Analytics Sunday: Top Ten Analytics Staffer Challenges

If you are an analyst, you can relate to the issues listed here.

Top Ten Analytics Staffer Challenges Within Companies

(Not in any particular order):

Number 10 = Information Technology Misalignment.  If you work in an environment where you require support from your information technology team, oh boy, good luck.  When you have a great IT team, you hold on to them like grim death!  Otherwise, you have to fight around these folks.  I've had great IT teams that supported my group so well you wanted to adopt them into your team!  And, I've been in situations where I hired my own IT-clones because I wasn't getting the support I needed.

Number 9 = Being Alone.  Your company hires you, and gives you absolutely no support whatsoever, right?  You are alone.  This is where Twitter is really important --- you have to act like you have co-workers, and build relationships with psuedo-co-workers.  When you're alone, you can't effectively serve your Executive audience, especially if they have a pre-conceived notion of what your job is or how fast you should do your job.

Number 8 = Systems.  You don't have an analytics system if you use Google Analytics --- rather, Google has a system where they get to know your business, inside-out, if they choose to look at it.  Analysts really struggle to communicate needs to Executives.  How do you tell your CFO that your PC is too slow to run a 100GB dataset through SAS?  How could the CFO ever understand that?  I recently watched as a SAS analyst printed a document on that green and white tractor paper from the 1980s.  Yes, this happened.  This person was leveraging a mainframe computing environment from the 1990s.  It is terribly hard for an analytics expert to convince anybody that the computing environment stinks.  Honestly --- if you can at all afford to do so, buy your own high-powered desktop PC --- you can get a rocket for under $1,200, and connect a 1.5 terabyte external drive to it.  Create your own data extracts from your own crappy database system, and do the heavy lifting yourself!

Number 7 = Competition.  Somebody in your company believes s/he can do your job.  I ran into this at Nordstrom all the time --- there were folks who actively recruited their own analytics projects, or refuted the work my group did with their own database analysis.  Even if you're the only analyst at your company, somebody in IT strongly believes s/he can do your job, and at times, is asked to do your job without your knowledge.  You combat competition by doing the job faster, more accurately, and in a more unbiased fashion than anybody else at your company.

Number 6 = Accuracy.  One of my best analysts made a ton of mistakes, and was not capable of catching the mistakes before the client caught the mistakes.  This is one of those "three strikes and you're out" scenarios ... you make mistakes that other folks catch three times, and you don't have a lot of credibility.  Try hard to tie your numbers, where possible, to an existing report or financial document.

Number 5 = Career Path.  You may not realize this, but there are business leaders conspiring to hold you down ... not on purpose, mind you.  I once had a business leader tell me that I was not capable of doing one other job in his company because I did not have any relevant experience outside of analyzing numbers.  If I could do it all over again, I wouldn't pursue a VP - Database Marketing job with zeal, I'd try to gain merchandising skills, marketing skills, finance skills, store management skills, creative skills, just about anything possible to broaden my horizons.

Number 4 = Politics.  You are not given complete information when you are assigned a project.  In a meeting at 8:00am, two Executives got into a spat about Facebook, one individual said that Facebook customers are worth $124.00 each, one individual said that the social media team should be fired.  At 9:00am, you're asked to demonstrate what the value of a Facebook user is.  You're not told that somebody wants to fire the social media team if your analysis suggests that there isn't any additional value derived from Facebook use.  When you present your findings ($2.00 incremental value), one VP just keeps beating you up over the results.  Well, this person doesn't want to fire her staff, so you are the problem.  Of course, you're not the problem, but you got caught in the middle of a political battle.  Interview the person giving you an assignment, try to ferret out as much information as you can.

Number 3 = Boss Can Do Your Job.  Oh, this one is terrible.  I've been the boss (my poor staff!), and I've been micro-managed by a person who used to do my job.  When you are in this situation, you often have to do each project twice ... do it the way the previous boss wanted it done, then do it the way you want it done.  Always create an appendix --- if your boss demands that your work has to be done a certain way, then do it that way, but refer others to the appendix for a new way to look at the information.  Never give up, never give in.  Your job is to reflect the behavior of the customer, not the methodology of the boss.  Honestly, you can accomplish both tasks.

Number 2 = Misinformation.  What you read on Twitter is not representative of the actual challenges the average analyst faces in a company.  You have few places to get viable information ... on Twitter, a half-dozen or a dozen individuals control the discussion.  Be careful here!  Try hard to get a neutral, unbiased, reasonable view of what is happening.

