A Peek at the Shopping Cart

 

analytics

Big data and analytics can be applied over broad parts of the organization to enhance and optimize operations, 360 degree customer analysis, business decision optimization, security intelligence and a range of other value adding factors of the organization. Today we will delve into pattern evaluation and informed decision making through the observation of the single most important part of businesses- the shopping cart.

Usually the analysis of shopping carts in Data sciences is part of Affinity Analysis- an analysis of the property of patterns arising from co-occurrences in user/user group behaviour. This analysis is a key driver into providing valuable insights into cross selling and upselling of products to existing customers. This analysis could also empower organizations with the ability to provide strategic decision making guidelines for marketing and sales.

A well-known example of this kind of analysis, is Amazon.com ‘People who bought this item also bought’ section on details page of the item being viewed.

Affinity analysis of the shopping cart is done from different perspectives- the order level, the item level and customer level. The order level analysis provides insights into the opportunities for cross sells and upsells. Customer level analysis provides insights into the evaluation of the products that can be sold in a customer’s lifecycle- this information becomes more potent when combined with the segment of the customer. The bright side of customer level analysis and implementation of decisions made on this evaluation could lead to the further sale of products which could increase customer loyalty – involves the evaluating the customer needs and providing them with what they need, before they consciously state the need for the product/service.

In the hospitality industry evaluation for cross sell and upsell is immense. Even in markets lie India, it is would be surprising how often customers are keen about availing deals either in the form of extra services that they would inevitably avail as a traveller (cross sell- as in local public transport, sightseeing packages, etc) or affordable upgrades (upsell- as in moving to a better room).

Hotels that sell directly through their hotel websites, can now understand their customers better. Affinity analysis on their guests’ shopping cart can tell them a lot about their guests buying behaviour, buying patterns and means of increasing sales closures.

Hotels selling rooms directly on their own website have access to large data that could then be used for improving and furthering their marketing and sales efforts through means like the above explained affinity analysis of their shopping cart. Apart from the commonly known disadvantage of empowering middlemen, the loss of valuable marketing and sales that can be gathered on direct sales done on hotel websites, lead to a major flaw in future business decision making.

A seemingly valid argument against the application of big data for small businesses/hotels is the expense and know how involved in ETL (Extraction, transformation and loading) for the organization. However, the counter argument for this line of argument is also straight forward through two possible solutions:

  • Tools for the extraction and utilization of data across transactions and also the evaluation and application of advanced analytics are becoming increasingly simple to integrate with your website.
  • Organizations and businesses now working in the niche hospitality segment provide for analytics, analytics based decision making and implementation of decisions made through analytics, provide for out sourced solutions that are connected directly to the hotel website.

While the first means might still be error prone for non-tech intensive small/medium size hotels, the second means is easily accessible and affordable with more guarantee of control over data and strategy for sales of room nights for hotels.

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