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What is stream analytics?
Streaming analytics is a term commonly used to describe extracting actionable information from continuously flowing/streaming data sources. A stream is a continuous sequence of data elements. The goal of stream analytics is to translate transaction-level logic into real-time business insights by letting companies extract, inspect, and analyze their data while it is streaming into applications.
Stream analytics is becoming more popular due to two causes. First, time-to-action has become an ever decreasing value, meaning we need real-time data insights. Second, we have the technological means to capture and process the data as it is being created.
There is a myriad of different sources of data. Data in retail can source from POS systems, (e-commerce) transactional data, in-store sensor data, supply chain systems, and social media.
If you don’t use or analyze the data quickly enough, it can lose its value. When it does lose its value, it will result in extra costs such as operational, administrative, reduction in productivity, inability to make informed decisions, and reduces a company’s competitive edge. Streaming analytics has many benefits. For now, we’ll focus on the benefits in the retail industry.
Benefits of stream analytics in retail
Better product recommendations
By using stream analytics, you’re not only analyzing real-time data, but you can also match it with purchase history, current seasonal trends, inventory status, current promotions, and data on similar customers. Which can elevate the product recommendation process to increase the likelihood of purchasing, and with having the insight of a market-basket analysis, you can offer additional products as well.
Prepare for market trends
Not only can it help with the product recommendation process, but integrating your stream analytics, network, and historical data will give many insights. Such as dynamically match data against market penetration, real-time trends, purchase behavior, and available budgets, and to make advert bidding decisions in real-time.
Location-based marketing
By using location-based marketing, streaming analytics can help physical stores track the location of their clients and match it with real-time data and historical customer data in other; to determine most likely product to be bought per store visit, to offer the right product at the right time, to the right person, and to collect data from each interaction between company and customer. A good use case example of this is Helzberg Diamonds, which will be explained later.
Personalize customer experience
Matching stream analytics with other data, and optimizing location-based marketing, you can ultimately deliver personalized user experiences to your customers. This data will give you insight into what your customers want when they want it, enabling you to provide the best offers to your clients matching their personal preferences.
Follow your customers and employees in-store. By enabling WiFi access points, as soon as your customer connects, you are capable of tracking this valuable motion data.
Online benefits
The benefits of stream analytics not only exist in-store but also online. Real-time data show retailers what their customers look for online, offering retailers an opportunity to capitalize on this data. For example, providing your customers with recommendations based on their purchase history, similar clients, and seasonal trends. Stream analytics makes this possible by analyzing all the different components and reacting as quickly as possible.
User case examples
E-commerce giants
Online retail giants, think Amazon or eBay, are always looking to get the most value out of the data that is generated when a customer is on their website. Every product looked at, each search conducted, and each click is carefully monitored and analyzed.
You might have noticed that on big e-commerce websites, even when you’re not a member, after a few clicks and page visits, you get exciting offers. This has everything to do with the behind the scenes. That is used to create the best value for customers and generate the best revenues for the company. Offering well-formed recommendations are the reason behind 35% of Amazon’s revenue.
Tesco
The large grocery store chain from the United Kingdom invested in real-time data analytics a few years ago to benefit the store as well as its customers. Tesco wanted to understand their quickly changing customer needs better, but also to reduce in-store food waste.
By monitoring real-time data for each of their products, which are up to 40,000 in each store, Tesco discovered that the way they thought we bought products, was not the way we bought them at all. By having these insights, they were able to adapt and therefore reduce food waste and adjust to their customer needs.
Helzberg Diamonds
Helzberg Diamonds was one of the first that has started using a Concierge app for its employees, which was built to ensure better jewelry-buying experiences at the physical store. The app provides workers with real-time access to shoppers’ purchase history, wish lists, and other pertinent information, including contact details, but also enables them to check in-store inventory instantly.
By relying on the captured data, in-store personnel should find it easier to make product suggestions. Based on the information provided about stock levels, associates can avoid disappointing customers by discussing jewelry that’s not immediately available. The app also shows whether or not the customer is part of the Helzberg Diamonds’ reward program, which could lead to promotional discounts.
Challenges of stream analytics
Act quick
Search queries give marketers an idea of what their customers find essential. However, you need to act fast because today’s customer changes their mind in a matter of seconds. To keep up with the rapid shift in consumer interest, marketers should stay one step ahead.
Different mindset
What can be a challenge for some is that streaming analytics requires a different mindset than traditional analytics. As explained earlier in 2015, with traditional analytics, you request something and wait for the data to respond. But since stream analytics is a constant stream of incoming data, you should start detecting interesting events as they come in instead of waiting for a response request.
Conclusion
The benefits mentioned here were just a few of the many benefits that stream analytics can generate for your business. As is shown by the different user examples, you can implement stream analytics in many different ways, to fit your business and customer needs. Streaming analytics is a valuable component to add to your day-to-day operations.
