Trends

Web-based Point of Sale

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Google Gears

For years several manufacturers have tried to sell web-based POS systems, but until now with limited success. The benefits of a web-based solution are clear: lower maintenance costs, easy to upgrade and above all accessible from anywhere.

But the downside of a internet based solution is equally clear: What happens when the internet connection fails? Yes, you’re out of luck, and have to fall back to pen & paper. This disadvantage has kept many retailers from going web-based, and rightly so, as internet connection do fail once in a while.

But now Google presented Google Gears. Google Gears is an open source browser extension that enables web applications to provide offline functionality using the following JavaScript APIs:

  • Store and serve application resources locally

  • Store data locally in a fully-searchable relational database

  • Run asynchronous Javascript to improve application responsiveness

In short, Google Gears allows software manufacturers to create web-based applications that can be used off-line. Although Google Gears is still a beta product, there are already a number of sites using it. Now just wait until a Point Of Sale manufacturer implements Google Gears…

Market basket analysis

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Shopping basket

Market Basket Analysis is a modelling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items. For example, if you are in an English pub and you buy a pint of beer and don’t buy a bar meal, you are more likely to buy crisps (US. chips) at the same time than somebody who didn’t buy beer.

The set of items a customer buys is referred to as an item-set, and market basket analysis seeks to find relationships between purchases.

Typically the relationship will be in the form of a rule:

IF {beer, no bar meal} THEN {crisps}. The probability that a customer will buy beer without a bar meal (i.e. that the antecedent is true) is referred to as the support for the rule. The conditional probability that a customer will purchase crisps is referred to as the confidence. The algorithms for performing market basket analysis are fairly straightforward . The complexities mainly arise in exploiting taxonomies, avoiding combinatorial explosions (a supermarket may stock 10,000 or more line items), and dealing with the large amounts of transaction data that may be available.

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New uses for customer displays

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Customer display

Customer Pole Devices (CPD’s) have long been considered merely bells and whistles at the Point of Sale (POS). They were seen as nice to have if extra money allowed, but were often the first item to be eliminated from the POS station when the budget limits were reached. Their main function was to display to customers information that was being displayed elsewhere and the redundancy was what made them seem to be superfluous. Note that they do not always stand on a pole, but sometimes can be incorporated in the EPOS terminal.

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A New Kind of Data Need for a New Kind of Retailer

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Chart

Now that Point Of Sale systems collect more and more information about the customers, retailers are able to optimize their marketing and inventory in ways that were not possible before. But the collected data is not only helpful for the retailer; manufacturers love to get access to shop-level data as well. eWeek published an article about a report conducted by Forrester:

“Syndicated POS data supports strategic marketing, not field execution. Traditional syndicated data has defined data hierarchies like account or channel that facilitate strategic planning and reporting,” the report said. “This data can take days or weeks to prepare, and manufacturers can’t easily use it to make ‘in-flight’ adjustments to trade promotion and replenishment activities.”

Manufacturers “haven’t gotten this information back from the retailers,” Overby said. Why? Ahhhhh, that’s where things get a wee bit political.

Some of the reasoning is indeed technical, with the expected difficulties in associating so much additional data with customers and products. But a more significant issue has been a political hesitation to share too much information with a supplier that is also sharing data with direct rivals. The retail-manufacturer relationship is not exactly overflowing with blind trust.

RFID tags can catch viruses

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RFID Label

Researchers at the Vrije Universiteit in Amsterdam gave a live demonstration of an RFID virus at the Fourth Annual IEEE Conference on Pervasive Computing and Communications (IEEE PerCom) in Pisa, Italy. The attacks exploit the same software weaknesses that PC viruses and worms do and can have the same devastating consequences.

Melanie Rieback, a Ph.D. student supervised by Andrew Tanenbaum, showed that an infected RFID label can infect a middleware database, which could potentially trigger the middleware to produce more infected RFID tags. The group set up an informational web site about RFID Viruses and Worms to give information about their work on RFID malware.

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