Conventional (TV, print, radio) advertising often relies on research and targeting methods such as focus groups or demographic targeting to increase brand awareness and sales. These methods seem to be more and more outdated as targeting technology is already delivering better results.
A (very) short history of advertising research and targeting
In the past, as media was unidirectional (broadcaster to consumer), there were few ways retailers could efficiently target potential consumers. Advertisers would use consumer profiles and split purchasing options through demographic indicators (age group, location, education, sex etc.). By using statistic results they could outline marketing opportunities for certain demographic groups (Ex. “Women between 25 to 35 years, urban, having higher education are more likely to buy Product X”).
Having (theoretically) discovered a potential consumer profile they would then buy media in newspapers, radios or TV stations that would best appeal to that certain demographic group.
Of course this is just a skeletal description of the whole targeting process but it explains the process pretty well. Many companies have benefited greatly from this targeting and advertising system. Most of the brands we now know and buy were built this way. Even now, decades after the likes of David Ogilvy were setting up the rules on research-based advertising, the system is virtually unchanged.
“I notice increasing reluctance on the part of marketing executives to use judgment; they are coming to rely too much on research, and they use it as a drunkard uses a lamp post for support, rather than for illumination.” – David Ogilvy
How did the Internet change research and targeting?
Few could have predicted the impact Internet was to have on commerce and economy. Even less would have guessed how this initially “exotic” media would impact research and targeting.
20 years ago there was no marketing concept that could explain AdWords targeting and not be considered science-fiction.
Internet targeting and advertising renders most of conventional knowledge on research obsolete as technology has achieved what was once impossible. 30% of all human population is now in reach of all advertisers and they can now target more than just demographics.
Behavioral marketing is a concept that could not be possibly be achieved with conventional media. Using consumer behavior rather than demographics advertisers can target real time preferences and individuals rather than demographic groups. Say a user is known to have previously visited a car dealership website. He then browses websites in search of reviews on different car models. The car dealership could potentially target this exact user and serve him the most informative ads. Advertising ROI is sure to increase this way.
Some companies have become increasingly good at Internet research and targeting. One of them is now the most valuable company in the world in terms of market capitalization. Let’s have a look at how Apple, Amazon, Facebook and Google use large data to target and monetize consumer traffic.
How did Amazon, Apple, Facebook and Google changed consumer targeting ?
Amazon personalized recommendations
Amazon is well known for its personalized products recommendations. How can it do this? Short answer: large data on consumer purchases and mathematics. Longer answer: Amazon holds a patent on its product recommendations which you can have a look at here (issued in sept. 2006). Although rather technical it focuses on certain key elements:
- user profiling: Amazon holds valuable data on user demographics and previous purchases. Using this data it can map users in specific consumer groups. User profiling combines shopping cart contents, item ratings and recent purchases as purchase intents seem to change in time.
- similar products information: say you bought three SF books. Similar products would be other books in that category. Some of these books would be more popular in terms of item ratings, reviews, views and purchases.
- item affinity is the probability of some products to be purchased together. Say you are buying a Kindle on Amazon. You are very likely to buy a cover or case to protect your device. That means these products have a high affinity index
- driver items are those products that are most likely to drive traffic to store. Again – the Kindle, Amazon’s best seller is not only a driver item but also a platform that insures further product purchases.
- user path: the consumer will follow a certain path until it ads a product to the shopping cart or confirms a purchase. These paths are very important as they can be used to “guide” consumers to products they are most likely to purchase.
Using these information (and probably more) Amazon can first map users in consumer groups (1), extract popular, affinity and driver products (2), compile most profitable user paths based on previous history and other users actions (3) and than recommend the items most likely to increase basket size.
Recently Amazon announced the launch of its Kindle Fire product. This product is built on a Android platform and uses a proprietary web browser called Silk. The browser optimizes web traffic by routing it through Amazon’s servers. As Amazon already holds information on user profiles (users will have to login to synchronize their book collection) and now data on web traffic it can further improve its recommendations.
Apple Genius recommendations
Although Apple does not explicitly state it monitors iOS user actions it doesn’t deny it either. If it does, however, it might access a huge pool on users data such as web traffic, mobile purchases, locations, call history, social networking information (through access to contacts information, call history, SMS and iMessage history etc.). Basically everything there is to know on its customers profile.
For now the most visible way Apple uses data to increase sales is iTunes Genius, the music and video recommendation system. iTunes Genius uses purchase history and iPod activity to recommend potentially interesting songs, albums or videos.
Although iTunes Genius probably uses a system similar to Amazon’s it is not yet known to be as accurate. The performance issues are probably connected to the number in customers and items on sale. Amazon has a wider products inventory and a larger pool of potential customers. This leads to a larger database and increased accuracy.
Technology based companies have changed the way we think of consumer targeting and advertising. Innovation lead to profits and behavioral targeting will probably develop in the future. Tomorrow we’ll have a look at how two of the largest advertising – revenue based companies, Facebook and Google, use large data to improve consumer targeting. Stay tuned.