How to avoid web analytics douchiness

I grew up friends with a kid that was a year older than me. We were great friends, even through elementary school where the difference between being in one grade vs. the next was practically equivalent to 10 current-day years of maturity. Never was this more apparent than when I was just starting 4th grade and he was beginning 5th grade. In “after school” (our parents both worked so we were in the after school program) one day early into the year, he comes over to my desk and drops a trapper keeper that must have weighed 15 lbs, stuffed so tight it looked like he tried to fit a medium-sized pet inside of it. As it thudded down to rest, he leaned over and explained to me that I would never be able to handle 5th grade. There is more learning, more homework, more studying, and more paper than anyone is capable of negotiating. 5th grade was going to kill me.

At the crossroads of every stage of life, I’ve tried to consult people who are on the other side. And in almost every case, a large percentage of the people on the other side report back that it’s harder than they imagined, more complex, and they literally call into question the human capacity to handle it. This was the case with 5th grade, high school, many tough college courses (thermodynamics does actually test the limits of the human mind and body), marriage, having children, and I’m sure it’ll be the same with other things. Every time someone has gone through it, they somehow survived, but question whether any other human could handle it (even though most every human for the last several thousand years has somehow survived the same).

This is called douchiness.

Where does douchiness come from?

In a nutshell, I believe it comes from a need to look smart; to feel better than the average bear and to celebrate our success in overcoming a challenge. How do I know this? Because I’ve been douchy many times in my life (and I knew it when I was doing it).

Our jobs are tough. Really tough, in fact. The debate about whether web analytics is easy or hard has been raging on for centuries (ok, maybe not centuries). But like our douchy definitions for the term “web analytics,” our focus on whether or not what we do is difficult is something we have constantly evaluated from our own perspective, rather than trying to appreciate what an outside perspective of our trade should be.

What kind of watch are you?

A metaphor I’ve been rolling around in my mind for a while relates what we do to how high-end wristwatches are designed. I do not have the money for these things, but maybe some day. Maybe I should put a paypal button on here. Anyhow…

Which of the two watches is more useful? Which of the two watches could potentially be perceived as douchier (to some, cooler, but in terms of actual usefulness)?

What we, as analysts, are paid to do is make sure we have an opaque face obscuring the complexity of our jobs. In watches, each feature the watch can handle is called a “complication.” It’s an apt name, because adding the ability to display a date, make that date learn which months have 30 days and which do not, add a chronograph, a lunar phase display, or any other feature significantly complicates its “motion,” or the guts of the watch. Classy watches like the one on the left simply allow that complication to exist in the background, electing to confidently show the user only what’s necessary; what they paid for. Watches like the one on the right are designed to impress. They are actually super cool looking, but to the user, they sometimes just present extra noise around the core message that is supposed to be delivered.

People in our industry believe that because our jobs are hard or complex, we can wave that flag to show the world how smart we are. We want to leave the face transparent to show our organization, our peers, our parents how smart we are. And worse yet, when someone gets into this trade, we tell them how hard it is. We tell them we don’t know if they’ll survive it. We tell people who say SiteCatalyst is complicated that they just don’t “get it”. That the complex, “enterprise” tool is like a Ferrari. Well I have news for you, my mom can drive a Ferrari. She can start the engine, put it into gear (which usually isn’t necessary with Ferraris since they have idiot-proof automatic shifting transmissions), turn on the radio, and drive wherever she likes. If my mom enters a race, yes, the car allows her to adjust camber, downforce, steering stiffness, single-nut tire changing access and all of that, but she can still go buy milk in the damn thing. To buy milk with SiteCatalyst (and just about all tools), you have to rewire a bomb. So don’t tell people they “don’t get it.”

In a nutshell, we slam our 15 lb trapper keeper down on the table to show the unprepared wussies what they are in for and how badass we are.

That’s douchy.


(Get it, like de-duping? Such industry funnies!)

What organizations need from us is the appearance that what we do is simple and straightforward, because that makes what we reveal actionable. Business is simple. You have revenue, you have expenses, and you have capital (or assets balanced against liabilities). Those are the only things that exist, relevant to us.

