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Product driven or Customer driven? Therein lies the rub for GoDaddy and others.

Previously published on LinkedIn Oct 12, 2016

Recently I was in a meeting reviewing a contract involving data, technology and ecommerce enablement. To be simple, it was a subscription service (e.g. SaaS) and it involved multiple products. The pain point in the discussion was billing and contract renewal. You know, the customer wants one bill, one renewal date and one due date on the invoice. The provider – was only capable of delivering a different renewal experience, renewal date, invoice and due date for every product.

This is not uncommon, even among industry leaders like GoDaddy and many others…

Last Saturday, I received three separate renewal notices from GoDaddy. Three days earlier I received a single notice from GoDaddy listing ten separate domains…and two days before that an email containing my "October Account Summary" which of course only listed a few products that I subscribe to. Not counting order confirmation emails, I've received no less than 32 emails from GoDaddy this year.

Recognize the problem? You should…if you have multiple products or brands in your company and have responsibility for customer lifecycle marketing (or in the current parlance – the customer journey), the question of whether your company is product driven or customer driven is very fundamental.

Based on the emails I've received and saved, GoDaddy and others like them, are trying to embrace the practice of "householding". This is a practice I'm familiar with from my days in magazine publishing. For many consumer subscription magazines and consumer catalogs the idea of householding is a way to minimize cost and understand who lives in the house. You know, mom, dad, Joey, Billy and Sally. The household has two adults and three children. Sending one catalog or marketing one subscription to Sports Illustrated makes a lot of sense when marketing to a "household."

The obvious point in householding is that individual records must be linked together or aggregated into a single entity. By doing so the marketer gains a much clearer picture of what types of products to promote to that household and gains efficiencies in their operations.

In B2B marketing this becomes much more complex because it can involve organizations with many physical locations, legal entities, accounts payable locations, subsidiaries, brands, credit lines, etc. Corporate linkage often shows up in the conversation, financial professionals step in to manage credit risk and sales compensation plans step front and center in the discussion. It's not an easy place to embrace the wonders of customer lifecycle marketing, marketing automation, personalization, etc.

If your company, like GoDaddy is struggling with how to implement customer lifecycle marketing in a complex B2B environment, I would offer a couple of suggestions:

1) Create a clear definition of the customer. Is the customer defined by a product, or a subscription, by site, by contract, by billing location, etc. This definition is critical when constructing predictive models base on "customer value" or customer lifecycle marketing objectives. As you can see, it will be nearly impossible to create a useful or accurate customer experience if you don't have a clear definition of the "customer."

2) Focus on householding and the operational impacts this implies. If a customer wants a single bill or a single renewal or a single unified customer experience, my recommendation is that you give it to them. However, it will be very difficult to respond favorably if your systems are in conflict with your customer definition.

Depending on the size of your organization and the breadth of your product offerings, this may be a short discussion with easy enablement. Or it may be a multi-year effort to align your company with your customers. In this age of instant information and customer-centric rhetoric, I can only wish you the best of luck... and may the force be with you in creating a seamless, real-time, responsive, socially enabled, 360 degree view of the customer journey.

Is GoDaddy Shifting Gears?

Customer lifecycle marketing provides an approach to business that can be extremely useful in understanding customer behavior, company behavior and whether or not a company and its customers are "getting along with each other." Join me as I experiment in a series of posts with lessons learned in customer lifecycle marketing and apply it to GoDaddy and their public records to see what the numbers tell us about GoDaddy, the world's largest domain name registrar.

Remember, this is an experiment, to poke and prod and "peel back the onion layers" and see what we can see using customer lifecycle marketing methods about GoDaddy's customer loyalty, revenue and business model assumptions.

Okay, here are a few reference points for your consideration:

GoDaddy has 14 million customers today (their web site says so) and had 13.8 million on December 31, 2015. On March 31, 2014 they had 12M customers and taking it back to Dec 31, 2009 they had 7M customers. That's 100% growth over a six year window of time. Not bad.

