How to Acquire Your First Million Customers - The Book

As you may have noticed, I haven't posted here in a little while. That is because I was busy releasing How to Acquire Your First Million Million Customers, a book on how to grow your site to a million customers or more.  The book is now available on Amazon and we have a site up with a blog on growth hacking, digital marketing, and other customer acquisition tips.

I would encourage you to check out the book and the new blog.  The new blog will be more active going forward than this site.

Ken

Overconfidence in Analytics – Why You Need to Dig Deeper into How Your Analytics Platforms Work

Marketing professionals and analysts often exude a level of confidence in their data that is misguided.  I regularly see people state with confidence what their return on ad spend is on a given campaign down to three decimal points.  They will tell you that their analysis is based on Google Analytics or a similar web tracking tool and a certain attribution tool.  Yet, they fail to state that these tools have a significant margin of error.

For example, Google Analytics and similar tools have a host of issues:

• Missing data. Look at the list of transactions as recorded in Google Analytics and compare that to what you see in your transactional system.  You will be surprised by how much is missing in GA and at some of the bogus transactions GA records.
• Malformed or bad referral sources and URLs. Look at the sites that supposedly refer traffic to you and will see all kinds of odd sites that don’t exist.  Look at the top pages on your site according to GA and you will find pages that don’t exist. 
• Self referrals. Ever look in GA and see that one of the main sources of traffic is your own site?  If so, you are polluting your data and overwriting valid traffic sources.

Attribution engines are even more fraught with issues especially depending on your implementation.  For example:

• Missing sources. Are you looking only at click conversions or click and view through conversions?  A lot of folks only consider click conversions which leaves out a lot of information.  If you are looking at view through conversions, are you tracking all of them or just view throughs from one ad network?
• Overconfidence in a given attribution model.  Do you have the analytics to prove that the attribution model you picked is the right one?  How do you really know which touch drove the conversion and therefore should get the credit for the sale?
• No consideration of offline. Do you have offline marketing that isn’t factored into your attribution model?

Do yourself a favor and learn how the internals work on whatever analytics platform you use.  The more you know, the better you will understand how much confidence you can have in a particular analysis. And ignore the vendor hype – none of analytics tools work as cleanly as the vendors promote.  Get over it.

Do these issues mean you should give up on analytics?  Absolutely not, but stop reporting on data down to three decimal points and make sure that whoever consumes your analyses understands that there is a non-trivial margin of error.  Said another way, use the data directionally.  The data is not dogma so keep your common sense engaged when thinking about the results.

Bringing Data to the People

One of my big themes lately at TeamSnap has been "bringing data to the people."  Our approach has been to push broad swaths of the company to immerse themselves in the data through better tools and a lot more training.

As I have talked about before, we leverage Tableau to push analyses down to marketing, bus dev, and product people.  It allows relatively non-technical people to run sophisticated analyses without any knowledge of the underlying data.  What is really amazing is that the underlying data is often coming from multiple data sources in the cloud and is joined through some sophisticated data hygiene and migration techniques that these business users don't need to know about.

Over the past two days we pushed harder on a different technique to democratize our data - training.  We did an intensive 2 day training session on Google Analytics with Analytics Pros.  It was a huge commitment in money but more importantly in time.  We had a wide range of employees from across the company attend.  It was amazing to see people really get the religion about how much you can improve your product through better data and that you don't need to rely on the data scientist types to get you data.  Google Analytics is incredibly powerful, but also very usable by non-technical folks especially if they some training.

One of the key takeaways for a lot of people was how flexible Google Analytics is.  I often say that Google Analytics is a platform, not a tool.  It can be whatever you want it to be if you know all the ways it can be used. 

Upon the conclusion of the training, my main advice to most people was to find one or two ways to integrate what they learned into their daily routines.  If you manage social, build a report or a dashboard or a segment that drills down into social.  Have that report or dashboard show up in your email box on a regular basis so you can make decisions from it.  Then tweak that report or dashboard over time to improve how you manage social.  By taking these small, incremental steps to analyze the data yourself, you set yourself on the path toward data empowerment.

Are you investing enough in the analytical skills of your rank and file employees?

Slicing and Dicing Your Way to Paid Advertising Nirvana

Digital marketing professionals who work in paid online marketing often get so focused on campaign and targeting level data that they forget to dig deeper and find the really interesting matches between those campaigns and their customer segments.  By doing so, they waste large amounts of their marketing budget. 