But from that point, the business breaks down into smaller pieces that affect one or more of those things. That’s when it starts to get complex. And we can decide to talk about that those little pieces, or we can simply say things like:

  • We have found a way to make x% more revenue
  • We have found a way to trim y% expenses
  • We have found a way to use our capital more efficiently

These are simple messages; compelling messages. And believe me, when the big cheese starts asking questions and digging into the claim you’ve put forth, there will be plenty of opportunity to look smart. But wait for the digging, for the love of God.

We are paid to make it look easy and have the goods to back it up. Any professional athlete makes it look easy. But they have 10,000 hours behind them that got them there and the confidence to not have to rub that in the world’s face all the time. Let’s do that. Let’s make it look easy. Let’s back that up with everything that exists behind the face, because we do have the goods to back it up. If we didn’t, we wouldn’t be running around talking about these amazing business outcomes we can drive.

And finally, let’s be nicer to each other and stop making people feel stupid for saying something complex is complex.

Let’s move past all of this inconsequential tool obsession, let’s extinguish the burning desire to look smarter than the next person, let’s be the classy and incredibly valuable resource the world needs us to be, and let’s reap the rewards. We are one community stuck in a rut, and we are digging. We are better and smarter than this.

With love,


Don’t believe everything you read.

Just because it’s written doesn’t mean it’s true.

I think we should read, and we should read a lot. But with everything we read, we need to ask if we really believe it. Don’t just take the author’s word for it. Don’t think that just because they got published that the ideas were vetted. Any specialist in any industry can point to 5 or 50 books that they feel are completely and utterly wrong or misleading, from golf instruction to Ruby on Rails programming to our own world of web analytics.

When you read Avinash’s books, Eric’s books, or anyone elses’ books, blogs, or even worse: companies’ sponsored whitepapers or adversising, just ask yourself, “Do I really believe this is 100% right? Is this the only way of doing this, or are there alternatives? Am I believing something because it is charted or has a seemingly legit data source?”

If you don’t ask yourselves this, I know someone looking to sell their shares of Enron and mortgage backed securities for a good price. They have all the charts and legit data sources you could ever want.

When you ask yourself these questions, however, one of two things happens: you either decide that the point was an interesting one but there may be another way of looking at the world (no, this doesn’t always mean a person is stupid or wrong, you may just feel differently about something), or, better, you confirm what the author said in your own mind, solidifying your respect for their work and thoughtful approach, building more personal conviction and ownership of the concept. In either case, your dissection of their words is what they wanted. No decent human being wants anyone reading their work like a manual for life, because they know that type of reader is a weak, fickle reader who could just as easily be turned by the next post. Writers write to get you thinking so you can reach your own conclusions. Which is exactly why I and so many others have such a great deal of respect for Avinash, Eric, and other major contributors to our industry. They care enough about us to let us reach our own conclusions, and they are comfortable enough with their own lives to be okay with small or even big differences of opinion.

There has been a great deal of manure written and spoken in our industry’s (and others’) history. Microsoft and its fans told the world that Apple was meaningless and Linux less so. How’s that going? And now Adobe is on this rant about the “cost of free” and building fear around Google Analytics and its suitability for enterprise. And even worse, other people are jumping on the bandwagon, trying to earn pageviews by shooting holes in GA and other players in the industry with bazookas. It’s a shame: everyone has been so sensationalist that it’s hard to distinguish the good, valid points from the total nonsense. Some of the points are legitimate, but not all are. It’s also a shame because Adobe/Omniture is a fantastic company that I’ve had nothing but positive experiences with, and they are acting like Tonya Harding rather than working on and talking about their own strengths, of which there are many.

So when it comes to blogs, co-workers, agencies, big companies or anything, listen to your grandpa: “Don’t believe everything you read.”

The other qualitative side of web analytics

Sometimes we let ourselves get a little rusty. It’s been a while since we talked to some people in our business who we don’t run into in the course of our daily routine or normal work.

