Tips and tricks for Retail advertisers

Please enjoy the second post in the series of tips and tricks for advertising within your industry. This week: Retail! Up next is Travel.

There are countless varieties of products, services, and companies within the Retail industry, but one thing unites us all: seasonality. Whether you’re selling school supplies, MP3 Players, or sports gear, you probably have a ‘hot season’ and some semblance of a ‘down season.’ I think our biggest trap as Retail advertisers is to only focus our efforts on the hot season; whereas Retail marketing should really happen year-round.

You have a lot of options and resources -- both free and paid -- available for marketing your business throughout the year. Below I’ve outlined a few of my favorite tools and tips.

• Figure out when your hot season actually starts. Many marketers assume they know when their hot season starts and ends or they base this year’s strategy on last year’s season. Use Insights for Search to better understand when customers begin to search for your products. If you look at search volume on ‘swimsuits,’ you’ll notice that queries actually begin to rise in January and maintain steady volume throughout July. Don’t fall into the trap of advertising swimsuits only in the summer!

• Use different types of campaigns at different times. Once you fully understand your seasonality, think critically about the different types of advertising you’ll do throughout the year. For AdWords customers, for example, I recommend exploring theRemarketing Tool to keep track of customers who may have visited your site during the down season. When hot season strikes again, you’ll be able to reach these customers when they are more actively pursuing your products. Remarketing is also a great way for AdWords customers with multiple seasons to reach people throughout the year. For example, if you sell flowers, you can set up a cookie to target people who purchased flowers from you on Valentine's Day, and then begin showing them ads for your Mother's Day specials in April.

• Through it all, continue to optimize your account structure. Once you’ve established the initial structure of your account, be sure to track performance and optimize your keyword lists and ad texts, particularly during down seasons. I recommend using the ‘Search terms’ report on your broad match keywords or the Opportunities Tab to identify new keyword ideas as well as negative keywords. For your ad text, look at which ads converted best in the previous hot season, and see if any consistent themes jump out. You may notice that mentioning ‘Save 20% on Gifts’ worked better than ‘Save Big on Gifts’ and you can edit your text accordingly for the next hot season.

• Take advantage of free offerings, particularly during the down season. For Retailers with physical locations, it’s imperative that you create a listing on Google Places. Particularly as consumers increasingly search while they are on the go, it’s important that people know when they are near your physical location!

Think of how much more time you have when you’re in the down season -- and use this time to optimize and grow your business in advance of the hot season. Our customers’ interests and behaviors change each year and yes, it can be difficult to keep up. Lucky for us, we have plenty of options to reach our customers at different phases of the conversion cycle and many tools to better understand our customers. Best of luck in 2011!

Posted by Tim Freeth, Team Lead, AdWords Retail

Google Hotpot now on Google.com and around the world

[Cross-posted from the Hotpot Community Blog]

Back in November, we introduced Hotpot, a new local recommendation engine powered by you and your friends. Using Hotpot is simple: you rate places on google.com/hotpot—restaurants, hotels, cafes—and add friends on Hotpot whose opinions you trust. Then the next time you perform a search, Google will serve up personalized results, listing places based on your tastes, as well as recommendations from your friends.



We’ve watched Hotpot grow and change over the last couple months, and today Hotpot is really going places: to a Google search box near you and around the world.

You can now enjoy Hotpot recommendations in your regular search results on Google.com. So say you’re looking for a restaurant in Barcelona. Go to Google and search [restaurant barcelona]. If a friend has rated a particular restaurant, you might see their rating and what they had to say about it—as well as their name and photo—directly beneath that restaurant’s listing. To see all recommendations by your friends, click “Places” on the lefthand side of the page, and choose “Friends only.” Remember, you’ll need to be logged in to your Google account in order to see recommendations.

Seeing place recommendations based on your tastes and those of your friends across more Google searches will make results more relevant to you and maybe lead you to discover a new gem. If you don’t have Hotpot friends yet, you can invite them to share all the places they love with you by using the “Friends” tab on google.com/hotpot.