Let's say that you are running marketing campaigns in AdWords.  You almost certainly will be looking at your CPA (cost per action) at a campaign, keyword, and a device level (desktop, tablet or mobile).  If you know what you are doing, you hopefully have also thought about whether you want to target men or women and what age groups you want to go after.  In addition, with luck you thought about your geo targeting as well as whether there are certain times of the week when your product is going to sell better.

The next step, and it is a big one, is to link AdWords to your back end database so you can track campaigns and keywords all the way through to what customers spend over time.  How much do you spend on a given keyword compared to the LTV (lifetime customer value) you get from customers coming in on that keyword?  Were these new customers or existing customers coming back again?

Now most folks are thrilled to just get this far (and only about 5-10% of the folks I speak with make it this far).  However, the reality is that the fun is just starting at this point.  On the back-end you probably have additional information about your customers.  For example, their industry vertical, the product they purchased, their role, etc.  So you now have the following dimensions to slice and dice your data by:

• Campaign
• Keyword
• Ad
• Device type
• Geo
• Gender
• Age
• Time of day / day of week
• New customer vs returning
• Industry vertical
• Product purchased
• Role

Here is where the fun begins.  Most people look at the dimensions one at a time.  They optimize the ROI for a given keyword or they optimize the ROI for a given campaign.  However, the real efficiency gains can be had from analyzing multiple variables at once.  Maybe certain keywords only work with certain devices at certain times of the week for certain age groups.  Maybe certain keywords play better for existing customers who are interested in a specific product whereas other keywords work well with new customers in a given industry.  If you haven't looked at the data at this level, you are missing key insights.

When you start dicing up your marketing spend at this granular a level, you start to realize that have an enormous amount of fat in your marketing spend.  You will find certain combinations of the above dimensions where the ROI is extremely poor and you will find other segments where the ROI is excellent.  If you take this approach, you often realize that 10-25% of your marketing budget is completely ineffective.  Rebalancing the budget to double down on the most impact programs can have a huge impact.

Analyzing your data at this level definitely isn't easy.  This approach takes a tremendous amount of work to set up and process.  However, think about it this way.  If your manager was to come to you and offer you an increase of 10-25% in your paid marketing budget, you would probably be ecstatic.  If you slice up your data enough to find 10-25% fat in your budget, you are essentially getting a lot of extra money to spend each month.

Wrangling the Google Analytics Sampling Beast

After reading my last post, you have determined that you are really struggling with sampling in Google Analytics.  Now what do you do?

Your first option is to stick to the main reports on the left hand rail of Google Analytics.

The reports under these main categories rely heavily on roll-up tables in Google Analytics and therefore generally do not have sampling.  However, once you start adding secondary dimensions, filters, or custom segments, all bets are off and your sampling is likely to come roaring back.  While this can be very limiting, using these main reports is an option to remember.

A second option is to reduce the amount of data you are looking at, particularly by reducing your date range.  On the positive side this is a quick and easy way to deal with sampling.  The negatives are that this is really time consuming if you want to look at a large date range.  Second, you have to reduce your date ranges as your site grows.  Lastly, for some queries (e.g., unique users within say a month or a year), you may not be able to use this trick.  If you are looking at monthly unique users, you cannot chop the month into two date ranges because you don't know how much overlap you have between the two periods.

A related option is to use Analytics Canvas.  This tool basically takes the date range trick and automates it for you.  You can tell it to take a Google Analytics report for the past year and split it into 365 separate reports, each one day in length.  This is highly effective in dealing with sampling but again doesn't work for metrics like unique users.

The most comprehensive solution for dealing with sampling is upgrading to Google Analytics Premium.  There is no doubt about it - it is an expensive product at $150,000 per year (as of the time of this article).  In addition, to really exploit the data in GA Premium, you need to learn BigQuery, Google's Big Data tool.  If you know SQL, this isn't too hard, but there are some quirks for sure.  All these cons aside, the data that you can get out of GA Premium is amazing.  GA stores your data at a very granular level behind the scenes.  If you have worked with the GA API before, you will have seen a taste of what GA stores although the BigQuery interface to GA Premium exposes a far more extensive set of data that GA is collecting.  It can take some time to wrap your head around the data, but it gives you an almost limitless number of analyses you can run about your customers.

Sampling in Google Analytics can be a real drag, but now hopefully you will be able to wrangle that sampling beast into submission.

Using the Presidential Election to Understand Google Analytics Sampling

When I coach people on how to use Google Analytics, I see one issue come up over and over - not understanding sampling.  In many cases Google Analytics will use a sample of your data instead of all your data when you run a report.  Is that bad?  The answer is not necessarily.  However, you need to understand sampling to be aware of the potential impact.

