Prelert, the leading provider of behavioral analytics for IT security, IT operations, and business operations teams, today launched a new Retail Order Analytics solution, which helps online and multichannel retailers identify technical and operational issues as they’re happening in order to stem losses and protect revenue streams. The technology is already being used by a number of retailers to improve digital commerce efficiency, including one of the world’s largest multichannel brands and one of the world’s largest pure-play ecommerce sites.
.@Prelert launches new #BehaviorialAnalytics solution to help #retail and #ecommerce organizations protect revenue
Prelert’s Retail Order Analytics solution can be broadly applied to analyze real-time transaction metrics such as orders per minute, carts created per minute, invoices per hour, or deposits per hour, so that revenue-impacting events can be found and fixed quickly. For example, after automatically learning what normal behavior looks like within any given metric, it can identify issues such as an unusually high number of abandoned carts, an unusually low number of completed checkouts, or even an invoice brown-out, so the root cause can be identified and addressed in near real time.
Periodic Retail Order Data: Challenging to Monitor
Accurately modeling periodicity – also known as seasonality – is such a difficult data problem for retailers to solve. As a result, automated data analysis is becoming necessary for retailers to identify critical problems and avoid drowning in false positive alerts. Due to the varying nature of periodic data, writing rules that can accurately monitor constantly changing behaviors is nearly impossible. Employing humans to watch dashboards and graphs is expensive and subject to human error. Even using supervised or trained machine learning is a poor solution because it can generate a stream of false alerts as data patterns change.
Built with unsupervised machine learning technology, Prelert’s solution automates data analysis and automatically detects the periodicity of daily and weekly order cycles. It adapts to changing data patterns that may result over time due to factors such as a new product becoming available or current events that cause a spike in product interest, and constantly updates its self-generated models of normal baselines. As a result, it accurately finds deviations in expected behavior that can indicate costly problems.
Analyzing Orders Per Minute
One of the world’s largest multi-channel retailers uses Prelert to analyze a massive volume of orders per minute on its global ecommerce site. Anomalies in this data could indicate issues that are preventing customers from placing orders and could easily cost the company hundreds of thousands of dollars if gone unchecked. Using Prelert, this company has detected a number of different issues that could have had a major impact on revenue if not detected early, ranging from process-related issues such as failure to renew an SSL certificate, operational issues such as server failures or application errors, and even external factors such as aggressive competitive marketing campaigns.
“A significant drop in the number of orders taken by an e-commerce site during a particular day might be obvious in retrospect, but can be very difficult to catch in near real time without automated machine learning. Static thresholds and even moving averages can’t reliably identify issues,” said Mark Jaffe, CEO of Prelert. “Our anomaly detection algorithms have been proven to work and provide significant ROI within hundreds of progressive IT organizations around the globe. We can provide the same value now for retail and ecommerce organizations, with a solution tailored specifically for them.”
Analyzing Real Time Revenue
Another ecommerce site tried using analytics tools that required its team to set thresholds for alerts, manually update configurations and perform maintenance on a regular basis. However, this solution could not account for slow hours or slow days, or any other periodic dips in performance metrics, causing the incident response team to be deluged with false positives – and still miss critical incidents. After using Prelert to monitor hundreds of metrics in near real time, the company found anomalies in its revenue data. It exposed a bug in its currency conversion code that was costing 40 cents on the dollar every time a transaction originated from Japan.
Prelert is easy to deploy, bringing analytics to where an organization’s data already resides to analyze it in near real time. In contrast, other solutions require data to be moved, and some even require batch uploads, eliminating any possibility of real time results. In addition, an open API allows developers to use Prelert in their own products or environments.
For more information on Prelert’s Retail Order Analytics solution, visit: http://info.prelert.com/retail-analytics
Prelert is the leading provider of behavioral analytics for IT security, IT operations, and business operations teams. The company’s solution analyzes an organization’s log data, finds anomalies, links them together and lets the data tell the story behind advanced security threats, IT performance problems, and business disruptions. Leveraging machine learning anomaly detection and other behavioral analytics capabilities, the solution automates the analysis of massive data sets, eliminating manual effort and human error. Hundreds of progressive IT organizations rely on Prelert to detect advanced threat activity, reduce false positive alerts and enable faster root cause analysis. Prelert lets your data tell the story. Please visit www.prelert.com or follow @Prelert to learn more.