Number 1 = Sanity.  Your organization is going to take you down an unpredictable path.  When I worked at Lands' End, everything was about measuring the incremental value of advertising (what is today known as attribution).  At Eddie Bauer, everything was about the 'small stuff', how many customers responded to a promotion.  At Nordstrom, it was generally about customer value in a store environment, or the incremental value of an online customer to our store division.  The company you choose to work for shapes how you develop.  If you want a well-rounded experience, if you want to maintain your sanity, you may need to work for 3-4 companies in the first decade of your career.

Analytics Sunday: Profit, Profit, Profit

Dear Analytics Experts:

You are really smart people with a bright future in a field growing in importance.  Seriously.  A recent college graduate, with significant mathematical aptitude, asked me which profession she should consider.  I told her "Web Analytics", of course!  If I were twenty-five years younger, this is the path I would choose.


For the past eighteen months, I actively watched or participated with you in conversations on Twitter.  Continually, I am frustrated by the content.  Not the technical content, that's good stuff, there's tons of good advice out there for the aspiring analyst.


I'm frustrated by the fact that you probably don't have a mentor in your company, a mentor who helps you focus on communicating important results to management.


A mentor is that gray-haired individual who shares war stories with you, helping you get around all of the roadblocks that stall a person with great technical skills.  A mentor is somebody in your company who pushes you to be better, from a business and inter-personal standpoint.


Analysts, for instance, are taught to focus on data, they're taught to make data-driven decisions, to create a culture of analytics.


Your Executive team isn't interested in a culture of analytics.  Your Executive team is interested in profit.

Let's focus on a very simple example.  In this example, you work for a typical $100,000,000 e-commerce brand.  Your analytics skills and your A/B tests yield a strategy that increased conversion rates by 8%.

Now, how are you going to communicate this nugget to your Executive Team?

Here's what you should avoid:

• Avoid conversion rate discussions.  Conversion rate discussions don't resonate with members of your Executive Team.  They simply don't understand what eight percent means ... is that good, is that bad, is it eight percent of a five percent conversion rate (yup) ... if it is, they'll say, "well, that's nothing, what about the ninety-four percent of people who aren't converting, what are we doing about them?"  In other words, conversion rate discussions can become mysterious and mindless strolls into a maze of circular psuedo-logic.
• Don't talk about ROI.  ROI has a negative connotation among many folks --- so many vendors and marketing experts have manipulated numbers to make "ROI" look great.  You don't need to manipulate anything, you're smart, you're savvy!
• Avoid geekspeak.  I know you want to show off your A/B test design, you want to prove that your results are statistically significant at a 99% level, you want to show how you used Omniture to make magic happen.  Not one person on your Executive Team cares.  They do, however, care about the outcome.  So focus on the outcome!  And have your ducks in a row ... write up the results as if your Executive Team cares, and pass them out if questions start flying.

Here's what you can do:

• Focus on profit.  I know, you think profit is some boring outcome of an old VisiCalc spreadsheet that is housed on an IBM AT computer with a 20mb hard drive in the finance department.  It isn't.  Profit is the currency of your Executive Team, your shareholders, or your owner.  DO NOT be seduced into all that stuff about "scale" and finding investors like Goldman Sachs or VCs or whatever ... you work in the real world, and in the real world, somebody has to make money or your business doesn't exist.  So focus on profit.  People understand profit.

We're back to the image at the top of this post.  Your test shows that you can increase conversion rates by 8%.


Convert this increase to profit.  Sit down with your finance team, and demand that they give you the tools necessary to calculate profit.  Unless your finance team is doing something illegal, they will give you enough data to make a rough profit calculation --- it's their job.

In our example, an 8% increase in conversion rate results in $2.9 million in incremental, annual profit.

Your company makes $9.0 million in annual profit.

This means that you, a humble analyst, found a solution that increases company profit by 32%.  Thirty-two percent!  You didn't just find an 8% conversion rate increase, you likely made a contribution to profit that is bigger than any other employee in your company!


That's important!

Make two of those contributions, per year, coupled with good interpersonal skills, and you'll be a member of your Executive Team in no time.


You create a data-driven, analytics culture by demonstrating that your work maps directly to company profitability.



Focus on profit.  Start making a difference with your Executive Team.

Analytics Sunday: United Or Divided

We care about Analytics Systems, because by understanding where we stand, and where our Executive teams stand, we better understand why we succeed or fail.

Take this brief, two-question quiz.  You'll understand where you fall on the grid.  Answer how your Executive team might respond, and plot where they fall as well.

