business intelligence and visualizations. Predictive analysis, on the other hand, attempts to predict the outcome by employing techniques such as regression analysis, forecasting and predictive modeling. Finally, prescriptive analysis is an advanced form of analytics that aims to find suitable solutions to the problems identified in the first and second levels of analytics. Some of the techniques employed in predictive analytics include complex event processing, simulation and recommendation engines.
The Pros and Cons of Utilizing Market Analytics
One of the main challenges of using market analytics revolves around integrating complex interfaces for accessing data. In fact, only 26 percent of the polled marketers believe that their systems are properly set up to work seamlessly together. The second key challenge revolves around a user’s ability to employ analytics data effectively. On this front, only 28 percent of the polled marketers said they were able to do this. The third key challenge has to do with data verification and validation. In particularly, outdated, inconsistent and irrelevant data poses a big problem to 59 percent of the businesses interviewed.
According to polled US executives, American companies that invest in big-data initiatives enjoy enhanced decision-making, improved collaboration and sharing of information, as well as greater customer satisfaction and retention. This is particularly important because 72 percent of the polled executives reported increased competition for customers. Market analytics gives businesses an edge over their competitors that have failed to invest in big-data initiatives.
The global digital advertising space was worth $154 billion in 2015. By 2020, the industry will be worth over $250 billion, largely driven by big-data initiatives including mobile internet, display internet, paid-search internet and classified internet. For this reason, more and more chief marketing officers are allocating more money to market analytics, with the average American business allocating 6.5 percent of its marketing budget to analytics. Additionally, analytics are increasingly driving marketing decisions. When making such decisions, 40.5 percent of CMOs consider consumer insight, 42 percent consider customer acquisition and 35 percent consider customer retention. Some of the techniques marketers use to gain consumer insight include location-based targeting, customization, and an increase in mobile and real-time reporting. The sources of business data include internal, external and a combination of the two sources. There are three levels of data analytics: descriptive analysis, predictive analysis and prescriptive analysis. The pros of big-data initiatives include better insight and decision-making, greater customer satisfaction and retention, and enhanced collection and dissemination of information. The cons of big-data initiatives include technological challenges, data verification and validation challenges, and the ability of users to interpret and utilize big data effectively.
The Evolution of Data Collection and Analytics