Non-retailers E-tailing
Over the past few months I’ve had many clients outside of retail (banks, airlines, and manufacturers) approach us about learning how to merchandise and target online.
It seems that having your burger “my way” has permeated to all types of goods and services. Companies selling services online are beginning to deconstruct their services into products. Something as simple as an airline ticket is now broken down into a boarding pass, an upgrade, a bag of peanuts, and rumor has it Ryanair has even considered selling access to the restroom.
As a result, the online shopping experience starts with a base purchase, such as an airline ticket. Through to checkout is a challenge to see how much merchandise or service can be tacked on to that base purchase without losing the customer. So how do I maximize the size of what is now my shopping basket?
The answer is to target effectively. Everyone knows that targeted mail is more effective than spam or junk mail. Online presents a unique challenge. There are at least 3 types of data that can drive the offer:
1) Segmentation – the tradition method for marketing, coming from loyalty programs or straight transaction history
2) Browse History – customers browse activity in a particular session tracked via Web Analytics
3) Product Relationships – products related by being similar, complimentary, mandatory, or premium
So how do we use it?
First and foremost, use all of it. Use it wisely though. Segmentation is the traditional method. Be careful how you use it. Segmentation data coming from data warehouses is by nature relatively old. It has been updated via several data sources, most likely via batch updates. It is good for banner ad space and other lifestyle offers. It is not always great to integrate into the shopping process.
Recent is relevant. Browsing and search history is as recent as it gets. Even using other shoppers history is still used, the old Amazon phrase “people who purchased X also purchased Y.” Real estate on the right side of the web page typically holds browsing related offers. These can be truly effective if your recommendation engine is smart enough to detect a pattern in the browsing. This can be extremely affective for a bank. As users research funds or loans, a bank can offer the hottest product related to the category the user was browsing. If this is tied to the customer segment as a secondary filter, this is like the nirvana of offers.The foundation for these offers is the product data. It’s hard for non-retail or consumer goods companies envisage their services and products in a catalog. Retailers deal with tens of thousands of products and variants and they get it done. Spend the time with your data to set up the product relationships needed to effectively merchandise. Don’t be restricted by how you have sold in the past. These relationships are important, as are their integration to browsing patterns or segmentation data.
So what offers do you click on? How often do you buy complimentary or upgraded products? How often do you click on an offer because it fits your lifestyle? I’d love to hear your opinion.
Comments
Interesting observation, especially how you broke down the entire process into a series of logical offerings.
Posted by: udidknow | September 29, 2009 6:03 AM