Deriving Customer Insights Leveraging Real-time Streaming Analytics

Real-time streaming analytics is transforming business intelligence by empowering leaders, marketers, service teams, and other business units to address events as they occur. Not only can this provide actionable insights more quickly, but you also can act on intelligence that generally isn’t available or useful after-the-fact.

With intelligence and analytics capabilities following the cloud trajectory, it’s become easy for businesses of all sizes to perform real-time data analysis, staying relevant in the moment. However, the promise of these capabilities still comes with concern for enterprises.

The best way to understand the potential of this technology is to look at proven cases where it is already making a significant business impact. Let’s explore some of those capabilities and how they’ve benefited Impetus customers.

Easily create a deeper customer understanding

Perhaps most important for real-time stream analytics is the ability to contextualize experiences. Impacts are most profound in the customer realm, where companies can understand interactions and questions or build personalized communications at any moment.

Micro-segmentation of customers

The more personalized your customer experience, the more likely they are to leave satisfied. It also helps with company efforts to increase customer lifetime value (CLV) and increase the effectiveness of promotions and marketing campaigns.

A real-time streaming analytics solution like StreamAnalytix provides context via real-time analysis of all customer interactions. For a pay-TV provider, for example, this can include purchases, channel usage, error reports, complaints, and outside efforts such as ads. Leveraging this data can allow a company to specifically target individual user preferences and present the most compelling offer. Here, it might be sports packages based (think NBA vs. NFL or MLS vs. Premier League) on existing habits or new service offers for the early-adopting elite.

Multi-lingual sentiment analysis

Companies looking to enhance offers and services need to know their customers and understand preferences to get the best data.

It would take considerable time to train every single marketer or service agent on your entire portfolio, customer segments, and user plans. However, real-time sentiment analysis can operate as that brain, with no delay in how a user is profiled.

By enabling this capability across customer locations and languages, a company can mine feature-specific opinions and even dynamically change the questions a customer is asked to get a statistically relevant sample size for as many questions as possible. A real-time streaming analytics solution like StreamAnalytix can enable your survey platform to adapt and adjust based on real-time metrics of customer segmentation or concerns.

Improving service and security capabilities

Real-time streaming data typically rely on direct customer input. For many companies, the most abundant source of that direct interaction is in their support and call centers. So, call center analytics has become an early source of use cases and gains for these advances in analytics.

Real-time agent support

Call centers can handle millions of minutes’ worth of calls each day. Each call provides dozens of unique data points from feature issues and software versions referenced to wait times and complaint resolution speeds.

Companies not only want to understand what this information means but are also looking for call center analytics that gives this data proper context. With a robust, real-time analytics solution like StreamAnalytix, call centers can adjust the volume a specific agent receives, route calls to agents with the best track record for addressing a problem, and identify who may need additional training.

By optimizing agent-caller pairings, companies can generate higher customer retention rates and improve their customer satisfaction index (CSI) scores. It’s a wealth of data to improve customer service as well as systems and processes that are proven to improve agent productivity and reduce after-call work.

Risk-profiling in real-time

Sometimes it isn’t what the customer says but what they’re doing that businesses need to address. The major areas here are risk assessment and anomaly and fraud detection.

Real-time data shows how people behave, which is vital for industries like auto insurance, where company risk and liability depends on these actions at a particular moment. Driver behavior, vehicle sensor data, overall usage data, etc. can be collected and processed through machine learning to create the most accurate risk profile of a customer. It would help the insurer protect their business and also potentially provide greater savings for customers who are safe or infrequent drivers.

StreamAnalytix also helps companies understand threats from fraudulent customers in real-time. By enabling banks to process the data in 5x more applications, at a fraction of the cost of previous systems, financial institutions can look for malicious attempts to use stolen personal information, see and potentially match patterns of fraud, and improve risk scoring models to understand historical data accurately. Platform management not only speeds up the processing of applications but can significantly reduce false positives while also highlighting behavior that is proven malicious.

Conclusion

Today’s cloud infrastructure makes real-time data available to companies. The next step is implementing stream analytics, designed to turn that data into relevant, useful business intelligence.

Now, affordable platforms exist to empower enterprises of all sizes to unlock this potential. It often starts with call center analytics, but the possibility for in-stream understanding exists anywhere real-time data has a use.