Revenue Growth Through Business Analytics: The Ultimate Guide
Once used only by a niche subset of businesses, data analytics has grown into a valuable, wide-scale tool that is being applied across a multitude of industries. Companies have found a range of uses for business analytics, from helping to pinpoint new potential markets for products to predicting how much an unexpected shortage in labor or supplies will impact overall profitability. And this increased desire for not only data, but the effective interpretations and recommendations for increasing revenue based on that data, has sparked the need for expertly trained analytics professionals in these industries.
The importance of analytics makes sense when considering just how much data is regularly produced and shared. Domo, a cloud-based operating system, released an infographic illustrating different types and quantities of data and content people share, consume, and produce every day. According to the infographic, during each minute of every day, nearly 100,000 hours of Netflix video were streamed, over 750,000 songs and audio files were streamed on Spotify, and 3,877,140 searches were conducted on Google.
Organizations recognize the importance of collecting data and analyzing it, but not all of them are fully aware of how to effectively use analytics to increase revenue. These organizations may not be educated in best practices for using business analytics tools, nor do they have the necessary staff or resources to effectively mine and utilize their data. This guide will show companies, regardless of their size, scope, or industry, how they can use data analytics to increase revenue and achieve business goals.
Chapter 1: History and Common Application of Business Analytics
While the collection and analysis of data is a widespread practice across virtually all industries today, this hasn’t always been the case. Companies have always had an urgency to understand what is currently happening in markets and to forecast the near future. Recording past data and information may be pertinent to that company’s activities by helping identify patterns in sales and customer cycles. In the recent past, companies or organizations that wanted to see how historical data could be used to affect future decisions might not have had the necessary technology or business analytics tools available to review that data.
A. Early analytics during the 1970s computer age
Throughout history, different societies and civilizations had unique ways of organizing and archiving data, like the abacus counting system used in Europe, China, and other regions, or Roman numerals, which allowed Romans to effectively organize and write down important figures. But the initial boom in the modern business analytics movement happened in the 1970s with the rise of the computer age.
As businesses and corporations increasingly utilized the services of computers, they took advantage of these devices’ decision support systems (DSS), which not only organized large quantities of data but helped to sort and locate which data was important to revenue growth. “DSS systems helped collate data from various areas of business, for example, production and sales, to give key decision-makers a bird’s-eye perspective of business in a way that hadn’t really existed before,” according to Cyfe, a business intelligence company.
Executive information systems (EIS) is a type of decision support system introduced during this time that executives could use to improve their decision-making abilities and extrapolate useful insights from data. The adoption of online analytical processing (OLAP) provided businesses with valuable marketing data.
Along with the increased use of these analytics tools into the 1980s, there was also the introduction of data warehouses, which are centralized locations where data can be stored in massive quantities, making it more easily and quickly accessible. “Prior to data warehousing, a significant amount of redundancy was needed to provide different people in the decision-making process with useful information,” according to Dataversity.
B. Introduction of analytics tools
Even though the insights these early analytics tools provided were valuable, one of the challenges was that they were not especially user-friendly. Then Microsoft’s Excel program came along. First introduced for Apple’s Macintosh computers in 1985 and then for Windows in 1987, this groundbreaking program would change the way industries organized and analyzed data. But the program faced some initial troubles with developers trying to incorporate both features and speed.
“Those early PCs couldn’t crank through numbers the way today’s powerhouse machines can, with the result that changing a number in a spreadsheet could bring things to a halt while the change rippled through all the interconnected calculations,” Doug Klunder, the lead developer of Excel, told The Daily Beast. Excel would later thrive, and analysts at companies are still using this business analytics tool’s simple spreadsheet format and advanced applications to organize and interpret their data.
With the increased popularity of the internet in the 1990s and early 2000s, vast quantities of data were being created and accessed at fast speeds. The introduction of Google Analytics in 2005, now the most widely used analytics service online, provided individuals and businesses with an advanced but user-friendly method of collecting and interpreting valuable internet data. Originally developed from Urchin on Demand, a business analytics tool created by Urchin Software, which was purchased by Google, Google Analytics enables users to implement, analyze, and track the effectiveness of specific internet advertising and business campaigns. Methods in which these campaigns can be tracked include determining how visitors respond to different types of online ads or how many people visited a certain website during a specific time frame.
