It's no secret that analytics has become a crucial aspect helping to facilitate most companies' operations. Consumers today create endless streams of data that is vital to understanding their shopping preferences, health profiles, and interests. If you could know exactly what your customers were going to want before they do, wouldn't you invest in that technology?
Analytics as a field, however, is still very much evolving and constantly improving. More importantly, technological innovation and new paradigms are continuously changing how data is gathered, processed, visualized, and turned into actionable insights.
This means something different for each industry, but the reality is such that expanded use of analytics and business intelligence is a plus for every field. As companies become more skilled at perception, they can also change the way they do business, helping to improve customer satisfaction and their processes.
For retail, insurance, and marketing, analytics is rapidly emerging as a central decision-making pillar. The degree of understanding data offers companies about their consumers means they can and already have reconsidered their offerings. From the way they communicate with consumers to how services and products are presented, these analytics trends are about to transform these three industries tremendously.
It's easy to see how analytics has already changed the retail industry. Inventory management, payroll, promotions, and even prices have all become more dynamic as retailers understand how to best manage these components to maximize their bottom lines and offer better customer service. For retailers, understanding the flows of their stores and products, as well as knowing how to streamline their sales across platforms, is essential to spurring continued growth.
IoT and Understanding Consumer Behaviors
The Internet of Things has long since stopped being a buzzword and is now a real innovation industry. As more devices connect to the internet and provide data, companies will be able to analyze and collect almost endless streams of actionable information from their consumers.
The Silicon Valley giants are already well ahead in the industry, with devices such as Google Home and Amazon Alexa already collecting consumer shopping preferences, online habits, and other usage data. Mobile phones and wearable tech also mean companies can understand how their customers shop at their stores and the places they frequent most.
WiFi sensors can be used to locate store hotspots, visualize consumer flow through stores, and even track visit history. This information can be used to improve operations and enhance efficiency. More importantly, these IoT data-streams can lead companies to turn this information into action across multiple channels.
Omnichannel Data Integration
This flood of behavioral data will come from multiple channels and can help retailers find the best ways to expand their omnichannel operations. Retailers can turn this data into a more streamlined multi-channel funnel, as well as expand their services across all avenues. For retailers shifting towards e-commerce from brick and mortar, it also means that cross-border sales will become significantly easier to generate. Most shoppers already buy internationally every month, and e-commerce is likely to develop into a trillion-dollar industry in the not too distant future.
Turning IoT data into action means that companies must correctly parse and interpret information about their consumers' behaviors and shopping preferences. Powerful solutions empower companies to uncover the best ways to improve the customers' journeys across their entire platforms.
These two interrelated retail analytics trends will continue to change how stores are built, which products or services are delivered, and how companies offer their services across channels.
The insurance industry has always been data-driven. To approve claims, policies, and determine premiums, insurers must analyze thousands of documents effectively and properly interpret the information to make it actionable. Until recently, however, the industry still relied on outdated methods that include having humans review hundreds of applications, medical forms, records, and tests to determine risk and premiums. Now, these new insurance analytics trends are redefining these processes.
Risk Analysis With Real-Time Data
Modern insurance works essentially the same way it did 100 years ago. Though companies now use computers to collect data, their processes still mean humans are the ones analyzing the outputs. Moreover, they're still focused on historical data, which doesn't always provide the full picture. In the near future, insurers will be able to collect data from a variety of sources, including online profiles and medical databases, driverless cars, wearable medical devices, and even social media. This will change how the industry views and determines risk by turning it into a dynamic and forward-looking process. By employing predictive analytics, insurers are more likely to give customers better premiums and coverage.
Most importantly, this real-time analytics are more likely to help customers. Auto insurers can track vehicle data to determine safer driving habits and automatically adjust premiums as would health insurers that detect healthy lifestyle choices and patterns of activity. With information arriving in real-time, insurers can also change how they underwrite policies by offering on-demand coverage and more personalized policies.
AI and Machine Learning Will Speed Up Insurance
Consumers today hate waiting, and insurance is notoriously slow. This is partly due to the complex underwriting process, and to how long agents take to analyze data. However, AI and machine learning will and are already improving the insurance application. With AI's ability to scan through thousands of documents in a brief time alongside machine learning able to apply data to more complex problems, the embrace of analytics will lead to much faster approvals. Already, insurers can provide same day coverage after application.
AI can scan through hundreds of sources simultaneously, meaning that as algorithms improve their understanding of the data they parse, insurers can offer better policies and more accurate risk assessments faster.
AI and real-time data is already proving effective. Companies can now take advantage of these massive amounts of information to enhance how they deliver services and offer more flexible insurance products.
For marketers, understanding behaviors and user journeys is a must. Analytics has already proven to be a goldmine, but evolving data analysis and data analytics tools will accelerate the trend. As companies forge better understandings of motivations and usage patterns, marketers will be able to more clearly direct their efforts while anticipating consumer desires and needs.
Better Business Intelligence Leads to Prescriptive Analytics
Business intelligence is about understanding how data translates into actionable outcomes in the short-term. This includes an aspect of predictive analytics as companies must understand how historic data points to future tendencies. This type of data trend analysis gives marketers the opportunity to latch onto new fads, habits, and cultural changes. However, analytics will soon be able to go a step further than simply predicting what will happen.
Companies will also be able to determine the optimal steps to take. Instead of struggling to understand what data means, BI tools will suggest the optimal courses of action for each customer, segment, or demographic. This will provide marketers with the ability to target their messages across channels better than ever before.
Analytics Will Enable Marketers to go Granular
Some products are global events, and marketing them is easy. Most products, however, are not so lucky. Instead, many goods and services range from industry-specific to incredibly niche, and marketers must find innovative ways to offer them to consumers. New analytics and better AI will lead companies segmenting industries more appropriately.
More importantly, the focus on prescriptive data also means that companies must be able to pinpoint their audiences more successfully. Various sources of consumer data, from mobile and wearable to home-use, means marketers can create hyper-specific market segments to distribute more personalized campaigns, smooth customer journeys with more accurate content, and drive results.
This focus on segmentation and granular analysis will require companies to invest heavily in business intelligence tools that can accurately connect data points to build relevant correlations. A paradigm based on predicting and targeting consumer groups will give marketers better multi-channel outcomes while improving conversion and onboarding funnels.