Online retailers make up more than 8% of the total retail market, and this is expected to grow to 11% by 2018, according to Forrester. The speed and complexity of modern life is driving new classes of customers to use online retailers for more and more of their shopping needs. Online retailers are branching out with their offerings, providing customers with new online services such as insurance and groceries. Mobile and tablet technology has created new touchpoints with the customer and has fueled much of the growth in online sales. Generational differences are also impacting the trend: Forrester research shows Gen Y consumers spend more online than any other age group.
Clean and consistent Customer Experience Management (CXM) is the hallmark of a top-notch online retailer. Giving the customer the best experience typically requires integration of many systems behind the scenes, with the goal being to achieve end-to-end visibility of everything your customer is doing in realtime and delivering the right information in the right context at the right time. The “right time” window is short, which increases the challenges of processing volumes of customer, product, intention, click-stream, social, and transactional data quickly enough to still be meaningful. As more real-time customer context data is added to the mix, complexity quickly increases, as do system requirements.
Once online retailers can process multiple data streams and reference data in realtime, they gain the ability to serve customers contextual offers, thereby increasing basket size and revenue per customer. Home-grown solutions may seem appealing at first, but as the requirements become more complex, cost of ownership becomes increasingly daunting. Extending open-source frameworks could be an option, but this approach isn’t as “free” as it seems at first: it will require investment in talented developers, scalability, redundancy, and security features for each roll-your-own solution, creating significant cost and management challenges.
Offering real-time customer context marketing requires retailers to coordinate all their systems in real time at the highest level, across all relevant enterprise and external systems. Data from the retailer’s ERP and CRM systems, plus current and historical purchasing data, website analytics and other contextual data, must all be brought together and processed instantaneously to make the right offer to the right person at the right time. It doesn’t do any good to make a marketing offer even one moment after a customer browses away from your website.
Striim assists online retailers by bringing all streaming data together in a single platform in real time (including: streaming log files, streaming transactional information via change-data-capture, historical data, contextual data, and social feeds). Striim easily brings together multiple streaming and static data sources from across the business to correlate and aggregate.
As data flows through the platform, business users can apply logic using simple, user-friendly tools to continuously query streams and react instantaneously when specific correlated conditions occur. For example, a user browsing specific items using a mobile phone can then switch to his laptop to finish browsing, and finally call the company to make sure his order can be fulfilled in the time required. The browsing history of all those touch points can be tied together and compared against inventory items identified as “items to move” in real time.
Benefits from the Striim solution can include increased average basket size, increased inventory turns, and improved sales on slow-moving goods. Most importantly, it creates more satisfied customers who feel better understood. The unified platform gives the opportunity to analyze all data in realtime from across customer touchpoints to identify the best offers for each customer.
Inventory related to other items a customer has reviewed through any channel (mobile, web, call center, etc.) can be correlated to recommendations in real time. Offering a customer the right product as she is in the purchasing process provides a much higher likelihood of increasing the basket size. Generally more product is sold once a recommendation engine is initiated, increasing inventory turns. On the long tail, the potential customers interested in slow-moving inventory can be identified and specifically targeted with offers.
As requirements change, so can your custom real-time recommendation engine. The platform is built for scalability and flexibility. Any micro application deployed can be opened in the Striim Flow Designer and reconfigured as the business processes changes. Business logic is represented in a simple, SQL-like language, within the reach of a much broader audience than Java programmers alone.