Companies are complex places. They’re complex like the olympics. Everyone is striving for a gold, but some people are skiing downhill and other people are brushing the ice to make a huge stone land in a target. It’s when the curlers are curling in the middle of the downhill race that things go wrong. It’s when different departments and different people are playing different sports that conflicts pop up and we have this, “We’re all on the same team,” BS talk with our managers. Yes, we are all playing for the USA or wherever you are from, but we earn our gold medals in decidedly different ways.

So something that is of huge value to businesses is objective people like us understanding the viewpoints of people in different disciplines. What pain are people feeling as a result of being aligned to a different goal than the people who are shoveling work on their plates? Where are the failure points?

I know that in many companies, it feels a little unrealistic, but if we can make some friends around the organization and go out to lunch with them once a month (individually), or at least have a standard 15 minute coffee every few weeks, we will have an unparalleled understanding of our organizations. We will start to see that peoples’ complaints are legitimate, we can identify the parts of the complex ecosystem that may be to blame for those complaints and pains, and we can give each of these people some context from something we’ve learned from another part of the organization. And we will learn things a little differently than the “team USA” coaches in the company (HR, centralized execs., etc.) because we are part of the infantry; we are the ones with the guns (not just the maps) in our hands. Even if you are an executive in analytics, you are still a practitioner, because this isn’t a profession you can avoid getting your hands dirty in.

When we get back to our analyzing and identify issues with the site’s performance or customer experience, we will have a new understanding of the types of conversations and decisions that led us to this point. We can also see these potential points of failure, losses of efficiency, and causes for bad decisions in current conversations taking place. We can become better analysts by putting our UX, IA, marketing, etc. “opportunities” into a qualitative context: that these could and should have been better experiences with higher ROI and overall performance, but there were giant stones sliding across the downhill course, causing the skiers to go flying into the woods.

When we do our analysis not just on the customer and the web site, but include some findings on the internal workings of the business, our work can take on a whole new light and reach a whole new audience. We can become the resource we should be to all arms and all sports, and we can do a huge service to the individual athletes by pointing out their overridden efforts to avoid these pitfalls when decisions were being made. We don’t need to point fingers or name names of those who steamrolled the “right” decision, just merely recap the facts in an effort to learn from our own mistakes.

So, who’s our next lunch going to be with?

How to measure web engagement, for real.

Ever wonder why “engagement” and “enragement” are spelled so similarly? Measuring engagement is tricky business, made even trickier by the use of esoteric indices and web-centric (as opposed to economics-centric) metrics to describe the concept.

So, here are some thoughts on how to measure, evangelize, and create delta in engagement.

First things first

We have two key issues in the area of measuring and expressing engagement to our businesses:

  1. Most of the makeup of “engagement” consists of metrics that have not been translated to economic value. This requires extra explanation as the measures are passed around the organization, and may alienate certain very important audiences.
  2. Largely, the types of “engagement” indices and calculations I’ve heard of are typically non-predictive in nature. They do not fold into or constitute a model that can be used to calculate the return of incremental investment or a shift in budget allocation* across media or resources. Typically, these engagement models are used to show delta in an index when delta in revenue or ROI is hard to create or isn’t enough for a pushy organization.

* Shifting your budget allocation based on distribution of historical ROI values is a highly unsophisticated long term strategy (fine in the short term) because it fails to consider the impact you can have on ROI by delivering better messaging and a better experience (read more).

It’s the economy, stupid!

The first thing to deal with is a nugget of wisdom made popular by James Carville. Fortunately, it’s a concept that the measure (argg! hate that word) community is transitioning toward as we speak: making measures economic in nature. Tweets, Likes, Pageviews, subscriptions, shares, etc. are the language of web metrics, and the good news is that they are all worth something. We just have to figure out what.

So let’s get started. The first thing to lay groundwork on is that there are two great, pre-existing models we can use to estimate the worth of something.

