Agile Humor – You Might Be a Project Manager If…

If setting your alarm for 6:30 am means you’re “sleeping in”…you might be a project manager.

If the time it takes to microwave your Lean Pocket in the breakroom and the time it takes to eat it are coded to two separate job numbers…you might be a project manager.

If you’ve fallen asleep on a status call with the offshore team…you might be a project manager.

If your client team hates you for taking too long to bring the project in, and your dev team hates you for not giving them enough time to bring the same project in…you may be a project manager.

If you’ve called someone a “scope creep” or “legacy hugger” under your breath…you may be a project manager.

If you are too tired to celebrate at Dave and Buster’s after the app finally goes live…you may be a project manager.

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Data Analytics

Avinash Kaushik, a personal hero of mine, gives the best definition I’ve ever heard of the difference between a KPI (key performance indicator) and a metric.

A KPI has a direct line of sight toward the bottom line. A metric is useful for tracking and evaluating specific tactics.

Revenue is usually a KPI. Bounce Rate usually isn’t.

Posted in A Marketer's Guide to Agile Development, Data Analytics | Tagged , | 4 Comments

Agile Humor – Knock knock…

Knock knock.
Who’s there?
Processes and tools.
Processes and tools who?
See, I told you he was Agile!

Knock knock.
Who’s there?
The user.
The user who?
Yup, we’re at Microsoft.

Knock knock.
Who’s there?
Done who?
Depends on who you ask.

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Data Analytics – Why The C-Suite Ignores Data

The data is too math-intensive to hold their interest.

Eyes glaze over. Gazes wander. Executives shift in seats. Expensive pens tap on conference tables. Math does that to people. You remember high school math class when you had to turn word problems into equations? Well, now you have to do that in reverse.

They really believe in their hunches.

C-Level executives are paid a lot of money on the assumption that they know stuff mere mortals don’t. And they got swagger. Who the hell are you to tell them their intuition doesn’t reflect reality? Well, it’s actually your job to tell them, isn’t it? Break the ice by asking if they saw Moneyball. Then slip the name Nate Silver into the conversation at some time in the presentation. You’re doing them a huge favor correcting assumptions that are untrue. But telling the truth isn’t enough – you have to sell the truth. Get your own swagger on.

They get a conflicting story from their own tribe.

Department heads like to say “Don’t bring me problems, bring me solutions”. So, unfavorable data within a department will often be put through the “spin cycle” before they are presented to the C-level person. Or, the data may be buried altogether so as not to make the middle managers look bad. For instance, maybe the IT team only presents the CTO cumulative data on users of the new app – the arrow on the graph is a never-ending march upward. It’s no wonder, then, that the CTO may not embrace Marketing telling her that customers don’t like the new app, and never come back after their first visit. It may be the first time she’s heard it.

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Agile Humor – The Cocktail Hour

Signature drinks:

The Sprint – Grey Goose and dissolved Skittles, the only mixer available from the vending machine at 2 am.

Velocity Sour – Jack Daniel, club soda and lemon juice, already 3/4 finished.

Harvey Kanbanger – Smirnoff, Galliano, and orange juice with an orange Post-it garnish.

Scrum Punch – Bacardi Silver, Hawaiian Punch and frozen lemonade concentrate, drunk standing up at 8:30 am.

Lean Martini – It’s not necessary to know the full recipe up front.

Backlog Captain and Coke – Captain Morgan. That’s all. The Coke will be added in the next sprint.

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How Data-Driven Marketing Contributes to the Class Divide

Once on a calibration call, I heard a customer service rep actually tell a customer who was doggedly asking for a better rate something like “You’re one of our low-segment customers. No matter how many times you ask, none of you guys can get that rate.”

Clearly, that rep was seriously off-script. But the segmentation score on her CRM screen told her that the company didn’t value this customer very much, so she didn’t either. I built the segmentation model that told her that. For me, it brought into stark relief how marketing segmentation can affect dynamics far down the road.

In many years in the field, I’ve seen the good that data-driven marketing can do. It makes online life more relevant. It helps businesses stock products their customers want to buy. But the intrinsic power of data analytics to segment a population can also be wielded to divide it.

The last twenty years in which database marketing has hit the big-time coincides with a period of increasing polarization between rich and poor. I’m not suggesting that segmentation causes this polarization. Rather, it’s that it helps drive the wedge, already in place due to a complex mix of social, economic and political factors, deeper.

The objective of segmentation is to enable businesses to target their marketing capital toward the acquisition and retention of those customers yielding the greatest profit. There isn’t anything wrong with this, per se. Making money is what businesses are supposed to do, and it is the responsibility of their marketing organizations to help make that happen.

Customers and prospects are identified by their potential to enhance the bottom line, and strategies are crafted to reward the more desirable segments for doing business with them and not reward less desirable groups for it (or even subtly discourage them from it). The most profitable customers are not always the wealthiest – but let’s face it, it’s often the way predictive models will tell you to bet.

Predictive and yield models tell builders how to market and build most profitably. A prospect who can only afford a $195K house is courted by no one and can’t find a new house to buy. A prospect who can afford a $950K house is courted by everyone and has plenty of choices.