1. When deciding who should receive a marketing promotion, do you like to segment customers into like groups (1), or do you like to create models that predict how likely customers are to respond to marketing messages (9)?
2. When considering a new marketing strategy, do you like to run a series of simulations that predict how customers might behave in the future (1), or do you like to run a series of tests that reveal an optimal marketing strategy to employ against your customer base (9)?

Plot the answer to the first question on the following chart ... (1) on the left, (5) in the center, (9) on the right.

Plot the answer to the second question on the following chart ... (1) on the bottom, (5) in the center, (9) on the top.

If you and your Executive team aren't plotted near each other, you've got the potential for communication problems.

Analytics Systems matter, they represent the difference in philosophy that divides or unites us.


Here's an old-school example:  Catalog marketers often employed a Segmentation/Forecasting hybrid model to measuring advertising effectiveness.  For instance, Lands' End used a segmentation strategy with "current" customers, followed by 0-3 month recency, 4-6 month recency, 7-9 month recency, 10-12 month recency, and beyond in three month increments.  Eddie Bauer and Spiegel used a similar strategy ... "current" customers, followed by 0-6 month, 7-12 month, and beyond.  This segmentation strategy served dual purposes.  First, catalog performance was measured within each segment, and that was important (part of the Segmentation system).  Just as important was a subtle twist incorporated within a Forecasting system.  Customers were locked-into the segment at the start of a quarter/season, and only moved out of the segment into "current" quarter/season status if the customer purchased.

• January 1 = 0 Current Season Customers, 100 0-3 Month Buyers.
• June 30 = 67 0-3 Month Buyers.
• Rebuy Rate For 0-3 Month Buyers = 1 - (67/100) = 33%.

This was a subtle, but very clever implementation of a Segmentation/Forecasting hybrid system.  The analyst knew how customers performed in each catalog (Segmentation --- understanding marketing campaign effectiveness), and just as important, the analyst was able to understand overall repurchase dynamics independent of marketing activities, and understand file strength implications for upcoming quarters/seasons (Forecasting --- understanding and forecasting customer performance independent of marketing campaigns). 


Back in 2001, I was trying to implement an Optimization/Prediction hybrid system, so I abandoned this type of segmentation scheme at Nordstrom ... the decision did not sit well with a faction of individuals who were loyal to the Segmentation/Forecasting hybrid system mentioned above.  Heck, it was as if I just obliterated all analysis reporting in the company!!!  My decision was not popular, at all ... when you and Management are far apart on the systems grid, it doesn't matter how bright you are, you're in big trouble.


Web Analysts frequently run into this reality, just read their tweets, calling Executives "HiPPOs", meaning "Highest Paid Person's Opinion".  You see, the Web Analyst often uses a Segmentation system that is hard-wired into the software solution used by the Web Analyst.  The person the Web Analyst is trying to convince frequently uses a Forecasting system ... take a merchant, who only cares if merchandise sells, pure and simple.  This person isn't evaluated on the performance of an individual catalog, this person is evaluated on customers loving her merchandise on an annual basis.  These are two individuals, running two different systems ... the merchant wants to know what is going to work in the future, the Web Analyst cares about what happened to conversion rates last month.  When individuals run different systems, disagreements happen.


It's really important to know where you stand on the Analytics System continuum.  It's just as important to understand where your Executive team stands on the continuum.  If you're in a similar place, you are united.  If you're in different places, you may be divided against each other.

Analytics Sunday: Which System Do You Run?

Do you know which "system" you run?

In football, there are many offensive and defensive systems.  Defenses run a 4-3 or a 3-4 scheme.  Offenses run a west coast offense, spread offense, spread option, option, wishbone, veer, run & shoot, I-formation, wing-T, on and on and on.

The system you run dictates the talent you need.  The system you run dictates your worldview, it dictates how you approach problems.

The same issues apply to the analytics community.  You may not know it, but you "run a system".  And your system, whether you know it or not, defines your relationship between your co-workers, your Executive team, and your public persona.

The most important part of defining your system is understanding what your primary area of concern is.  You can only align yourself with one of the following two statements.

1. Your primary concern is to understand how marketing campaigns perform.
2. Your primary concern is to understand how customers behave.

You do not measure marketing campaigns to understand customer behavior ... that is fool's gold.  Either you are trying to optimize marketing campaign performance, or you are trying to optimize customer spend.

And mind you, every analyst worth her salt uses pieces of every system.  But her first instinct, when any question arises, is to fall back on her primary system.


If your primary concern is to understand how marketing campaigns perform, then you are likely to run one of the following systems.




