Analytics is transforming retail in myriad ways — a significant aspect of which is the ability to harness and analyze machine data, making a substantial impact to the bottom line for many retailers.
Machine data is one of the most rapidly expanding categories of data capture today. Everything from mobile devices, web sites and POS systems to RFID and many others create digital footprints detailing how customers interact with a company. The result is a gold mine for understanding ways in which improving a customer experience can drive up sales.
However, even data-rich retailers may be surprised to find that valuable information found only in this machine data isn’t making it back to traditional data warehouses or business intelligence systems, so these retailers are effectively blind to any potential insights. Why? Machine data is challenging because it’s typically in an unstructured or semi-structured format that doesn’t lend itself to traditional analysis. It also is frequently siloed in many different systems, with many different formats, and several different interpreters.
However, excellent solutions are now available that can overcome these obstacles. Splunk, for example, is one platform that excels at handling machine data. Splunk ingests machine data in its native format and indexes it, and can even enrich it by tapping into traditional relational databases or social media. Robust analytic capabilities enable retailers to create rich dashboards, providing the right level of information to people with a wide variety of informational needs throughout the company.
Tesco.com is a British retailer, for example, that leverages Splunk to learn how customers interact with their web site and subsequently better understand what products customers were interested in — and which pathways led to the best conversion rates. With these insights, Tesco.com can copy these pathways for future products to maximize sales. Additionally, they can make note of ineffective pathways that lead to abandoned customer carts, enabling them to intervene before sales are negatively impacted.
Another example of leveraging machine data comes from Sonic restaurants. Sonic was migrating over 3,500 locations to new Point of Sale (POS) systems and they needed a way to ensure that pricing was consistent across different POS systems. Using Splunk Enterprise, they were able to pull in data from a wide variety of systems in different formats and identify and remediate pricing errors. They also were able to pull in data across mobile, interactive terminals and drive-in systems to gain insights into their customers’ experiences and plan new products.
Innovative retailers are even pulling data to understand how foot traffic flows through their stores. With this information, retailers can begin to see customer patterns.
When are locations the busiest? Are customers waiting at checkout lines? Which departments are visited most frequently? With this valuable information, retailers can modify the in-store experience to keep customers satisfied and coming back.
But machine data isn’t only relevant to the customer experience. It also provides valuable information on IT operations. For example, Kohl’s leverages Splunk to pull operational data from a vast array of IT systems that together deliver hundreds of critical applications for their business. They also pull data from their POS systems into Splunk to visualize the complete lifecycle of transactions as they flow through their systems.
Target leverages Splunk to improve robot operation within their distribution centers. Splunk allowed the company to gather a baseline performance of their robots to be able to identify when they were operating outside normal parameters. Leveraging the predictive analytics capabilities of Splunk, they discovered that they could also predict when a robot was about to fail. Now, when these data patterns indicate a forthcoming problem, they can automatically initiate the appropriate parts order and generate a technician’s work order to service the robot. This ensures the robot is serviced before a problem occurs, preventing operational down time and saving Target money.
These examples only scratch the surface of how machine data and analytics are transforming retail today. Reach out to your CDW account manager and let’s discuss your business and the art of the possible with machine learning.
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