C. Growth of descriptive, predictive, and prescriptive analytics in business
Throughout the early 21st century, as internet users produced exponentially more data each year, more and more companies recognized the need to effectively capture and analyze this information, also known as big data.
“Big data is a term that describes the large volume of data — both structured and unstructured — that inundates a business on a day-to-day basis,” according to SAS. “Big data can be analyzed for insights that lead to better decisions and strategic business moves.” Writing for Forbes, Louis Columbus notes that “big data adoption reached 53% in 2017 for all companies interviewed, up from 17% in 2015, with telecom and financial services leading early adopters.” As companies prioritized big data, they increased their usage and application of descriptive, diagnostic, predictive, and prescriptive analytics to their business activities. The following are descriptions of how these four types of analytics are used to analyze and interpret business data.
Businesses can use descriptive analytics to answer questions as to what happened in the past, while diagnostic analytics can be used to help determine why the events occurred. While data on its own can reveal that a company missed its revenue growth goals for the past year, diagnostic analytics can be applied to understand the reasons for that loss. As IBM describes it, diagnostic analytics help to answer the question, why did it happen?
Predictive analytics can be used to forecast future results based on a company’s current data. If a company is testing a new location in a different city, it can use the data received from that test along with predictive analytics to forecast potential outcomes. According to analytics leader SAS, “The goal is to go beyond knowing what has happened to provide the best assessment of what will happen in the future.”
Prescriptive analytics, then, can be utilized to help determine from test data what steps a company should take, like whether it should open another location in a year, or how much it should invest in an opening. IBM says that this type of analytics “prescribes the best course of action when making complex decisions involving trade-offs between business goals and constraints, using optimization technology.”
Chapter 2: Ways Business Analytics Is Used to Drive Revenue
The collection and analysis of data, no matter how it takes shape, can help businesses achieve their goals. But when it comes to specifically growing revenue, businesses can benefit from using the following analytics procedures. Below are hypothetical examples of conflicts that businesses may face throughout their day-to-day operations and how analytics can be used to develop effective solutions.
A. Creating more responsive relationships with consumers to drive retention
A fruit juice company notices on Google Analytics that its number of visitors declined by 50% over the past 30 days. The company wants to bring more visitors back to the site but doesn’t know how to best engage them.
This company can use the popular business analytics tool Google Marketing Platform to better understand customers’ concerns. The company’s CEO or a marketing executive might discover through responses to a survey that customers did not care for a new product line, or by analyzing comments on a Facebook post can find out that consumers found an advertising campaign distasteful. From there, the company can make the necessary changes to increase revenue.
B. Interpreting and shortening sales cycles by using analytics
A sports clothing retailer knows there likely will be a decline in sales between the winter holidays and summer. The retailer isn’t sure if its best course of action would be to increase production during this period to try and lure in more customers or if it should decrease production until the summer and continue selling existing inventory.
The business can find the best course of action by using diagnostic, descriptive, predictive, and prescriptive analytics. Diagnostic analytics can show whether consumers actually spend during these months, descriptive analytics can illustrate why they do or don’t shop much from January through May, predictive analytics can forecast the potential positive or negative outcome if the retailer does increase production, and prescriptive analytics can recommend the best strategies for increasing that production. “Diagnostic analytics are also valuable at an interim stage before an organization is ready to put its models into action,” according to Deloitte.
C. Predicting through analytics which strategies will advance supply chain procedures
A foods manufacturer in the United States is debating two different options to increase efficiency and cut down on costs long term after missing revenue growth goals. The company is considering either investing money into hiring and training more factory floor staff or purchasing expensive equipment that can deliver products more quickly.