The way I explain sampling to folks is that when you see poll results for who is leading in the presidential election, the pollsters are only calling a sample of the 220 million eligible voters in the U.S.  They often only call several thousand people.  As long as those people are representative of the overall voter base in the U.S., you can accurately predict election results with a very small sample.

If you have a large site, you have a lot of data in Google Analytics.  Some reports would take a very long time to run if Google were to use all your data.  Hence the use of sampling.

One of the big issues with sampling is that Google doesn't tell you the statistical significance when it uses sampling.  Think back to the election polls.  If you read the fine print, you will almost always see something that says, "Margin of error for this poll is +/- x%."  Often the margin of error is just a few percent, and it gives you confidence in the results.  However, Google Analytics doesn't tell you the margin of error.  If you have an event that doesn't happen very often, the margin of error can be huge.  One of the first things that a lot of people notice when they have sampling is that their results are very inconsistent.  They run a report one day and then the next day and get very different results.  Similarly, if they pull the same data using two slightly differently approaches in GA and get very different results, they likely have a lot of sampling going on and a large margin of error in the results.

The first thing to check is how much sampling Google is doing.  If you see a message like this in the upper right hand corner of your report, you have sampling going on.



So how much sampling is bad?  A lot of people say that below 5-10% and you have a problem.  However, it really depends.  In the presidential polls, they only call a couple thousand people out of 220 million people.  That is ~0.001% sampling, well below the rule of thumb some people call for in Google Analytics.  However, in the election polls the statisticians can calculate the margin of error.

The margin of error depends a lot on how often the event you are measuring happens in GA.  For example, let's say you are looking at device category under the mobile overview.  Because there are only three choices in that report (desktop, mobile, and tablet), each session has a device, and each category shows up fairly often, you can have a low sampling percentage and you can still be confident in your results.

However, let's say that Google Analytics is using 10,000 data points in your report, and you are trying to estimate the frequency of a rare error message on your site.  Let's say your error only happens 1 in every million times although you don't know that yet.  The problem is that in this case GA is almost always going tell you that this event happens 0% of the time because the odds of the event happening in your 10,000 data points are very low (10,000 / 1,000,000).  When GA does see the event in your sampled data, it is usually going to say the event happens 1 in every 10,000 times because it has 10,000 data points and it sees one occurrence. However, both answers, 0 and 1 in 10,000, are way off from the truth.  Sampling is a huge problem in this case.

In the next post we will talk about your options if you suspect sampling is impacting your GA results.  Until then watch for the yellow sampling message in the upper right hand corner of your GA reports to see how much sampling you are experiencing.

How Big Is Your Analytics Team?

As Chief Growth Officer at TeamSnap, I am often asked how big our analytics team is.  TeamSnap has about 10 million customers.  Those customers are highly engaged in the app so we have a lot of information about what is happening in recreational and competitive sports, especially youth sports.  We have 75 employees and are quite analytical as a company.  Yet, we only have one full time data analyst.  Instead we have taken a bit more of a distributed approach that I will describe.

Our basic philosophy is to get different people around the company engaged with the data.  We do that by creating simple interactive dashboards that show our progress against targets.  For example, in business development we have a dashboard that shows how we are doing in working with other partners to drive business to TeamSnap.  The dashboards are so closely tied to comp plans that we use the same data to figure out quarterly bonuses.  At the same time, the purpose of the dashboards is to allow people in those functions to interact and drill down on the data.

What we typically do is allow the end users to filter the dashboards by a variety of factors including geo, sport, type of customer, etc.  If our business development folks are working with soccer partners in Arizona, they can isolate our performance in soccer in Arizona.  Most of the analyses are actually much more complex and deeper than that example, but the interesting thing is that they are being run by business folks who are totally abstracted from the underlying data.  They are just using a simple point-and-click dashboard.

By empowering the team with these interactive dashboards, we tend to pull out far more conclusions from the same sets of data.  We have a lot of eyes on the data and different folks approach the data different ways and ask different questions.  I often tell the team, "With these dashboards you can slice the data almost an infinite number of ways so your findings may vary." Sure enough the team often teases out all kinds of insights that we never would have seen with a more top-down driven approach where the analytics team spoon fed the data to the business team.

Several of our most analytical people in the company are ones who had little to no prior experience in digital analytics.  Many of them came out of sports backgrounds or creative professions.  It always puts a smile on my face when someone who does not have a formal data analytics background incorporates some great analyses into their work.

