How to Achieve the Full Value of Real-time Customer 360
How to Achieve the Full Value of Real-time Customer 360
Effective data management creates and maintains long-lasting customer satisfaction and loyalty. It allows businesses to create authentic connections at scale and give people exactly what they want, exactly when they want it. It also gives buyers the customized experience they desire — research shows that 71% of consumers expect companies to deliver personalized interactions.
That’s why real-time customer 360 — unifying data to create a complete, integrated picture of what a customer is experiencing in the real world — is increasingly popular with businesses across industries. However, achieving real-time customer 360 can be challenging because it involves parsing through huge amounts of data (streaming and at rest) to find small portions of relevant information to drive real-time decisions.
Why is real-time customer 360 valuable?
Real-time customer 360 is valuable for consumers because it drives a smoother buying process and customer service tailored to their needs. Real-time customer 360 is valuable for businesses because it boosts revenue and customer retention. Benefits of this symbiotic use case include:
- Enhanced customer experience
- Improved upselling and situational awareness
- Proactive customer support
Real-time customer 360 challenges
Even when businesses understand the benefits of real-time customer 360, they may struggle to execute on it due to challenges posed by their existing stream processing architecture. Some of the most common challenges that occur when using traditional data pipelines and streaming architectures include:
- Volume: Data streaming for real-time customer 360 involves massive volumes of data generated at high velocities. Processing and analyzing this data in real-time requires a highly scalable and efficient infrastructure. Traditional stateless systems often cannot handle this continuous influx of data without bottlenecks or latency issues.
- Alert fatigue: Related to the volume challenge, alert fatigue happens when the number of state changes occurring across a system makes it hard to identify the most important events and respond accordingly. Businesses need a way to reduce redundancy and make better decisions about what data is truly helpful in a given situation and at what level of detail.
- Time to value: Traditional architectures make it difficult to deliver actionable insights — whether to a human or automated system for decision-making — fast enough to actually make a difference. The myriad systems required to process data lead to compounding latencies that make traditional architectures ineffective at scale. Context: Real-time customer 360 requires bringing in wide-ranging context across entities and their source streams. Benefits such as personalized offers and proactive support are very difficult with a stateless approach because each customer’s state is spread out across many different sources and only small portions of this data are relevant.
- Integration: The need to integrate data from many different sources adds complexity and can impact quality, consistency, and accuracy across diverse data types. In the absence of effective integration, departmental silos can also limit critical data sharing across different business units.
There is a common misconception that these challenges demand a complex solution. In reality, real-time customer 360 does not actually require a large, intricate architecture to power valuable insights and automation. There is a simpler, faster, and more scalable way to achieve the full value of this use case and get closer to your customers.
Instantly act on your data with streaming data applications
To solve the challenges of executing real-time customer 360, the data pipeline’s application layer needs contextual, real-time data to better act upon streaming data. Applications must be able to preserve their data and compute context locally between operations (aka maintain statefulness). That’s exactly what streaming data applications do — they allow businesses to instantly process and analyze data in the context of the underlying entities that are relevant to the business (i.e., maintain a complete view of the real-world objects and their state in real time). This allows companies to respond quickly to the events and opportunities that matter most to customers.
A critical aspect of streaming data applications is their ability to run full-context business logic on data in motion. Business logic — underlying rules and intelligence about how the business actually operates and the goals it wants to achieve — allows applications to interpret the meaning of streaming data.
Put another way, business logic answers the question, “Do we need to respond right now?” when something changes across the business. If the answer is yes, it also helps facilitate the appropriate response. For example, instead of a customer arriving at a store to find that an item is out of stock (despite the company’s website saying otherwise), business logic applied to contextual streaming data could be used to notify the customer as soon as the last item has been purchased and reroute them to a different, fully-stocked store.
Using Nstream for real-time customer 360
Nstream offers the fastest, most effective way to build streaming data applications that unlock the full benefits of real-time customer 360. It is not a general-purpose stream processing technology — rather, Nstream is a full-stack streaming data application platform for entity state modeling that evaluates the business logic of real-world objects against streaming data.
Streaming data applications built on Nstream’s platform leverage a stateful approach capable of isolating events and streams at a real-world object level (e.g., a customer, a product, a delivery truck, etc.). This means when a small bit of data changes for a customer, there is no need to retrieve every piece of necessary data because it is already held together in memory, ready to be added on to. The new event simply needs to refresh a portion of state so the logic can be re-evaluated. Personalized offers and recommendations
Research from McKinsey found that the majority of today’s consumers (71%) expect companies to deliver personalized interactions, and over three quarters (76%) are frustrated by lack of personalization. To deliver the custom-tailored experience that buyers desire, businesses can use Nstream to quickly build streaming data applications that understand a shopper’s past spending habits, as well as current digital activity and real-world behavior (e.g., walking into a store) to identify and extend the most relevant offer, such as a trial, coupon, or voucher.
For instance, you are likely familiar with the annoying déjà vu that occurs when you purchase a product online yet continue to get served ads for the same item everywhere you look. With Nstream, companies can update their customer profiles in real-time and eliminate redundant ad targeting to restore a quality customer experience.
There are other ways in which personalization can positively impact satisfaction and loyalty in the post-purchase phase. For example, Itron uses Nstream to build streaming data applications that combine database data (e.g., power grid topology, electricity tariffs, vehicles/charger types) with streaming data (e.g., smart meter, connected vehicle, and geolocation data) to surface insights for optimizing electric vehicle charging. As a result, Itron’s customers enjoy real-time recommendations for the fastest and cheapest charging options available for their vehicle.
Improved upselling and situational awareness
By using Nstream to analyze customer data — both data in motion and data at rest — in real-time, businesses can better understand customer preferences based on their geolocation, purchase history, and buying patterns. This information allows them to make relevant personalized promotions to customers at the right time, increasing the likelihood of additional revenue generation.
Superior real-time situational awareness is especially important when you consider the time-sensitive nature of certain assets, such as expiration dates on food in a grocery store. Real-time customer 360 allows companies to reduce waste and strengthen customer trust by more accurately reflecting the status of their products.
As the customer service gurus at Zendesk once noted, waiting for a customer to notify you of a problem is like waiting for your houseplants to start wilting before you water them. That’s why proactive support has become table stakes for businesses looking to offer a five-star customer experience. In fact, one survey from HelpLama found that 89% of consumers find proactive customer service to be a pleasant surprise or a positive experience.
Nstream can help organizations build streaming data applications that perform automatic, real-time scoring to determine whether or not a customer is having an optimal experience. If the score dips below an acceptable level, alerts can be sent so that other teams (e.g., customer support) can proactively — not reactively— reach out to the customer to resolve the issue and/or provide some type of compensation (e.g., a discount, voucher, or freebie) if the problem cannot be fixed immediately.
For example, one of the top three telecom providers in the U.S. uses Nstream to perform real-time experience scoring for more than 100 million customers. This customer has built streaming data applications that calculate hundreds of millions of customer experience KPIs per second, allowing the company to gauge the quality of each customer’s last phone call and immediately reach out if they are experiencing any issues.
The fastest way to real-time customer 360
Undoubtedly, companies capable of mastering real-time customer 360 will have a major competitive advantage for the foreseeable future. Nstream is the easiest, fastest way to streamline data management and achieve the desired benefits of real-time customer 360. Our full-stack streaming data application development platform provides a radically simple, low-code approach to personalized offer/recommendation and proactive support use cases that can be achieved at a much lower cost compared to other solutions.
Looking for more details about Nstream for customer 360? Read How Real-Time Streaming Applications Keep Shoppers Happy and Ready to Spend More.