Monday, December 22, 2014

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.

Tuesday, November 25, 2014

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.

Tuesday, November 11, 2014

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.

Monday, August 18, 2014

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.


Friday, May 23, 2014

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.

Tuesday, April 22, 2014

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.

Monday, February 3, 2014

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.