Three Things You Need to Know to Get a Job in Analytics

You would love to work in analytics.

You could tackle lots of interesting and challenges. You could help people by solving their problems. You could change the course of your organization.

The pay isn’t bad, either.

But how do I get there? What should I be focusing on? What skills do I need to have, and where can I learn them?

These are good questions. And the answers are exactly what you need to know to land a career in analytics.

 

Answering the Questions

Let’s look at one of them. “What should I be focusing on?”

As I have gone through interviews, read job postings, and worked in the field of analytics, I’ve noticed a pattern. Three things that seem to come up time and time again. Three things that every hiring manager is looking for in a good analytics employee.

Three things that every analytics professional worth his salt is good at.

They are as follows.

  • Technical ability
  • Business vision
  • A dazzling aptitude for communication.

These are the core. They are fundamental. Make them yours, and nothing will be able to stop you.

 

The Big Three

Technical ability, of course, makes sense. It’s usually what everyone thinks of when they think of analytics. It’s the gritty, in the weeds kind of work.  Writing code, working with databases, and building models.

But it’s only a third of the story. If technical ability is all you’ve got, you’ll be a squirrel for the rest of your career.

And you don’t want to be a squirrel.

Which is why you will want to throw some solid business vision into the mix.

Some people can cut through the waste and get right to the heart of what’s important. Some people know the difference between a task that’s critical and a task that’s trivial. Some people know what the real problem is, and how to fix it.

These people have business vision. And you want to be one of them.

But that’s only two thirds of the story. If that’s all you’ve got, you’ll be a lemur for the rest of your career. And even though a lemur is better than a squirrel, you still don’t want to be one.

Which is why you will learn to be a brilliant, bright, and vivid communicator.

Why? Because you have important things to say! You’ve done the work to answer hard problems!

But if you don’t tell anybody, it doesn’t matter. Or, if you tell people and they don’t understand you, it still doesn’t matter. The only way analytics ever matters is when something valuable is communicated. Otherwise it’s all a waste.

And it would be a tragedy for you to waste all your hard work.

 

In Summary

There they are. The three critical elements. Hold them close to your heart and never forget them.

  • Have technical skills.
  • Have business vision.
  • Communicate brilliantly.

If you can manage those three things, you will never be without a job (a good one).

If you are ready to start developing these skills, you may want to have a look at this E-book.

 

The Blueprint For Any Analytics Project

Having a blueprint to follow helps you see the big picture, stay on track, and know what to do next. Being by nature complex and time consuming, you need one if you are going to succeed in the field of analytics. Here is the thought process I go through for any project I am doing (or thinking about doing).

ONE:
What is the strategic goal of the project? (Who will benefit and why?)

TWO:
What is the measure of success? (How will things be different if we succeed, and how can we observe that?)

THREE:
Who (or what) will the output be designed for? (what information will be conveyed, and to what end?)

FOUR:
How will the output be designed?

FIVE:
How will the output be delivered?

SIX:
Detail the method of analysis?

SEVEN
How will it be done efficiently and automated? (what are the tools and how frequent is the analysis done?)

What It’s All About: One Rule to Save Everyone Around You From Dying of Boredom When You Talk To Them About Analytics

Analytics is about one thing: Intelligence. That is what we as analysts work long into the night for and torture thousands of rows of data for. It is the gem, the perl, and the sapphire of our very existence.  It is the end all and the be all.  Show me an analyst that has a tenacity for producing large volumes of relevant and important intelligence, and I will show you an analyst that will always be employed, praised, and loved.

 

Perhaps we should spend a moment defining what analytics is not.  It is not a fancy graph, a lot of graphs, a lot of fancy graphs, statistics, methods, data tables, data bases, SQL queries, excel spreadsheets, pivot tables, or anything else that makes people instantly die of boredom at their mere mention.  Analytics uses those things.  But the end result of analytics is not those things.  They are merely vehicles.  And vehicles for what?  Can we guess?  Vehicles to deliver intelligence. Don’t get caught up in the vehicle.  Be familiar with the vehicle and how you can use it to produce intelligence, but never forget what you are really doing.  What your true purpose is.  The product is the intelligence, not the graph or the speadsheet.

 

Remember this one rule and you will save hours of frustration for yourself and an insane amount of irritation from everyone you work with.  They don’t care about your analysis.  They don’t care how hard it was.  They don’t even care about how amazingly close to 100% your Pearson’s coefficient came.  They only care about their questions getting answered.  They care about increasing their intelligence.  And as an analyst, so should you.

Why Learning SQL Will Change Your Life

Working with all your data in Excel is like trying to manage all of the financial transactions of a large bank with a pencil and paper.  Grossly inefficient, prone to errors that are hard to fix, and with the technology available to us today, rather unwise.  I think one of the reasons better analytics hasn’t taken hold in our organizations is because we are all still trying to use Excel for analytics and data processing.