The first one is obvious: real return, which describes the actual dollars we get in a transaction. In a paid search campaign, we can see the money that the campaign drove in real terms. In truth, we only see a certain percentage of the money, because real return models don’t intrinsically consider ripple effects: that a single click could lead to a positive experience, a sale, lifetime value that can’t all be captured in our data systems, the potential for word to spread to their family and friends, etc., etc.

Real return is both a blessing and a curse: it gives us cold, hard data, but it also impregnates our businesses with the idea that everything is suddenly measurable in cold, hard terms. That is completely inaccurate.

The second model is probably the one that will be more powerful for us in translating engagement into value, and doing so in a predictive way: willingness to pay (WTP). WTP describes the investment a business is comfortable making to get a desired outcome, often an outcome that is non-financial in nature, or difficult to translate to financial terms. Investments in radio advertising, for example, are based largely on willingness to pay: “We can deliver your message to over 300,000 listeners in our lunch time block.” The advertiser does some back of the napkin math to figure out what the financial impact of 300,000 listeners may be (the business owner obviously wants some sort of a return), and they decide on whether the ad is worth the cost.

Truthfully, WTP models can and should be derived from real return data, but getting too obsessed about turning WTP into real return will bring your decision making to a halt. You must fight the urge to get stuck in this trap; keep moving.

Getting your Financial Figures

Now, it’s time to figure out how to put dollar signs on your tweets, likes, etc. One of the best places to start (if your business is large and does traditional advertising) is by finding proxies in the traditional advertising side. If a single post on Coke’s facebook wall reaches an audience of 3mm viewers (measurable), and is liked/commented by 10% of viewers (measurable), and that is seen by their friends (measurable), you can estimate the total reach. How much does it cost you to get that same reach with a billboard?

Now add a little premium to that figure because a) facebook is an interactive medium and builds affinity and b) you may have learned something about your creative or brand from the comments you received, which has value far above and beyond what non-interactive, traditional media can provide (you have to pay extra for the focus groups, in fact).

If you aren’t a big place with huge advertising budgets, you can just investigate what it would cost you if you did. Make a few phone calls to the newspaper, TV station, radio, or an agency, and see what you will get for what price in terms of reach for an audience that fits your target market.

You’ll have to go through your whole organization and look at each action, trying to understand the value of each effort (you will probably be using a mix of real return and WTP/proxies in each scenario):

  • What am I willing to pay for a paid search click, email open, or display impression? (ROI + extra lifetime value + word of mouth potential)?
  • What am I willing to pay for an additional developer? (cost or incremental revenue of releasing a feature or product earlier)?
  • What am I willing to pay for a usability person or designer? (potential upside of a range of basis-point deltas in conversion rate [a fancy way of saying build a matrix] for applicable traffic sources).

ROI is NEVER the answer to any of these questions because ROI fails to consider anything but a moment in time. On the WTP side, don’t forget to factor in other costs like salary, resources, efficiency loss, etc. in addition to the hard cost of the media or whatever it is you’re looking at.

No, this is not going to be something you do in a week. But it’s also not something you have to do for your whole company before you can start making predictions. Take off a few bite-sized pieces and see where you get with it.

Making it predictive

Now that you have your models for valuation and a method of getting some numbers to work with, it’s time to make sure that your model is a predictive one, not a backwards-looking one.

Creating engagement data for the past is easy. Creating it for the future is much harder. The good news about a WTP model is that it is pretty forwards-looking, though: you are focused on what an incremental set of eyes, an incremental sale, etc. is worth to you. If you are stuck in the ROI trenches, you will never be able to understand what a sale is worth, and you will never be able to state what you are wiling to pay to create a new customer or rekindle the flame with an old one. As long as you get things back to WTP, you will have a predictive model on your hands (it would take me 20x the length of this post to actually start discussing a viable model, so if you’d like a post that dives into real models, please leave a comment!)