The vast majority of our dashboards are created in Tableau.  We love the way it dynamically syncs to various data sources and presents the data to users in a way that is comfortable for anyone who has used Excel before.  While Tableau isn't cheap, it was a much better fit for us than the half a dozen tools we tried before.

So I circle back to my question of how big our analytics team is.  Our goal is to think of our analytics team not as our one data analyst, but rather as our entire employee base. We want every employee teasing out great insights from the data and integrating data into what they do.

Want to Improve Your Paid Ads? Get out of Your Ivory Castle!

Last blog post I argued that it was pretty hard to become a slave to data.  While I strongly believe that, I also believe that sometimes you have to put your analytical tools aside and get out into the real world.  Leave your ivory castle!

Paid advertising teams often are very analytical folks.  They speak in the language of CTRs (click through rates), CPAs (cost per acquisitions), view through conversions (conversions that came from folks who saw ads but didn't click on them), etc.  Many spend their days analyzing numbers, moving money across keywords, ads, campaigns, etc.  I too spend a tremendous amount of time looking at these metrics and often say that my job is akin to being a portfolio manager.  I look at numbers much of the day and move money around between projects to optimize our spend.

However, I feel strongly that marketing folks, and in particular paid advertising types, need to go out into the real world and mix it up with customers.  One of my favorite ways to do that is to go to tradeshows.  I try to go to roughly only show a month.  People (especially my wife) often ask me, "Why are you going to so many shows?  Don't you have people who can run the shows for you?"  Of course I have very competent team members who can run the shows.  However, I go to listen to our customers.

I go to the booth and ask questions.  I often don't do a lot of talking myself.  Here are some of the questions I ask:

To Existing Customers:

• What do you like best about the product?
• How did you find out about us?
• What other solutions did you consider?

To Potential Customers:

• What are the biggest challenges you face today?
• What options are you considering to address those challenges?
• How are you researching those options?

My favorite scenario is to have one or more existing customers in the booth try to sell some potential customers.  This happens a lot with TeamSnap because customers generally love the product and are pretty vocal about singing our praises.  I just sit back and listen to how the existing customers sell the potential customers.  They often can really crisply describe the benefits of the product.  Best of all, they use language that potential customers understand. They don't have the problem of being too close to the product.

During these types of interactions, I just sit there and take notes.  After the show I huddle with the folks working with me on our paid ad programs.  We almost always come up with new campaigns for AdWords, Facebook, etc.  As well, we typically come up with alternate versions of existing ads that more succinctly describe the product benefits.  These new campaigns and creatives generate lots of new analyses and learnings.

The more varied your customer base, the more you need to get out and meet folks.  TeamSnap is used in over 100 different sports.  As well, we have customers in 196 countries.  As such, there are a ton of ways customers use the product.  An adult hockey team in Vancouver has very different needs than say a youth travel soccer team in L.A. or a high school rugby club in London.   You cannot pick up those sort of differences from behind a desk - sometimes there is just no substitute for leaving the ivory tower.

Can You Become a Slave to Data?

In a recent discussion with my colleagues about data-driven decision making, one of them asked, "Can you become a slave to data?  In other words, can you become so focused on the numbers that you ignore common sense and make bad decisions?"  It was a great question.  After a little reflection, I responded that yes in theory you can be a slave to data.  However, in the real world I struggle to think of companies that truly have become slaves to data.

On a scale of 1-10 (where 1 means totally winging it and 10 means being completely data driven), most companies are much closer to 1 than 10.  When other Internet professionals ask for my advice on issues they are facing, I am often shocked by how little analysis they have done.  I often will say something like, "That is a good issue.  I bet looking at metrics X, Y, and Z will tell you your answer.  I am sure you have looked at X, Y, and Z, right?"  Usually I am greeted by a long pause and a lot of foot shuffling.  If an average company is a 2 or 3 on the data driven scale, they can easily move up a number or two (or more) and not become slaves to data.

Secondly, if you are using a hypothesis driven approach as I talked about in my last blog post, you already are bringing intuition and common sense into the equation.  You are only running analyses on things that you have already thought about.

Lastly, if you really understand the limitations of the common analytical tools, you know that there is a pretty large margin of error built in.  Let's take the free version of Google Analytics (GA).  Try running a report one way and then run the same report a different way.  The results are often a little different, even when GA doesn't say it is sampling the data.  When GA says it is sampling the data, the variance between reports can be pretty large.  Alternately, hook up the GA e-commerce tracking to your website and compare how many transactions GA gets to how many your e-comm platform says you actually received.  You will often see that GA misses about 15% of the transactions.  Drill down on some of the transactions in GA and you will find garbage transactions.