To be a valuable analytics professional, you have to know how to leverage SQL.  You will be able to do things you’ve never imagined possible trying to pound things and program things and automate things in Excel.

SQL is the de facto standard coding language that is used to communicate with databases.  With it, there is no more downloading endless worksheets, manipulating them by hand, and doing complicated analysis that is not easily reproducible.  You can communicate directly with the database, extract exactly what you need, and because your procedure is there written in code and not lost in a series of manual copies, pastes, and filters, you can re-run it at any time and easily find and fix errors in your process.

Don’t get me wrong, Excel is still good for specific, limited cases – but if you are doing any kind of analyics that requires downloading data and taking that data through certain manipulations, you need SQL.  You will be so much more efficient you’ll wonder how you ever survived without it.  It is well worth the small learning curve to get acquainted with it.

And I wouldn’t just recommend it for analytics professionals.  If you are a marketer that knows how to use SQL, you are probably part of 1% of marketers who do, and with that ability you will be able to do analysis and make discoveries no one else in your organization can.

Take some time and learn SQL.  I recommend these free tutorials from the w3 schools.  Insane efficiency awaits you.

Marketing & Sales: Frenemies No More

If you’re a solopreneur or small business, marketing and sales are one and the same thing. As your company grows, the work starts to get divided up. Marketing focuses on building brand awareness, stimulating interest, and generating leads. Sales works to turn prospects into paying customers.

Because sales people want a sale now, and marketing is working to build long-term brand equity, the timelines, goals, and outcomes are different enough as to make the teams basically ignore each other at best, or work against each other at worse. They are both friends and rivals. In a word: frenemies.

But Sales and Marketing share the same goal: increase revenues. Nothing else matters. As a company, there are 4 main ways to increase your revenues.

1. Increase your number of paying customers
2. Increase the quantity of goods or services a customer buys per transaction
3. Increase the price per transaction per customer
4. Improve retention rates of existing customers and increase the overall number of purchases they make with you over time

To achieve these, Sales & Marketing must bury the hatchet of frenemyship and become Smartketing. To achieve this level of nirvana, the two teams must sit down and define the customer lifecycle, mapping the major points along the timeline:

Customer path from awareness to purchase to referrals

This enables both teams to know exactly where they stand in terms of increased revenue today, next week, and 6 months down the road.  They can better forecast where they’ll be, identify chokepoints in the process that cause lost revenues, and work more closely together towards goal achievement.  In the next post, we’ll discuss more about these goals and how to measure the impact of micro-conversions in the customers buying journey.

One Must-Know Excel Function to Save Hours of Time

The Vlookup

Have you ever had to compare two lists of email addresses? How about flag a certain set of products out of a large transaction list? Or maybe mash up a few lists of ugly-looking customer IDs? This function will allow you to do all of it!

Lets take the example of two lists of customer IDs that you need to mash up to see which customers are members of both lists:

Vlookup 1

Rather than reading through and trying to spot the matching pairs, we will employ the powers of the Vlookup to show us where the matches are. This will produce the following result, where the matches are called out:

Lets have a look at how to set this up:

Vlookup2-1

Notice that there are only 4 elements in our unassuming Vlookup:

  • The first item is the cell value we want Vlookup to check for in the lookup-list (in this case, A2)
  • The second item is the lookup-list Vlookup will use to check for the first value you specified (in this case, D:D)
  • The third item tells Vlookup which value from the look-up list to return. Here we specify column 1 of the list (because, after all, there is only one column)
  • The forth item you should almost always enter “False”. This tells Vlookup to match values exactly.

Not so bad. Now just double click the little black box in the lower right corner of the cell to fill in all the cells below:

Vlookup2-2

Viola! You have found the matches! Now all that’s left is to distill down that big ugly list to the matches you care about. Drop a filter on it:

Vlookup5

And de-select that uncouth #N/A:

Vlookup6

BOOM! There are your customers who attended your webinar and are subscribed to your blog.

Vlookup7

Congratulations, you are well on your way to saving yourself hours of time. Before we move on to the next 2 functions, however, let’s take a look at one more awesome capability of our friend the Vlookup.

Remember that we mentioned you could return a different value from the lookup-list? Let’s see how that works – we will modify what our starting example looked like:

Vlookup8

Notice that our lookup-list now has two columns – the customer ID and the engagement that they showed during the webinar. What if we want to know which customers are members of both lists, AND now how engaged they were on the webinar? We can do that. Let’s modify our Vlookup:

VlookupFormula2-New

Notice two changes to our first Vlookup:

  • We’ve expanded our lookup-list to contain both columns instead of just one (D:E)
  • We’ve specified Vlookup to return the second column of the look-up table with the number 2

That’s it, we’re done. Just fill the formula down and you’ll have your matches showing webinar engagement.

