Big Data in Retail
The rapid growth in accessibility to transaction, interaction and processing data is leading to a paradigm shift in the way retailers do business.
There is a vicious circle at play that is forcing retailers to turn the concept of Big Data into practical value-adding outcomes. Consumers are demanding more and more transparency from retailers in order to compare and contrast products and prices prior to making a purchase. As a result of this, tighter margins are in turn driving the need to gain a competitive edge through innovative Big Data initiatives.
Bigdata Experts at Sentienz understand the game of scale, retail companies easily hit 100s of terabyte storage. Also we respect the existing IT Infrastructure and allow smooth adoption. Our platform enables you to fasten the road to advanced analytics.
Bigdata the “Opportunity”
Big Data is much more than a huge dataset and access to cutting edge analytics tools; it is the phenomenon that will separate the winners from the losers in Retail. The growth in importance of Big Data is the latest evolution of the role of data within an increasingly competitive retail environment.
Bigdata “What” Data
For retail companies, it is important to tailor big data in multitude of databases that are unstructured and unrelated, in order to meet the growing challenges faced by the industry in the present age. . The most significant objective of customer analytics is to understand and target most valuable customers and maximize profits and loyalty. It further tap the transactional data to coordinate customers, stores, products and promotions. Big data also helps the company to draw a comparative analysis of the dealings of its competitors, to have a competitive edge over others
Bigdata Retail “Scope”
Many existing business capabilities can be enhanced when more and varied data becomes readily available for analysis, expanding the scope of opportunities and a breath of optimizations.
Bigdata Retail Usecase
Enhanced Customer Service
Excellent customer service is critical to the success of an ecommerce site. Zappos and Netflix are examples of terrific customer service. But Big Data has made customer service a challenge by requiring seemingly every interaction with a shopper to be used for serving that shopper. To continue to excel at customer service, online retailers need to overcome this challenge.
For example, if a customer has complained via the contact form on your online store and also tweeted about it, it will be good to have this background when he calls customer service. This will result in the customer feeling valued, creating a quicker resolution. More timely responses to information requests
Personalized Customer Experience & Targeted Marketing
Recommend items that may catch customers' attention by cross-analyzing their online behaviour with their in-store interactions. Suggest new online purchases to customers by examining their online transactions and level of promotional engagement. Offer more value to customers who are less likely to purchase by analyzing web logs and in-store traffic patterns.
Insights provided by geographic, demographic, and promotional data assist in upsell and cross-sell products. Secure more sales by targeting promotions to the customers most likely to purchase again. Expand your market segments by offering your loyal customers incentives to share coupons and promotions with others.
Optimizing the Sale channels & Merchandising Range
Boost your sales and margins by looking at both
online data and historical POS transactional data. One
can Minimize losses by matching product success and
failure with geo-locational, psychographic, and
Its important to maintain the right inventory by
correlating external factors such as weather, major
events, and potential natural disasters to purchase
behaviors. Improved merchandising decisions drive
topline and bottomline improvements through
Analyze customer sentiments via their tweets,
facebook likes/dislikes, photos etc… and Analyze click
You’ve been segmenting your customers for years, but now it’s time to micro segment them. Big data empowers organizations to tailor their products and services to meet the very specific needs of each customer. An example the report gives is tailoring applications on a Smartphone based on the owner’s personality.
Consumers shop with the same retailer in different ways. Data from these multiple touch points should be processed in real-time to offer the shopper a enhanced experience, including content and promotions.
Supply chain visibility
Customers expect to know the exact availability, status, and location of their orders. This can get complicated for retailers if multiple third parties are involved in the supply chain. But, it is a challenge that needs to be overcome to keep customers happy.
A customer who has purchased a backordered product would want to know the status. This will require your commerce, warehousing, and transportation functions to communicate with each other and with any third-party systems in your supply chain. This functionality is best implemented by making small changes gradually.
Managing Fraud & Abuse
Larger data sets help increase fraud detection. But it requires the right infrastructure, to detect fraud in real-time. This will lead to a safer environment to run your business and improved profitability.
Most online retailers need to process their sales transactions against defined fraud patterns, for detection. If it's not done in near real-time, it could be too late to catch the fraudsters.
You need dynamic pricing if your products compete on price with other sites. This requires taking data from multiple sources, such as competitor pricing, product sales, regional preferences, and customer actions to determine the right price to close the sale. Large merchants like Amazon already support this functionality. Overcoming this challenge will give your business a huge competitive advantage.
Predictive analytics (Supply Demand forecasting)
Analytics is crucial for all online retails, regardless of size. Without analytics it is difficult to sustain your business. Big Data has helped businesses identify events before they occur. This is called "predictive analytics." Predictive analytics is becoming an important tool for many businesses.
A good example of this is predicting the revenue from a certain product in the next quarter. Knowing this, a merchant can better manage its inventory costs and avoid key out-of-stock products.
In Store Experience
A grocery chain’s free shopping list App feeds into their plan-o-gram logic to optimize product placement and increase cross-selling
ever-growing intelligence on what products shoppers are likely buy together
more effectively cross/up-sell related items through co-locating them
leverage the mobile shopping-list App to recommend forgotten items (i.e. bread, olive oil & vinegar)
Analyze Staff performance
Upper management will be empowered by the collection of more accurate and detailed personnel performance data that can be reported in real or near real time. Find out instantly your company’s turnover rate or its total number of personnel sick days, according to the report, to try to understand the root causes of certain performance-based issues.
Identifying Hidden Insights
Imagine if you could correlate sales patters to some unconventional source of data like weather or stock market etc. BigData opens up new avenues by unlocking the hidden insights in data
Store promotions such as Clearance sales or product launches can be floated by customers first to determine if they are worth doing, or whether it needs further refinement.