I picked on Google Analytics, but you can find similar limitations in most of the tracking tools.  Let's not even get into attribution modeling.  I can barely explain to you what made me buy product A over product B.  How is a relatively simple attribution algorithm able to describe what drove my buying behavior?  It can't.

If you understand these limitations in today's tools, you realize that our tools are best used as directional indicators.  They cannot give you exact answers, but they sure can point you in the right direction.  Your brain must be fully engaged at all times - you cannot blindly trust the numbers that are spit out.  I often like to triangulate my results using a couple different methods or tools to increase my confidence in my measurements.

The bottom line is that if you have a solid analytics team, the chances are extremely slim that you have to worry about becoming a slave to data.  Whew, you can cross that off your list of things to worry about!

Are You Boiling the Ocean?

You have just been assigned a new project at work, and you think to yourself, "I am going to be more data driven on this project."  The first thing you do is start daydreaming about all the data you want.  Before you know it, you have a list of reports you want that is longer than War and Peace.  Heck, you don't what you are going to do with all the data, but you want it!  You are now officially boiling the ocean. Your analyst will get all the reports done about the same time as you boil the ocean.

A far more time efficient process is to be hypothesis driven.  You have a hunch about what is going on.  Why not leverage that intuition to reduce your decision making time dramatically? By creating a hypothesis about what the problem might be, you do two things.  First, you limit the data you need to collect.  Second, you pull data that you can take actions on.  Both make you go faster.

Let's say that you have been assigned to improve your account creation funnel.  You have a four step funnel and you have a hunch that step #3 is where you lose everyone.  You can postulate a hypothesis that says that you think that you are losing more people from step #3 than any other step in your funnel.  If the hypothesis is true, the implication is that you should focus on improving step #3 before you work on other steps (all other things being equal).  You now have a fairly straightforward analysis that your analyst should be able to bang out in Google Analytics or a similar tool.

Let's say that your analysis comes back the way you expect.  You now can formulate a hypothesis about what is wrong on step #3.  For example, I think that customers are getting confused about what to input into field XYZ.  You can now use a tool like Mouse Stats to see if customers are pausing longer on field XYZ or exiting on that field.  If they are, you know where to focus your actions.

At the end of the day, making rapid improvements to your site or app is the name of the game.  Boiling the ocean is your worst enemy when trying to make rapid improvements.  Let hypotheses guide your actions and forever be more efficient!

How Grow Can You Go?

More than ever, a company's growth rate drives its ability to raise money, at least when the company is an early to mid-stage tech company.  In the SaaS world, a growth rate below 50% means it will be virtually impossible to raise money.  At above 150% investors will practically fall over themselves to give you money. 

Because growth is so important, the ability to forecast a company's growth is critical.  A good growth model sets expectations and drives the company's strategy. Yet, this forecasting is often done with a wing and a prayer and perhaps a little bit of historical data (which often is barely relevant when looking forward). A better way to forecast growth is to look at your baseline growth rate and then think about the major things that could impact that growth.  The main drivers of the model typically are:

• Baseline growth rate.  If you shut off all your paid marketing and left everything status quo, what would happen to your business?  This usually is a good measure of your virality minus your churn.  In other words, how many customers do you get from word of mouth minus how many do you naturally lose through attrition each year?  If you have ever had a period where you didn't spend a lot on marketing, you may have come close to this baseline growth rate.
•  Change in revenue in per user (aka ARPU).  This can include raising prices, selling customers a higher quantity if applicable, up-selling customers on higher cost products, and cross-selling customers on other product lines.  Raising prices is always a little difficult for early to mid-stage companies, but up-selling and cross-selling are often very viable strategies for this stage company, especially if you can regularly create new products.
• Improving the conversion / close rate.  In a B2C environment you should be regularly running A/B experiments to improve your conversion rates.  In a B2B environment that involves sales reps, you can make tweaks to your sales process to improve your close rate.  Your success in these areas will often depend a lot on how far you are down the road.  For example, at TeamSnap we have run thousands of A/B tests.  As such, improvements in conversion rates are much harder to find at this point.
• Increase your paid marketing / sales team.  If you know your cost to acquire a new customer (CAC), you can always see how many extra customers you can acquire if you up your marketing budget or increase your sales team.  However, there are diminishing returns to this strategy.  CAC can grow as you ramp up spend, so it is best to plan for CAC to rise with spend.
• Increase virality.  If you can increase your virality through enhancements to your product, this can be one of the most powerful ways to jumpstart growth at minimal cost.  For a lot of companies there are a lot of things they can do to increase virality.  However, forecasting how much virality will increase is not an easy task.
• Reduce churn.  Growing your business with a high churn rate is like putting out a fire with a leaky bucket.  In the early days this is an area that can bear a lot of fruit.
• M&A.  Do you plan to acquire any companies this year?  Acquisitions can increase traffic, your customer base and/or revenues.

