Big Data in retail
In the recent years, the term Big Data has been discussed almost everywhere, first among the new age online business, but increasingly now in the traditional offline businesses as well. One industry which was first to identify the power of Big Data technologies and stands out in the use of Big Data is the Retail industry.
One may ask, why Retail?
It is because the number of data points collected by a retailer is more than that in any other industry. Any retail store will stock 1000s of SKUs, transact with 100s of customers each day, deal with dozens of suppliers and log data for each of these individual items, customer transactions, buying patterns, weather information, supplier transactions.
And if this store is a part of a large chain, the company owning this chain will have dozens of store in the same region and 100s of them nationwide. All these stores collect the same amount and type of data 365 days a year. Just imagine the amount of data created.
But why would Retailers want to collect so much data?
The answer is simple, the better the retailers anticipate their customer’s need based on the buying patterns, seasonal requirements, personal preferences, weather conditions and many other factors, better the chance of their making a sale.
In case of a neighborhood grocery store, the store owner knows each and every customer of his personally, knows their buying patterns and knows their preferences, this knowledge allows him to recommend his offerings to his customers. Large retailers possibly have millions of customers among their 100s of stores and it is not possible for the store managers or sales staff to know each of their customers personally.
In their case collecting all types of data and analyzing it to derive patterns and make sense out of it is extremely important. By tracking every customer transaction, retailers can look out for patterns of behavior and learn more about their shopping habits, helping serve the customers better.
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How are Retailers using Big Data?
Here are some ways, how Retailers use Big Data.
Personalized Recommendations
This is probably the most widely cited and used use case for Big Data not just for online retailers but also for offline retailers.
Retailers gather data for all the transactions you’ve made at any of their stores and store them in their databases. Data collected over a period allows retailers to identify patterns in customers buying and send out suggestions via post or emails or mobile apps. Retailers also gather data about your gender, age, locality, income group and share generic recommendations based on the shopping habits of customers who share your demographic
Amazon Retail Outlets
traits.
In case of online retailers, data is collected for every click you make on the online stores and recommendations are real time.
Amazon.com has championed the personalized recommendations for it’s customers based on their browsing and purchase history. Amazon also provides recommendations by comparing your browsing and purchase history with other customers and identifying similar interests.
Personalized recommendations work very well with the customers if implemented in the right way. But certain discretion needs to be followed and customers must be reassured that their privacy is not being breached in anyways. There are plenty of examples where things have gone wrong, something that could have been avoided, here are such experiments that didn’t go down well with the customers of Amazon and Target.
Dynamic Pricing
Online retailers like Amazon.com routinely adjust item pricing for their products several times a day or hour for multiple items. The prices of items also vary depending on the customers, for example a discount may be extended to a loyal customer than a new one.
This is called dynamic pricing and is achieved as a result of analyzing a vast and constant data flow in real time, Big Data. Various inputs like demand, popularity, competitor prices, weather conditions, location and customer’s purchase history are some of the parameters that prompt real-time fluctuations in the pricing.
Offline retailers have started experimenting with dynamic pricing, some even have implemented electronic price tags enabled with RFID which change pricing depending on the customer’s nearby the tag.
If implemented correctly by processing and analyzing huge sets of data, dynamic pricing can enable retailer to optimize their pricing based on real time inputs, as opposed to setting a price over the long term and either pricing too low and giving up margin needlessly or charging too much and losing sales.
Sears uses Big data to set prices in near real time and sending customized coupons to loyalty shoppers that eventually helps moving in-store inventory faster. Going by the success of their internal implementation and knowledge gained on Big Data, Sears also started a Big Data consulting firm, MetaScale to help organizations outside Sears with their Big Data requirements.
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In Store Experience
Offering deals and information about the items that a customer would be more interested in based on his search patterns using the smart phone as well as his location within the store, retailers are able to improve the in store experience of a customer by sharing relevant information at the right time.
Large retailers are implementing geo-fencing in their stores along with Big Data to improve real time offers and recommendations to their customers.
Walmart Store
A geo-fence is a virtual perimeter around a physical space such as retail location or a bus stop. Retailers are able to send notifications on customer’s mobile device when customer enters or exits the geo-fenced area.
For example, if you had searched for a particular clothing item online a week back and now are in a store and passing by the clothes section, you might be notified about the availability of that particular toy in the store.
Wal-Mart is implementing geo-fencing with Big Data with intention of suggesting users to find real time information about items in it’s stores by matching users requirements with the in store inventory.
Retailers are also using Big Data and geo-fencing to improve store layouts by tracking the movement of customers in the stores, the time spent in each aisle and items sold in each aisle.
If you have any other Big Data use cases in Retail and I am sure there are dozens of them out there, feel free to share them with us. If you are looking for implementing Big Data for your retail business, do get in touch with us at info@localhost.
Check out more from the Big Data Blog Series:
1. Hadoop in the AWS Cloud – Elastic MapReduce (EMR) A Complete Guide2. The 3 Top Hadoop Distributions Compared
3. 4 Faces of Big Data you Can’t Ignore
4. The Big Data Life Cycle Stages Briefed
5. The 3 V’s of Big Data – Volume, Velocity & Variety