Segmentation:  The primary goal of the Segmentation system is to categorize customers into homogeneous groups, for the purpose of optimizing marketing performance.  Segmentation was popularized by the RFM, or "Recency - Frequency - Monetary" system.  RFM is seldom used to understand customer behavior ... you don't ever know what is in the mind of a customer when the customer is a 4-6 month, 2x+, $100 AOV buyer.  You don't know what is in the mind of a customer who is an "Existing Visitor" in Coremetrics.  You simply categorize people in like groups for the purposes of understanding the effectiveness of your marketing activities.  Segmentation has many cousins.  CRM blends automation with segmentation.  PRIZM clusters classify customers into similar lifestyle profiles.  Web Analytics is a modern fusion of segmentation in an OLAP environment.  If the primary goal of your analytics department is to classify customers for the purposes of measuring marketing activities, then you run a Segmentation system.

Optimization:  The primary goal of Optimization system is to use testing to significantly improve marketing performance.  The Optimization system is fundamentally different than the Segmentation system in approach.  The Segmentation system presumes that various segments outperform other segments, allowing the marketer to target a customer with a message.  The Optimization system presumes that testing strategies yield the optimal strategy to pursue within a customer segment.  Instead of believing that marketing is right, the Optimization system believes that testing methodology reveals truth.  If, when asked a question, you immediately think about "testing" the question against another strategy, then you run an Optimization system.  Optimization systems are highly popular among the online marketing community, as well as many in the database marketing community.


Prediction:  The primary goal of the Prediction system is to create statistical models to explain customer response to marketing, or to target individual customers for marketing efforts.  This system is different, in that it believes in one-to-one communications automated by mathematical equations.  Database Marketing, by and large, is a fusion of Segmentation and Prediction, with a skew toward Prediction.  Online Personalization is an outgrowth of Prediction.  E-Mail "relevancy" requires a strong "Prediction" system.  If you build statistical models to rank-order customers for catalogs, you might run a Prediction system.  If you create statistical models to measure online influence, you run a Prediction system.  If you build statistical models to identify customers for telemarketing campaigns, you run a Prediction system.  If you build statistical models to identify the impact of a loyalty program, you might run a Prediction system.  The key is your "first instinct" when approached with a marketing problem ... if you immediately think about models, you run a Prediction system.




If the primary purpose of your marketing activities is to understand the impact of customer behavior on a business, independent of marketing activities, then you are a bit different.  You run a Forecasting system.


Forecasting:  The primary goal of the Forecasting system is to holistically understand how customers evolve and change over time.  Those who run a Forecasting system move outside of marketing campaigns, instead seeking to link macro-level customer habits to current and future events.  The style of database queries is different ... using pre/post periods to understand customer behavior, coupled with forecasts/projections for future customer activity given prior trends.


Now, obviously, you use all four systems in your analytics work.  You skew toward one system, using elements of all systems.

That being said, why does any of this matter?


Well, it turns out that the system you run needs to be in harmony with the system that your Executive team believes in.


For instance, how would you approach answering the following question:

• Conversion rates on your website are down eleven percent in 2011.  What is causing this problem, and what should be done to fix it?

Each analytics system approaches the question from a different perspective.

• Segmentation folks look at conversion rates among buyers, existing visitors, new visitors ... they analyze conversion performance by traffic sources, trying to identify the underlying problem.
• Optimization folks attempt to solve the problem first, looking to test different strategies that might resonate with the customer ... why look to the past when one can fix the problem by influencing the future?
• Prediction folks attempt to solve the problem first, by targeting those who are visiting the site with relevant content.  Or, this system might "equalize" customers using a statistical model, in order to understand underlying problems in past marketing campaigns.
• Forecasting folks combine macro trends with changes in customer behavior across channels, looking to identify trends (i.e. Mobile) that influence the future trajectory of website performance, forecasting the impact of lower conversion rates going forward.

Say your team runs the Prediction system.  Say your Executive team responds to the Segmentation system.  Well, you've got a disconnect, don't you?  Your Executive team wants to understand marketing performance, and you're out there promoting a personalization plan to boost conversion rates.  Ooops.  Or worse, say you promote a Forecasting system ... you are constantly talking about the impact that changes in long-term customer behavior caused by search/social/mobile will have on the business, while your Executive team craves campaign-based KPIs and Dashboard results of marketing campaigns.


