Today, enterprises are looking for innovative ways to digitally transform their businesses - a crucial step forward to remain competitive and enhance profitability. There are key technology enablers that support an enterprise's digital transformation efforts, including smart analytics. Real-time insights and data in motion via analytics helps organizations to gain the business intelligence they need for digital transformation. From a business perspective, the potential benefits it can offer an organization are many - you can use location and contextual data to create better customer experiences; create radically new data-based products for your business; make more informed decisions in complex scenarios; carry out effective monitoring and analysis; detect even the smallest change and trigger immediate action; and extend your solutions to analyze the past, present, and the future.
While these benefits are applicable to most organizations across diverse industries, a key advantage of smart analytics is that it can be customized to create solutions to meet the specific requirements of a particular industry.
This white paper will focus on the business benefits extended to the retail industry and discuss some common use cases within this domain.
2. Analytics for the Retail Industry - An Overview
Consumers today have ready and easy access to information on anything, anytime, and anyplace. Likewise, enterprises too have access to large amounts of data that contain customer demographics, usage patterns, and preferences. Organizations are often faced with information overload and too many choices; as a result, enterprises that fail to cater to specific customer needs lose out and customers soon switch loyalties to their competitors.
To address these challenges, enterprises can leverage analytics to help them become more familiar with customers and their requirements, and to meet these, optimize communication so appropriate messages are pushed to them at the most opportune time and place.
3. Sample Use Cases
Ad optimization, product recommendations, and proximity marketing are some common buzzwords we hear often. But most enterprises lack the knowledge and expertise to leverage these new techniques that help to optimize business given the new generation of 'informed' customers. On one hand, enterprises are struggling to handle the enormity of data, the variety of data channels, and the inability to correlate these disparate data streams to identify useful insights. On the other hand, organizations are puzzled as to when, where, and how product specific information should be communicated to customers in order to move them along a buying cycle.
Streaming analytics can not only dig out relevant insights from large amounts of data in order to optimize an organization's marketing strategy, it also guides enterprises on how best to communicate with their consumers. For a detail discussion on different kind of applications, please refer to 13 Stream Processing Patterns for building Streaming and Realtime Applications.
3.1 Proximity Marketing
A typical example of this is the use of streaming analytics for proximity marketing, incorporating beacons and mobile infrastructure to locate customers and analyze their behavior and enhance their experience by providing them with exactly what they need. This enables organizations to highly customize their offerings based on context, instead of pushing out random product offers via text messages or emails to a mass audience.
Streaming analytics helps retail stores track the indoor location of their customers, discover the most attractive product in the shop, send an 'offer of the day' when customers within a certain radius, provide offers related to their specific products of interest, suggest other products relative to what they are looking at right now, offer coupons based on customers' previous purchases, and obtain more advanced statistics for the store manager to refine and redefine business strategies (Figure 1).
See how international airports have optimized operations by employing streaming analytics on data generated through ibeacons.
3.2 Contextual Recommendations
Some e-commerce portals now incorporate online product recommendations. These recommendations are usually based on other products that were purchased by similar customers. In a world with too many choices and an array of brands of anything you want to buy, this has been very effective. For example, Amazon generates about 35% of their revenue through recommendations (Figure 2).
While this is useful, it overlooks many other possible behavioral purchase patterns. Streaming Analytics elevates the product recommendation process by providing suggestions not just based on similar customers, but also based on the customer's own purchase history, current seasonal trends, and product combinations that are not intuitive, yet can be found through data mining and machine learning (e.g. beer and diapers). This is achieved by correlating the incoming order data with summarized historical data, such as customer's buying history, personal details as well as seasonal trends, inventory status, current promotions, and product correlations based on machine learning models. This real-time correlation and analytics results in intelligent recommendations that have a higher purchase probability rather than generic recommendations that are not customized based on the customer or the current context.
3.3 Ad Optimization
By employing streaming analytics for digital marketing, enterprises can dynamically decide when and how to bid for digital ad space based on market penetration, real-time trends, purchase behavior, and available budgets. At present, most enterprises are struggling to achieve effective conversions through their digital marketing strategies as they cannot optimize ad placement contextually in real time. Hence, most organizations rely on static distribution of their digital marketing budgets to blindly place pre-determined amounts of different advertisements through multiple social media channels without being able to optimize ad placement in real time based on which ads and channels that get more conversions.
Streaming analytics seamlessly solves this issue by correlating online user views/clicks with user demographics on social media and available marketing budgets and make advert bidding decisions within milliseconds so ads can appear on the web page that the target customer is currently viewing within average human reaction time. See how Experian uses Streaming Analytics to optimize ad placement in less than a millisecond.
Analytics used to be a term reserved for data scientists - a word heard by many, but understood by a few. This is no longer the case. Enterprises that do not reap the benefits of analytics will soon be edged out by their competitors. With data being a key component in any business today, enterprises are forced to look for new ways to analyze this data and gain insights into their business. Thus, in today's business world, analytics has become vital to improve customer experience, increase market reach, optimize budget spend, enhance business processes, and find and eliminate anomalies. All of these eventually translate to improved revenue for any business.
Retail industry players can particularly benefit from analytics as it enables them to communicate more effectively with their customers in an era where customers are more informed and respond better to customized marketing efforts. Moreover, streaming analytics provides the organizations with useful insights on customer behavior, which in turn can help them to refine their marketing strategies.
Analytics can be used for specific solutions and use cases in other industries as well. Refer to our white papers that cover other industry solutions for more details:
With the pace at which the world is transacting, analytics that are computed as batches will no longer be relevant. The current need is to perform complex analytics in real-time so enterprises can act on them before the opportunity goes by. Streaming analytics is a perfect fit for this role as it can receive multiple types of data from multiple sources, correlate them, process them, and provide meaningful insights all in a matter of milliseconds. Irrespective of the industry, streaming analytics can create a winning strategy for your business.
If you want to try out these ideas, please checkout WSO2 Stream Processor. WSO2 Stream Processor (WSO2 SP) is an open source stream processing platform. It can ingest data from Kafka, HTTP requests, message brokers and you can query data stream using a “Streaming SQL” language. With just two commodity servers it can provide high availability and can handle 100K+ TPS throughput. It can scale up to millions of TPS on top of Kafka.
If you found these use cases helpful and/or applicable to your organization or have similar use cases, we're happy to further discuss your requirements and take you through a demo. Please contact us and we'll get in touch.