How many Nexus 7 did Google sell and at what margin? Revenues and Cost of Revenues Analysis

Summary: Google likely sold one million Nexus 7 last quarter but did not make a dime of profit from the sales.

I have presented similar such numbers for devices sold when companies do not release their numbers. Such analyses are based on the three accounting statements – balance sheet, income statement and cash flow statement, after all businesses are required to disclose all their revenues, expenses, inventories and cash flow.

Om Malik did similar analysis of Google’s latest  earnings release. Om looked at earning statement line item on “Other Revenue” and how it jumped from $385M to $666M from same quarter last year and stated that Google likely sold million units of Nexus 7.

Since Google launched Nexus 7 in July 2012 it is better to use the jump in Other Revenue from their second quarter to third quarter.

Other Revenues was $439M in Q2 and $666M in Q3. The difference $227M. But a look at past few quarters show Other Revenue is on the rise by about 5% every quarter. Accounting for that $21M growth, Google made additional $206M in unaccounted for Other Revenue which likely all came from Nexus 7 sales. At an ASP of approximately $210 that points to indeed million units of Nexus 7 sold.

The rule of accounting requires revenues from same period must be matched with costs incurred in the same period. Let us reconcile the cost of selling  1 million Nexus 7.

Google does not make it easy for us by itemizing Other Costs.  The details are in the text of the 8-K but Google combines all of the Motorola COGS numbers which adds additional step to our math.

Other Cost of Revenues – Other cost of revenues, which is comprised primarily of data center operational expenses, amortization of intangible assets, content acquisition costs, credit card processing charges, and manufacturing and inventory-related costs, increased to $3.78 billion, or 27% of revenues, in the third quarter of 2012, compared to $1.17 billion, or 12% of revenues, in the third quarter of 2011.

In Q3 this was $3.78B of which $2.11B was from Motorola. So Google’s cost of revenues is $1.67B.

In Q2 this was $2.4B of which $1.029B was from Motorola. So Google’s cost of revenues is $1.371B. This is all their Cost of Revenue without Nexus 7.

The difference, $1.67B-$1.371B= $299M is unaccounted for. Not all could be due to Nexus 7, just like we did for revenues we should account for other growth.

The Other Cost of Revenue number in Q1 was $1.28B (no Motorola all Google). This is all their Cost of Revenue without Nexus 7. Assuming the same growth percentage from Q1 to Q2 applied to Q2 to Q3, the Q2 number including growth of other costs is $1.46B

So Cost of Revenues for Nexus 7 is  $1.67B-$1.46B= $210M, same as the Revenue from Nexus 7.

Remarkable coincidence. May be not.

For one thing it confirms the math that Google most likely sold one million Nexus 7.

More importantly they are not making a dime from Nexus 7 sales, selling it at cost like Amazon is.

4 Ways You Can Put Google Customer Surveys To Work Today

As I previously wrote, Google Customer Surveys is a true business model innovation. It helps publishers unlock value from their digital assets and enables market researchers reach new audience they otherwise would not have found. I expressed my reservations on their positioning in my previous article

But I do not get what they mean by, “look for correlations between questions” and definitely don’t get, “pull out hypotheses”. It is us, the decision makers,who make the hypothesis in the hypothesis testing. We are paid to make better hypotheses that are worthy of testing.

Since I wrote that article, their Product Manager emailed to say they removed their statement on, “pull out hypothesis”.

This is a limited tool with ability to ask just one question and no way to ensure that the same user will answer multiple questions for doing customer level analysis.

There is one more item which is their minimum sample size. You cannot order anything less than 1000 samples.

Despite these reservations I see Google Customer Surveys as an effective tool for product/brand managers, researchers and small businesses for these purposes:

1. Aided Recall:  Present them a choice of different brands ask them how many of these they recognize.
When you are trying to get very quick and high level data on customer awareness or preference of your brand, this is a great tool. The results are especially actionable when you get extreme results like no one knows about you.
If you are trying to find which brand they recognize the most then you can do that as well with different question type. However, due to its question format limitation, Google Customer Surveys cannot help with Unaided recall.

2. Finding Consideration Set: Present them a choice of different brands and ask them how many will they consider buying for solving a particular need. This is similar to Aided Recall but the question is more focused. You are not simply asking about awareness but whether your brand makes it into their consideration set.

3. Brand Association: Present them an image or a statement and ask them to pick a tag-line or brand they believe goes with it. Another variation of this question is asking them to associate your brand with an unrelated field. A typical example is, “if our brand were a movie actor, who will it be”.

Ability to use images is a very powerful feature. It creates many different opportunities. For example for testing your advertising copy or the images you use in your collateral. It is better to poll your audience whether the image you used looks more like a bean bag or boxing glove before you launch your expensive advertising campaign.

