The Right Questions for Personalization Success

“I’m a guy, and I don’t ski. Why are you showing me pictures of a woman skiing?” I wish I could remember the name of the man who said this, because it is a great summary of the customer perspective of personalization. The implication is, he’d be more responsive to offers that featured guys doingContinue reading “The Right Questions for Personalization Success”

Use Cases in Cloud Infrastructure Management

Well, with a snappy title like that,  I expect I am now all alone in this room. 😉 My cloud research this year will be focused on use cases in two areas: consumption and service. I will delve into the tasks involved, and how commercially available tooling addresses those tasks. Consumption and Cost Management ApplicationContinue reading “Use Cases in Cloud Infrastructure Management”

Evolution to Personalization: 3 Maturity Levels

How will you mature your digital marketing? If you expect to excel in your market via superior customer experience, targeting, or personalization, you need a culture of optimization – measuring, improving, and predicting. The strategy of using audience data to improve customer experience and optimize results has become this decade’s gold rush — for marketersContinue reading “Evolution to Personalization: 3 Maturity Levels”

3 Foundations for Personalization

“Personalization” online has its roots in the mid-90’s, when industry leaders such as HP and Wells Fargo had big buttons on their home pages, “PERSONAL” and “COMMERCIAL.” From today’s perspective, that’s pretty lame: those categories represent myriad audiences and personas with very different needs. To meet today’s customer expectations, marketers need to recognize dozens, hundreds,Continue reading “3 Foundations for Personalization”

Better Know a Data Scientist

Michel Manago is CEO of Kiolis, a Paris-based startup that develops software for personalization and recommendation. I interviewed Michel about the MyCoachNutrition project, which applies case-based reasoning and collaborative filtering technologies to guide subscribers to eat and exercise well. It strikes me that the project combines 3 of Michel’s keenest interests: algorithms, starting companies, andContinue reading “Better Know a Data Scientist”

Untargeted (and Undirected) Advertising

Isn’t this a lovely map? I call it an Ad-Map, and I sometimes use them to get about a strange town. Businesses pay to be listed, tourists get an orientation. Tragically, at least for advertisers and tourists, maps such as this are of little use. Advertisers in each category are listed in seemingly random order. Numbers onContinue reading “Untargeted (and Undirected) Advertising”

Google Offer for Nobody

My weather app offered me this banner this morning: OK, Google, take me to the nearest hair salon. (Try it now) I’ve been looking for a new salon. “Nearest” has never been in my criteria. Under 10 miles would be a plus, but “nearest”, nope. Granted, there are certainly people who LOOK as though that was howContinue reading “Google Offer for Nobody”

Data Scientist: Your New PartNerd

Inescapable: data science delivers the insights you need in order to be effective with digital marketing. Short-term avoidable: hiring a data scientist. Embraceable: Your new data scientist.Creative people and data science people should work together all day, every day, until they can understand each other, with a shared vision and shared vocabulary. Your data scientist must beContinue reading “Data Scientist: Your New PartNerd”

Data-driven Decisions? I’ll get back to you later.

Who wouldn’t love love love having data to support decision-making? Interviews with two dozen people, mostly marketing managers and executives, on the subject of data-driven decision making, reveal deep-seated ambivalence. There are valid reasons to reject the value of data for decisions. Data quality is a pain point for everyone, and quality may be so poor thatContinue reading “Data-driven Decisions? I’ll get back to you later.”

2 Surprises in Data Science Jobs

1. Surprise, the tools of data science, such as hadoop, python, and SAS, are far down on the popularity list of skills words. No surprise, the ideal candidate has experience, and uses computers and statistics. My take: hiring a data scientist is unfamiliar to most companies, so it feels risky. Managers are not confident ofContinue reading “2 Surprises in Data Science Jobs”