Growth is the key to every business and monitoring business performance is the key to growth. Current generation businesses have multiple offerings in terms of products or services and span over multiple geographies which make collaboration and monitoring even more complex. “Time is money”, so companies leverage Machine Learning and Artificial Intelligence to come up with smarter techniques to have competitive advantage and growth potential to succeed in the marketplace.
We live in a world that’s driven by data and the insights it generates. So, we need to spend more time innovating or strategizing rather than suffering from downtime while searching for data anomalies.
Understanding the challenges with manual data monitoring
Can you recollect the number of times you have logged onto E-Commerce websites?
There are thousands of individuals who visit various E-Commerce websites at any given time. Some may be booking flights for an upcoming vacation or planning quick weekend breaks; others may be opting for special discounted rates or taking-up on summer holiday offers, etc.
Heavy traffic leading to buffering and payment gateway failures along with innumerable other challenges that make users switch over to other websites as options are plenty.
The onus falls on internal teams to find the time to manage the overwhelming number of metrics on a day-to-day basis. They need to tackle this on the go while conducting routine activities like manually updating thresholds and working on alarms which are often found to be false positives. The list is endless and they often find their hands full when it comes to acting on the problems that arise due to the heavy footfall on such sites – detecting anomalies and rectifying the outages that occur due to them.
As an E-Commerce enterprise, the manual way causes business losses or opportunity losses.
- Customers report faults to customer service
- High downtime due to delay in detection and resolution
- Fraudulent activities go unnoticed until the volume of these transactions goes high enough to be detected
- Anomalies in seasonal events and business anomalies go unnoticed
- Failure to effectively engage customers in the ‘Earn & Burn’ programs
If you are unaware of metric normal behaviour, how can you track it or set up an alert to be triggered if it happens?
Upping the Game with Augmented Analytics
To tackle the highly sophisticated nature of transactions, E-Commerce retailers need to adopt solutions that go beyond the traditional methods to detect anomalies. CrunchMetrics offers comprehensive resolutions that encompass state-of-the-art anomaly detection techniques which alert you in real time and enable instant actions to safeguard your business.
CrunchMetrics prepares your system for proactive monitoring instead of being reactive. Its AI-enabled, self-adaptive algorithms ensure you have zero dependencies on manual maintenance thresholds along with real-time monitoring of all metrics.
Here are some of the key advantages you enjoy with CrunchMetrics:
- Automatic anomaly detection which triggers actionable alarms
- Machine Learning algorithms that learn from user feedback and eliminate the future occurrence of false alarms
- Real-time monitoring and anomaly detection that allow internal teams to resolve issues immediately, significantly lowering downtime of the terminals
- Possible fraudulent activity detection, preventing revenue loss
- Correlation and Contextualization
- Help in identifying the probable root cause of the problem
For your business operations to run seamlessly, you need to have the tools necessary to interpret available data and derive meaningful insights from it. CrunchMetrics helps businesses do the same.
Summon the power of Augmented Analytics to help you identify risks and business incidents in real-time.
Kshitish Sahoo is the product manager at CrunchMetrics. He has more than 7 years of experience in domains such as Energy and Utilities, QSR, Healthcare, CPG, Real Estate, Banking and Insurance and E-Commerce. He has worked on setting the strategy, developing the feature propositions, marketing the product and handling the financial metrics of the product.