Vlookup10

Excellent! Our advanced implementation of Vlookup was a success.

Using the ‘Perfect Information Concept’ for Smarter Analysis

In the presence of perfect information, there is no need for analytics. In fact, the whole aim of analytics, with all its methods, models, algorithms, and statistics, is to get as close to perfect information as possible (or, better said, as close as necessary). The analytics professional would do well to keep this aim in mind, as it has a way of focusing the analysis and getting rid of any wasteful processes.

Let’s take an example – one that illustrates well the value of aiming for perfect information. We will have a look into the field of web analytics.

Suppose we were just given access to a large set of web analytics data for a company that sells pugs (yes, the animals), and they want you to ‘optimize their website for pug sales’. The novice analyst, without any regard to what the phrase ‘optimize for pug sales’ even means, would no doubt jump right into the boundless data set and get lost in a fog of pageviews, unique visits, time on site, and convoluted navigational summaries. But this is not what is going to happen to us, because we are going to pause, consider what the concept of perfect information can tell us, and then dive into an effective session of analytics with the aim of selling lots and lots of pugs.

A Simple Question

First, we’ll consider a question. If we could know everything we needed to know in order to optimize pug sales, what would we want to know? Don’t be realistic here; remember, we’re discussing the inherently unrealistic concept of perfect information. Here’s what my pug selling informational paradise would look like:

I would know every single person in the United States of America who wants to own a pug. I would know them by name, where they lived, how much they were willing to pay, and how many pugs they would want. I would know the best time to reach all of these people, and I would know what method they would prefer to be communicated with, whether that be phone calls, direct mail, digital media, personal visits, smoke signals or carrier pigeons. I would know what motivates their love for pugs and what need having a pug would fulfill in their lives. And remember, I would know all this about each person individually. Further, I would know, by name, each person in the United States that could be persuaded into buying a pug. I would know all of the above information about them, along with what exactly it would take for them to make the decision to take home a pug, whether that be an Internet advertisement, a informational video, a seminar, a mailer piece, a discussion over a croissant lunch, or whatever else. I would know this about each and every one of them.

Phew! What a paradise! Unfortunately (or rather, we might say, fortunately), it is impossible for me to know all of these things. Getting all this information would take an infinite amount of time, and that’s to say nothing about keeping it updated. So what are we to do? We’ve taken the proverbial journey into pug selling informational heaven, only to realize we’ll never achieve it in this life.

But we don’t need to. The picture of everything we would want to know, in a perfect situation, is a useful framework to use as we start to list out all of the things that we can know.

We can’t know by name everyone in the U.S. that currently wants to own a pug. But what indications can we use to find and get to as many of them as possible? One – people searching for anything related to ‘pugs’, especially ‘buying pugs’, in search engines, will be an attribute of these people. Optimize for these terms and put up some relevant search engine advertising. Two – people looking at dog sites or pet sites, like petfinder.com, are also in the market, perhaps, for a pug. Advertise on these sites. Three – people that find your site and actually take time to browse the ‘pugs for sale’ pages or start entering in information on your shopping cart. Optimize this process for them. And on and on.

Above, we talked about persuading people to buy pugs – about knowing what it is that will convince them to make the buy decision. We can’t know this for every single person, but we can know it for certain types of people. I’d start by doing some analysis of the various past blog posts that the company has put up – which ones have been the most popular, induced the most checkouts, and brought the highest level of engagement? What were they about? You may notice that they were about finding companionship, or about giving a dog a good home, or about guarding your house from intruders (a stretch for a pug, I know, but perhaps they’d be effective at keeping away small cats). These are key findings into what is motivating people to buy pugs. (And if such a blog with diverse topics doesn’t exist, perhaps it’s time to start one. . .)

Conclusion

We can go on and on here. But I think the point is clear – start with what you would know in the perfect scenario, and work your way backwards into what you can know with the available resources and data. As you do this, you’ll notice that the individual information we knew in paradise will manifest itself as understanding certain groups of people with similar tendencies, or customer segments. These segments are the currency of effective analytics.

Please also take note that we did not actually stay within the realm of web analytics click-stream data as we were doing our analysis. This is important! Just because you have endless web analytics data doesn’t necessarily mean it is the most useful way at getting the information you need to know. Don’t let yourself get cornered – understand what you need to know, and then use the best resources available to you, whatever they may be, to know it.

Perfect information to knowable information to the data resources available for knowing it – that’s the thought process. If you do it the other way, you’ll soon find yourself spending many hours sifting through a sea of data, only to come up thirsty for insights. Don’t let that happen to you.