But Hotpot will only be half the fun if you can’t share it with all your international friends. So starting today, we’re making Hotpot available in 38 new languages—including Chinese, French, German, Italian, Korean, Polish, Russian and Spanish—allowing people to share their favorite places in their native language.

Start rating and sharing recommendations with Hotpot everywhere, anytime: at google.com/hotpot, on Google Maps, using Google Maps for Android with an easy widget, and on our new iPhone app.

Happy rating!

Posted by Lior Ron, Product Manager

Tips and tricks for Financial Services advertisers

We’ve asked our AdWords Team Managers to provide tips and tricks for online advertising within particular industries. Over the next few weeks, we’ll feature these guest posts on the Small Business Blog. We're kicking off with a guest post on advertising within the Financial Services industry -- check back for information on your industry!

In thinking about ‘online advertising’ it’s easy to focus on keywords, ad texts, and clickthrough rates. Now with more online media channels than ever, it’s important we think about our online identity holistically as well -- and that we consider how this identity might impact our advertising both on and offline.

This practice is particularly important for advertisers in the Financial Services industry. Our businesses and customers are greatly impacted by economic trends and world events. For this reason, I encourage you to consider the following tips when developing your advertising strategies.

• Be an industry expert. As a member of the Financial Services industry, you must be an expert on the economy as a whole -- not just an expert of your own particular niche. One thing we’ve learned from the recession is just how interconnected the Financial Services industry is: decisions and regulations in one sector often impact dealings in another. For example, as interest rates were lowered through the recession, consumer interest in refinancing increased at a sustained level. As a marketer, you must stay on top of these trends to understand how they impact your business and your customers.

• Adjust your online advertising strategy in reaction to consumer behavior or industry trends. If you notice a particularly pertinent financial ruling or piece of news, use Insights for Search to learn how this change has impacted search behavior. For example, as Congress considered extending the Bush Tax Cuts recently, consumer searches spiked. What an excellent opportunity to get your name in front of your target audience in order to get the most out of your marketing budget!

• As an example, Humana insurance recently noticed changing consumer behavior and incorporated industry trends into their marketing strategy. In reaction to changing healthcare reform and later medicare enrollment, Humana took the opportunity to educate their customers via a YouTube brand channel and edited their ad text to include pertinent terms, such as ‘affordable.’

• Manage your online brand and reputation. For better or for worse, there are now countless channels through which you can influence your brand online. I recommend setting up Google News Alerts that trigger when your company name is mentioned. I also recommend creating Profile Pages, listing your business on Google Places (only, of course, if you have a brick and mortar address), and testing new Google ad formats that showcase your product or service, such as SiteLinks.

• Engage with your customers online. And finally, to build upon your online brand, it’s important to interact with your customers online. Engaging with your customers online can also help you better understand if/how economic trends are impacting them -- and this can help you make business and advertising decisions. I recommend creating Twitter, Facebook and YouTube identities to solicit feedback and engage with your customers!

2011 is an exciting year to be an online advertiser! In planning your advertising strategy this year, make sure you take a step back to see the bigger picture. For more tips on Google’s tools related to online business, please visit the Small Business Center.

Posted by Payton Dobbs, Team Manager, AdWords Financial Services

Julia meets HTML 5

Posted by Daniel Wolf, Software Engineer

Today, we launched Julia Map on Google Labs, a fractal renderer in HTML 5. Julia sets are fractals that were studied by the French mathematician Gaston Julia in the early 1920s. Fifty years later, Benoît Mandelbrot studied the set z2 − c and popularized it by generating the first computer visualisation. Generating these images requires heavy computation resources. Modern browsers have optimized JavaScript execution up to the point where it is now possible to render in a browser fractals like Julia sets almost instantly.

