What’s the Significance of Real-Time Insights in a Digital Landscape

For companies that have a mature business online or for the ones who are growing online, leveraging real-time data – information available within milliseconds of an event – is becoming a must-have due to the competitive landscape. Making decisions and reacting while the user is still on the page requires analyzing information and personalizing experiences in a fraction of a second. In this article, we discuss the concept of real-time data – why it’s needed, where it comes into play, and why it’s still challenging for many businesses.

What is real-time data?

The term “real-time data” is used loosely by technology vendors to describe “data that is made available close to the time it is generated”. Many of them call it “In-session data”. In many cases, ‘real-time’ refers to data that is available within seconds or milliseconds – from the time the event happens to when action can be taken based on that event.

From real-time data to real-time insights

Beyond just having the data available in real-time, companies also need the ability to draw insights and conclusions from that data and trigger actions (typically programmatically), still within a very narrow timeframe. This could involve performing calculations to identify anomalies or compare specific data points to a historical dataset, and then using those real-time insights to drive automated decisions and experiences.

For example, when a user lands on a website, real-time would mean making decisions like which content to display to that user within a split second, before the page even loads – based on data points such as the location the user is browsing from or which content they have already interacted with. This is a much narrower window than waiting an hour or even a minute to take action based on that user’s visit. True real-time data enables split-second decision-making to drive personalized, seamless digital experiences.

For Online Businesses, Real-time Insights are Indispensable

Some decisions can be made on ‘slow’ data. One could even argue these are most day-to-day decisions that a business makes – such as whether to start a new marketing campaign, change sales targets, or hire another product manager. In most cases, you do not need split-second data; these decisions will be made based on information gathered weeks or months prior.

However, there are cases where every second matters. Digital-native businesses, which generate the lion’s share of their revenue from their online presence (such as their website or mobile app), often need to identify and act on insights very quickly due to the nature of the online world. For example, an outage or broken interface that prevents users from completing their purchases can completely erode profitability if it is not dealt with swiftly.

Another major area of focus for online businesses, which is entirely dependent on real-time insights, is online personalization – where the digital experience is tailored to meet the specific needs or desires of the particular customer. 

The importance of real-time personalization 

Personalized experiences are what set successful websites apart. Showing each visitor content that’s tailored to them leads to higher conversion rates, lifetime value, and revenue growth. Personalization is now a competitive necessity. This can be seen in industries such as eCommerce, gaming, and publishing, all of whom have invested heavily in understanding their visitors and molding their journeys to maximize engagement.

However, with users jumping between devices, with the trend of extinction of third-party cookies, and with the growing awareness to privacy, the majority of website visitors are anonymous. Their preferences are not evident ahead of time. The intent and interests of these users can often only be inferred from signals in their current browsing session.

To serve anonymous visitors personalized recommendations before the page even loads, businesses need to collect and analyze real-time session data. The complexity of these calculations dictates that they would usually be performed by an artificial intelligence (AI) model rather than a traditional algorithm. This real-time insight then fuels a decision (almost always automated) about which product, content, or offer to show the next visitor.

The session data fueling personalization is only available after the visitor lands on the page, and often needs to be made before the page is fully rendered. To prevent a broken user experience, this time frame needs to be measured in milliseconds. The data simply wouldn’t be actionable using traditional, slower methods focused on historical trends.

Real-time data capabilities are essential for digital-native businesses to understand customer intent and deliver tailored journeys. In a highly competitive environment and with consumers expecting more seamless and personalized experiences, real-time data becomes more central than ever.

Acting on Real-Time Data is Challenging

While having access to real-time data presents opportunities, actually leveraging it to drive decisions and experiences can prove challenging for data, product, and engineering teams.

The first major hurdle is data processing within milliseconds or seconds to generate insights. Doing so requires infrastructure for scalable and high-performance data intake and analysis, which can ingest streams of real-time events and make AI-based inferences within tiny fractions of a second. Building and running these systems is highly complex. 

Once the data is available, the company needs to be able to immediately trigger actions based on those insights, typically in a way that’s completely invisible to the user – which means integrating real-time insight into other systems, and with minimal room for error.

Together, this means that leveraging real-time data at scale requires a meticulously engineered technology stack spanning data collection, AI models, and software integrations. While the potential value is immense, realizing this potential necessitates overcoming these non-trivial barriers. Many companies find that specialized platforms, purpose-built for real-time predictive AI, can help them close this gap more quickly.

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