In 2013 they added 1.3M customers. In 2015 they added 1.1M customers. I am inclined to believe these are "net adds" and not gross numbers of new customers acquired during the year. Hence I would expect the growth rate to drop to a number close to 1 million customers added in 2016. If correct, that would be roughly 30% slower than three years ago.

Okay, GoDaddy has also been kind enough to publish that 700,000 customers spent "over $500" each in 2015. That tallies to $350M in annual revenue if they "average" $500 each, but they don't they spend "over $500 each." Let's use $1,000 as the "average" for this group. This tallies to $700M.

Based on the 700k customers that spend "over $500 each" the above point leads me to believe 5% of their customers generate between 18-35% of total revenue. That's (700k high value customers / 14M total customers and $350M -$700M high value customer revenue / $1.9B total revenue). Okay, so far so good.

Now the GoDaddy retention rate is at 85%...unless the customer has been with the company three year or longer, then it jumps to 90%. We're going to stick with the 85% number. On a base of 14 million customers, with 85% retention rate, GoDaddy loses over 2M customers per year. In order to achieve the net add of 1M in 2016, this suggests they need to "acquire" 3M new customers in order have a net increase of 1M.

Turning to profitability, the company has made steady progress in shrinking their annual losses. 2015 showed a small loss of $52M. Based on the last three years, it is possible that GoDaddy will achieve break-even by the end of 2016.

So, here's my early thesis or my rough observation, GoDaddy is shifting up market. 5% of the customers could be generating 35% of revenue. The profit picture is improving and the net add in customers is becoming increasingly difficult. The bigger the base, the more difficult it is to maintain rapid customer growth across the entire customer base.

GoDaddy continues to make acquisitions, as evidenced by their recent purchase of ManageWP in order to add a wider and deeper offering to their existing customers. In other words, the GoDaddy business is shifting from high customer acquisition growth to revenue and profit growth from existing customers. This is a fundamental shift in their business and will alter the way they sell to and service customers. It is likely their segmentation models will change, their customer lifecycle methods will change and their product offerings will change. There's a whole lotta change coming to GoDaddy...

But then again, this is very rough, and very we peel back the layers… with a very limited number of data points. Time will tell if I'm barking up the wrong tree. After all, rumor has it there are over 1B registered domains around the globe and GoDaddy says they only have 63M under management.

Does your customer journey start like this?

Recently I signed on to receive emails from a specialty clothing retailer - just to see how they do their marketing. What you see above is the result of my first 20 days of emails received. Notice that the Welcome Email came on day one, then a pause until day 7, then every day a new email. A few other things I'd like to point out:

  • No personalization exists in any of these emails
  • No dynamic content has been delivered
  • No gender specific offers

Customer Lifecycle Metrics, Part 4: Convert, and Create a Customer

This article talks about the "customer lifecycle" but in realty the author is talking about "prospects." and the use of "conversion" metrics from a Prospect to a Customer. The lifecycle being talked about is BEFORE the person or company even becomes a "customer." My definition of a customer being someone that buys something. Meaning if they haven't given you money for your product or service, then they're not a customer.

We may be splitting hairs on this one, but Lisa still has some valid points and it's worth the read.

Is it time for GoDaddy to Turn Off the Super Bowl Ads?

Previously published on LinkedIn - Oct 4, 2016

Brand marketing is fun, sexy and very expensive – at least in the case of SuperBowl ads. But do they move they needle for the bean counters? We'll come back to that one later, but in the mean time…

Something about my last two posts bothered me. They were too rosy, too upbeat on results. Something didn't' quit feel right. But then again, as I mentioned, we're peeling back the onion layer, by layer – and we're working from the outside in with assumptions.

I was going to move onto householding, deferred revenue as it relates to acquisition costs, etc, but those will have to wait for another day…

From what I can tell, GoDaddy is losing money on new customer acquisitions. This feels contrary to my previous two posts…you know, average customer revenue is $137 per year. Cost per acquisition is just $67. It looks good when considering only those two points.

Yep, until you pull back the numbers a bit more. The illustration below, shows what happens when we remove all that juicy, easy renewal revenue and leave Customer Acquisitions standing in the harsh light of the bean counters.

