There’s little question about Big Data’s ability to deliver solid insights. What may not be so clear, however, is exactly how organizations can convert the knowledge obtained from these analytics into concrete action.
Big Data analytics are useful across every industry, but they are of particular benefit to consumer-oriented businesses. Marketing professionals, financial institutions, healthcare providers, insurance companies and retailers can all leverage Big Data analytics to optimize the management of company resources, create better customer experiences, revitalize their brands and gain a strong competitive advantage.
Outside marketing agencies and marketing departments within organizations may stand to gain the most from Big Data analytics. The right Big Data analytics platform can streamline an organization’s marketing efforts by providing deeper insights about a customer’s past buying behaviors, current needs and possible future buys. Big Data analysis also optimizes micro-segmentation of the customer base to provide targeted messaging and tailored encounters. There’s also increased opportunities for multi-channel marketing that can create seamless experiences for customers across digital, social and physical channels. These can include location-based marketing via mobile device notifications as well as opportunities for cross- and up-selling.
A concept known as sentiment analysis offers powerful business intelligence to enhance the customer experience by collecting and assessing data from a number of sources including transaction data, social media, mobile interactions and other sources within the Internet of Things that reveals customers’ perceptions and desires regarding products and services.
These tools can also help companies correlate of data from the IoT to provide a 360-degree view of customer actions and behaviors.
The bottom line – Big Data can be integrated into every aspect of the marketing strategy and at every point of the purchasing process.
In the finance industry, Big Data analytics is not only changing how firms interact with their customers, but how they address back-office tasks such as risk and fraud management.
Wealth management is one of the most prominent aspects of financial services, and it’s also one of the most criticized. The most common complaints center around extraneous fees and the length of time it takes to accomplish simple tasks such as fund transfers. Big Data analysis allows banking and investing institutions to decrease the time frame in which everyday financial functions are performed by exploring historical customer data and predicting which accounts have the most chance of bouncing checks or making regular direct deposits. This, in turn, assists financial service firms in determining how long finds should stay on hold or whether to clear a check the same day it’s deposited. The same information can also help financial service organization to offer personalized services such as overdraft protection or interest-bearing checking accounts.
Investment firms can offer their customers more reliable predictions with analysis backed by historical market data as well as predictive algorithms that can give a clearer view of the outcome of a particular investment strategy. This decreases the risk that investors take on, increasing their return — and their satisfaction with their financial advisor.
Fraud protection is also enhanced by Big Data analysis. Financial firms will be able to track customer transactions and behaviors, spot atypical practices and take the appropriate action.
Healthcare organization work on an evidence-based basis, and this is exactly where Big Data analysis shines. Information that is collected from individual patients can be amassed into a web of interconnected symptoms, diagnoses and treatments. This collection of data can help healthcare professionals pinpoint separate co-morbid issues or identify conditions that might have otherwise gone under the radar. As a result, the quality of patient care goes up, as well as positive outcomes.
On the business side of things, reimbursement for services can be expedited by Big Data analysis that detects billing codes and compares the related combinations to patient and historical data to determine if they’re likely to raise red flags for the insurer. It can also help healthcare organizations track denials and ascertain if there are recurring problems.
Risk assessment and avoidance are cornerstones of the insurance industry, and they’re no strangers to the power of Big Data analysis. Insurers already utilize it to create risk pools and determine coverage rates, but many don’t leverage the full force of these tools when it comes to the customer experience. Clickstream analysis, the evaluation of how customers behave in and navigate websites and mobile apps, can help improve user experiences by tailoring each interaction based on how a customer has patronized the company in the past. It can also use data from the IoT to customize insurance offerings based on lifestyles, life stages and upcoming milestones. As with the finance industry, insurers can benefit from the early fraud detection abilities that Big Data analytics provides, reducing the number of bogus claims.
For retailers, adapting the Big Data analytics attitude is a highly lucrative strategy. Customers want seamless shopping experiences across all channels, from websites and mobile apps to customer service calls and brick-and-mortar interactions. In fact, many customers start their purchases with research via the web and then complete their buys after an in-store inspection of the goods. Big Data analytics can boost a retailer’s ability to configure each interaction a consumer has by evaluating past purchases, clickstream analysis and sentiment analysis. Having access to a plethora of historical and company data regarding supply and demand can help retailers optimize the supply chain by providing a more accurate, birds-eye view of the entire process, from manufacturing to final delivery.