What Uber Really Fears

Isabel Roughol posted a very interesting piece on LinkedIn today about how Uber’s Privacy scandal is a symptom of Uber’s culture. She paints a cautionary portrait of a company that is endangering its future with its wanton disregard towards privacy and threatening its enemies, even if these “enemies” are journalists doing what real journalists are supposed to do.

Uber will probably survive this “scandal” because it doesn’t fear the media nor does it worry about a large-scale customer abandonment because of its corporate behavior. Nor does it fear its old school competitors because they lack the organization and political juice to pressure government leaders.

Simply put, the service is far superior to anything out there and most people put a value-added service like this ahead of their ethics (ever try to get a cab in New York when it rains?). Call it situational ethics, but when a service like Uber is so seductively easy and efficient, and you’re talking about a low out-of-pocket expense like a cab fare, a blind eye will be turned.

The only thing Uber fears is regulation and taxation on a local, state and/or Federal level, which frankly is inevitable. When these government agencies figure out how they can get a slice of the Uber revenue pie, the situation will change.

And this is the real cautionary tale: The parallels to Zynga are eerie. Zynga thrived by exploiting a loophole in Facebook’s ad policy and declined – okay, crashed – when that loophole was closed.

Your move, Uber.

Big Data – Useless If You Don’t Know What You Are Doing

In a wide-ranging interview posted last week on IEEE-Spectrum, “Machine Learning Maestro” Michael Jordan discussed the current state of Big Data; its strengths, its shortcomings and the ways it can be misused. Here are a few excerpted quotes which highlight the key issues, in my mind, that people must be aware of when they use Big Data.

“When you have large amounts of data, your appetite for hypotheses tends to get even larger. And if it’s growing faster than the statistical strength of the data, then many of your inferences are likely to be false.”

“I think data analysis can deliver inferences at certain levels of quality. But we have to be clear about what levels of quality. We have to have error bars around all our predictions. That is something that’s missing in much of the current machine learning literature.”

“It’s not a year or two. It will take decades to get right. We are still learning how to do big data well.”

“Spectrum: What adverse consequences might await the big-data field if we remain on the trajectory you’re describing?

Michael Jordan: The main one will be a “big-data winter.” After a bubble, when people invested and a lot of companies overpromised without providing serious analysis, it will bust. And soon, in a two- to five-year span, people will say, “The whole big-data thing came and went. It died. It was wrong.” I am predicting that. It’s what happens in these cycles when there is too much hype, i.e., assertions not based on an understanding of what the real problems are or on an understanding that solving the problems will take decades, that we will make steady progress but that we haven’t had a major leap in technical progress.”

Okay, so here’s the deal for marketers:

• Using Big Data successfully to identify customers and prospects is totally dependent on
  • the strength and reliability of your data; and
  • your ability to ask the right questions
• At this point, Big Data is another thing in your marketing tool box. It’s still in its infancy.
• Implementation still rules. Big Data is useless unless you create, execute and deliver marketing campaigns that resonate with customers and prospects, driving them to buy your products and services.

 

 

 

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Sticking With What You Love (and Do Best)

Tristan Louis posted a wonderful piece comparing Apple circa 1997 and Puls’ recent launch. Either will.i.am is an astute student of history or he and his team have no sense of history.

While the post cites the eerie parallels, there are two key differences:

1) Apple was around 20 years old when it launched that campaign in 1997 and Jobs came back to save the company; so it had some cache and history. There was an effort to build an expand the company, ideally maintaining cutting edge innovation. As the article inferred, the bigger you get, the harder it is to innovate, be leading edge. You have to buy new ideas while preserving what you have. Institutionalization sets in.

2) I think Puls is a product/business that is being built to be sold (a la Beats). The mindset and preference of will.i.am is to create, build and flip, so he can continually create and not have to worry about maintaining a business long-term; that is not in his DNA.

Which brings me to a key takeaway from the piece – stick with what you love and do best. There are very few people who have the whole package – the dynamic depth of intelligence, managerial skills, business savvy/people skills, product expertise and vision –  and the PASSION to take a company from start-up to Fortune 100 company. In this case, will.i.am just wants to create.

More power to him.