While some of these drivers of growth are harder to forecast than others, the most important part is to take a stab.  Having growth targets for each of the above drivers will help you set your strategic priorities. If most of your growth is going to come from one or two drivers above, you need to make sure your team is focused on those items and that you are closely measuring your success in those areas.

Do You Really Understand Virality?

Everyone loves to say that their product is viral, but is it?  I used to work in the online photo space where everyone said that their product was "incredibly viral" because they let customers publish photos to Facebook and share photos via email.  However, most of the online photo sites didn't do the analytics to prove if there was any virality from this photo sharing.  Heck, many of the sites weren't even checking to see if anyone was sharing their photos.

Let's look at how you test the hypothesis that your product is viral.  We'll stay with the online photo example.  In this case to confirm your product is viral, you would need to:

1. Measure how often people are sharing via email and social media.  You might do this by building tracking into your platform.  In other words, record in your database when a photo is shared.
2. Ensure that customers can easily order photos that have been shared.  This is basic usability blocking and tackling.
3. Track all customers who click on those shared photos and come back to your site.  I personally like Google's UTC tracking links in Universal Analytics.
4. Set up a mechanism to track how many of those visits turned into orders.  The e-commerce tracking module in Universal Analytics is a powerful tool for doing this.
5. Track the lifetime value of these customers.  Do these customers just order the shared photos or do they actually place new orders with their own photos?  To do this, you will probably need to merge together data from your web analytics platform with your transactional back-end database.

Once you have completed these tasks, you should have a decent picture of whether anyone is sharing, and if so, whether it is helping at all.  The next step is to build a customer funnel. Where are customers dropping off?  There are a likely one or two places where you are losing a lot of folks.  Work to optimize those places.

You also can build a virality model from this data.  I often refer people to "Lessons Learned - Viral Marketing" by David Skok as a starting point.  Not only do you want to compute your viral coefficient, but more importantly, you want to find out the time delay.  How long does it take from sharing to the time people place their orders?  The time delay has a far more powerful effect than the viral coefficient.  That means that optimizing your funnel to increase your conversion rates isn't as important as making sure that those people who convert do so quickly.

The next time you start to say that your product is viral, challenge yourself to walk through these steps.  See if you have done the homework to really prove that you have a viral product.

Top Customer Analysis - You Probably Are Doing It Wrong

When marketing professionals think about their best customers, they often do the same boring analysis. They pull a list of their customers who have spent the most over some time period and call the folks at the top of the list their best customers. They then look at the demographics and a few other traits of those customers and declare victory. However, especially in an online world, this misses a lot of really interesting insights. If you stop with your analysis at this point, you are only about 5% done.

A natural extension of the analysis mentioned above is to factor in customer acquisition costs (CAC) and marketing source. You already calculated how much the customers spent - extend that to calculate Lifetime Value (LTV) of those customers. (You will need to know your churn to compute LTV.) If you have the analytical tools to pull the CAC for these top spenders, you can generate a ratio of LTV to CAC and segment the data by marketing source. What marketing sources have the best LTV to CAC ratio for these top spenders? What does that tell you about marketing activities that were really effective at generating top customers?

The next step is to broaden the analysis beyond just the top spenders. Look at LTV to CAC for all customers acquired by marketing segment. Which marketing segments are most effective using this lens? Until now, the analysis has assumed no virality. But most products, especially online properties, have at least some virality built into them. If you haven't looked at virality before, here is a great primer by David Skok.

Now instead of just tallying up your best customers in terms of spend, look at those customers who shared your product the most. Better yet, see if you can link those shares back to actual new customers acquired. If you can, keeping going - you aren't done yet! Look at those downstream customers acquired by the sharing and see how much they spent. Let's take those dollars and attribute them back to the customers who did the sharing. In other words, we are going to look at which customers spent the most AND how much money you got from their sharing activity. Once again take those top customers and compare the value they brought you to their CAC and segment the data by marketing program.

