Goals

OpenTable just wrapped up Q2 goal planning. Every time we go through one of these, I refine my approach to goal setting, and wanted to codify some of my thoughts.

I’m a big believer in OKRs (aka “Objectives and Key Results”). They’re originally from High Output Management by Andy Grove, and were largely popularized in the tech sector by Google.

Writing a good OKR is hard

My standard rules:

Objective

  • The objective should be aspirational, and be something that can get people excited. For search, for example, one of my objectives is “make deploys, indexing, ranking, and autocomplete crazy fast and stable.”
  • For teams, I think many good objectives can stay the same quarter over quarter, serving as the core mission of the team.

Key results

  • Measurable (numbers, not “crazy fast”).
  • A spectrum — not boolean (e.g., “launch X” is just succeed/fail), because you want to be able to measure progress over the course of the quarter.
  • Aggressive. Google’s standard is to hit a 0.6 average in grading OKRs. People who are new to OKRs are often uncomfortable with this, but managers need to push hard on this.
  • Intelligible. Key results are a valuable communication tool to engineering/design teams and to counterparts throughout the rest of the company. People need to be able to understand what you’re measuring.
  • Time boxed. We do quarterly planning, so our goals are typically time boxed to the quarter. For some big new launches (that might take more than a quarter of work) I’m okay with doing goals a quarter in advance, but this should be rare.

Key Initiatives

  • Key initiatives are not part of classical OKRs
  • For product managers who are working with engineering teams on a day-to-day basis, I think it’s also important to highlight big projects, because these big projects are a good opportunity to align with other teams and highlight complicated/impressive projects the team aims to work on.
  • Initiatives are the PM or team’s hypothesis on what they’re going to do to meet the key results they’ve outlined. It’s really critical that key initiatives are hypothesis though — teams succeed or fail based on their key results, not on whether they deliver specific products.

OpenTable does OKRs by product development team, led by product managers. This means that my team has their own set of OKRs, but I think we’re missing out on OKRs that aren’t always tied to the business and on OKRs for other functions (like individual engineers).

Communication of the OKRs is just as important as writing them

We generally set aside a week at the end of every quarter for a series of death meetings to review every single team’s set of goals. It is striking how variable the approaches are for different teams.

I think the best goal meetings:

  • Has a POV, and is making the case for why the work of this team matters in a macro sense
  • Tell a story about what was accomplished, what will be accomplished, and how they’re related to the team’s core mission — and makes the delivering team the hero.
  • Explains some of the hardest problems the team has faced and is trying to solve in simple terms. I work with our data science team, and it’s particularly important for them — even a highly technical CTO won’t know what ensembles and mixture of experts are in the context of our business.

Finally, I tried something new this planning process which I’ll be sure to include in the future. A demo. In retrospect, this seems obvious — every good planning or mid-sprint check-in should have demos, so why shouldn’t an end-of-quarter? — but I think it’s easy to get too focused on the stats. Product companies should show how they’re building products.

Communication

Product managers are in the business of communication, so concise and cogent writing is one of the most powerful tools available to us. It’s a hard muscle to develop, requiring consistent feedback and deliberate practice.

Business writing is a core part of the curriculum at the consulting firm McKinsey. Managers pore over every word written by new analysts, setting a high quality bar and demanding frequent revisions. Most companies don’t invest as much energy into training people how to write, so we’re left to figure it out on our own. It’s not easy.

One place to start is Barbara Minto’s Pyramid Principal. I was introduced to Barbara’s work by Harrison Metal and it was immediately familiar: She helped codify McKinsey’s writing style.

An easier place to start might be Julie Zhuo’s recent article on “Escaping Email Hell.” Julie offers specific changes to writing style which improve emails — and most of these tips could be applied to any kind of writing. Her best advice is to “know your end before you start.” Great writing should be a condensation of your thought — not every step in the winding journey you took.

Other books I’d recommend:

 

Rewards vs. Culture

When I was in college, I spent two years as editor in chief of our daily online-only newspaper, The Daily Gazette. One of my first projects was leading a redesign of our website — and that included adding advertising; overnight, we were pulling in a thousand dollars a month.

I didn’t really know what to do with the money (the college paid our costs), so I came up with a scheme to divvy up the money as a salary of sorts to our team. Each semester, we’d pay writers, photographers, and editors a few hundred dollars — but they had to be active participants, meeting a quota of contributions. I imagined this could lead to a golden age for quality content.

Instead, the program failed miserably. Story contributions and staff attendance dropped rapidly over the course of the semester.

Next semester, we changed course. We stopped paying the team (apart from a couple, more significant, work-study scholarships) and instead used the money to buy t-shirts, send people to conferences, hire a journalism coach, and throw parties. This became a turning point for the Gazette: Our staff grew, the team wrote a lot of great stories, and we were named one of the top three college news sites in the country.

Looking back, I now realize that this challenge is a great lesson in the difference between rewards and culture.

What I realized after that first semester is that when we started paying our staff, working for the Gazette became a job — not a passion hobby. Our staff had tons of other opportunities, and a couple hundred bucks wasn’t worth hours of their life. When we pivoted how we used the money, though, we built tight relationships within the team, we created a sense of camaraderie, and we made the Gazette a social touch point at the school.

People want to get paid appropriately, of course, but as people move up Maslow’s hierarchy they want more than just a paycheck. I’m always thinking back to my time at the Gazette, and wondering how I can focus on building a culture so that work isn’t just an obligation.