Delivering the message

Now here’s the critical part: when you deliver the message, deliver a prediction, not a predictive model. Use your model; don’t ask others to use it. Here are a few examples:

  • “Mr. CEO, our work has turned up an opportunity to bring in an incremental 5% market share with a $1.75mm investment, all in.”
  • “Mr. CMO, we have found a way to reallocate 10% of our radio budget to social media and improve that budget’s reach by 50%.”
  • “Mr. COO, we have found that there is a significant tradeoff between process and productivity in the IT department that we estimate has cost us $7.5mm in revenue in the last 2 quarters. Some changes in our workflow can close that gap in the next 6 months.”

Also, deliver the right message to the right people. CEOs care about stock price and company valuation, which means cash flow, market share, and innovative things to talk to the world about. COOs care about efficiency and good ideas that will impact the business directly. CMOs care about using their budget effectively and being able to show the value of their existence in terms of changes since the last CMO was fired 3 months ago.

Your mileage with your executives may vary, but keep in mind that different executives come with different goals and perspectives. Try to guess what motivates them and reposition your idea in a way that suits their personal or professional goals.

What happened to engagement?

Nobody gives a crap about engagement. Engagement is just a stepping stone on the way to “worth,” and it’s a non-predictive stone. Once you’ve moved on, you’ll never look back.

The most important skill in web analytics

What is the most important skill and greatest determiner of your success in web analytics, out of the list below?

  • Statistics
  • Modeling
  • Tool implementation
  • Tool use
  • Data visualization
  • Reporting
  • Teasing insight out of the data
  • Marketing channel expertise
  • Usability
  • Information Architecture
  • A deep understanding of your business
  • Sales

If you guessed “sales,” you are right. If you can’t sell yourself, sell your ideas, sell the need to implement well, measure, report, analyze, test, and optimize, you will be a servant to your organization and its various stakeholders, pushy personalities, and its operational issues. You will be infinitely more likely to be less productive, less impactful, frustrated, unhappy, unfulfilled, and paid nowhere near what you are worth. One skill can fix that.

Start with some good books:

These are going to feel cheesy to you. They may feel weak on facts. They may feel lame or even a little manipulative. It’s up to you to decide what you will put into practice. I’m not suggesting you read any of these books (or any book, for that matter) without a healthy dose of skepticism or desire to challenge the thinking, but you won’t be worse off for reading them. You will find things you will put into practice that will wow you.

After you start selling internally, the next step is to find various internal partners. People who can teach you the ropes of what gets things done, get you to the right people with the right context, and make your selling and influence life infinitely easier.

Don’t make the mistake of thinking irrefutable evidence does its own selling. We spend our days building cases, and really good ones. But everything we do and everything we find needs a sale or it will be sidelined by other, sexier ideas that were sold better.

You need to be competent in the other skills, but if you’re not working hard on improving the one skill that will make you happer, earn you more money, and make you feel more valuable, it might be worth a second look.

Have a great weekend, hopefully with some good reading!

Why a BI mindset can be BS

“All of your data, under one roof.”

So good in theory, but in reality, the marketing promise has written checks the hiney is unable to cash.

Data centralization is something that people have been working on for 50+ years. Our digital data warehouses are only one of the more current attempts at the myriad ways businesses have tried to get it all in the same place so we can see the whole picture. And you know how much of a struggle this effort has been for us in digital? It’s always been that way. It’s actually always been worse.

Unfortunately, the promise that data centralization makes will never be realized. The promise is that centralization will get us away from the clutter of decentralized data and give us one place to build from. I don’t think we will ever get there.

For one thing, the data fights centralization. In all fairness, it is very, very hard to centralize data and link information from our various systems (CRM, point of sale, web analytics, web marketing/advertising channels, call center, etc.) And once the data is linked, it’s incredibly hard to collapse trees of data where metrics don’t match up (web uniques, customerIDs, credit card #’s, email addresses, invoice #’s, TV and radio performance data, coupon redemption, finance metrics, etc.).

This is HARD. Being a CIO would suck.