Whew! If those analyses seem fairly complex, you are right. These are going to take you a while. Even with the best of tools and people, this isn't going to be a quick process. However, if you can complete these analyses, you will likely have a totally new perspective on where you should be focusing your marketing efforts.

Don't Forget about Demographic Data in Universal Analytics

Google has slowly added demographic information to Universal Analytics (formerly Google Analytics).  Because of this phased rollout by Google, many marketers have forgotten to enable this capability.  It is a simple yet highly important setting in Universal Analytics.
Setting up demographic tracking is very straightforward.  In Universal Analytics you navigate to Admin and then Property Settings.  Then enable the slider for "Enable Demographics and Interest Reports." 
 

Universal Analytics Property Setting for Demographics Data

From there you will need to make a change to your Universal Analytics code deployment.  I am a big fan of deploying the Universal Analytics code (and any other tags you have) via Google Tag Manager (GTM).  In GTM to enable demographic data collection for your Universal Analytics account you just need to check a box under the main settings page for Universal Analytics.

Demographics Setting in Google Tag Manager

As in the case of most Universal Analytics changes, you need to exercise a little patience. It typically takes a day or two for the data to start to populate.

Now that you have the data capture working, head over to the audience reports in Universal Analytics.  

Demographics Reports in Universal Analytics

You can drill down by age and gender.  Assuming you enabled the E-Commerce Capabilities (shame on you if you didn't), you should be able to see how your conversion rate compares for men versus women and by age group.  

It is important to realize there is likely a healthy margin of error in these reports.  One of the more common issues is that people share devices which can wreak havoc on these types of reports.  As such, it is good to double check the data against information you capture directly from your users.

Once you dig into the data, it is critical to feed the results back into your paid advertising efforts.  Let's say you discover that certain demographic segments are more inclined to purchase from you than others.  You will then want to adjust your bidding strategy on paid search, Facebook and wherever else you advertise.  If you have segments that perform very poorly you can shut them off entirely in paid advertising.  Alternately you can take a strategy that says you want to bid for all segments, but you are willing to bid much more for your best segments.

This is a very quick win so stop what you are doing and implement demographic reporting now.

Good Marketing People vs Bad Marketing People

I have been reading Ben Horowitz's book, "The Hard Thing about Hard Things" and have been inspired by a section where he describes what a good product manager does versus what a bad product manager does.  As such, I am taking my crack at what a good online marketing professional does versus what a bad online marketing professional does.

A good online marketing professional:

• Constantly looks for ways to do his or her job more efficiently without anyone ever telling him to do so.  S/he aggressively tests out new tools and implements them as they make sense.  S/he has a strong bias toward automating rote tasks.
• Presents conclusions and recommendations instead of just presenting a mountain of data, thereby making the receiver interpret all the data.
• Is fluent in analytics and can back up his/her conclusions and recommendations.  As the saying goes, "In God we trust - all others bring data."
• Is passionate about technology - s/he is always reading about new technologies that could impact his/her job, his/her product, and the world around him/her.  S/he never waits for someone to tell him to read up on a subject.
• Recognizes that the online marketing world doesn't fit neatly in a 9-5 world.  Sometimes the most interesting thing of the week will happen on Sunday morning, whereas things can be quiet  during normal business hours.  When it is quiet, go for the long bike ride you have wanted to go on.  When you strike gold on a Sunday morning, sometimes you have to buckle down and start digging right then.
• A/B tests everything in life down to how s/he makes his/her coffee in the morning. 
• Is intellectually curious.  When s/he spots some interesting data, s/he always digs into it to see if it could improve his/her performance.

A bad online marketing professional:

• Blindly follows what has been done in the past.  S/he never asks why it was done that way before.
• Acts like math is like cooties.
• Doesn't know how to lead people to conclusions.  S/he usually presents no data or enough data to overwhelm a mathematician.
• Is afraid to take risks.  S/he doesn't understand that what you do in online marketing this year cannot be what you did last year.
• Is scared of technology.  Let's face it.  Some parts of this job are technical - deal with it.

Improving Things One Step at a Time

Online marketing can be overwhelming at times because there are so many vehicles and each one requires flawless execution of many, many details.  If you join a company that has little online marketing infrastructure (or perhaps worse, a very disorganized marketing program), it can be a daunting task to put everything in place.  The best approach - take one vehicle and improve it methodically step by step.

