Category Archives: A Marketer’s Guide to Agile Development

A Marketer’s Guide To Agile – Top 7 Reasons Why Your Data Is So Crappy

How and when data is captured is a vital part of decision-making in call center scripting, digital architecture, sales presentations, just about everything that touches a customer. There are things you can do to help make it better, but some issues will be always with us. So, here’s why your data is crapola, in no particular order…

1. TEXTING (yes, TEXTING)

In the data world, spelling counts. Whether data is being entered by professionals or by the customers themselves, text fields will be rife with errors. That is true no matter how clever you get with the drop-down boxes or type-ahead functionality. The proliferation of text-speak has made this worse, and spelling is rapidly becoming a matter of personal choice. That doesn’t bode well for text-mining. And no, spell-correct is not the antidote. Entire websites are devoted to how that can go horribly and hilariously wrong.

2. IT WORKLOAD

“Why can’t the digital dev folks just put another box in the online form so the prospect can enter their promo code?”

They can! They will! Just as soon as that work order rises up to the top of the priority list. Right now the project manager has it in the backlog. Where work orders go to ripen, get covered with brown spots, and die.

3.BAD DROPDOWNS

The data was shocking. Month after month, nearly a third of all disenrolling customers were dropping service due to bankruptcy. Nationwide. All segments. So I planted myself in the call center for a few days. In the CRM system, the drop-down box marked “Reason for Leaving” had 35 choices. “Bankruptcy” starts with a B, so it came up at the top of the drop-down. Too bad “Alien Invasion” wasn’t one of the choices, it would have been caught more quickly. So, yeah, that happens. In my experience, 12 choices is the limit for an effective drop-down in a call center. Any more than 12, and boom – 30% of the country mysteriously goes bankrupt.

4. POOR REQUIREMENTS

The marketer: “This email address is blank – it should be a required field.”
The analyst: “Someone input their mailing address in the email field.”
The email vendor: “Lots of these email addresses are undeliverable.”

Coding an email field with an input mask requiring a “@” character and a period will solve the analyst’s problem. Making it a required field will mean 100% of records are populated, which will satisfy the marketer (at least temporarily). Coding an email field with that input mask plus making it a required field will get you a lot of bad email addresses like “noneofyourbiz@buzzoff.com”. That can get you blacklisted with ISP’s. It can also spike your abandonment rate on the digital form page.

But marketers – it’s not the developer’s job to know that – it’s yours. He or she will code to your requirement specs. Think through the ramifications of stakeholder requirements – and make sure the requirements reflect a considered decision.

5. USER EXPERIENCE

First, let’s get one thing straight. UX and good data capture are NOT mutually exclusive. But what’s great for data capture isn’t always great for the user experience, and the two must be balanced. I once had an HR stakeholder tell me that data was vitally important to her, so it was a requirement that users submit 10 fields of personal data into a web form before they could browse jobs on the company’s career site. I told her that strategy would get her the best data and the three most committed job seekers she ever saw. The rest would bail.

6. COMMUNICATION

The promo postcards start hitting consumer mailboxes at 8am, and the TV ad blitz started airing at 9am. The campaign is a hit! Responses galore! Great! Except Marketing forgot to notify the Customer Service Manager that the promo schedule had changed, so she isn’t staffed up for the traffic onslaught. The unfortunate reps that are schededuled today can’t keep up with the volume. Calls are going unanswered. Their priority today is not great data capture. Their priority today is to take as many calls as possible and make it through their shift alive.

7. INCENTIVES

Sales reps’ income largely depends on how many sales units and revenue dollars they bring in. For customer service reps, bonus criteria often include average handle time, time-to-answer, off-hook time, etc. Sometimes data accuracy is part of the incentive formula, but it’s almost never the lion’s share. Generally, when short-term incentives are introduced for better data capture, data capture gets better. And when the incentive period is through, the gains recede, although hopefully to a little higher than baseline.

Agile Humor – Definitions

Kanbanter – Small talk exchanged while you and a fellow developer view progress on the board.

Custermation – When a project’s resources estimate is about as accurate as Custer’s prior to the Battle of Little Big Horn.

Reverse Time Lapse – When seven hours elapse while finishing a piece of code with a one-hour estimate.

Pessimestimation – When you start padding six extra hours onto every hour of estimated work.

Doubtsourcing – When stakeholders start contracting out work because all of the internal team’s time estimates appear six or seven times too high.

