E-commerce sales continue at a blistering pace. As per the U.S. Department of Commerce, third quarter 2015 e-commerce sales increased 15.1% from the third quarter of 2014; while total retail sales increased 1.6% in the same period.1 This trend continued in the fourth quarter. U.S. online sales on November 30 – Cyber Monday – saw a 12% year-over-year increase, climbing to a record $3 billion in one day.2 Meanwhile brick and mortar sales at U.S. stores open on Black Friday actually fell from $11.6 billion in 2014 to $10.4 billion in 2015, according to the retail researcher ShopperTrak.3 While e-commerce sales are operating from a smaller base – in the third quarter of 2015 accounted for 7.4% of U.S. retail sales4 – we predict the continued influence of two major trends here:
Consumers are asking for a 1:1 relationship with retailers – with customized products, promotions and rewards. They also expect the same personalized relationship with the brand across every channel of interaction along the path to purchase – online, in-store or via their mobile devices.
Yet while “personalization” is the hottest buzzword in the retail industry today most retailers can’t deliver on its promise. A survey by EcoConsultancy found that while 96% of retailers say personalization is awesome just 6% are doing it! That’s because delivering on the promise of personalization calls for a 360-degree view of your consumers, products and in-store data – something most retailers are unable to do. Their existing stack of legacy mainframe and relational technologies – just don’t talk to each other to make this happen. And multimillion-dollar spends on systems integration and ETL vendors have not resulted in solutions that meet consumer needs.
In retail today, a product is not just a physical stock keeping unit (SKU) but a complex bundle of integrated information that includes digital images and videos, catalog descriptions, customer ratings and reviews, dynamic pricing and promotions, as well as in stock availability.
Unfortunately, most retailers with relational systems have significant issues managing product and supplier data complexity limiting competitiveness. This puts traditional brick and mortar retailers at a huge disadvantage versus Amazon.com. As per a recent article by Profitero covering the toy category, “Even when Amazon curates its massive assortment to a more manageable list of products for its holiday promotions, other retailers have difficulty matching the breadth of Amazon’s listings. Selling 68% of Amazon’s Holiday Toy List, Toys “R” Us was the retailer listing the most, followed by Jet.com at 63%. Wal-Mart and Target sold just 59% and 57% of Amazon’s Holiday Toy List, respectively.”
Simply put, if you’re offering consumers a more limited selection of toys because of your limited supplier on-boarding and product data management capabilities – you will end up with lower sales too.
The advent of mobile apps providing easy access to competitive pricing information puts retailers at risk of ending up as showrooms for Amazon.com. This is especially true in categories like toys, electronics, or even diapers – where Amazon.com offers a broader selection, typically lower prices, and second day unlimited “free delivery” for Prime customers at $99 a year.
The only way around this is to sell bundled solutions. Consumers are looking for not just comparative pricing information but solutions with comparisons and context (e.g., recipes for dinner or an entertainment system that includes a TV + sound box + installation + cables + a service plan).
Yet most retail data today lacks semantic relationships that can link and enrich the data with context and sell solutions to consumers. Thus, the associate in the store selling the TV often lacks the context to link the TV with the right set of cables or sound box. While online this is even more difficult – since putting together the right bundle and then making comparisons across competitive products and pricing schemes is impossible to do on most e-commerce sites.
In retail today, an estimated 80% of new data sources involving in-store transactions, e-commerce, and consumer and product information are not considered for analysis. That’s not terribly surprising considering that retailers face immense challenges arising from the sheer volume, velocity, and variability of the data they must manage.
Retail systems largely utilize relational database management systems (RDBMSs) that are driven by Oracle, IBM DB2, Microsoft, and SQL as the programming language for managing the data stored within them. For RDBMSs to perform well, data flowing into them must first be mapped with a predefined schema or a set of constraints that define how it is structured and organized for analysis using a “rows and columns” approach.
We predict a continual shift to Enterprise NoSQL (Not Only Structured Query Language) technologies that offer greater flexibility at data management. NoSQL technology takes a schema agnostic approach enabling loading up the data first and then seeing where the problems lie. This problem-oriented approach focuses on how the data will be used (queried) rather than how the data must be structured to fit within a traditional RDBMS.
In summary, traditional retailing faces a significant threat via shifting channels and changing consumer preferences in 2016. We believe winners will use big data and analytics to make the right decisions? Will you?
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