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Earnings and Sales From Google Disappoint

4/17/2014

2 Comments

 
Picture
SAN FRANCISCO — Alexander the Great is said to have wept because he ran out of kingdoms to conquer. Google is eager to avoid such a miserable fate.

Its core digital advertising business is so dominant that analysts are questioning just how much it can continue to grow. So Google is unleashing its vast cash hoard on robotics, artificial intelligence, smart thermostats and, just this week, high-altitude drone satellites.

The only thing all these acquisitions have in common is a focus on the future — often, the distant future.

The risk in thinking about what will be big in 2050, however, is that you can lose sight of 2014.

Google’s first-quarter earnings report, released after the market closed on Wednesday, surprised Wall Street. The company has traditionally gushed profits without breaking a sweat. Now it takes more of an effort.


One big reason was a problem of several years’ standing: Internet users are migrating to mobile devices, but ads on phones and tablets still do not have the familiarity and appeal they do on bigger computers. And they are not as profitable for Google. Google’s ad volume jumped 26 percent in the quarter, which sounds good but is less than expected, while the amount advertisers pay dropped 9 percent, which sounds bad and is.

Continue reading the main storyGoogle shares, which were about $600 in March, came under pressure in the recent tech sell-off.

There were other potentially worrisome notes. Operating expenses were 35 percent of revenue, compared with 31 percent in the first quarter of 2013. One reason: acquiring companies at a rapid clip entails specialist fees and other costs.

Then there were real estate and construction costs, as Google races with Amazon to build out the computing cloud for potential customers. The company needs a lot of data centers. That raised capital expenditures to $2.35 billion, up from $1.2 billion in 2013. Google said it expected expenditures to remain high.

Revenue was ostensibly impressive for the quarter, rising 19 percent, to $15.42 billion, but that was about $100 million short of expectations. Net income was $3.45 billion, and earnings per share were $5.04, compared with $4.97 in 2013, slightly weaker than forecast.

The stock, which was up strongly earlier in the day, immediately fell 5 percent before partly recovering. Google split its shares this month, a move that solidified the founders’ control over the company.

“The issue with Google is, you want to support the management in their efforts to find new revenue streams, but you don’t want them to act careless with shareholder capital,” said Colin Gillis of BGC Partners.

Google’s efforts to find those new streams have intensified recently. It acquired several robotic companies, including Boston Dynamics, maker of BigDog, Cheetah and other mechanical creatures. It bought Nest Labs, which developed an innovative thermostat, for $3.2 billion.

And just this week it bought Titan Aerospace, which makes drone satellites. Google said Titan, which was founded in 2012 and has about 20 employees, could help bring Internet access to millions and help solve problems like deforestation. The purchase price was not disclosed but is believed to be around $75 million.

With $59 billion in cash in the bank and a well-oiled machine that every quarter generates billions more, Google can clearly afford to buy all sorts of companies. Generally Wall Street has indulged these acquisitions, even the unusual ones.

“All the crazy stuff like robotics is the best thing for the company,” said Gene Munster, an analyst with Piper Jaffray. “Investors feel like it’s a company that going to continue to find ways to grow. It’s a big contrast with Apple, whose investors are begging them to do more crazy stuff.”

Mr. Gillis is more skeptical. “Do you trust Google’s management as visionaries?” he asked. The analyst questioned the Nest purchase. Making thermostats does not fit in with Google’s core advertising operation, he said. Neither do the robots.

In absolute terms, Google is doing very well. Here is one way to measure its heft: The company is projected to increase its digital ad revenue this year by more than $5 billion, which is more than the total ad revenue of Yahoo or Microsoft.

The only viable threat to Google comes from Facebook, whose ad revenue is forecast by eMarketer to jump 50 percent this year. Facebook’s revenue is about a quarter of Google’s.

Google’s position on the decline in its profits for mobile ads? Don’t worry about it.

“I believe in the medium to long term that mobile pricing has to be better than desktop,” Nikesh Arora, Google’s senior vice president and chief business officer, said on a conference call with analysts. His reasoning is that knowing where the customer physically is will command a premium.

The “holy grail,” he added, will be when they start their campaign on the site instead of merely concluding it there.

One analyst noted on the call that Google had 10 percent of the worldwide advertising market.