If it's not immediately obvious the mix is 36.84% from the Top 5% customers, 60.84% of all other Renewals and roughly 2.32% from New Customers. Admittedly, I've assumed the top 5% is all renewal revenue, but how often do you see a new customer land in the top 5% of customers, especially with a customer base of 13 million? Not often. Which is why I counted the Top 5% as renewal revenue and then backed into the New Customer number. Here's the detail, just in case anyone wants to see it.

So, why do we care? Well, in the words of… we have an elephant in the room: GoDaddy is losing money during acquisition. This isn't really uncommon for companies, so it's a little elephant, but it may be one of the reasons why the GoDaddy growth rate in total customers is slowing. It is very likely one of the reasons why the company is shifting their customer lifecycle focus to renewals and larger companies. It can also be seen as a red herring in the GoDaddy story to "radically shift the global economy to small businesses". It makes for a good story, but the cost to acquire all those small companies and "passionate individuals" is expensive. At least through current methods at their current size… hence my poke at the Super Bowl ads.

Marketers count on short-term memory loss with customers

It must be one of the "secrets of the trade." Marketers count on short-term memory loss in their customers.

As mentioned earlier, we are tracking the email marketing of a $100M+ retailer. So far nearly 80 email offers received over the lat 3 months or so. One of the offers that sticks out is the "Last Chance for Free Shipping." Only it's not the last chance. The "free shipping" offer with "last chance", "2 days left" and "ends tomorrow" type of language has been received no less than 15 times.

Do they really think the customer is going to believe them the next time they say "Last chance...?" Nope. They're banking on the belief that customers have short-term memory loss OR that customers don't believe what they say anyway, so they can say whatever they want just to get a response.

Brings to the forefront why "truth in advertising" laws came into place in the first place. And it's a no-so-subtle reminder that when talking about "relationship marketing", "customer lifecycle marketing" or the "customer journey" it's important to be honest and truthful in your marketing.

Deductive reasoning in the age of Big Data and Pervasive Marketing Technology

One of the more challenging aspects for today's marketers is the analysis of large data sets and the use of sound deductive reasoning – the ability to reach good decisions based on the data in front of them.

Many people, including marketers, are capable technicians. They can build statistical models, write code, setup and manage automated processes in the tools of the trade, but doing analysis, interpreting data and drawing useful and correct conclusions is elusive. Too often, marketers, executives and statisticians see what they want to see and find the data to support their own emotional bias or wishful thinking.

For the fun of it, and for the controversy that surrounds it, I'd like to use global warming for illustration purposes, before returning to marketers and the dilemma they face in a massive sea of data.

Perhaps we can all agree we want to be good stewards of the earth and our natural resources. However, we may not agree on the conclusions being derived from the evidence presented. Let me offer a few questions that may challenge today's accepted norm – in other words, let's play what if and challenge the foregone conclusion on global warming that…"everyone knows the science is factual and only idiots will think otherwise."

Question #1: What did man do to cause the end of the last ice age? Clearly if mankind is the cause of global warming, then the obvious relevant question is, how did we end the last ice age? As reference, some authorities suggest the last ice age ended about 11,500 years ago.


Question #2: What did mankind do to cause the earliest recognized ice age? If we are the cause of global warming, then the reverse conclusion has merit. In other words, on the basis of what action(s), or the absence thereof, did we cause the earliest ice age? The earliest ice age started about 2.6 million years ago.


Question #3: When the earth rotates around the sun, how does it warm the sun? Oh it doesn't? Okay, then what does the earth do to cause the sun to warm mother earth? Ah, perhaps the earth generates all of its own heat and the sun has no influence on the warming of our little globe. San Diego State Geological Department identifies extraterrestrial impacts, gravitational contraction and radioactive decay as the three main sources of Earth's internal heat.

In contrast the Ohio State University points to the Sun as the primary energy source of earth. They further state, "Gradual changes in Earth's rotation and orbit around the Sun change the intensity of sunlight received in our planet's polar and equatorial regions. For at least the last 1 million years, these changes occurred in 100,000-year cycles that produced ice ages and the shorter warm periods between them."

Clearly this thing called "global warming" is a complicated topic and requires some more study.