Julia Map uses the Google Maps API to zoom and pan into the fractals. The images are computed with HTML 5 canvas. Each image generally requires millions of floating point operations. Web workers spread the heavy calculations on all cores of the machine.

We hope you will enjoy exploring the different Julia sets, and share the URLs of the most artistic images you discovered. See what others have posted on Twitter under hashtag #juliamap. Click on the images below to dive in to infinity!

Google at NIPS 2010

Posted by Slav Petrov, Doug Aberdeen, and Lisa McCracken, Google Research

The machine learning community met in Vancouver in December for the 24th Neural Information Processing Systems Conference (NIPS). As always, the single-track program of the main conference featured a number of outstanding talks, followed by interesting late night poster sessions. A record number of workshops covered a wide variety of topics, while allocating sufficient time for skiing in Whistler - after all, many of the most interesting research conversations happen while riding the lift in-between ski runs. This year’s conference also featured a symposium dedicated to Sam Roweis, providing a retrospective on Sam’s life and work. Sam, a fellow Googler and professor at NYU, was at the heart of the NIPS community and is terribly missed.

As always, Google was involved in various ways with NIPS. Here at Google, we take a data-driven approach when solving problems. Therefore, Machine Learning is in one way or another at the core of most of the things that we do. It is therefore unsurprising that many Googlers helped shape the program of the conference or were in the audience. This year, three Googlers served as area chairs and even more were reviewers. Googlers also co-authored the following papers:

• Label Embedding Trees for Large Multi-Class Tasks by Samy Bengio and Jason Weston
• Learning Bounds for Importance Weighting by Corinna Cortes, Yishay Mansour, and Mehryar Mohri
• Online Learning in the Manifold of Low-Rank Matrices by Uri Shalit, Daphna Weinshall, and Gal Chechik
• Deterministic Single–Pass Algorithm for LDA by Issei Sato, Kenichi Kurihara, and Hiroshi Nakagawa
• Distributed Dual Averaging In Networks by John Duchi, Alekh Agarwal, and Martin Wainwright

Additionally, Googlers co-organized three well attended workshops:

• Coarse–to–Fine Learning and Inference by Ben Taskar, David Weiss, Benjamin Sapp, and Slav Petrov
• Low–rank Methods for Large–scale Machine Learning by Arthur Gretton, Michael Mahoney, Mehryar Mohri, and Ameet Talwalkar
• Learning on Cores, Clusters, and Clouds by John Duchi, Ofer Dekel, John Langford, Lawrence Cayton, and Alekh Agarwal

Finally, Yoram Singer gave a great talk on Learning Structural Sparsity at the Sam Roweis symposium and Googlers presented the following talks during the workshops:

• Online Learning in the Manifold of Low–Rank Matrices by Uri Shalit, Daphna Weinshall, and Gal Chechik
• Distributed MAP Inference for Undirected Graphical Models by Sameer Singh, Amar Subramanya, Fernando Pereira, and Andrew McCallum
• MapReduce/Bigtable for Distributed Optimization by Keith Hall, Scott Gilpin and Gideon Mann
• Self-Pruning Prediction Trees by Sally Goldman
• Web Scale Image Annotation: Learning to Rank with Joint Word-Image Embeddings by Jason Weston, Samy Bengio, and Nicolas Usunier
• Coarse–to–fine Decoding for Parsing and Machine Translation by Slav Petrov

Overall, it was a very successful conference and it was good to be back in Vancouver one last time. This coming year NIPS 2011 will be in Granada, Spain. Hasta luego!

Tips for creating a free business listing in Google Places: Adding useful descriptions and relevant categories

(Cross-posted from LatLong)

With this blog post, we’re concluding our three part series about the Google Places quality guidelines. Today, we’ll discuss how to choose the best fitting categories for your business listing as well as how to provide a useful description. In case you missed the first two blog posts, you can find here the first post about business titles and here the second part about business types.