4. Consumer Behavior Research: This is a whole class of hypothesis testing you can do with Google Customer Surveys. While it is not a tool for A/B split testing, you can use it test your hypothesis on customer preferences or their susceptibility to anchors and other nudges. Before collecting results you need to specify a reasonable hypothesis that is worth testing. When you collect data you can test for statistical significance using Chi-square test to validate your hypothesis. Do keep in mind that sometimes data can fit more than one hypotheses

There is however a big limitation because of the length of questions you can ask (as you see in the third option in the image on the left).

There you have it. A tool with limitations but is effective for specific areas. It opens up new ways to collect data and test when none existed before.

A corollary for this post would be cases where you should not use this tool. That includes finding price customers are willing to pay or asking them about how important a single feature is. You have to wait for another post for the reasons.

Google Customer Surveys – True Business Model Innovation, But

Summary:Great business model innovation that points to the future of unbundled pricing. But is Google customer survey an effective marketing research tool? Do not cancel SurveyGizmo subscription yet.

Google’s new service, Customer Surveys, is truly a business model innovation. It unlocks value by creating a three sided market:

  1. Content creators who want to monetize their content in an unbundled fashion (charge per article, charge per access etc)
  2. Readers who want access to paid content without having to subscribe for entire content or muddle through micro-payments (pay per access)
  3. Brands seeking customer insights, willing to pay for it but have been unable to find a reliable or cheaper way to get this
When readers want to access premium content they can get it by answering a question posed by one of the brands instead of paying for access. Brands create surveys using Google customer surveys and pay per use input.

Google charges brands 10 cents per response, pays 5 cents to the content creators and keeps the rest for enabling this three sided market.

Business model is nothing but value creation and value capture. Business model innovation means innovation in value creation, capture or both. By adding a third side with its own value creation and capture Google has created an innovative three way exchange to orchestrate the business model.
This also addresses the problem with unbundled pricing, mostly operational challenges with micro-payments and metering.

But I cannot help but notice severe sloppiness in their product and messaging.

Sample Size recommendation: Google recommends brands to sign up for 1500 responses. Their reason, “recommended for statistical significance”.
Statistical significance has no meaning for surveys unless you are doing hypothesis testing. When brands are trying to find out which diaper bag feature is important, they are not doing hypothesis testing.

What they likely mean is Confidence Interval (or margin of error at a certain confidence level). What is the margin of error, at 95% confidence level? With 1500 samples, assuming 200 million as the population size it is 2.5%. But you do not need that precise value given you already have sampling bias by opting for Google Customer Surveys. Most would do well with just 5% margin of error which requires only 385 responses or 10% which requires only 97 responses.

Recommending 1500 responses is at best a deliberate pricing anchor, at worst an error.

If they really mean hypothesis testing, one can use a survey tool for that, but it is not coming through in the rest of their messaging which is all about response collection. The 1500 responses suggestion is still questionable. For most statistical hypothesis testing 385 samples are enough (Rethinking Data Analysis published in the International Journal of Marketing Research, Vol 52, Issue 1).

Survey of one question at a time: Brands can create surveys that have multiple questions in them but respondents will only see one question at any given time.
Google says,

With Google Consumer Surveys, you can run multi-question surveys by asking people one question at a time. This results in higher response rates (~40% compared with an industry standard of 0.1 – 2%) and more accurate answers.
It is not a fair comparison regarding response rate. Besides we cannot ignore the fact that the response may be just a mindless mouse click by the reader anxious to get to their article. For the same reason they cannot claim , “more accurate”.

Do not cancel your SurveyGizmo subscription yet. There is a reason why marketing researchers carefully craft a multiple question survey. They want to get responses on a per user basis, run factor analysis, segment the data using cluster analysis or run some regression analysis between survey variables.

Google says,

The system will automatically look for correlations between questions and pull out hypotheses.

I am willing to believe there is a way for them to “collate” (not correlate as they say) the responses to multiple questions of same survey by each user and present as one unified response set. If you can string together responses to multiple questions on a per user basis you can do all the statistical analysis I mentioned above.<;

But I do not get what they mean by, “look for correlations between questions” and definitely don’t get, “pull out hypotheses”. It is us, the decision makers,who make the hypothesis in the hypothesis testing. We are paid to make better hypotheses that are worthy of testing.

If we accept the phrase, “pull out hypotheses”, to be true then it really means we need yet another data collection process (from a completely different source) to test the hypotheses they pulled out for us. Because you cannot use the very data you used to form a hypothesis to test it as well.

Net-Net, an elegant business model innovation with severe execution errors.

