The app Shopsavvy for the Android and Redlaser for the iPhone are pretty sweet. With the iPhone 3GS you can use its video camera to capture a barcode, then the app shows you price comparisons for that product online as well as comparing the price to other stores near you.

This makes me think of all the cool things grocery stores could do if they harnessed the internet’s data collection and social networking abilities. A few ideas:

1. Grocery stores already keep track of purchases with their loyalty cards when you check out, why not share that information with the consumer? Give the customer a personal page on their website that shows their shopping habits and make recommendations like Amazon does – 90% of people who purchase Cool Ranch Doritos also buy Cherry Coke.

2. Being able to see everything that you have purchased and the quantity of what you have purchased would help you plan your shopping better. Like what Mint does for personal finance, you would know more accurately how often you need to buy milk. You could see pie charts that show you how much of your food purchases are made up of candy.

3. With more data, grocery stores could give highly relevant and targeted coupons designed individually for the consumer. With enough time the grocery store will know which kind of offers – buy one get one or % off – and on which products incentivize customers to buy. They could figure out that my cookie of choice is Oreos and any discount below 20% off won’t make me buy, but as soon as an offer comes for 30% off Oreos, I’m there. The store could effectively maximize every purchasers buying ability.

9. Brands could set up loyalty products for each of their items. Your 10th Kraft purchase gives you 10% off your next purchase of cheese.

7. Or how about instead of going through a checkout line you put your cart through a conveyor track, like an x-ray machine at the airport, that scans all your items immediately and gives you the price.  No more paying price checkers and no more lines.

8. Or, what if your fridge had bar scanners on the side of it so that it knew everything that you had in the fridge. It could tell how often you take things out so it would alert you if some food was about to expire soon. If you needed to go grocery shopping, just push a button on the fridge and have it print out everything you have run out of, or better yet, send it to your handset.

5. What if every time you put an item in your cart, a digital read-out of the total price of everything in your cart was displayed on the cart handle; take something out of the cart and the price goes down. This would be awesome for customers to be sure they weren’t spending too much while picking out the groceries. Of course, grocery stores probably like that we don’t know how much everything costs until we get to the check out line. But think off all the cool stuff you could do:

6. Supercook is an online tool asks what’s in your kitchen and then uses that information to provide dozens, if not hundreds, of unique dishes that you probably would never have thought of on your own. What if the grocery store kept track of what you put in your cart and gave you ideas of what dishes you could make while in the grocery store  – add Country Crock butter and Pace Picante Salsa, and you have all the ingredients you need to make zesty enchiladas.

4. Looking at the data you could tell in what order most purchases happen in. So they find out that statistically, after people buy meat, the next thing they buy is beer. Up-sell by placing selected items next to, or in-between the meat and the beer for that purchaser to see like barbecue sauces or beer coasters.

10. Let customers connect with other customers who buy similar things. Looks like you buy a lot of spices and ingredients, would you like to join a recipe community in your area? You BBQ a lot, compete in your local community cook-off.

Maybe some of this stuff seems a little too much big brother, but I would let companies know my purchasing habits in exchange for relevant coupons, food suggestions and insights to my food buying habits.

Saw this on TechCrunch this week:

Twitter [and Facebook] “will surpass Google [as a source of traffic] for many websites in the next year.” And just as nearly every site on the Web has become addicted to Google juice, they will increasingly try to find ways to get more links from Twitter. Because Twitter equals traffic…these Twitter links “convert better” than search links because they are often pre-filtered and come in the form of a recommendation from someone you are following.

This is a good point. Now that there are no more gate keepers to filter what gets published, everything gets published and we rely on filters (bloggers, friends online) afterwords to help us find the best stuff. Twitter is a great tool for networking with like-minded people to help you find the stuff you’re interested in. I rely heavily on the opinions of bloggers that I trust to point me to where the good stuff is at. This is the best reason, so far, for getting a business involved in Twitter.