Adding useful descriptions

As a business owner, we encourage you to add a specific description of your business in the “description” field. This gives potential clients more information to understand what your business is about and see if your business matches what they are seeking. You can also use this field to provide further guidance about the location of your business which might be useful in some cases where it is hard to find, e.g. if the entrance of your business is only accessible via the rear.

Keep the description clean and concise, so it is helpful to users and catches their attention. A series of repeated keywords or categories may turn off potential customers, but a crisp and catchy summary of the services you offer help users determine if your business is right for them.

Choosing relevant categories

If you provide appropriate and accurate categories, we can better match your business listing to relevant user searches. We recommend choosing specific categories that describe the core of your business well instead of broad ones. A good way to find representative categories for your business is asking yourself the question “What is my business?” Be sure to capture what your business is as opposed to what it offers or sells - in that sense, “bakery” would be a good category as opposed to “cakes” or “bread”.

Also, do not include location information in the categories field. If you would like to provide such additional information about your business, you can use the description field and, if appropriate, the service areas feature.

You will be asked to choose at least one category from our standard list - just start typing in the categories field to see what is available via the auto-suggestions.

We recommend always choosing the best matching and most specific category for your business - for any specific category, Google will be able to automatically determine the more generic category as well. That means, if you are a Mexican restaurant, you should go for ‘Mexican Restaurant’ and not ‘Restaurant’ - Google then automatically knows that if you are a Mexican restaurant, you are also a restaurant.

You can provide up to five categories for your business listing. After picking a standard category, you can add up to four customized categories. To add another category, just click on ‘Add another category’ and an additional field will be triggered. Put only one category per entry field. Entering more than one category into a category field is not compliant with our quality guidelines and could result in your listing being suspended and not appearing in Google Places. In case you find it difficult to find an appropriate standard category to start with, just pick a category that fits best and add more specific custom categories. If you are uncertain about categorizing your business, you can also ask for advice in the Google Places help forum and discuss with other business owners.

We hope that this information helps you add a concise description and accurate categories to your business listing in Google Places. This gives potential clients more information to determine if your business matches what they are seeking. For further questions you can visit our Google Places help forum.

Posted by Sabine Borsay, Consumer Operations

More Google Contributions to the Broader Scientific Community

Posted by Corinna Cortes and Alfred Spector, Google Research

Googlers actively engage with the scientific community by publishing technical papers, contributing open-source packages, working on standards, introducing new APIs and tools, giving talks and presentations, participating in ongoing technical debates, and much more. Our publications offer technical and algorithmic advances, demonstrate things we learn as we develop novel products and services, and shed light on some of the technical challenges we face at Google.

In an effort to highlight some of our work, we periodically select a number of publications to be featured on this site. We first posted a set of papers on this blog in mid-2010 and subsequently discussed them in more detail in the following blog postings. This blog posting highlights a few new noteworthy papers authored or co-authored by Googlers from the later half of 2010. In the coming weeks we will be offering a more in-depth look at these publications, but here are some summaries:

Algorithms and Electronic Commerce

Robust Mechanisms for Risk-Averse Sellers
ACM Conference on Electronic Commerce (EC)

Mukund Sundararajan and Qiqi Yan, Stanford University

In his seminal Nobel prize-winning work, Roger Myerson identified the revenue-maximizing auction for a risk-neutral auctioneer. In contrast, this work identifies good mechanisms for risk-averse auctioneers. These mechanisms trade a little revenue for better certainty, in the best possible way. We expect this work will help guide reserve-price selection in auctions where auctioneers/sellers want better control over their revenue.

Monitoring Algorithms for Negative Feedback Systems
World Wide Web Conference (WWW)

Mark Sandler and S. Muthukrishnan

In negative feedback systems, users report abusive content at a site to its owner for consideration or removal, but the users might not be honest. For the site owners, this represents a trade-off between vetting such user reports by humans vs. accepting them without vetting. This paper presents a mathematical framework for design and analysis of such systems and presents algorithms with provably good trade-offs against malicious users.

