TechCrunch recently did a writeup on a new web 2.0 product called Tailgate. The software, dubbed as “Worlds First fully transactional web 2.0 banner system,” allows users to make purchases directly through a banner ad.

It’s a strange concept, but take a look for youself.

Several questions come to mind:

• Success rate - banner ads typically have very low click through rates, are users now going to interact with the ads?
• Security - will users feel comfortable entering in their personal and credit card information in a banner ad on a third parties website?
• User Experience - the advertiser/seller loses all control of managing the users’ experience and opportunities for building a relationship. Will retailers take to this approach?

Would you purchase from a banner ad? As a retailer, would you want to sell your products through a banner ad?

BusinessWeek has a great article/chart illustrating who is doing what online.

Online activities are broken down by:

• Creators - publishing content
• Critics - commenting on content
• Collectors - gather information using RSS
• Joiners - use social networking sites
• Spectators - readers and viewers
• Inactive - are online, but are not participating

To no surprise, the younger end of spectrum are the most active. How long will it take for the Baby Boomers and Seniors to get involved? Do you think they ever will?

As selected by TIME Magazine. These five websites suffer from marketing infestation, are in need of an overhaul, notoriously slow to load, discriminatory, and just plain annoying.

1. eHarmony
2. Evite
3. Meez
4. MySpace
5. SecondLife

read the full story

In reviewing the success of your email marketing campaigns, there are several data points that provide valuable insight. These are the things that you should be tracking. They include:

• Delivery rate – percentage of individuals that receive the message (delivered divided by sent)
• Open rate – percentage of individuals that open the message (unique opens divided by delivered)
• Open to click rate – percentage of individuals who opened the message that clicked on a link (unique clicks divided by unique opens)
• Conversion rate – percentage of people who clicked on a link that converted on the landing page

While most email marketing suites provide some of these numbers as part of their reporting, it is important to note that our definition and value of what we track is slightly different. For example, typical click through rates are measured as the percentage of clicks to the number of messages sent. But if a recipient does not open the message, how can they produce a click? This typical measurement of the “click through rate” doesn’t tell us much.  By measuring the percent of clicks to opens (click to open rate), we are able to determine how effective the message is at producing action (clicks).

The goal is to evaluate the success of the campaign along each step of the following process:

Send -> Deliver -> Open -> Click -> Convert

Setting benchmarks and measuring the success of each step allows us to determine where the areas for improvement are in the email marketing process.

Here are some things to look at for poor performance at each step of the email marketing process:

• Low deliver rate - how clean/up to date is the list? how are messages being sent out? is there a server issue? are you on block lists?
  
• Low open rate - are your subject lines compelling? is your from name/email recognizable? are messages being caught in SPAM or bulk folders?
• Low open to click rate - is your message compelling? is the creative and copy engaging and focused on actionable items? are images being blocked? are the messages targeted to specific market segments?
• Low conversion rate - how compelling is your landing page? do you even have a landing page?

Constant fine tuning at each step of the process can help to improve the overall results of your email marketing campaign.

Often times the focus of ecommerce and online marketing is on conversions - making sales to your sources of traffic. But what about selling MORE to the people who already want to buy from you?

In the July 2007 edition of Business 2.0, the article “Rethinking The Recommendation Engine” discusses some of the latest in ecommerce cross selling and up selling technologies (aka recommendation engines).

According to Forrester Research, consumers spent $220 billion online last year and it is estimated that 10-30 percent of that is accounted for by recommendation systems. That is anywhere between 22 and 66 billion dollars!

So what is a recommendation system? The most recognized recommendation system is Amazon’s. Based on the items you have purchased and what you search for, they do some complicated calculations to find items that you might also like.

A ‘recommendation system’ is a friendlier name to the traditional sales terms of cross selling and up selling.
“Would you like to to super size that? Would you like fries with that?”
“If you love that shirt, take a look at this one. Do you want some new pants to go along with that as well?”

The article goes on to talk about some new recommendation platforms out there used by some heavy hitters.

• ChoiceStream - suggests movies and songs, used by Blockbuster, Comacst and iTunes
• CleverSet - Scans products descriptions, prices and ratings to make recommendations
• Aggregate Knowledge - Uses “supercomputers” to make connections between consumers and products - used by Overstock.com and WashingtonPost.com

These technologies are not well suited for the average online store. But that doesn’t mean you can’t, and shouldn’t, have some means of cross selling or up selling. Creating a simple recommendation system not only creates value by selling more, but it also enhances the customers’ experience, introducing new products and helping customers find what they may have forgot or need.

Here are some concrete recommendations for creating a simple system to capture that 10-30%, making the most out of your sales:

• Start simple - build in to your ecommerce platform a way for you, the store owner, to associate different products with one another
• Suggest similar and complimentary products - if they have a new pair of shoes in their shopping cart, suggest some shoe polish
• Get more complicated - make recommendations based on previous the buying patterns of the individual as well as other customers
• Up sell - show the higher end models of the same product
• Make recommendations along the way - show your recommendations on the product page, shopping cart page, checkout, and after checkout (do you need a car charger for that new iPhone?)
• Continue the dialog - follow up in the confirmation email with other items they might like or may have forgotten
• Monitor, analyze and adjust - constantly adapt to changing demands of your customers, especially if your software can’t