The manufacturer can use the four types of business analytics to determine the best solution. Diagnostic analytics can be used to determine the extent of the loss, descriptive analytics can help show why the loss happened, predictive analytics can illustrate the potential benefits of more labor and of more equipment to compare the two, and prescriptive analytics can help the manufacturer decide on the potential solutions.
D. Using aggregate data to adjust marketing procedures
The owners of a new yoga studio launched a digital advertising campaign targeting individuals who live in the neighborhood. The campaign has successfully generated interest among the community, but the business owners are not sure how many people who responded to the ad will end up taking classes at the studio.
Aggregating data would be of great use to the yoga studio in this instance. “After the data is aggregated and written to a view or report, you can analyze the aggregated data to gain insights about particular resources or resource groups,” according to IBM. Professionals of the yoga studio could use Microsoft Excel to organize information from each of the respondents. From there, that data can be further aggregated into larger groups, allowing the yoga studio to see that 49- to 70-year-olds are its most engaged market, and that its average customer will likely be 55 and female and will reside within one mile of the studio. Having this information in place will allow the studio to adjust its advertising and marketing plans to reach these new demographics to meet revenue goals.
E. Weighing pros and cons of future business actions with prescriptive analytics
A popular domestic hotel chain is considering opening its first international location. Not only does the chain want to consider which location might be most profitable, but it also wants to look at how political and social forces in those international regions may impact business long term.
Here, after using analytics tools to interpret relevant past data, the hotel can apply prescriptive analytics to determine which international location might be best. One country provides a nice tax incentive, but insights from prescriptive analytics show that unstable political forces there may result in new government leadership and revocation of that credit. Additionally, another country may charge the hotel chain expensive fees for development and construction, but the long-term, relative stability of its government will likely lead to more revenue growth.
“By pinpointing the best outcomes and making recommendations, companies can focus on delivering an amazing customer experience. Prescriptive analytics help companies make decisions that reflect customers’ needs and changing trends,” according to Forbes.
F. Using descriptive and diagnostic analytics to develop current solutions from past errors
A successful bus carrier notices that its fuel reserves are low around the 10th day of each month, causing it to offer shortened services on the 11th and 12th days until it receives new fuel supplies on the 13th day. The bus carrier hasn’t had the time or resources to devote to figuring out why this shortage continues to happen.
This bus company could use diagnostic analytics to find that it has been purchasing its fuel in bulk on the 13th of each month to save money, but that supplies dwindle weeks later. The company can use descriptive analytics to see that even though it is initially saving money by purchasing in bulk, it’s losing more money long term by offering shortened services on those two days and should make purchases three times a month to achieve stronger revenue growth. These types of findings, made possible by descriptive analytics, can be presented in different ways. “Some common methods employed in descriptive analytics are observations, case studies, and surveys. Thus, collection and interpretation of large amount of data may be involved in this type of analytics,” according to Dataversity.
Ultimately, companies can use many business analytics tools to meet their goals, just as long as they effectively collect data, understand what data is important, and know which tools to use to extrapolate insights from that data.
Sources:
CYFE, “The History of the Evolution of Business Analytics”
The Daily Beast, “Microsoft Excel: The Program’s Designer Reveals the Secrets Behind the Software That Changed the World Twenty-Five Years Ago”
Dataversity, “A Brief History of Business Intelligence”
Dataversity, “Fundamentals of Descriptive Analytics”
Deloitte, “Five Types of Analytics of Things”
Domo, “Data Never Sleeps 6.0”
Forbes, “53 Percent of Companies Are Adopting Big Data Analytics”
Forbes, “Descriptive Analytics, Prescriptive Analytics, and Predictive Analytics for Customer Experience”
Google Marketing Platform IBM, “Data Aggregation” IBM, “Diagnostic Analytics 101: Why Did It Happen?”
IBM, “Prescriptive Analytics” OLAP.com, “OLAP and Business Intelligence History”
SAS, “Big Data: What It Is and Why It Matters” SAS, “Predictive Analytics”
ScienceSoft, “4 Types of Data Analytics to Improve Decision-Making”