Common Outcomes of Data Bottlenecks
Retail information bottlenecks are best understood in the context of the value chain: Assortment Planning; Merchandising; Availability; Fulfillment; Marketing and Customer Loyalty.
- Information about products is not as good as it could be – data received from the vendor community can be incomplete, inconsistent and sometimes just plain incorrect. At best poor item information holds up the Assortment planning process and at worst creates lost sales or causes compliance problems with taxes and fees.
- Poor item detail data impairs the Search and Browse experience, a problem that results in early session abandonment and sales lost to competitors
- Maintaining price competitiveness is difficult in today’s environment of flash-sales, offers and aggregators where consumers are able to discover the cheapest prices that are only a click away – affecting online and in-store items alike
- Incorporating Drop Ship Vendors and 3rd party logistics operations will increase the assortment, but this obscures visibility ultimately leaving items unavailable for sale and creating back-orders leading to a poor customer experience and lost sales
- Finally and most significantly, when customer data is not available to every process, both Site Merchants and Marketing are at a competitive disadvantage
Behaviors of the Data Starved Organization
How much time is spent by Engineering, Reporting or Business departments wrangling data to make a critical decision? Wrangle it right: identify the data and locate the source, then ensure it is accurate and viable, and finally format it so that a person or a system can act on it.
For strategic decisions this is an overhead that is both acknowledged and justified. However for daily tactical decisions, the difficulty in getting data is often treated as dirty laundry, neither being recognized nor accounted for – which means not only a hidden resource and time cost, but more significantly, the opportunity cost of less agility in responding to market, competitor and external factors.
Making Sense of the Data
Once the underlying data around Item, Customer, Inbound Order and Outbound Order is properly mapped into the retailer schema (or Master Data Management System if available) then it opens up whole new capabilities for the IT department to deliver the systems that create competitive advantage.
|Dimensionality of all attributes, Item class(es), Item Component Breakdown, Public Domain Information Concerning Item, Product Reviews and Ratings
|Improved search via Google Shopping, LinkedOpenCommerce, Bing and Siri, Price Comparisons, Competitive Market Analysis
|Simple customer attributes, Social Graph Data and Psycho-demographic profiles, Online customer behaviors, Customer transaction history
|Basic Segmentation, Simple Collaborative Filtering, Social Shopping, Complex Recommendation, Personal Site Curation (Pico-segmentation); Dynamic User Experiences
|Order Breakdown details, Manufacturer Vendor details, Payment Details, Shipment Tracking, Weather Status, Labor Issues
|Ability to better manage inventory by machine insight into order status and ability to expedite orders and smooth out peaks and troughs in availability, ability to carefully manage costs
|Order details, Shipper Details, Shipping Statuses
|Manage Order Breakdown Details; Shipper Details; Fees, Tax and Customs Payment Status
The Item, Customer, Inbound and Outbound Lifecycles are documented here.
First Retail’s Vision of Semantic Retail
If First Retail achieves its objectives in Semantic Retail, then the term “Semantic Retail” will disappear. All data will be marked up so that machines can understand retail process transactions to make decisions with minimal human curation. Agent Technologies shop for consumers.
Examples of this include:
- Mapping semantically available weather data over time to determine and predict the effects of weather on sales. This enables pro-active availability strategies or the ability to track a shipment from manufacturer to distribution center, via vendor, shipper, customs and finally freight company to the back door. At each stage conditions that will impede with on time delivery in any way (e.g. GoodRoute for HazMat transportation
- Using Social Graph data mapped to the Product Taxonomy to recommend products for self-purchase (e.g. ShoppyCat) and for gifts (e.g. Wantful)
- Enabling a consumer to issue a request in natural language text or verbally (e.g. Siri) and then matching those requests with offers in real time for vendors and to your networks in a similar way that Zaarly and Ubokia envisage.
First Retail is creating a Semantic Matching Engine that will ingest item catalogs at web-scale in real-time. Coupled to its Unstructured Text Classification capability, this will form part of the new infrastructure required to succeed as a Retailer in tomorrow’s marketplace. For further information email us at email@example.com
- How To Leverage Structured Markup To Create E-Commerce Web Portals Search Engine Land (Nov 2011)
- The Value of Semantic Markup to Retailers SemanticWeb (Nov 2011)
- Good Relations Website – The Web Vocabulary for E-Commerce Martin Hepp
- Furor Surrounds Amazon’s Comparison-Shopping App – Price Check LA Times (Dec 2011)