Hey folks!!!! Hope you all are doing great & had a great weekend.
Today we will be discussing the use of Big Data in Retail industry –
Retail industry is among the early adopters and innovative users of big data. But they had the challenge of tackling the huge data since 1970s when barcodes were first introduced to scan the products at POS along with surveillance cameras sending huge amount of data to data centers. All these are challenges Retailers face to capture, store, cleanse & analyze all the data they collect. Further, to add to the challenges, consumer’s interaction with social media & internet which generates billions of data points that can be measured via clicks, page views, time spent on per page etc. flood the data centers.
Big Data analytics is helping retailers to capture, store, cleanse, process & analyze data. According to Mckinsey report, Big Data Analytics can raise the operating margins by as much as 60%.
Here is a list of main categories where retailers employ Big Data Analytics –
1. Assortment Optimization – Deciding which products to carry in which stores based on local demographics, buyer perception, and other big data; this can be termed as assortment optimization that can increase sales materially
2. Placement & Design Optimization – Brick-and-mortar retailers can also gain substantially by optimizing the placement of goods and visual designs by mining sales data at the SKU (stock keeping unit) level
3. Seasonal Optimizations – Consumers make purchases of certain products during certain period or season. This data can be used to maintain minimal availability of particular products during particular season thereby avoiding stock outs
4. Pricing Optimization – Retailers today can take advantage of the increasing granularity of data on pricing and sales and use higher levels of analytical horsepower to take pricing optimization to a new level
5. In-store Staffing Optimization – Depending on the data of consumers visiting a brick & mortar store, staff can be hired optimally to serve the consumers better
1. Inventory Management – With the additional detail offered by advanced analytics & mining multiple datasets, big data can continue to improve retailers’ inventory management
2. Supplier Negotiations – Leading retailers can analyze customer preferences and buying behavior to inform their negotiations with suppliers
3. Warehouse Space Optimization – Warehouse space can be utilized optimally with the data available for production, shipment of products & so on
4. Distribution & Logistics Optimization – Leading retailers are also optimizing transportation by using GPS-enabled big data telematics and route optimization to improve their fleet and distribution management
1. Sentiment Analysis – Sentiment analysis leverages the voluminous streams of data generated by consumers in the various forms of social media to help inform a variety of business decisions
2. Web Analytics for Online Consumer Behavior – To analyze consumer behavior online, frequent pages visited, products usually viewed etc. specific products can be targeted to every consumer
3. Customer Micro-segmentation – The amount of data available for segmentation has exploded, and the increasing sophistication in analytic tools has enabled the division into ever more granular micro-segments. This is known as Customer Micro-segmentation
4. Advertising & Promotion Campaign Analytics – Companies can analyze data to understand where to spend their advertising & Marketing budget in order to effective
5. Cross Selling – Cross-selling uses all the data that can be known about a customer, including the customer’s demographics, purchase history, preferences, real-time locations, and other facts to increase the average purchase size
6. Location Based Marketing – Location-based marketing relies on the growing adoption of smartphones and other personal location data-enabled mobile devices
7. In-store Behavior Analysis – Analyzing data on in-store behavior can help improve store layout, product mix, and shelf positioning
8. Multichannel Consumer Experience – Usage of big data to integrate promotions and pricing for shoppers seamlessly, whether those consumers are online, in-store, or perusing a catalog. This is known as multichannel consumer experience
9. Loyalty Programs – Loyalty programs are structured marketing efforts that reward and therefore encourage, loyal buying behavior – behavior which is potentially beneficial to the firm
1. Performance transparency – Retailers can now run daily analyses of performance that they can aggregate and report by store sales, SKU sales, and sales per employee
2. Labor Inputs Optimization – This can create value through reducing costs while maintaining high service levels
3. Demand Based Production to avoid Stock-outs – Depending on the data available for demand of certain products in the past, future predictions/forecasting can be done & accordingly production can be followed to avoid stock-outs & over-stocking
4. Personalized offerings for Consumers – With the availability of micro & minute data available of consumers, personalized offering of products can be possible. This helps in building & maintain relationships
New business models
1. Price Comparison Services – It is common today for third parties to offer real time or near-real-time pricing and related price transparency on products across multiple retailers. This way price comparison can be done
2. Web-Based Markets – Web-based marketplaces, such as those provided by Amazon and eBay, provide searchable product listings from a large number of vendors.
I hope this information helps!!!!