“That tells me there’s 90 percent more opportunity around the world,” Mr. Arora said. “We don’t constrain ourselves and our thinking. We’d like more than we have in every market out there.”

A version of this article appears in print on April 17, 2014, on page B1 of the New York edition with the headline: Earnings and Sales From Google Disappoint. Order Reprints|Today's Paper|Subscribe



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  • Home
  • Applied Analytics
    • Analytics for Decision Making
    • Analytics for Marketing
    • Data Engine
    • Customer Insights
    • Analytics for Advance Marketing
  • Soft Skills
    • Adaptability
    • Confidence
    • Change Management
    • Unlearning and Learning
    • Collaboration and Teamwork
    • Cultural Sensitivity
  • Marketing
  • Finance
  • Economics
    • Introduction to Managerial Economics >
      • Basic Techniques
      • The firm: Stakeholders, Objectives and Decision Issues
      • Scope of Managerial Economics
    • Demand and Revenue Analysis >
      • Demand Estimation and Forecasting
      • Demand Elasticity
      • Demand Concepts and Analysis
    • Prodution and Cost Analysis >
      • Production Function
      • Estimation of Production and Cost Functions
      • Cost Concepts and Analysis I
      • Cost Concepts and Analysis II
    • Pricing Decisions >
      • Pricing strategies
      • Market structure and microbes barriers to entry
      • Pricing under pure competition and pure monopoly
      • Pricing under monopolistic and oligopolistic competition
    • Narendra Modi Development Model of Gujarat
  • JBDON Golf
  • Let's Talk
  • MBA Project Sharing
  • About Us
  • CET Knowledge Zone
    • Tips From JBIMS Students >
      • Prasad Sawant
      • Chandan Roy
      • Ram
      • Ashmant Tiwari
      • Rajesh Rikame
      • Ami Kothari
      • Ankeet Adani
      • Sonam Jain
      • Mitesh Thakker
      • Tresa Sankoorikal
    • Speed Techniques
    • CET Workshops
  • What is Cluster Analysis
  • Preparing Data and Measuring Dissimilarities
  • Data Reduction and Unsupervised Learning
  • Hierarchical and k-Means Clustering
  • Cluster analysis using excel and excel miner
  • Adding Uncertainty to a Spreadsheet Model
  • Defining Output Variables and Analyzing the Results
  • Using Historical Data to Model Uncertainty
  • Models with Correlated Uncertain Variables
  • Creating and Interpreting Charts
  • Using Average Values versus Simulation
  • Optimization and Decision Making
  • Formulating an Optimization Problem
  • Developing a Spreadsheet Model
  • Adding Optimization to a Spreadsheet Model
  • What-if Analysis and the Sensitivity Report
  • Evaluating Scenarios and Visualizing Results to Gain Practical Insights
  • Digital Marketing Application of Optimization
  • Advanced Models for Better Decisions
  • Business Problems with Yes/No Decisions
  • Formulation and Solution of Binary Optimization Problems
  • Metaheuristic Optimization
  • Chance Constraints and Value At Risk
  • Simulation Optimization
  • Marketing Analytics and Customer Satisfaction
  • Customer Satisfaction
  • Measurements and Scaling Techniques – Introduction
  • Primary Scales of Measurement
  • Comparative Scaling
  • Non-Comparative Scaling
  • Experiment Design: Controlling for Experimental Errors
  • A/B Testing: Introduction
  • A/B Testing: Types of Tests
  • ANOVA – Introduction
  • Example -Inspect Spray and Tooth Growth
  • Logit Model - Binary Outome and Forecastign linear regression
  • Social media Microscope
  • Text Summarization
  • N-Gram - Frequcy Count and phase mining
  • LDA Topic Modeling
  • Machine-Learned Classification and Semantic Topic Tagging
  • Visualisation and statistics (Political Advertising,Movie Theater and Data Assembly)
  • Excel Analysis of Motion Picture Industry Data
  • Displaying Conditional Distributions
  • Analyzing Qualitative Variables
  • Steps in Constructing Histograms
  • Common Descriptive Statistics for Quantitative Data
  • Regression-Based Modeling
  • Customer Analytics
  • Illustrating Customer Analytics in Excel
  • Customer Valuation Excel Demonstration
  • Understanding The Growth Of Data
  • Evaluating Methods Of Data Access
  • Communication journey
  • Data Journey
  • Finding Patterns In Data
  • New Page