P.S. Google has added a Creative Commons filtering capability to its Image Search results to allow you to find images without having to pay a stock photo company or steal an unlicensed picture from Image Search. The option isn’t currently available on the user interface, but you can enable the filter by adding a certain parameter to your search URL (in this case, just replace “mountains” with whatever you are actually searching for)

• For public domain images — http://images.google.com/images?q=mountains&as_rights=cc_publicdomain
• For images licensed with Creative Commons Attribution (that is, images you can use as long as you attribute the image back to the creator) — http://images.google.com/images?q=mountains&as_rights=cc_attribute
• For images licensed using Creative Commons Attribution Share-Alike — http://images.google.com/images?q=mountains&as_rights=cc_sharealike

I got the image above from Wikimedia Commons. Nice!

Great talk at TED by Clay Shirky:

In a world where media is global, social, ubiquitous and cheap – in a world where the former audience are increasingly full participants – in that world, media is less and less about crafting a single message to be consumed by individuals, and is more often a way of creating an environment for convening and supporting groups.

What an exciting world we live in. The ability to create movements, be heard, and make things happen has never been easier. Now it’s about asking the question, what do you want to create?

I think pre-internet, most people trusted big companies more than little ones because they were bigger. If you have never heard of a company then how can you trust it? Now that any company is findable online the tables have turned. Big companies that have static brochure websites, where they have their corporate jargon and stock photos on every page leads one to think, what are they hiding? Meanwhile little companies whose websites consist of blog posts and insights into who is behind the company and what they think leads one to believe, these people are just cool normal people who do what they do well. Cool.

I want By Data Be Driven to be my personal Internet marketing consulting brand but I choose to make my site a blog that shows who I really am. I think that goes father than a lot of pages about services offered and my unique strategies of how to “effectively market your brand online using integrated search marketing to help companies get noticed, retain customers and continue growing.” If you really want to know what I can do you’ll find out from someone else other than me, so why put up the front and just be as personal as I can?

I think before long, most sites will have a blog, feeds from their interaction with others and interactive widgets/applications on their home page instead of a stock photo of a happy customer, their tag line and links to about us, services and contact. Just wait.

I finished reading the book Super Crunchers by Ian Ayres. I thought it was good. I liked his explanation of randomized a/b or multi variant testing done online and off.

He explains that randomly dividing prospects into two groups and seeing which approach has the highest rate is one of the most powerful super crunching techniques ever devised.

When you rely on historical data, it is much harder to tease out causation. The sample size is key. If we get a large enough sample, we can be pretty sure that the group coming up heads will be statistically identical to the group coming up tails. If we then intervene to treat the heads differently, we can measure the pure effect of the intervention…after randomization makes the two groups identical on every other dimension, we can be confident that any change in the two groups outcome was caused by their different treatment.

Of course, randomization doesn’t mean that those who were treated differently are exactly the same as those who were not treated differently. If we looked at the heights of people in one group, we would see a bell curve of heights.  The point is that we would see the same bell curve of heights for those for those in the other group. Since the distribution of both groups becomes increasingly identical as the sample size increases, then we can attribute any differences in the average group response to the difference in treatment.

In lab experiments, researches create data by carefully controlling for everything to create matched pairs that are identical except for the thing being tested. Outside of the lab, it’s sometimes simply impossible to create pairs that are the same on all peripheral dimensions. Randomization is how businesses can create data without creating perfectly matched distributions.

The power behind randomized testing is undeniable. So should we just have computers make all our decisions for us? With that question in mind is were he goes throughout the majority of the book.

Randomized trials require firms to hypothesize in advance before the test starts. Historical data lets the researcher sit back and decide what to test after the fact. Randomizers need to take more initiative than people who run after the fact regressions.

The most important thing that is left to humans is to use our minds and our intuition to guess at what veriables should and should not be included in the statistical analyisis. The regressions can test whether there is a casual effect and estimate the size of the causal impact, but somebody (some body, some human) needs to specify the test itself.

So then the question becomes what do we test, and after we test the question becomes, what are the results telling us?