Advanced analytics is the autonomous or semi-autonomous examination of data using sophisticated techniques that go beyond traditional business intelligence (BI) to help businesses uncover connections and make better predictions.
By leveraging advanced analytics techniques and tools, companies can easily gather, integrate, and analyze data to produce fast, accurate insights. As a result, business leaders who previously relied on gut instinct can now turn to finely calibrated, easily accessible evidence.
Advanced analytics techniques include predictive analytics, complex event analysis, cohort analysis, retention analysis, machine learning, and data cluster analysis. These tools enable powerful predictive analytics that can instantly suggest relationships and clarify previously hidden patterns, issues, and opportunities.
Because advanced analytics provides insight in real time, organizations can get data insights faster and immediately adjust decisions as their business shifts. What’s more, advanced analytics tools require minimal support or training, so companies can immediately begin reaping the benefits while easing the burden on in-house IT and data scientists. This helps organizations keep up with the head-spinning pace of change in today’s business environment and gain critical competitive advantages.
This guide will dive deeper into advanced analytics and how it can drive better business results.
Big data—a term that has existed in some form for more than 75 years—refers to the ever-increasing volume of data that inundates the globe as technology’s processing power increases.
Today, big data grows in volume, variety, and velocity every day. Existing data sources collect increasing troves of information, while new sources gain traction. As the Internet of Things expands, connected devices create vast amounts of data from sensors attached to machines and appliances, or from the words we speak to smart speakers.
This data has the potential to transform business in many ways: by allowing businesses to track customer behavior and preferences, by improving supply chain efficiency, and by mitigating fraud.
But these massive reams of data are worthless without analysis. The trick, then, is determining how to interpret big data to address previously unsolvable business problems. This is where analytics comes into play.
Big data analytics is a form of advanced analytics that uses predictive models and statistical algorithms to make faster, better decisions. These decisions can lead to new revenue opportunities, improved operational efficiency, increased profits, and more satisfied customers.
Big data analytics can benefit businesses in a variety of ways:
Chief procurement officers are under more pressure than ever to contain costs and increase efficiency while also adding strategic value.
Big data analytics can help procurement professionals better predict suppliers’ lead time and late deliveries. By tracking real-time deviations in normal delivery patterns, as well as information about weather patterns, bankruptcies, or other disruptions, companies can take decisive action early and ultimately improve performance while reducing costs.
In fact, a recent report by strategic consultant the Hackett Group found that organizations using advanced analytics to streamline their procurement processes have 22% lower labor costs than typical organizations.
Big data analytics gives a 360-degree view of a company’s customers, taking customer relationship management to new levels. Predictive analytics allow companies to determine which customers will experience an issue before the problem arises, allowing them to determine solutions, contact those clients proactively, and increase the likelihood of maintaining satisfied customers who continue buying the product or service.
These happy, loyal customers boost an organization’s revenue and are critical to long-term success. In just one example, Coca-Cola revamped its marketing strategy by building a data-enabled, digital loyalty program. After collecting consumer data through social networks, the soft drink behemoth used advanced analytics to gain valuable customer insights, which helped it boost consumption of its products and upsell new products.
No wonder a McKinsey survey of more than 700 businesses found that spending on analytics to target customers generated operating profit increases of 6%. Investing in analytics that can pull together rich customer profiles allows personalization and optimization that can deliver five to eight times the return on investment and can lift sales by 10% or more.
Big data analytics can conduct sophisticated analysis of consumers’ online activity and point-of-sale transactions, allowing advertisers to create targeted and personalized campaigns that put the right product in front of the right people at the right time. This ultimately saves money and increases organizational efficiency.
Examples of data-enabled, highly targeted ads are everywhere today. Just think of Instagram’s personalized ads, which let businesses target users based on the accounts people follow and the content they like and share.
It’s not just Silicon Valley companies that leverage data in advertising, either: Venerable “old-school” businesses like grocery giant Kroger have also overhauled their marketing strategy, using data to create personalized coupons that offer special pricing.
Using big data analytics uncovers hidden patterns and enables predictive capabilities that lead to actionable insights and drive better business decisions. Whether a company is deciding to launch or discontinue a service, enter a new market, or shift marketing strategy, advanced analytics allow robust data visualization that help leaders make more informed decisions.
These enhanced decisions lead to better bottom lines. Highly data-driven organizations are three times more likely to report significant improvements in decision-making compared to those who rely less on data, according to a survey of more than 1,000 senior executives conducted by PwC.
Advanced data analytics can be broken down into four general categories, including descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. We’ll review each of these in more detail below.
Descriptive analytics examines data to answer the question, “What happened?”
This form of analysis depicts exactly what has happened until the current moment, providing context that allows business leaders to see trends and find problems that would otherwise remain hidden. Descriptive analytics is often communicated via visualizations like graphs and dashboards. Most business users will interact with descriptive analytics through traditional reports that review past operations or sales results.
Diagnostic analytics examines data to answer the question, “Why did it happen?” It builds on the “what” of descriptive analytics to understand the cause of past events. Sometimes called root-cause analysis, diagnostic analysis relies on techniques such as data discovery, data mining, correlations, and drill-downs. Growth marketing teams use diagnostic analytics to understand ad performance and further optimize channels, e-commerce companies use diagnostic analytics to understand what sales channels and products are most effective, and finance companies rely on diagnostic analytics to pinpoint the causes of fraud.
Predictive analytics is a form of data mining that predicts what is most likely to happen in the future. By leveraging machine learning and AI, predictive analytics analyzes current and historical facts to determine the likelihood of future outcomes. Many businesses use predictive analytics to identify customers likely to stop using a product or service.