Pricing Kindle Books

Sometime back I wrote this model for pricing a book

Price of a book  = Content   + Consumption  + Convenience

To be correct it should read

Value of book = Content    + Convenience

Content is the information content and Convenience is ease of access of to the content based on your usage scenarios. The Consumption component is really folded into Convenience. This equation is for a given format of the book.

then we can write  price for any given format  as

Price =  F(value)   +  G(price of all other formats)

The function G() boils down to reference price. Note that this is a recursive function but its base case is the price for the current most common format, the hardcover book. The sign of G() is usually negative, in other words it prevents the publisher from capturing the full value of the book.

So how should the Kindle book be priced? Amazon wants to price most books at $9.99 but the publishers are against such flat pricing. They saw what Apple’s 99 cents pricing did to music labels and want to have better control over their pricing. Recently one publisher declined to have their book available on Kindle at the same time as their hardcover book. Other books like Dan Brown’s upcoming book may not be available in eBook format for a long time.

If $9.99 is not the price, how should the eBooks in Kindle and other formats should be priced?

Sony executive, Steve Heber used cost argument to justify the lower price tag:

Steve Haber, president of Sony Corp.’s digital reading business, says it is logical to expect that digital books should cost less, because of the lower production costs, such as for paper. “There should be significant savings” for consumers, he said.

It is quite possible Mr.Heber is using this cost argument more towards publishers to put pressure on them to reduce their  prices to Sony than as a blanket statement on pricing based on cost. Cost is irrelevant to pricing a book  or anything else and Mr. Haber knows that as well since this part of Sony’s DNA, judging from pricing  for other digital content from Sony.

A Forrester analyst, Sarah Rotman Epps,  said,

“What we’ve seen in other industries and in the evolution of digital content is that consumers are not willing to pay as much for content that is separated from its physical medium.”

I am not sure if Ms. Epps has looked at data on consumer behavior with respect to pricing for digital content. But I disagree with her assessment.  First, the low price expectation for digital books comes not because of separation of content from physical medium but due to low reference prices set by other free digital books. Second the new medium, eBook reader, is not net negative it adds several convenient factors that increase the value to the consumers.  Customers are willing to pay for convenience and willing to pay for it.

Now questions arise, whether the value added by eBook format comes from the format, the distributor  or the device and how this value should be shared by the three. In Amazon’s case they are both the distributor and the device maker and they captured significant part of the value through Kindle pricing. Publishers are afraid, based on iTunes history, that  Amazon with its Kindle store and  Kindle reader will gain upper hand in the value chain. They are going to seek out other distributors, like Google, who allows them to set prices.

Are the publishers losing out on potential eBook sales by refusing to release in that format at the same time as the hardcover book? According to The New York Times article, eBook sales are 1-2% of total book sales. So if these eBook readers want to read the latest books in their Kindle or other readers then they should be willing to pay for the convenience.

The net is, this is  new battle in the publishing value chain. Amazon wants to win the platform  battle with its set pricing but this is far from over. New players like Google is already in the fray and we should expect other players like Adobe and Microsoft to enter as well.

Under every Complex Formula is …

Look at some of the quotes from the news media on companies with a data driven approach to management and business decisions

  1. Applying a complex equation to a basic human-resource problem is pure Google, a company that made using heavy data to drive decisions one of its “Ten Golden Rules” outlined in 2005. (WSJ on Google’s HR plans)
  2. Like Google, Facebook calculated the relevancy and authority of information before deciding to display it to me. The News Feed was shockingly complex — calculating and ranking more than a trillion items per day — and the results were very satisfying.  (WSJ on Facebook Newsfeed)
  3. Mr. Donahoe installed an entirely new system to determine which items appear first in a search. It uses a complicated formula that takes into account price and how well an item’s seller ranks in customer satisfaction. (WSJ on eBay)
  4. What could be more baffling than a capitalist corporation that gives away its best services, doesn’t set the prices for the ads that support it, and turns away customers because their ads don’t measure up to its complex formulas? (Wired on Google)
  5. CineMatch, on the other hand, is all math. It matches your viewing and rating history with people who have similar histories. It uses those similar profiles to predict which movies you are likely to enjoy. That’s what these recommendations really are – predictions of which movies you will like. (Netflix movie recommendation)

I am willing to bet that underneath the complexity is a multiple regression model, built with multiple variable and constantly tuned to better future behavior from past actions. Every business collects or has the opportunity to collect significant customer data. Companies like Google and eBay strive to be accurate 99% of the time or more. But building a regression model even with a handful of variables can improve decision making over driving without a dashboard.

Are your decisions data driven? If not I can help you build a model.