For example, let's assume that this company you have joined has no formalized email newsletter to its customers.  You would love to have a robust newsletter program using email best practices.  However, you will never get there in one giant leap; you need to make regular, incremental progress.  For example, the steps you take could be something like this:

1. Set up a hosted email service with a one-time email list (ensuring opt-out data is retained) and custom newsletter content
2. Create an HTML template that you will use for each newsletter
3. Create an automated or semi-automated program to populate the newsletter member database
4. Master whatever tracking tools are in your email program
5. Implement Google Analytics UTM tracking links
6. Personalize the name in the email
7. Personalize big chunks of content in the email based on data from your customer database
8. Incorporate A/B testing of headlines
9. Incorporate A/B testing of content
10. Incorporate an ad server (even if you are just using it to target internal ads to your customers)

Each time you send an email you try to bite off the next step in your email improvement program.  If you send 1-2 newsletters a month, it won't take long before you have gone from a rudimentary email program to a state-of-the-art program.  At the same time, because each new newsletter is incorporating just one new change, you minimize the chances of things going wrong.

Being state-of-the-art requires constant forward motion.  You won't get there over night, but you will get there quickly if you keep improving.

Portfolio Management in Digital Marketing

Years ago I seriously contemplated taking a job in the finance industry, but eventually decided to pursue a career in technology instead.  These days I have come to the conclusion that being an executive in digital marketing is a lot like being a portfolio manager in the finance industry.

When you run a digital marketing department, you have countless experiments going at any time.  You are running parallel programs in SEO, paid search, banner ads, social media, content creation, PR, etc. In each of these areas you can be testing dozens or hundreds of different concepts.  Add this up and it can easily mean thousands or tens of thousands of things going at once.

With this many experiments running at any one time, overseeing a digital marketing department really becomes portfolio management.  You need to constantly compute the ROI of your investments and move your money into the initiatives with the best returns.

On the surface that sounds simple, but the reality is that some marketing activities are harder to measure than others.  That is why digital marketing executives have to be experts at analytics.  Sure they should have an outstanding analytics department behind them, but at the end of the day they need to be analytical experts themselves.

I spend 3 to 4 hours a day in Google Analytics, SQL, Tableau, Optimizely and other analytical tools.   Sure I am an analytics fanatic, but more and more digital marketing execs are putting a considerable amount of their time into analytics so they can focus their team’s marketing efforts on the most valuable areas.

Slow and Steady Wins the Online Marketing Race

Back in June I wrote a piece called, "Pssst, What Is Your Secret to Online Marketing?"  In that post I laid out the case that analytics were the most critical component to an online marketing strategy.  However, months later I continue to get people who ask me for the silver bullet to online marketing.  I guess that post wasn't too convincing so this month I am going to lay out another overarching principle in good online marketing - being slow and steady.

Am I really suggesting that you should move slowly in online marketing?  No, move slowly in the Internet business and you will get left behind. However, success in online marketing often comes from paying attention to a million and one details, and you cannot pay attention to details if you are moving too quickly.  Even those companies that really break through and create a nice buzz often do so by doing many little things right.  Customers see the company over and over in a positive light and build a great impression of the company.

What sort of details matter in online marketing?  Quite frankly because all these pieces build on each other, the answer is pretty much everything does.  Here are examples of details that I often see companies overlook:

• SEO - are you creating good title tags, creating alt tags for your images, writing good page copy, etc.?
• Website hygiene - do you have dead links, old content and other major mistakes on your site?
• Retargeting - do you have a re-targeting program that puts customers into all the appropriate segments and hits them with the right follow-up messages?
• Triggered emails - are your targeted messages crisp, well designed and regularly revised based on data?
• Paid search - are you constantly reviewing your campaigns, allocating more budget to the good ones and tweaking or killing off the dogs?
• A/B testing - are you A/B testing, well, everything?  No, I really mean everything. 
• Cross marketing - are you actively looking for good cross-marketing partners?  Do you regularly see who already sends you a lot of traffic and then reach out to them?
• Relationships with key customers - do you look to build deeper relationships with key customers?  Do you thank them in any way for their support?

This isn't an exhaustive list by any means but it should give you a taste of all the little details that any online marketing teams needs to pay careful attention to. 

What do I mean when I say these things build on each other?  If you can double the number of people coming to the front door of your site through a lot of little things and then double the conversion rate to trial and then double the conversion from trial to paid, you are talking about an 8x improvement in revenue!  Now we are talking about some big results.

Folks, it is time to stop looking for silver bullets and focus on the blocking and tackling.  Put enough blocking and tackling together and you will see big results.