Shopping Search Engines, The Next Generation
Bastien Duclaux, CEO Twenga
Shopping is not what it used to be. Long gone are the days of the catalogue and trawling the high street to track down the best bargains and find the latest deals. Today’s shoppers are living and breathing the evolution of next generation shopping. As the world becomes faster paced, the number of cash-rich, time-poor shoppers that demand access to quality services and products in a fraction of the time is booming. Consumers have turned their back on the high street and are increasingly using shopping search engines to find what they’re looking for quickly and at the best price. However, today’s services are lacking many features that would make them truly useful to the consumer – advice, customisation and reliability.
Social networking, content sharing sites and web-applications have made the Internet more social than ever before and this has had a significant impact on shopping search. Consumers are now starting to make a vast amount of purchasing decisions based on peer reviews and recommendations. The next generation of search engines are positively embracing this shopper and revolutionising their services to ensure the user always enjoys a social shopping experience and receives search results that are most relevant and suited to their needs. But is this easier said than done? Is it just the minority of shopping search engines that will actually be able to meet rapidly changing consumer requirements, quickly?
Look at vertical search in its totality; it’s unique in its own right, bringing together suggested search results within a subset or category that are based on the shopper’s specific criteria. In order for the industry to move forward, search engines must focus and build on that personal criterion by becoming more customised and exhaustive, without the complexity. The challenge lies in determining whether the industry is firstly intelligent enough and secondly mature enough.
At Twenga, we’re already taking a different approach to that of our competitors as we rank search results naturally, in line with our algorithms. In addition, we’re working on a review aggregation service that identifies expert reviews on specialist websites by key words and publishes them on our site, providing our users with intelligent information that will help them come to a purchasing decision.
To shape the future of shopping search, new ways to engage with the shopper need to be developed and the power of human search needs to be unleashed. This in turn will make shopping search engines highly effective and efficient destinations for brands looking to communicate key messages to the relevant consumer in a timely fashion.
The insurance, travel and health sectors are leading the field as they have effectively developed services that are completely tailored to the consumer’s personal criteria. How? By combining the power of comparison and search in one place giving rise to a much more convenient way of shopping in a market where individual requests are all so unique.
Alan Mitchell, industry reporter and respected blogger, recently predicted in an article for Marketing Week that the shopping search industry would mature rapidly over the next five years. In fact, I’m confident that shopping search as we currently know it will make significant strides over the next two to three years, attracting more and more consumers away from the high street in favour of a next generation shopping experience.
June 8th, 2008 at 6:14 am
“At Twenga, we’re already taking a different approach to that of our competitors as we rank search results naturally, in line with our algorithms.”
Could you explain what this exactly means ?
Nicolas
September 7th, 2008 at 9:02 pm
Algorithyms are used to produce a method of getting sites and Urls ranked in the search engines. Search for anything using your favorite crawler-based search engine. Nearly instantly, the search engine will sort through the millions of pages it knows about and present you with ones that match your topic. The matches will even be ranked, so that the most relevant ones come first.
Of course, the search engines don’t always get it right. Non-relevant pages make it through, and sometimes it may take a little more digging to find what you are looking for. But, by and large, search engines do an amazing job.
As WebCrawler founder Brian Pinkerton puts it, “Imagine walking up to a librarian and saying, ‘travel.’ They’re going to look at you with a blank face.”
OK — a librarian’s not really going to stare at you with a vacant expression. Instead, they’re going to ask you questions to better understand what you are looking for.
Unfortunately, search engines don’t have the ability to ask a few questions to focus your search, as a librarian can. They also can’t rely on judgment and past experience to rank web pages, in the way humans can.
So, how do crawler-based search engines go about determining relevancy, when confronted with hundreds of millions of web pages to sort through? They follow a set of rules, known as an algorithm. Exactly how a particular search engine’s algorithm works is a closely-kept trade secret. However, all major search engines follow the general rules below.
Location, Location, Location…and Frequency
One of the the main rules in a ranking algorithm involves the location and frequency of keywords on a web page. Little SEO SecretCall it the location/frequency method, for short.
Remember the librarian mentioned above? They need to find books to match your request of “travel,” so it makes sense that they first look at books with travel in the title. Search engines operate the same way. Pages with the search terms appearing in the HTML title tag are often assumed to be more relevant than others to the topic.
Search engines will also check to see if the search keywords appear near the top of a web page, such as in the headline or in the first few paragraphs of text. They assume that any page relevant to the topic will mention those words right from the beginning.
Frequency is the other major factor in how search engines determine relevancy. A search engine will analyze how often keywords appear in relation to other words in a web page. Those with a higher frequency are often deemed more relevant than other web pages.