Take a moment, and answer each of the following two questions:

1. When deciding who should receive a marketing promotion, do you like to segment customers into like groups (1), or do you like to create models that predict how likely customers are to respond to marketing messages (9)?
2. When considering a new marketing strategy, do you like to run a series of simulations that predict how customers might behave in the future (1), or do you like to run a series of tests that reveal an optimal marketing strategy to employ against your customer base (9)?

Plot the answer to the first question on the following chart ... (1) on the left, (5) in the center, (9) on the right ... then for the second question, plot a (1) on the bottom, (5) in the center, (9) on the top.


Repeat this exercise for the perceived answers your Executive team would provide you.

If you and your Executive team plot out in a similar manner, there's a good chance that you are aligned, that your Executive team understands what you are trying to accomplish.


If you and your Executive team are far apart on this graph, well, that doesn't mean you aren't going to be successful ... but it does mean that you come at problems from different perspectives, and that can lead to challenges and disagreements.  It can, by the way, also lead to spectacular breakthroughs, especially when each side respects the other.




P.S.:  Where do I (Kevin) stand, on this continuum?


I skew significantly toward Forecasting.  This is why my message is so different than what you hear in the Vendor community, is why my message is so different than those shared by leading analytics experts, and is why my message is not always welcome at leading conferences.


You really have to look back at the influences that shape each analyst, over time.  In my case, I was trained as a Statistician, which pushed me toward Optimization/Prediction.  Then I entered the Direct Marketing world, and noticed that Executives are not fans of Optimization/Prediction ... they tend to gravitate toward a Segmentation/Forecasting quadrant that was opposite of my training.  

In other words, I had to change my approach.


I borrowed heavily from things that were done at Fingerhut in the 1980s ... where they used a Segmentation/Forecasting bridge known as the rolling twelve-month file to demonstrate the health of the business independent of marketing campaigns.  I borrowed heavily from Jim Fulton, a former Lands' End co-worker who is now a Database Marketing consultant (Customer Metrics) ... he heavily leveraged the Forecasting system to demonstrate that new customers, not customer loyalty, lead to a healthy, growing business.  I borrowed from Lands' End, who skewed heavily toward a Segmentation/Optimization platform.  I borrowed heavily from Spiegel, a company that used a clever Segmentation/Forecasting bridge (I'll cover this in a future post) to measure file health.  I borrowed from Eddie Bauer, where pure Segmentation systems failed to improve the health of the customer file, pushing me toward channel interactions (Forecasting).  I borrowed from Nordstrom, a heavy Segmentation/Forecasting culture that "ripped the band aid off" on strategies without the need for Optimization.  I borrowed heavily from some of the early Forecasting/Prediction work done at the Santa Fe Institute in the mid 1990s, and borrowed heavily from Mathematical Biology (Forecasting/Prediction).


Notice that the word "Forecasting" keeps coming up in the activities that shaped my consulting focus.  I skewed farther and farther away from a focus on marketing campaigns over time, skewing more and more toward holistic customer migration patterns independent of marketing campaigns.


My experiences skewed me dramatically toward the Forecasting system.  I'm an enthusiastic advocate of moving beyond analysis of marketing campaigns.  I realize not many agree with me.  No worries!


Your experiences skew you to one system, or to a combination of systems.  No skew is right or wrong, all are appropriate.  You simply have to align your system with your core beliefs, and hope that your system is in alignment with those who are tasked with implementing your beliefs.

Analytics Sunday: Chip On Your Shoulder

This is a picture of Aaron Rodgers, the quarterback of the Green Bay Packers.  Today, Green Bay plays Pittsburgh in the Super Bowl.

It's been said that Mr. Rodgers sometimes has a "Chip On His Shoulder".  In other words, from time to time, he'll feel "slighted" when something happens to him, causing him to motivate himself to perform even better to prove others wrong.  He wasn't recruited by a big college (or, for that matter, any college).  He was drafted 25th by Green Bay in the 2005 draft.  He wasn't selected for the Pro Bowl this year, in spite of staggering statistics and a Super Bowl caliber team.  He replaced a beloved legend.  He used these issues to amp his performance, to prove the doubters wrong.  Heck, it has been said that he has a list of every NFL team that chose not to draft him ... he seeks to play well against those teams to prove to those teams that they should have drafted him!


Negative events can define you, or negative events can motivate you.  In Aaron's case, he uses each slight as an opportunity to prove his doubters wrong.  And here he is today, leading his team in the Super Bowl.