Prescriptive analytics suggests actions decision-makers can take in response to those predicted outcomes—whether by capitalizing on a future opportunity or mitigating a future risk. It relies on simulation, complex event processing, neural networks, and machine learning to process a combination of structured and unstructured data, and continually improves suggestions as it ingests new data. For example, companies can apply prescriptive analytics to determine the offer most likely to retain a customer.
Companies often use descriptive and diagnostic analytics to understand what has happened in the past and why. They then apply predictive analysis to determine what will happen next, and prescriptive analysis to determine the next best step to take in response.
By using statistical methods and machine learning to move beyond traditional BI, advanced analytics and businesses uncover trends and patterns, predict the future, and drive change using fact-based information.
Businesses that implement self-service advanced analytics can drastically cut down the time required to reach business-critical decisions while reducing costs. Because they have streamlined access to robust data and comprehensive data insights, these companies can also more accurately define customer trends and needs—making them better positioned to create successful products or services and delight customers.
Advanced analytics can particularly benefit marketers by allowing them to capture the specific characteristics of individual customers instead of analyzing them at a segment level. This granularity enables personalized marketing that improves conversion rates and increases ROI.
Overall, advanced analytics’ benefits can be broken down into several categories, including the following abilities:
Traditional BI makes it difficult to use the rich data around transactions, customers, and operations that companies generate every day to drive decisions. But thanks to advanced analytics and proactive diagnostic tools, organizations can now analyze this raw, denormalized data in real time. This granular, flattened data paints a more accurate picture of a company’s operations and helps leaders uncover specific, actionable insights.
Beyond delivering more actionable data insights, advanced analytics also leverages sophisticated algorithms, computational linguistics, and data mining to allow higher-level questions and more detailed, distilled answers. These produce fast, dependable insights and business-critical decisions.
By uncovering hidden patterns in data and enabling predictive capabilities, advanced analytics helps an organization determine what might happen in the future. This, in turn, enables clearer decisions. Advanced analytics platforms allow what-if analysis that can be used to consider any number of potential scenarios. They also offer machine learning-enabled visualizations that add valuable context while saving time.
Advanced analytics uses machine-learning and AI to uncover ways to optimize operations and predict customers’ needs. These insights help companies solve previously unsolvable problems, as well as those they didn’t even know existed. Advanced analytics allow organizations to examine their operations more deeply, find hidden demand drivers, and make smarter decisions to spur greater profits.
Advanced analytics relies on each of the following techniques to help organizations analyze their data:
This describes an approach to data mining that emphasizes real-time analysis to generate predictions about the future and how those predictions will affect business operations.
This type of analysis is a set of techniques that analyze data and allow businesses to identify opportunities or threats in real time and respond to events or trends in the data as they happen. Marketers can use this kind of analysis to recognize patterns in customer behavior so they can present relevant, real-time offers.
This is a type of behavioral analytics that takes data from an ecommerce platform, app, or website and breaks it into related groups for analysis. This allows businesses to ask more specific questions and better track and understand customers’ answers. For example, cohort analysis can help retailers understand behavioral differences between customers acquired at different times or the relative long-term value of different customer groups.
Retention analysis is the process of analyzing customer behavior to understand how and why customers churn. It often involves using machine learning—which leverages statistics and probability to find hidden connections among variables—to quickly determine the reasons customers leave, as well as predictive analytics to make predictions about customers’ future actions. Overall, retention analysis generates insights around how to keep customers while also boosting new customer acquisition rates.
Machine learning is a branch of artificial intelligence that uses data and algorithms to imitate the way humans learn. In advanced analytics, ML algorithms can adapt to learn from data without explicit rules-based programming—and, like people, these algorithms get better as they gain experience.
Data clustering is a machine learning technique that uses an algorithm to classify, or “cluster,” data points into groups. Cluster analysis organizes similar pieces of data to facilitate effective comparisons.
Business intelligence and advanced analytics both collect data and analyze it to create insights. But their approach to data collection and the results of their analysis diverge significantly.
BI uses descriptive analytics—often in the form of spreadsheets, pivot tables, and reports—to tell a high-level story. By aggregating past and current data and creating visualizations to explain insights in that data, BI adds context that helps decision-makers understand broadly where the company stands at a specific moment. The main drawbacks are that legacy BI is limited to small, narrow analyses and its aggregated data lacks specificity or obscures critical insights in the data.
Advanced analytics, meanwhile, takes a forward-looking data science approach, with a focus on diagnostic, predictive, and prescriptive analytics. It strives to offer forecasting for the future, and its powerful techniques allow it to analyze far larger and more complex data sets instantly.
Moreover, while traditional BI aggregates data, advanced analytics takes the opposite approach, capturing granular data and flattening it so insights can be uncovered at a specific, actionable level.
In other words, while BI makes sense of historical data and creates a 30,000-foot-high narrative, advanced analytics goes much further and flies much lower, uncovering details and patterns that yield insight into what’s coming next and how to respond.
To fully leverage the power of data in today’s frenetic business environment, organizations need advanced predictive and prescriptive analytics that explore overwhelming amounts of complex, transaction-level data.
Sisu offers a superior engine for advanced analytics. It’s designed to quickly drill down into the most important data dimensions and automatically surface key drivers and patterns in your data. This dramatically accelerates data analysis and creates clear, actionable recommendations that lead to better decisions and improved business outcomes.
Reach out to our team to get started surfacing meaningful insights faster.