Question #4: What is my sample size and is it statistically valid? When we talk about climate change and authorities make statements like the following, "The World is Warming. It's Because of Us. We're Sure" (National Geographic, April 2017) and reference data that shows global temperatures for the last 116 years as the proof point… is this a statistically valid sample size against 2.6 million years, or 100,000 year cycles, or even against the natural cycles of our earth, which has existed for 4.55 billion years?


Question #5: Where is my control group? In marketing we are often taught about establishing control groups in order to minimize the confusion that large numbers of variables might cause in drawing predictable, reliable conclusions. The same is true in scientific discovery, using the scientific method and control groups. definition by by Anne Marie Helmenstine, Ph.D.

So… where is my control group for planet earth? Oh, we don't have one. Planet earth is the only planet of its kind where science can point to a large population of living humans.

Okay, so now that I've set the stage using global warming for illustration, let's return to today's marketing realm.

Question #1: What did you your customer do to end the latest cold spell in your sales production?

The most obvious answer is they bought your product. But why? Why did they suddenly start buying your product again?

Question #2: What did your customer do to launch your company? Oh, they didn't? You started your company… perhaps even you started it to fill a customer need and they were happy to pay you to satisfy that need.

Question #3: When your company hovers and rotates around your customers – you know, capturing a 360 degree view of customer behavior, what do you do with all that data to create new products and generate more revenue and profits?

To put this into perspective: 1.2 million products, $15 billion in revenue; 15,000 employees, 100 million customer interactions monthly via telephone, mobile device, web site, email, text, social posts, etc. Geographic data points, demographics, firmagraphics, transactional details, including real-time, variable content, personalized to every individual at the right time, in the right place with the right product. Each interaction, unique and personalized to the user…. Get the picture? There's a lot of data in here and it's only going to get worse with the Internet of Things showing up.

Question #4: What is your sample size? Is your sample size a single interaction, a single purchase, a single conversation? Or a billion customer interactions? And is it statistically significant, valid and representative for the decision you're trying to make?

Question #5: Where is your control group for your 360 degree customer view? Oh, you don't have one? Better yet, what is the definition of your control group and how will you eliminate thousands or millions of variables in order to isolate cause and effect and produce predictable performance?

As you can see, today's marketers have a bit of a challenge when it comes to doing analysis and reaching sound conclusions.

Early in my career, in the magazine publishing industry, I enjoyed the benefits of a stable product with stable pricing and a stable control piece that lasted multiple years. It was tough to beat! When I moved into catalog retailing of technology products, I suddenly had 150,000 products, with declining prices and rapid product obsolescence. The rules of the game had dramatically changed and my control group practices, data analysis and assumptions likewise had to change. In a nutshell, I needed new perspectives and new skills.

Today's marketing leaders must have or must develop excellent skills in analysis, data interpretation and deductive reasoning. If they don't then like so many others in their companies, they will simply see what they want to see and look for data that supports their own emotional bias and wishful thinking.

We need marketing leaders that have wisdom, discernment and backbone to use sound deductive reasoning. If the prevailing opinions are overwhelmingly popular, but lack the support of basic, solid analysis (e.g. no control group, non-existent or miniscule sample sizes, short windows of time and a million variables), then stand up and admit to your CEO exactly what you're actually doing – taking a best guess to support your own wishful thinking.

As for climate change – that's an easy one– it happens every day, every winter, spring, summer and fall.

Is RFM still valid in the online world?

Years ago in New York City I was introduced to the concept of RFM modeling (recency, frequency, monetary value). I briefly met Donald Libey and sat through a discussion / presentation with him while he mopped his head with a handkerchief. He was very enthusiastic about the topic and worked up a sweat - I supposed the room and lights might have also helped. :) Ever since, I have been a fan of this approach to business and have found it to be exceptionally useful.

In my career, I have used RFM in many industries, have combined it with frequency momentum and extended it by adding "depth" metrics drawn from web site traffic. And yet, I pose the question today, is it still valid in the online world?

Can email delivery analytics help you market smarter to your customers?

