ComparisonEngines

Read Part 1 and Part 2 before reading this post.

By now you can understand how your intelligent shopping agent will get its data. You can hopefully also imagine its predictive technology, anticipating what you want, when you want it, and where you should buy it, well before you even need it.

OK, there are many other features of this intelligent agent, but I just want to focus on one more, the purchase process, before tying this together with some ideas of the business model.

Your intelligent agent will have your payment information and permission to automatically make purchases. If you’re not comfortable with that, there will be an option to opt-out of this automation, but it’ll be an opt-out; the general user of the agent will trust it because of how well it knows you.

Your agent will know your buying habits and be hooked up to your bank and credit card accounts, so it will know what you can spend on a purchase. But this doesn’t mean it will willy-nilly spend whatever to get a product for you. That’s unacceptable. Your agent will haggle on your behalf. CUC International (now Cendant Corporation) owns a patent for Hagglezone and once had a site which featured the haggling technology. Read this NYTimes article (which even mentions Mercata.com – oh, the good old days of ecommerce!) from 2000 on how this worked. Merchants aren’t currently setup to haggle, but merchants could be part of the intelligent agent preferred marketplace powered by the haggling technology. While your intelligent shopping agent will always scour the web for the best deal and could have hooks into distributors and manufacturers, it could feature a marketplace (just like the old http://www.hagglezone.com) with select merchants who want to be part of this bazaar and get first crack at the consumers. In the Hagglezone, your agent could always just set an offer price and wait – as it will know the urgency of your need, it doesn’t need to transact immediately. And if the agent is working for thousands or tens of thousands of consumers, it could haggle or negotiate a great group buying price (back to the Mercata model).

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So in my last post, I re-introduced the concept of my intelligent shopping agent. The foundation for the agent could be built by querying the consumer and analyzing past buying behavior. But that’s just the start. The focus of this post will be indicators (beacons?) which provide a deeper lever of understanding into a consumer’s tastes, and thus can start to intelligently make recommendations. That’s part of the point of the agent. It should always be on the lookout for potential products, deals, offers, sales, new trends, and more which might interest you.

Indicators:
-Search. This is a pretty easy one to understand. If you search for ‘red cashmere sweater’ or ‘cuisinart blender’ it makes sense that you might be interesting in acquiring those products. The intelligent agent will start to learn about brands or products that you’re interested in by these simple queries. But the value of search for the agent doesn’t end there. Search queries can make up a very complete picture of your life. We don’t typically think about this on a daily basis, but privacy advocates obviously do. Through my searches, the agent can figure out my socioeconomic status (Wealth. Am I searching for high end goods or coupons? Education. Am I searching for worldly issues or more pedestrian facts and figures? Occupation. Am I searching for computer courses or fundraising tactics?) It can determine my life stage (Am I searching for mortgages? Am I searching for a wedding planner? Am I searching for a SAT prep course? Am I searching for the AARP? Am I searching for income comparisons between two different cities?). There are some generalizations in there, but the idea that your searches can tell a lot about you and who you are today or who you’ll become tomorrow can be pretty powerful, especially when combined with other indicators. Interesting to note that Facebook doesn’t have complete search information.

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I’m still waiting for my intelligent shopping agent, but alas, it’s still nowhere to be found.

Back in 2006, I wrote the following description of the system that I thought would be prevalent by now:

In my vision of the shopping (and a lot of other activities) in the future, I will have an intelligent agent which understands my preferences, knows the marketplace (price trends, consumer sentiment, etc.) and is always on the lookout for products, deals, reviews, recommendations from friends (this is where social networks/communities become really valuable), etc. which I’d be interested in. My agent understands the competitive landscape and makes sure that my buying decision is well informed. My agent would have the authority to haggle with a dealer/distributor/merchant and make purchases according to criteria that I set or that my agent learned over time. It might take a while for the agent to understand my preferences and there would definitely be work involved with setting up such a system, but we will get there.

That was over 4 years ago. While this system might sound like science fiction (Snowcrash – if you want to know what will happen in the future, read the leading scifi writers), we have the platforms to enable this reality. There are a number of components that can come together through powerful APIs, but why not start with just querying users about simple preferences in a creative way.

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