The reason I don’t think we will get there is because today, linking data and creating models almost always means sacrificing fidelity. Take cross-channel attribution, for example. The problem is that revenue is duplicated across the reporting delivered at the channel level: email reports $10,000 and search reports $10,000, but we only sold $15,000. Many tag management solutions today “fix” that problem by choosing which of the tags to fire, excluding the other tags. But is this a fix at all? We just lost fidelity into the revenue that each channel participates in. If two different agencies manage this media, they just lost some ability to understand how their media influences revenue; they lost some ability to optimize and boost results, because now they are only working with part of the story.

These tools fail to take outside influences into consideration like the creative, changes in budget, visitor history, etc. They teach marketers to assume that media channels intrinsically are “closer” or “further” from the purchase, or more/less related, and that this will be consistent over time. In other words, they suggest that because email was an early touch and search was a late touch last month, it will continue to be that way in the future and that’s how you should optimize the campaigns. Hogwash.

Yes, it is true that guns don’t kill people and tools don’t tell stories; people do. But when we create technologies that “fix” problems, we are stating that what comes out of the tool represents the truth, the whole story, or something like that.

The promise of BI and data centralization is that when information is married and central, we get a better picture of what’s happening and can make better decisions. It’s this fallacy that has led your company’s management to make decisions that make your head spin. It’s that false promise that leads businesses to invest millions in a solution that blurs the focus and liposuctions critical details that will influence decision making.

Am I saying that progress hasn’t been made? No. Immense progress has been made, and data centralization is a hugely important thing. But it isn’t the answer. It’s one of two barrels we need to employ to understand what is happening. The other is the specialist view. The highly trained and sometimes myopic perspective that crafts per-channel, per-market, per-issue solutions. Specialists on their own are highly dangerous. Generalists on their own are, too. It’s only when both fire together and you can take superb, but realistic solutions to market that you win.

This is just a, “where do you spend your time,” idea. If you are investing an inordinate amount of your attention into centralizing, de-duping, common-keying, etc., at the cost of per-channel, per-market, per-idea optimization, I believe this isn’t the best use of your time, as a ninja analyst.

It’s your choice how to tackle optimization. You can start at the central: Omniture Genesis, multi-channel attribution, etc., but you MUST go to the individual branches. You can’t make decisions from the trunk. Or you can start at the branches to find opportunities, but you MUST look at the trunk to make sure that growing one branch won’t tip the tree over.

“Simplification” through centralization is an illusion. What we do is hard, and it’s going to stay that way. It’s just a decision on how we’re going to spend our time.

Get your web analytics t-shirts. Bring the pain to #nerdshirtfriday

I’m going to be getting some t-shirts made up for conferences and other places we can unleash the full measure of our nerdiness.

Before committing, I need to know if there’s some demand out there. Can you comment if you’re interested? If I get enough interest, I’ll get it all set up and post the ordering info here on the blog.

Let me know which designs you like!

#1 – Put that in my review!

#2 – Step it up

#3 – Patriotic Theme

#4 – He said what? Hay-ll no!

#5 – Yeah, take that!

#6 – Inspired by Michael Jackson

#7 – Please sir?


Don’t forget to share this junk with your friends on facebook, twitter, Google+, or whatevs. Trying to gauge interest here. Plus, every time you like or tweet, someone, somewhere saves a tree*.

*I am not positive about this part.

Analytics is everywhere. Take a break from the tools.

OK, last post of the week, I promise.

While I was waiting to cross the street the other day, I noticed something that made me ask the question, “Who in the world thought that was a good idea?”

About 50% of the cars making a right at this intersection were hitting the curb, ruining their pretty wheels, and looking generally silly. For the angle of the turn, the curb was just horribly designed. It extended into the intersection, and for cars to make the turn if a car was coming the other way, they practically had to run over the curb to prevent the front end of their car from sweeping out into oncoming traffic.

I see situations like this all over the place, both in the physical world and on the web. Most of the time, these types of problems seem so obvious, but not to the people who can actually fix the problem or who could have expected / avoided it in the first place. Why is that?