You are an analytics expert.  This means that, at some point, you're going to alienate folks, especially the punditocracy.  There are executives, psuedo-analysts, information technology leaders, data mining practitioners, business intelligence leaders, vendors, and web analytics experts who have a financial or political interest in protecting their position in the industry.  You will threaten their position, because you are going to innovate, you are going to offer metrics or analyses that contradict their worldview, undermine their business model, impede upon the message they actively sell to their own community, or diminish their perceived influence in the industry.


Their objective is to shut you down.  The executive won't share your data in important meetings.  The IT staffer won't create the database you need to conduct your analytics work.  The web analytics leader will produce research that illustrates the exact opposite fact that your research illustrates.


When the community attempts to shut you down, you have at least four choices.

1. You can get in line, an do exactly what the punditocracy wants you to do.  In kind, you will earn psuedo-love from the punditocracy.
2. You can fight, tooth and nail, to prove that you are right, and you may or may not win.
3. You can turn the other cheek.
4. You can place a chip on your shoulder.

Right or wrong, I tend to place a chip on my shoulder, though I know it's written that it is wiser to turn the other cheek.  I tend to be motivated by being challenged, motivated to demonstrate what the data is telling us is more relevant than the opinions of a select few.  I can look back over nearly 25 years in this industry, counting all of the innovations that grew from times when somebody disagreed with what the data suggested.


Now, there's an art to being motivated by negative feedback.  If you get negative feedback because what you are sharing is fundamentally flawed, well, then you need to accept the negative feedback.


But if you get negative feedback because what you are sharing stands in stark contrast to the published message of the punditocracy, and your message accurately represents customer behavior, then you've earned the right to soldier on, and you probably need to soldier on in a diplomatic manner.  You can still do this while having a chip on your shoulder.


Be motivated by the data, and the truth!  Don't let an executive or industry pundit squelch you, instead, find a forum to share your message.


An example:  I once worked with an executive who did not agree with the message I was sharing, though the message was going to be very important to the future of the company.  So I created a daily quiz that I shared with the executive team ... I asked a question, gave three choices, then shared the answer revealed by actual customer data.  Did this technique nearly get me fired?  Absolutely.  Was I able to evangelize my message and teach folks how customers truly behaved?  Absolutely!  Did I have a chip on my shoulder.  Oh yes!  I was not going to be stopped, I was motivated to share facts as revealed by actual customer behavior, I was not going to be motivated by a potential hypothesis that fit somebody else's worldview and was only valid for a minority of customers.


If you do your job well, you are going to be excluded, you are going to be criticized.  

Somebody is always going to demand that you "stay in your swim lane".  

If you're going to be forced to stay in your swim lane, you may as well try to win the race.

Analytics Sunday: 25 FAQs For The New Analytics Leader

A new generation of analytics leaders are about to be crowned.  What do they have to look forward to?  Let's find out.


Question #1:  Will management listen to me?  The reality is that some people will listen to you.  Your analytics projects are important, and what you learn is important.  People will listen to you when you craft a message congruent with the needs of the Executive Team.


Question #2:  Will my staff listen to me?  Good question.  If you came from a peer environment (i.e. you were part of a six-person analytics team), and you've been promoted to lead the team, you'll have one set of challenges.  There will be jealousy, envy, anger.  Folks will challenge you because they want to come across as being smarter than you.  If you come from outside the company, you'll encounter a different set of challenges.  You're not likely to be trusted, even with good credentials.  In either case, you will need to earn trust and respect, your title won't guarantee it.


Question #3:  Are all analytics skills equal?  Unfortunately, no.  I see this every day.  Web Analytics experts look down at the old-school skills possessed by Business Intelligence Analysts.  Business Intelligence Analysts complain that Web Analytics experts only know how to analyze one channel.  SAS Programmers are derided by both parties as having skills congruent with those needed in 1986.  Couple that with inter-industry challenges (i.e. the skills required to manage CRM in the casino industry are different than those in e-commerce), and you'll have challenges that will take some time go get over.  Just remember, the core of all analyst experiences is the same ... you're trying to understand customer behavior.


Question #4:  Can I fire people?  If you are going to fire anybody, do it in the first month or two in your new job.  If your team "isn't on your side", if you will, do your absolute best to move people on with dignity, grace, and respect ... because one day somebody will be doing the same thing to you.

Question #5:  Can I give people promotions or pay increases?  Oh boy.  Somewhere in your company, you have a person who is in charge of "compensation".  This person has a set of goals and objectives that are in direct conflict with what you want to accomplish.  This person has a budget, and salary increases cannot exceed what is budgeted, even if people in your department deserve promotions.  Again, do what you can do in the first two months in your new job, because after that, it becomes much harder to reward people for their efforts.