So what should you do? Events like OpenTable’s recent internal hackathon are great. Share books with colleagues, so that you establish common frameworks and mental models. Print t-shirts, host board game nights, sponsor wine tastings. And don’t give undervalued rewards.

Search, discovery and marketing

One of the clearest places to see this problem of ‘too much’ is Yelp. I’ve been fascinated by how many companies are effectively trying to unbundle Yelp, despite that fact that (unlike Craigslist) it’s a modern technology company that does most of the things one would expect it to. But where people unbundling Craigslist generally try to peel off a category and deliver a modern experience, the people going after restaurant listings are often doing so with constraint. That is, instead of giving you every single restaurant that’s within 2 miles and that lots of people liked, they give you 10 restaurants. … People are attacking crowdsourced universal scale with constraint, curation and personal preference.

Looking at these companies, it strikes me that actually, saying that ‘Yahoo’s directory didn’t scale’ misses the point. What we’re really seeing is a trade-off between two problems. You can have a list, solving discovery and recommendation, but once the domain gets big then your list is either unusably long or partial and incomplete (and perhaps uneconomic to maintain). Or you can have a searchable index of everything but you’re on your own working what’s good and finding things you didn’t know to search for.

Search, discovery and marketing from Benedict Evans

Curation also works best in places where taste matters — the “best” book, the “best” restaurant. An algorithm can’t provide social validation beyond “the most popular.” What’s interesting about Yelp is that their focus on user generated content  means it’d be hard for them to add sources of curation outside of their users — there’s no list of restaurants with Michelin stars, or James Beard winners, and there probably can’t be one without undermining the core conceit of the platform.

Killing the Creative Class

In Culture Crash: The Killing of the Creative Class, Scott Timberg explores the convergence of forces which have combined to “eviscerate the creative class” — those people who make, produce, review, and sell culture; he includes everyone from record store clerks to authors.

While I sympathize with Timberg, and share some of his nostalgia for the late 20th century, I deeply disagree with his assessment of what we’ve lost, and how we lost it. At a high level, Timberg blames the rise of the internet for the decline of the creative class, through piracy, disintermediation, and mass retail.

His piracy arguments feel tired; the point has been argued elsewhere (see this, this, this, and this). While counterintuititve, there isn’t much evidence to suggest that modern piracy substantially harms most artists; the rise of high-quality distribution services massively undercut piracy (see: Spotify).

On disintermediation, his description of record, book, and movie store clerks is striking. They were “men who’d given up something else do [this work] out of love for the music.” He describes they as having a moral commitment. It is as if cultured clerks are modern-day monks, dedicated to art instead of God.

But the decline of Western civilization’s long-time keeper, the Catholic Church (and with it, monks & monastaries) no more sounded a death-knell for our culture than the rise of the Internet over the past three decades has.

In particular, Timberg fails to place the creative class he mourns in the context of history: Mass-produced culture is a new phenomena. There were few book clerks before Gutenberg. Motzart and Michelangelo produced culture with funding from noble patrons, not from a creative middle-class. Art and culture will need to change in response to new technologies and social mores, but it will survive.

I suspect we will see a shrinking professional creative class, but a flowering of amateurs. I’m not concerned about the production of culture, but I’m less clear on how culture is discovered. Timberg suggests that the canon of classics is undersiege, and its hard to disagree. There are fewer “expert” voices saying what should be consumed, and its harder to find the signal in the noise.

This isn’t necessarily negative. Old cultural gatekeepers often enforced mass culture — disadvantaged or fringe artists could be locked out. But I find it hard to believe that The New York Times Bestseller List is the best way to identify our cultural canon.

In part, this is because I think why something is recommended is just as important as what was recommended.

For many pieces of culture, the story and recommendation is a critical part of providing value. What is the story of this piece? How does it fit into the larger movement? What is its place in history? Am I supposed to like it? I suppose I believe that culture benefits from a hint of elitism. And I’m not sure that algorithms can provide an elite judgement anytime soon.

For many, the consumption of culture isn’t too different from the consumption of wine: We take pleasure, backstory, and price — in addition to the culture itself.

Computer algorithms can provide recommendations (as shown by Netflix), and will improve. But the algorithms that undergird machine-learning recommendations work by finding counterintuitive and subtle links between people and the culture recommended — I don’t believe exposing these recommendations would be compelling to most users.

Some companies have two separate algorithms: one to produce a recommendation, and one to produce a compelling set of reasons. This should eventually produce compelling rationales (assuming “success” is properly defined and fed into the algorithm — easier said than done), but if people don’t have faith in the algorithm, it can’t impart greater meaning and value to pieces of culture.

So I’m not sure how we can protect this judgement.

I don’t think recommendations do well in a winner-take-all marketplace, and instititutions like Michelin or Pitchfork are struggling to remain viable as businesses.

In part, these publications and cultural touchstones are being replaced by user generated content [UGC]. But UGC-centric sites struggle with authority — is a lengthy review from a Yelp elite reviewer more important to Yelp than a review from Michelin? Should a university professor and expert in a subject have more power than a long-time Wikipedia editor to edit Wikipedia?

The issue might not be insurmountable — Stack Overflow seems to have resolved some of these issues, and FourSquare is playing with interesting mesasures of “expertise” — but there are no comparable cultural organizations.

Where do we go from here? I’m not sure.