Learn Analytics From Stanford, For Free

Analytics is a hard subject to learn well.  It involves the bringing together of several disciplines, such as statistics, business strategy, machine learning, data base management, computer science, etc.  It will therefore be necessary for the analyst to have a firm grasp and understanding of all of these topics and more – a daunting task, to say the least.

What if you could get Standford level learning and course material on these topics to help you become a better analyst.  And, what if you could get it without cost?  The world would be your oyster.

As it happens, today is the day the world becomes your oyster.  Stanford has started to leverage the capabilities of the internet to bring high level course work and education to the masses – for free.  And what’s more, the classes they are currently offering happen to be highly correlated with those skills necessary to be good at analytics.  Some of the free courses offered include Machine Learning, Introduction to Databases, Natural Language Processing, and Programming Methodology.

This is an awesome opportunity.  All you need to bring to the table is your time and some effort – the knowledge and course structure are there waiting for you.  Check out a class listing here, and the database class here.

Kudos, Standford.

 

Analytics People Will Pay For

What kind of analytics are people willing to pay for?  That’s a fair question – it shows, perhaps, where there is greatest need and where one might successfully start up a business.  Let’s answer that question with a little analytics ourselves.

First, we need a data set.  Where might we find data on the kinds of analytics people pay for?  Doubtless there a various choices, but I think it would be wise to have a look at the current top analytics vendor, which, as anyone in the analytics space knows, is SAS.  Assuredly, they must have some customer testimonials and success stories, from which we can extrapolate the kinds of analytics being sold.

We are not disappointed in this assumption; in fact, we are pleasantly surprised.  Have a look at the SAS customer testimonials page.

That’s a TON of analytics success story data; and what’s more, it’s already structured into a nice data format!  Scrapping a little bit of data from the page, we can produce the following table, which shows the number of customer success stories by industry-

WOW!  That’s 665 accounts of people paying for analytics (and being happy about it) across 23 industries.  I believe we’ve found a rich data source for understanding the value that analytics has and how we can in turn apply it to solve real world problems.

We could go and have a look at all 665 successes to get an extremely broad overview of the space, but since life is all about decisions and segmentation, I’m going to leave the banking industry to SAS and have look at some of the more interesting verticals.  There are some fascinating things here.

Google’s Pricing Strategy: The Automated Auction

Let’s take a moment and consider one of the more interesting of the pricing models – the auction.  And to see this model at its full potential, we’ll want to analyze the real world example of the company that got auctioning right, and is currently leveraging it to pull in billions of dollars every quarter: Google.

Yes, everyone’s favorite search engine is a giant auctioning machine.  You sign up for Adwords, put in a maximum bid, and BAM!  You’re in the largest and most sophisticated automated auction in the world.  There are many beauties and subtleties of the system.

Subtlety One

One, there is almost infinite segmentation.  If you can search for it, you can put an ad up for it – and with people searching for things like “pugs for sale in east sussex”, “ornithine transcarbamylase deficiency”, “my hamster is afraid of me”, and everything in between, there’s really no limit.  So how do you efficiently price an infinite amount of different ads? The automated auction.

Subtlety Two

A second aspect of Google’s Adwords sales is that the transactions need to be made quickly and effectively, because so many of them need to happen daily (on the order of billions per day, I’d estimate).  You therefore must have the auction automated, or else you could never hope to reach the scale necessary to accomplish the task.  So what else does the automated auction do for Google?  It provides the ability to scale.

Subtlety Three

What about a third aspect?  How about the fact that it’s impossible to know what the actual value is for a given ad space for a set of keywords at any given time?  How do you set a price for something like that, especially when it’s changing so often?  Again, the answer – the automated action.

It really is a genius concept.  But now we must be dutiful innovators and ask ourselves, what else could the automated auction be applied to?  Or, what other markets and product sets have the same kinds of characteristics as the three we just enumerated above?

Insurance

How about the insurance market?  Do we see infinite segmentation?  I think we do – you have people’s age, socio economic status, yearly income, health levels, gender, risky behaviors, etc. etc. etc., and all the combinations of all of those things – all of which could be used to determine a premium for health insurance, car insurance, home insurance, life insurance, etc. etc. etc.  Do transactions need to be made quickly?  Perhaps not on the daily basis that we see happening with Google, but with the number of people that could use an insurance policy (everyone) and the frequency that some of the above factors could change, it may be useful to have a system that would allow for more transactions to take place without demanding extra time.  And the third – impossibility of knowing the exact value of any given set of insurance policies for a given individual.  That one is in the bag – even with all of the sophisticated data crunching algorithms the industry uses today, we still haven’t quite been able to pin it down.

I wonder how the concept of the automated auction could transform the insurance industry.  (or any other industry, for that matter, that has a semblance of these same three characteristics.)