Data Analytics – Define Channel

“A third of our sales are coming in through the web channel – let’s move budget from direct mail into display ads!”

Whoa – display ads are in the digital channel – but is display actually driving those purchases? Would more display ads drive in more purchases?

What do you mean by channel exactly? What if I respond to a direct mail piece by calling your Inbound Sales Center, then go onto your website to make a purchase? Which channel do you attribute me to – Direct Mail, Inbound Call or Web?

The answer is that every response has two at least two types of channel attribution.

One is the marketing stimulus channel – in this example, Direct Mail. The other is the response channel – in this example, Inbound Call. In many cases, a third channel is the purchase channel – in this example, it’s Web.

Moving money from Direct Mail to Display might be the right move – or it might cut off the main pipeline into your purchase funnel. I don’t want to make your head explode – but there may be a combination of market stimuli that constitute the actual Market Channel. It’s another facet of multi-channel attribution.

So you’re not measuring all this precisely? You’re not alone – many firms, even some really big ones you’ve heard of, aren’t doing it all that well either. Getting attribution right is a commitment – time and money – and is an iterative process. It should ultimately answer the question of where to spend your marketing money, gaining more precision with time.

Agile Humor – More Agile Drinks

The Scope Creep

Gin and tonic. Wait, can you add some bitters to that? Oh, and can the tonic be in a separate glass? I’ll have it over at that table instead. And can you bring some peanuts with that? Then some buffalo wings?

Debug Sour

Whiskey and sour mix, sent back three times until the bartender gets it right.

Minimum Viable Martini

A martini glass with just enough chilled vodka for the first sip.

Cowboy Code Margarita

The best margarita you ever tasted, but the bartender can’t replicate it. Plus, the bar’s a mess.

Click here for more Agile drinks from a previous posts…

Click here for even more Agile drinks

Click here for still more Agile drinks

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.

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.

A Marketer’s Guide to Agile Development – Big Data Envy

Marketers are becoming as insecure about big data as they already are about mobile. It’s like the middle school dating scene – you think everybody else is doing it but you.

Don’t buy into the hysteria. Relax. Big data is real, alright. And, its potential really is quite vast. But its buzzword-du-jour status right now causes marketers unnecessary angst. Nobody wants to be perceived as anything less than cutting edge, and it’s a status thing to say you’re “doing big data.”

Before you hire the Hadoop gurus, ask yourself if your operation is ready to use big data. Are you already wringing enough insight out of your existing customer databases and retention data to move the attrition needle down a few ticks? Are you mining data out of your existing digital analytics tool sufficiently to inform decisions about content?

If the answer is no, then queuing up big data could be like buying your kid a new Escalade when he’s still learning to drive the 2003 Honda Civic.

As I said, big data has great potential – yet data won’t become insight until you ask questions and use it to get the answers. Let’s make sure that paradigm is working on the regular data before rushing to harness big data.

Agile Humor – Agile Billboard Top 10 – 11/28/12

1. Rihanna – Diamonds (the Vizio Flow Remix)

2. Maroon 5 – One More Night (Until Freakin’ Code Complete Is Done)

3. Ke$ha – Die Young (Yes, Scope Creep Kills)

4. Bruno Mars – Locked Out of Heaven (Server Credentialing Blues)

5. fun. – Some Nights (Red Bull, Get Me Through)

6. Ne-Yo – Let Me Love You (Until You Learn To Love Yourself or Until I Find a Girlfriend Who Plays Minecraft)

7. PSY – Gangnam Style (There’s Something Hotter Than Agile?)

8. The Lumineers – Ho Hey (Don’t You Come At Me With More Changing Requirements, Bro…)

9. Taylor Swift – We Are Never Getting Back Together (Pair Programming Fail)

10. Flo Rida – I Cry (Abandoned Code Remix)

Agile Humor – Halloween Costumes

Put on a rumpled plaid hoodie and old jeans. Smear some pizza down the front of the hoodie. Dab your face with ivory-toned makeup to reproduce the pallor of a four-day-coding-marathon and go as a developer.

Put on a hemp shirt and a pair of Seven For All Mankind jeans. Pick up a pair of Oakley sunglasses. Get a spray tan, grab a Vitamin Water and go as a marketer.

Put on an oxford shirt and a pair of Dockers. No food smears on the shirt, you don’t have time to eat. Knit your brow and go as a project manager.