In our industry, we tend to get consumed with the tools we have in our arsenal. We have a flow of data coming in, and like in the Matrix, we tend to watch that flow and look for kinks in the mesh. But there is a lot of data that doesn’t come in table or chart form. A lot of the data that our offering creates exists in a more qualitative sense. It’s there if we’re paying attention to the road ahead, rather than driving by watching the speedometer.

I think a lot of organizations miss out on some pretty high-potential enhancements to their offering, their marketing, to everything they do because they are looking at the dials rather than the road. This particular curb may not cause a “blip,” because maybe there are 9,999 other curbs with no issue. The averages smooth the curve. But it doesn’t mean that there isn’t a significant problem out there, and maybe the charts and tables aren’t the easiest way to see it.

Some of social media’s greatest untapped potential is this qualitative view. People are out there talking. While we are trying to produce an ROI on twitter, we’re ignoring the “curb checks” that our customers are screaming about. We’re stuck on the dials, rather than the road.

Companies spend hundreds of thousands (and often millions) of dollars trying to make the spreadsheet and chart-creating tools better and better, or to get more of these tools. These tools, sadly, will never, ever substitute for looking at the road. No speedometer, odometer, GPS, or anything will ever be good enough to prevent you from missing something big happening on the other side of your windshield.

Of all the tools in your arsenal, I believe common sense may be the best one. How can you use it — and get your company to use it — more often? Take a look through the windshield today. I bet you’ll see some pretty interesting stuff.

Go ahead and shoot your magic bullets. We’ll wait.

It’s been a long wait for the people who simply want to create or improve an offering and add value to peoples’ lives. The world has come up with some pretty great things to help businesses grow without actually doing anything better (and in some cases, like crap SEO content pages unfit for human use, do things worse). Wave after wave of things that will change our business forever have gone by and crashed on the shore. Yes, we got to surf for a while, but then it got more competitive. Noisier. And the change wasn’t forever. We’re still waiting. Right now, we’re wrapping up our wait on social media*. It’s okay, we’re patient.

When you are done with your next best things and your can’t misses, when the smoke blows away and your barrel cools down, you’ll still be sitting there looking at a pristine bullseye. If I looked for the safest place to stand while you brands unloaded all of your budget and efforts on me, it would be right smack dab in the middle of the target labeled, “offer your customers something genuinely better.”

So when the bullets are all gone, put down your arms and come have a talk with us. We’ll still be here. We have some ideas about how to do things better. Realistic ideas. Actionable ones. Ideas with lasting value, the ability to differentiate the offering; ones that may even earn a little trust and respect from our customers that will last.

Each of the efforts you’ve watched dwindle had lasting value, though. It wasn’t the channel that failed. It was the people who thought the channel would somehow take care of things that the business wasn’t willing to take care of. *Social media fits here. Wonderful opportunity for businesses, yet again not a magic bullet. In fact, more of a boomerang bullet.

There are no magic bullets. There is only the hard work it takes to do it better.

Maybe you’re just not meant to make a difference.

You haven’t been trained on that. You don’t have enough direction. People won’t cooperate long enough to make things happen at your company. There isn’t a process for this. Tell me how I prioritize this against the 100 other things I’m doing. That’s not how things are done around here.

Maybe you just aren’t meant to make a difference. Maybe you’re just being paid to measure stuff and produce reports. Maybe.

But maybe you’ve been hired because the company needs someone to stir the pot. They need someone to shine a light that finally unseats the loudmouths who make all of the decisions based on gut feel and steamroll their colleagues. Maybe you have talents that can help your company wake the hell up, unfreeze the crippling processes that have been put into place, rethink how they market and react. Maybe that, instead.

In the real world, there are obstacles. There are stubborn people. There is a lack of direction and leadership. Get over it. You aren’t writing term papers with word limits and assigned reading any more. The grade you get is subjective. That’s life.

As analysts, we are the last people on earth who need “direction” from some internal T-Rex with an agenda. We let the customer do the guiding, not the boss. Learn from what you observe. Craft a plan to put what you learn to action. Make your business react. Don’t let them call you a measurer. Make them call you a leader.

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