Question #6:  Do I have to work eighty hours a week?  No.  If you aren't going to work eighty hours a week, be darn sure that you are the most effective person on the planet while working thirty-five hours a week ... and you'll set a good example for your team in the process.


Question #7:  Do I have to set a good example for my team?  Yup.  Your team will watch your every action, looking for cues on how to think.  I once had an analyst tell me that my ears got red when I was angry, regardless of what I was saying verbally.  Spend a lot of time setting a good example on how to behave, this is more important than spending time crafting a vision.


Question #8:  Will I get to set my own vision for my department?  You better do this!  I used to go on a "road show", meeting with every important Executive, learning what they were going to focus on in the next year.  I did this in November or December.  Anything an Executive wanted to accomplish that was congruent with my vision became an objective.  Anything an Executive wanted to accomplish that wasn't congruent with my vision became an objective.  Any resources that remained were allocated against my vision for where to take the department and the company.


Question #9:  What's the best way to make a big splash?  It's not by doing the most complicated analysis project that you can do because you're now in a position of authority.  Do something neat and helpful for a member of your Executive team, and let that member evangelize your message for you.  I always wanted to do something analytically sophisticated.  My non-analytically oriented co-workers wanted simple solutions to complex problems.  You serve your company better by providing simple solutions to complex problems.  I know, that's hard to do.


Question #10:  The information technology department won't help me.  That's not a question!  And yet, it's a problem you're likely to run into over and over again.  My advice is to hire a person that is a "database guru" ... not an analytics guru, but a person who can create databases and link database together.  Honestly, you don't need your information technology people as much as you think you need them.  Have your database person create tables and then have her hand off her code to the information technology department.


Question #11:  What happens when Executives won't listen to me?  It's guaranteed to happen.  I recall sitting with a Merchandising leader ... I wanted to cut her catalog circulation way back, because it was terribly unprofitable.  She patiently listened to my argument, then told me that she was going to increase circulation because she needed to grow top-line sales and asked me to have a plan for her in a week, a plan that had significant circulation increases.  Well, you can fight this to the death, you can call this leader a HiPPO (highest paid person's opinion), or you can do your best to optimize her strategy.  I'd suggest that you do the best job you can to optimize her strategy.


Question #12:  How come my budget is so small?  This always amazed me.  As an analyst, I always wanted to purchase software and hardware and seldom had the ability to convince anybody to listen to me.  Then I became a VP, looked at the budget, and realized that nobody was getting hardware or software!  Well, that's not true, there was money to do stuff, just not enough to do what you wanted to do.  This is a good thing.  Software and hardware don't make the company money, hard-working, brilliant analysts make the company money.


Question #13:  Why won't my company let me speak at eMetrics or Internet Retailer?  Because your company believes that whatever you say at this conference will result in your competition acquiring the tools and techniques necessary to outperform your business.  You and I know that this isn't the case, but your boss is not likely to be an analytics expert ... he will not see the world the way you see the world.


Question #14:  How do I learn if I can't share?  This is a common problem.  Companies ram the analytics expert in a dark closet and then toss bread crumbs under the door to keep the analyst fed.  You see this on Twitter all of the time ... the same six Web Analytics or Business Intelligence gurus (folks who work at agencies or vendors or are consultants like me) dominate "the conversation" with their slanted point of view (me included).  Find a non-competitive company, and offer to visit that company for two days of "knowledge exchange".  Surprisingly, people are open to this, and Management is generally open to this.  Take advantage of what you can do, don't worry about what you can't do.


Question #15:  Our longest tenured person is likely to leave, what do I do?  Each situation is unique.  I've generally run across two situations.  In Situation #1, the business or the department is in dire need of a "turnaround".  In these cases, I hire talent from outside the company, because I need to fix things or I will lose my job.  In Situation #2, business is fine, or the talent level in your department is good.  In this case, I'm going to promote from within, and I'm probably going to hire an entry-level person to replace the person I promote.


Question #16:  My staff disagree with my approach to a problem.  How do I get them to do things my way?  Oh boy.  I've made this mistake 22,439 times.  This may go against every fiber in your body, but try to get your staff to focus on outcomes.  If they have to improve search performance by 15%, don't worry about how they analyze the business to get the increase, just worry about the outcome.  Offer suggestions, but don't demand one specific analytical approach to a situation.


