September 26, 2009
Customization is derived through explicit, stated preferences, while personalization is driven by both the explicit and implied – behavioral, demographic and brand specific information. How did the user get to the site (referral information like URL or search keywords), prior purchases, and onsite activity are key to driving relevance on a website.
Consider this, your brands’ website probably constitutes less than 0.5% of a visitors life experience, if you’re wildly successful… there’s a world of insight necessary and available to the purview of your personalization scenario. A world that requires integration with a more comprehensive data set: your marketing database, third party data and analytic models to decipher it.
February 19, 2009
Can a single algorithm deliver relevance… across seasons, different web properties, between global cultures and among differing offering categories? Personally, I have troubles trying to predict behavior in the people I’ve known for many years. The human heart and mind act in sometimes strangely unpredictable ways. Isn’t that the part of humanity that’s really great? I think so!
Algorithms to deliver relevance need to accurately reflect consumer information – behavior, demo / psycho-graphics, and other. The difficulty with this model is that only the smallest of slivers of a consumers life revolves around any single brand – though we would all like to believe differently:-) Also, until you get reams of data surrounding an individual, how do you actually recommend. In the on line world this is particularly debilitating because over half of website traffic bounces from good sites right away and only a small sliver (used the word twice) of traffic actually makes it to a conversion… and an even smaller sliver (gotta stop using that word) comes back and makes it to a second second conversion. So, algorithmic personalization or recommendations really are only capable of helping a small portion of your consumers, after you get to know them.
An actuary can build statistical models that deal with vast population samples, telling the breakdown of what will proportionally happen in certain events. That’s interesting but it also deals with averages across large groups of people. Not necessarily valid to the point of algorithmically driven recommendations or optimization of an individual and their purchase potential or drivers.
I don’t intend to close the door on the subject, I do believe these approaches are helpful when other data is not available or when you have A LOT of information and you want to solve a retention / lifetime value issue… which are both great issues to solve. From my experience though, many brands believe they can use these technologies, in particular recommendation engines, to help solve an acquisition problem. Hmmm.
Love to hear your thoughts!
September 29, 2008
Much has been written about on-site consumer behavior and its applicability to targeting programs. Despite this, little is really known about types of behaviors that are tracked or trackable, and which ones are truly indicators of or useful as a high-value targeting mechanism. It’s possible that we’ve over-inflated the importance of behavior and overlooked other things that should be considered. And, in doing this, headed towards a focus on a technology solution that relies upon the use of highly personal consumer information to fuel its’ engine… raising the concern of consumer protection groups and the governments of many countries.
Types of behaviors that are tracked or track-able:
- On-site, single domain behavior – probably the least contentious source of data, easily believed by consumers as a valuable source of data to help marketers connect with consumers. Often called re-marketing, in a recent MediaPost article, Jeff Hirsch, the CEO of Revenue Science and Behavioral Targeting Standards Consortiumfounding member, specifically stated that this a limited version of behavioral targeting.
- Cross-domain behavior – algorithms consume a large quantity of data, including searches, product views, and domains visited in the attempt to identify a sense of interest on the part of an individual consumer so a marketer can target ad messages. The aggregation of this data can occur via cookie tracking across a network or more recently there’s been attempts to validate the process of intercepting and interpreting all web activity from ISPs (i.e. NebuAd), creepy.
However, in all the dialog about behavior, what other data points are available that marketers can use as targeting methodology? Or, from a more important strategic perspective, what is the objective of a marketing program and which data points can help the marketer accurately identify and target?
My assertion is that other data points are more effective at targeting, achieving marketer objectives. Here’s why I say this:
- Today, only a minor portion of display ad spending is funded towards behavioral targeting. Display ads are largely brand oriented. Marketers tend to choose domains as their primary targeting mechanism. Behavioral targeting comes too late in the purchase cycle to affect brand preference and attitude, it’s more of a “hail-mary” or a direct response tool.
- Virtually no on-site targeting is the result of multi-domain data aggregation.
What other data points are available, without any fancy technology:
- Geography, language, and season – these have been used for generations in the offline environment very successfully, why don’t we use them on line more often? The interesting point is that these data points are largely available to everyone with very limited technology constraints. Between a site visitors IP address and their browser settings, these data points are easy to use. Whether display ad or on-site targeting, these are available today.
- Returning customer or new prospect – again, these are largely available to marketers today. Why not use them? Certainly, every marketer would like to speak to a new prospect in different terms than they would an existing customer.
In a later post I’ll try to talk about targeting options that require more sophisticated technology. Until then, send me your thoughts!
March 10, 2008
I’ve read articles and blog replies where others try to distinguish between the terms personalization and targeting, referring to personalization as one-to-one and targeting as one-to-many. As a person who has actually created terms and pioneered strategy in this marketplace, I see the two as more or less synonymous with far fewer distinguishing dimensions than others see.
While trying to create a communications strategy for Kefta’s multi-channel personalization solutions, we determined early on that we wanted to distance ourselves from the failed software based personalization solutions of the late ’90s. They were an expensive, IT driven failure. The key failure was that they were too “heavy” a solution to ever get off of the ground. From a marketing strategy perspective, they were a failure because they relied upon users to self express differences before they could start targeting content, and they simply served a different message, as if that was supposed to be better than the original message.
Targeting became a term that was descriptive yet avoided a reference to the prior perceived failures of what was called personalization in the late ’90s. Using this learning as a guidepost, we landed on “dynamic targeting”. Prior to our use of this term, little was used with reference to targeting and no one in the online marketing space had ever used the combined term “dynamic targeting”.
Today, few people use the term personalization as a description of a type of technology. It’s more frequently used as describing a type of experience, leveraging the term personal. Beyond that, personalization has been a dead term and trying to describe it beyond it’s prior history is futile.
Love to hear your thoughts -