One advantage to doing business online is price transparency. Because of the openness of the internet, online store owners can know exactly what their competitors are charging and have complete control over their own price positioning. Although using available web data to determine pricing is not new, there are new ways to utilize competitor data to make it more meaningful and make you more competitive.
EXAMPLE: Using an online digital camera retailer as an example business, we will look at how it’s possible to use your competitors pricing data to make sure your own products are competitive. For this example we will assume that your products are already registered on multiple shopping engines and indexed with Google.
First, to determine the price you should list your product at, you should search for your own product on a price comparison site like Shopzilla, Pricegrabber, Shopping.com, NextTag, Bizrate, Google Products, etc. These sites will show you a range of prices that other vendors are charging for the same product. Using this range, you can determine the “Buy Zone” for the product. The Buy Zone is the price range that most people will shop in until they settle on a vendor and make a purchase. Potential customers will usually only try 3-4 stores that are within the Buy Zone before making their final selection.
The screen shot below shows a range of prices for a specific Sony Digital Camera. The Buy Zone is calculated by taking the lowest advertised price and adding 10% to it to get your Buy Zone range. Secondly you look at the user feedback and free shipping offers which may affect the actual Buy Zone or deter you from trying a particular store because of poor user feedback.
It’s easy to see how a consumer can quickly determine their own Buy Zone and then systematically purchase based on the next best value. In this case the lowest price offering also had very high customer review rating and no tax or shipping fees. The next two had only a marginally higher price but better customer reviews. The last one had a higher price and less favorable reviews and therefore is a last choice of those selected.
Anyone can do this type of comparison for a single product, but what happens when you have hundreds or thousands of products that you need to do this for?
Fortunately, there are software solutions available on the market that can assist you in extracting large volumes of web data from a variety of websites. One such software is from Mozenda (www.mozenda.com). Mozenda’s Web Agent Builder software solution enables anyone to quickly set up an “Agent” to capture information from the web. Through a series of clicks and highlights, you can build an agent to capture such fields as competitor name, product, price, shipping, tax, consumer reviews, etc., and then save that information to your own database or spreadsheet for comparison purposes. Mozenda’s software also allows you to schedule your agents to run on any interval and send you the data automatically through its web service or via auto FTP. If your looking to automate the information gathering and process, this is probably the best method for doing so.
Once the data is in your database you can then effectively determine the Buy Zone pricing for all your products and make regular adjustments to those products to ensure that you stay competitive. You can even automate the process of price setting to stay within 5% of the lowest competing price by programming a simple comparison application that looks up data from your competitor pricing tables and then sets your web pricing appropriately.
This is just one way that online retailers can be more competitive and not loose out on traffic because they are priced out of the Buy Zone range. By automating this process the retailer will enjoy a competitive advantage over many of their competitors that are much less sophisticated.
In the past couple years the term “Web 2.0″ has come to mean a lot of different things to different people. So, what exactly is Web 2.0?
To understand where the term Web 2.0 came from, we have to go back to the dot com bubble burst in 2000 and 2001. The dot com burst left the Internet industry in shambles, but there were a few people that managed to survive. Google, Amazon, and EBay each emerged from the ruins of the burst and have seen remarkable success. As Internet gurus analyzed why such companies succeeded they realized that the web was going through a change–a series of improvements–much the same way software does as it moves through versions. So, the term “Web 2.0″ was initially used to describe what set those companies that survived the crash from those that didn’t.
That is a little trickier question to answer, as Web 2.0 means a lot of different things to different people now. So, we’ll try and break things down into a few different categories:
Web 2.0 in design has been characterized by a variety of trends and shifts in web design theory. Trends have included the prominent use of bright colors, rounded corners on DIVs and other containers, heavier use of gradients, incorporating more bubbly fonts, adding faux reflections on images, etc. Some of these design trends have already seen a decline in their use, while the shifts in web design theory are ongoing. Sites now tend to be less cluttered as white space is better used. They tend to be more legible as more thought is given to typography. Design throughout a site now tends to be more consistent as designers are striving for consistency and effective use of complementary color palettes.
Web 2.0, for some, simply means that users are now able to interact and network with one another. This has lead many website owners to try to integrate social networking aspects (friend connections, user ratings, user-generated or user-provided content, etc.).
Now that the term “Web 2.0″ is so pervasive and is such a hot buzz word, some people have begun describing any new tech related startup company as “Web 2.0″ whether their site or company embodies any of the other definitions of the term.
Tim O’Reilly gets the credit for this definition. O’Reilly found that in each of the aforementioned survivors of the bubble burst, collective intelligence was the driving factor behind the sites’ success. In the case of Google, collective intelligence was seen in the development of their Page Rank algorithm. The algorithm was based off of the idea that the more sites that link to a page the better resource that page is. This is allowing the community (of website) to determine (collectively) which sites really deserve top rankings. In the case of eBay the pricing and use of the website was determined largely by its users and the collective intelligence they supplied by way of their interactions. Finally, Amazon used the collective intelligence of its users to not only provide simple improvements (like book reviews), but also more complex marketing strategies (like making product recommendations based off of the purchasing habits of similar users).
At this point, the majority of the definitions above are pretty widely accepted. So, I would hesitate to call any one of them “wrong”. I do, however, tend to gravitate towards O’Reilly’s definition for the simple reason that it seems the one that will make the biggest impact on the Internet and it’s users.
At the recent Web 2.0 Expo in San Fransisco, O’Reilly illustrated the difference between Web 1.0 and Web 2.0 by using the following example. Consider a bank. A bank has at it’s disposal massive amounts of information because of all the transactions it processes on a daily basis. But, the bank doesn’t do anything with the collective intelligence it has laying everywhere. Contrast this with a website like WeSabe. WeSabe allows users to input daily financial expenses and to categorize those expenses as they see fit. In his presentation, O’Reilly used the example of a BMW repair. He entered an average car repair, performed at a prominent BMW repair shop in the Bay area into WeSabe. WeSabe searched through its records and returned an average repair cost, repeat customer percentage, and recommendation percentage for the popular repair shop which was pretty helpful. What was more helpful is that WeSabe also returned stats on an alternative (and far less known) garage whose average repair cost was lower, had a higher return rate, and higher recommendation percentage. So, the site was able to put the collective intelligence to good use by making a relevant and useful suggestion.
My hope is that as more data becomes available through the web we start to think about how it can be linked and combined to provide us with greater intelligence and understanding. Maybe we can call that World 2.0.
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