Question #17:  My team doesn't get along with each other, what do I do?  This is why you get paid the big bucks!  I once managed two feuding individuals, and no matter what I did, they wouldn't get along.  So I called each individual into my office, closed the door, and then scolded each individual just loud enough to make sure that a couple of people near my office knew I raised my voice, knowing that the two individuals who couldn't get along would be mortified by this revelation.  This only works when both individuals respond to that type of treatment ... other people will break down and cry, or become rampantly defiant.  Each case is unique.  Spend time studying the habits and personalities of each employee, because there simply isn't a right answer to how to treat everybody.  Sometimes team building works, sometimes scolding works, sometimes you wait for a new leader to emerge and solve the problem.


Question #18:  What is my career path?  Odds are that you've hit the glass ceiling.  I recall a CEO telling me that I was only capable of doing my current job, that I didn't have the background necessary to do any other job in the company.  That fact may or may not be true!  At some point, you're going to have to slide out of the analytics career path and try to acquire additional skills, or you're going to bounce out of your company and work for a vendor or on your own.  By the way, analytics staffers are notoriously bad at crafting a career outside of analytics, me included.


Question #19:  Why doesn't anybody understand what I am explaining?  You don't know this, but you're speaking geek-speak!  Your whole career was built off of impressing other analytics experts, so you unknowingly developed a language that allows you to communicate to other analytics experts.  Comments like "... our tagging solution allows us to do on-the-fly segmentation that is helpful in the analysis of multivariate test results" sound great within an analytics community, but are meaningless to a CFO looking to reduce expenses by eighteen percent.  In your new job as a Leader, it's imperative that you communicate like you are communicating to your Grandmother.  Nobody outside of your department understands what you do, but everybody outside of your department depends upon what you do.  Focus on changing your communication style over time.


Question #20:  I presented data like Edward Tufte would present data, and nobody understood my message.  What did I do wrong?  This goes back to communication.  There's a whole bunch of folks that will tell you to never present a pie chart to somebody ... well, that's all well and good unless the person you are presenting to requires data in a pie chart format in order to be able to understand the data!!  Don't do what the gurus tell you to do, do what is right for your audience.  Gurus are selling a solution, gurus are not selling a solution that works perfectly in your business environment.


Question #21:  How many meetings a week should I attend?  How about zero?  Seriously, keep meeting attendance to a bear minimum.  You are more valuable to the company analyzing data than you are attending a weekly committee meeting.  You have staff, encourage them to go to meetings and encourage them to make their own decisions.


Question #22:  Should I analyze my own data, or have my staff analyze data for me?  Oooooooh.  I think my answer is "yes".  You need to have your own projects, you need to stay sharp, and you need to discover changes in customer behavior on your own.  Having said that, you need to develop your people, so your people should get a steady diet of meaningful, meaty projects.

Question #23:  The gurus make it sound like they're brilliant and I'm stupid.  What's going on?  The new analytics leader has a problem.  The only thing she reads are the comments, thoughts, opinions, and pontifications of about seven people.  Here's a tidbit.  The folks working in the trenches are as qualified as the gurus, and in many cases, are more qualified.  The folks working in the trenches simply cannot talk about what they're doing, so it can sound like the gurus are on the cutting edge while the new analytics leader is dealing with simplistic, boring, pedestrian issues.  Never lose confidence.  You're doing work a guru couldn't hope to accomplish, your work simply isn't being published.


Question #24:  How do I know when it is time to quit?  Not many people talk about this topic, do they?  You'll know the answer to this question, trust me.  You may not act upon your instincts, but you'll know when it is time.  First of all, you're likely to be bored.  Second, you're likely to have tapped out what you can accomplish ... in other words, every company has a glass ceiling for what can be accomplished, politics will eventually limit your effectiveness.  Third, you are likely to have a set of "friends" at your level, and when those "friends" are fired or they leave the company, you end up at an important inflection point ... you'll either be re-energized and you'll start over, or you'll know it's time to move on because new leaders don't share your passion for topics you care about.  No matter the circumstance, you will intuitively know when it is time to leave.


Question #25:  How do I quit?  I'd plan ahead.  In other words, you spend a year crafting the next step in your career.  Write a booklet ... it's not terribly hard to write 40 pages, anybody can do that.  Start your blog now, so that you have a year of content when you finally leave your company and need a job.  Make connections on Twitter, not because you're a savant of 140 character communications, but because when you are ready to get a new job, you'll have 443 followers that can help you.  Make your connections on LinkedIn.  But do it ahead of time ... folks can sense desperation when you do it after losing your job, you're a savvy business person when you do it a year before leaving your job.


Ok, time for your thoughts.  Use the comments section or Twitter to state your question and the answer you'd like to provide to the analytics community.