Profiling will tell businesses which customers are likely to have the wherewithal to pay on time and upgrade to more profitable products. This insight will be incorporated into the firms’ CRM systems. Those segments will receive the best offers, the special concierge customer service phone lines, the waived fees. There might also be “aspirational” or “elite-in-training” groups that get slightly better treatment in hopes that they will start behaving like the elite groups. And the other segments?

It costs them more to do business. They pay more for products. They have to wait in a longer phone queue for customer service. As for the service they do get when the phone is answered, there is no scripting in the CRM system that explicitly says “you don’t have to go the extra mile to treat this customer well”. But it’s pretty much guaranteed that some harried customer service reps will (perhaps in a rush to minimize the Average-Handle-Time metrics they’re bonused on) interpret it that way.

Before analytics, businesses often had policies that every customer should be treated like they’re the best customer – because absent the data, the assumption was that every customer had that potential. But in the data age, there is no more benefit of the doubt. When people complain that customer service doesn’t exist anymore, they’re wrong. It’s still alive and well – it’s just heavily up-market.

To reiterate, marketing segmentation and analytics did not cause the class divide. That has existed for millennia. Let’s at least be aware about how marketing analytics contributes to it in present day.

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New Agile Jobs

Code-Alones – Programmers who lack the people skills to be developers.

None-Of-Your-Business Analysts – Requirements gatherers for skunkworks projects.

Projectile Managers – Representatives of death march projects who must appear before angry stakeholders in the Marketing Conference Room.

Time Bandits – Scheduler/Physicists who bend the time-space continuum at the end of a sprint.

Pester Control – Analysts who intercept and gently steer away stakeholders who try to bother the development team with scope creep requests.

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Data-driven Decisions vs. Decisions With Data in the Room

Billions of dollars are spent on data. Analytics. Business intelligence. Modeling and profiling. Surveys and focus groups.

This data is presented to decision makers. But that’s not the same thing as saying that data is driving the decisions.

Has this ever happened to you? You’re in a meeting with executives. The Marketing Department presents data supporting the necessity of a change in the way your company is currently doing business. The execs politely listen to the data read-out, then announce their plan to continue pursuing the current course of action. Why?

Lots of reasons. The data went against their intuition. The data was presented by a department other than their own. The data threatened someone’s fiefdom. The data pointed to a course of action that was difficult or complex. The data was too math-intensive to hold their interest.

If you ask these execs whether they are making data-driven decisions, they will usually say yes and actually believe it. They are, in the sense that they are making decisions in the same room where data was presented. But that’s not the same thing, is it?

It’s not enough that the data is solid. It has to be sold.

Sometimes the people who are the best at crunching the data aren’t the best at presenting it or pursuading others that it’s important. That’s why the presentation of data is best when it’s collaborative. Gather data, make it bullet-proof, weave it into a story non-geeks can understand. Then, let the best communicator on your team (or someone else’s team) tell the story and sell the story to the people who need to make decisions from it. Good analysis only gets you halfway there – pay attention to making it come alive.

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Agile Humor – Multiple Choice

Choose the most correct answer:

1. User Experience:
(a) is a distinct professional discipline focusing on how a product’s use is perceived and experienced by the people using it.
(b) finishes a sentence that begins “If I were a user I would want…”
(c) is that nice department of people on the third floor that we let decide whether the “Submit” button should be red or blue.
(d) means my experience. I’m a user too. You know, a really experienced one.

2. Code Complete:
(a) means all feature code for a sprint is written and documented, and ready for testing.
(b) is a cruel tease – it’s never, ever, EVER effin’ done.
(c) a prerequisite to all of us getting wasted at Dave and Busters.
(d) is the time when you discover what the words “welcome changing requirements, even late in development” mean to you.

3. Sprints:
(a) are a short time period, usually 2 to 4 weeks, during which portions of code deliverables are written, tested, and possibly pushed to production.
(b) run like mini marathons.
(c) are ten pounds of coding stuffed into a five pound bag.
(d) are given clever names to distract you from the fact you haven’t had a day off in two and a half weeks.

Answers: Aw, come on now…

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A Marketer’s Guide to Agile Development – Who Owns Analysis?

I got certified in Marketing Data Governance a couple of weeks ago – and on the first day of certification class, I heard something that made me choke on my coffee.

Our instructor told us that the question we should be asking is NOT who owns the data – it’s who owns the means of analysis. Because they who own the means of analysis ultimately get to control the story.

That makes sense, and explains why I’ve been fought over. As a data professional, I mean. I’ve spent most of my analytical career in Marketing – but Finance, Information Technology, and Operations have all at one time or another discussed bringing my Analytics practice under their managerial control at various stages of my career.

Sometimes Marketing analysis points up shortcomings in other departments – maybe even running counter to a department’s carefully crafted party line. Maybe Sales isn’t converting leads so well. Maybe IT’s app isn’t thrilling customers. Maybe Finance’s allocation of budget dollars to acquisition at the expense of retention isn’t such a great strategy.

That inevitably makes some political waves. Politics shouldn’t enter into Analytics – except it almost always does. If we can’t steer clear of it, we can at least be aware of it and craft our data presentation and messaging to acknowledge it. This will help minimize the “shoot the messenger” dynamic – or the spawning of competing analytical operations controlled by (and not coincidentally, producing analysis flattering to) the departments they measure.

Analysis has power. Which means control of it is a big deal, a big responsibility, and a big political advantage.

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