Data capture followed with data analytics or data capture that is done for conducting data analytics has successfully transformed the retail industry in countless ways. It seems the ability to bring together and analyze humongous machine data is what has helped it make a tremendous impact to the bottom line for retailers, one and all across.
Mobile devices, websites, POS systems, RFID and much more which come under the umbrella of machine data, leave or rather create a digital footprint about how customer interact with companies, what do they feel about the product or the services and how do they express their experience. It’s a gold mine that can help retailers understand how they can channelize their efforts to improve the customer experience and drive up sales. This is what makes it one of the fastest evolving categories of data capture.
“The” Gold Mine
Unfortunately, retailers who can be termed to be data-rich will be surprised to realize the kind of value addition only the machine data has but is not being implemented through conventional data warehouses or their business intelligence systems. This makes them practically blind to any kind of potential insight from their own data – the gold mine they are sitting on. So now the question is why is this happening?
The reason is that machine data, if not always – most of the times; is in an unstructured format or a semi-structured format. This makes it of absolutely no use to conventional analysis. Apart from the challenge of structure, this kind of machine data has the tendency to move away in silos, in various systems, various formats, and numerous interpreters.
However; commendable data analytics software and solution providers, now take care of such obstacles. They are designed to manage analytic activities starting from ingesting machine data in specified formats followed with indexing, and doing that value addition by adding up conventional related databases or social media. Strong analytic capabilities further empower retailers with data-rich dashboards, loaded with appropriate information to a wide array of users across the organization.
Taking this entire theory a step further, analytics are now learning as to how customer interacts with their website. This helps the retailer to assess what all products the customer was or would be interested in; and what is it that could be done to increase the conversion ratio. Apart from finding out ways and means to attract and sell new customers; these data capture based analytics also portray ineffective pathways such as abandoned customer carts, and how to mediate in time to avert negative sales.
Leveraging Machine Data is New Normal
If a retailer is to migrate a few of his locations to new Point of Sale (POS), one thing they are required to be sure of is that the pricing should be consistent across all POS systems. Advent in technology and techniques of data capture and analytics now facilitate such retailers to pull in data from a wide plethora of systems in dissimilar formats, identify and mitigate pricing errors. And this is not it. Pulling up data from mobiles, interactive terminals and drive-in systems enable them to gain insights about customer experience to use it for designing and planning new products.
Today, retailers are positioned to pull up data that gives them the insight about how foot traffic flows through their retail stores. Which locations are the busiest? Are customers waiting in checkout lines, and if yes – for how much time? Which departments are visited most frequently? All these details help retailers to conclude customer patterns and modify the in-store experiences to keep customers satisfied.
The benefits don’t end with data relevant to the customer experience. Valuable information in IT operations can also be extracted, as in data from a various IT systems which together deliver numerous applications for their business. Pulling up data from POS systems helps them visualize the complete life-cycle of transactions as they float around their systems.
Out of the Box Thinking
Improving the robot operation in distribution centers of these retailers is it seems is one of those out of the box thinking. Retailers can collect data about the performance of their robots to identify instances as and when they operate out operate, more or less than prescribed, parameters. Leveraging this predictive analytics could also give approximate time or a deadline when the robot is likely to fail. Now that these data patterns can predict forthcoming challenges, ordering spare parts and rolling out technician’s work order to service the robot – is like a piece of cake. All this makes sure that the maintenance activity for the robot is done even before the problem occurs, ultimately preventing downtimes and saving dollars.
Easier said than Done
All you would have been reading this article since the beginning where we have been harping on a single note – data capture and data analytics. However; the most important aspect to the entire story is that if data capture is not done appropriately conducting data analytics is not even a far fetched possibility. Yes, you heard it right.
Data capture strategy is something that precedes data analytics. An effective data capture strategy only can empower retailers to collect and manage information about their customers, clients, and prospects. Capturing as much data as possible from customers is crucial for retailers, particularly the ones who are planning to generate more profit by assessing customer behavior patterns. Data capture is something than certainly can be done in-house; but if your employees are busy doing it, how will you take care of your core operations.
Does this ring a bell? Yes, call in data management experts at the earliest.