Recently I reviewed some material from my early days in catalog marketing and magazine publishing. The context of "deliverability" back then, as it still is today in the physical world, was all about understanding the postal system. Things like sectional center facilities (SCFs), zip code sort, zip+5, carrier route coding, bar codes, containerization, in-plant post office, merge/purge reports and site-penetration were just some of the things that were used to make decisions about getting your catalog or magazine delivered to the right people on time.

All of that seems to have been stripped away when deliverability was netted down to one item: the email address.

Or does it?

Which data sources are you using to model your customers – what they say or what they do?

The black box of predictive analytics comes in many flavors and nuances. And of course which methods you use become part of the assessment on whether or not you fail or succeed in today's marketing tech driven world.

So – here are a few for you to consider if you're looking to boost your customer lifecycle marketing:

· Expressed intent – a fancy name for predictive analytics built from search data. Not too long ago I sat on a panel discussing customer lifecycle marketing. This was way down south, at a beautiful yacht club in the Florida Keys. The venue was extraordinary. The discussions were likewise stimulating. The idea of "expressed intent" and search came to the forefront. Every time we search online, whether that is through Google, Yahoo, Bing or others; whether on a desktop, laptop, tablet or mobile device, we leave behind a trail of digital crumbs, anxiously gathered and analyzed by some digital marketer. Predictive models built from this treasure trove of expressed intent can be extremely useful when combined with transaction history data, click-stream data, company data, etc.

· Transaction histories – every purchase has a story to tell. It may not be obvious, but it's there. Is it a first time purchase, an add-on purchase, an up-sell to a better product? Perhaps a new office location is opening, new employees are being hired, or better technology for process improvements so the company can cut costs. Purchase history data when combined with other data elements can be extremely useful.

· Predictive Index (PI) – PI is a tool commonly used for screening, selecting and coaching employees so they can be more productive in the work place. It is a self-reporting assessment tool that in my experience can be highly predictive. It identifies things like Dominance, Extroversion, Patience and Detail capabilities/assets of the individual. Take a look at this tool here and then imagine having this type of information on your most loyal buying customers. This may seem farfetched, but if you really believe your own rhetoric about customer relationship marketing and that your company is a "Trusted Advisor" then why wouldn't your customers be willing to spend 3-5 minutes helping you understand them better so you can better serve their wants and needs? Take a look and see what thoughts it prompts for you if applied to customer lifecycle marketing.

· Speech Analytics – call centers, sales centers, customer support lines, tech support lines and customer interaction centers are everywhere. The ability to capture, record and analyze today's conversations is impressive. How many times do you make a phone call to a company only to hear a message saying something to the effect of "this call may be recorded so that we may serve you better – for quality improvement purposes." Okay, so phone calls are being recorded in your company call center – are you using natural language speech analytics to understand the tone, urgency and context of the language your customers are using with you? If yes, once the speech patterns are understood, are they analyzed in real time, integrated with your CRM system and providing useful prompts to the agent/sales person on the phone? If not, are you recording every call for training purposes and building predictive models for every sales agent or customer service agent to help them improve their language and map their language (i.e. choice of words used) to the customer lifecycle experience? Lots of big data opportunities in this one, right?

This is a short list, and perhaps only the largest of companies can take advantage of these ideas. Or then again, maybe you should be asking your marketing tech vendors if they have these types of tools so you can add them to your marketing technology stack.

Now, let me tie this back to the headline question – what they say or what they do? Many companies use customer satisfaction surveys. Many are listening to and responding to social media. Some are using speech analytics. However, the bottom line is this - at some point all of this data and all of the predictive tools will be or can be used to increase revenue and profit. It's a conscious choice – or at least it should be. Companies need to decide what the right balance is for them. Some will focus only on bottom line results while others are okay with healthy profits so long as they maintain and keep happy and satisfied customer relationships. What is your company using and how do these types of data sources fit into your customer lifecycle efforts?

In my experience, behavioral tools far outweigh the value of customer sat surveys and other reference points that focus on what customers say – at least when it comes to designing and developing customer lifecycle models. However, with the continuing evolution of analytics, social media, cognitive computing and big data, this bias needs to be validated over and over and over again.

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