HCI

Children's Roles Using Keyword Search Interfaces in the Home
Computer Human Interactions (CHI)

Allison Druin, University of Maryland, Elizabeth Foss, University of Maryland, Hilary Hutchinson, Evan Golub, University of Maryland, and Leshell Hatley, University of Maryland

In this paper, we describe seven search roles children display as information seekers using Internet keyword interfaces, based on a home study of 83 children ages 7, 9, and 11.

Machine Learning

Large Scale Image Annotation: Learning to Rank with Joint Word-Image Embeddings

European Conference on Machine Learning (ECML) Best Paper

Jason Weston, Samy Bengio, and Nicolas Usunier, Universite Paris 6 - LIP6

In this paper, we introduce a generic framework to find a joint representation of images and their labels, which can then be used for various tasks, including image ranking and image annotation. We simultaneously propose an efficient training algorithm that scales to tens of millions of images and hundreds of thousands of labels, while focusing training on making good predictions at the top of the ranked list. The models are both fast at prediction time and have low memory usage making it possible to house such systems on a laptop or mobile device.

Overlapping Experiment Infrastructure: More, Better

Faster Experimentation, Knowledge Discovery and Datamining (KDD)

Diane Tang, Ashish Agarwal, Deirdre O'Brien, and Mike Meyer

Google's data driven culture requires running a large number of live traffic experiments. This paper describes Google's overlapping experimental infrastructure where a single event (e.g. a web search) can be assigned to multiple simultaneous large experiments. The infrastructure and supporting tools provide a framework that enables running experiments from design to decision making and launch, and can be generalized to many other web applications.

NLP

Products of Random Latent Variable Grammars

North American Chapter of the Association for Computational Linguistics (NAACL)

Slav Petrov

It is well known that the Expectation Maximization algorithm can converge to widely varying local maxima. This paper shows that this can be advantageous when learning latent variable grammars for syntactic parsing. By combining multiple state-of-the-art individual grammars into an unweighted product model, parsing accuracy can be improved from 90.2% to 91.8% for English, and from 80.3% to 84.5% for German.

Software Engineering

Contention Aware Execution: Online Contention Detection and Response

International Symposium on Code Generation and Optimization (CGO)

Jason Mars, University of Virginia, Neil Vachharajani, Robert Hundt, Mary Lou Soffa, University of Virginia

This paper makes a big step forward in addressing an important and pressing problem in the field of Computer Science today. This work presents a lightweight runtime solution that significantly improves the utilization of datacenter servers by up to 58% on average. This work also received the CGO 2010 Best Presentation Award.

Speech

Say What? Why users choose to speak their web queries

Interspeech

Maryam Kamvar and Doug Beeferman

Say What? Have you been speaking your search queries into your mobile device rather than typing them? Spoken search is available on Android, iPhone and Blackberry devices and we see an increasing numbers of searches coming in by voice on these phones. In our paper “Say What: Why users choose to speak their web queries” we investigate, on an aggregate level, what factors are most predictive of spoken queries. Understanding context in which a speech-driven search is used (or conversely not used) can be used to improve recognition engines and spoken interface design. So, save keystrokes and say your query!

Query Language Modeling for Voice Search

IEEE Workshop on Spoken Language Technology

Ciprian Chelba, Johan Schalkwyk, Thorsten Brants, Vida Ha, Boulos Harb, Will Neveitt, Carolina Parada*, Johns Hopkins University, and Peng Xu

The paper describes language modeling for google.com query data, and its application to speech recognition for Google Voice Search.
Our empirical findings include:

• 10% relative gains in WER from large scale modeling,
• a less known yet potentially quite detrimental interaction between Kneser-Ney smoothing and entropy pruning (approx. 10% relative increase in WER)
• evidence that hints at non-stationarity of the query stream, and
• surprisingly strong dependence across three English locales---USA, Britain and Australia.

Structured Data

Dremel: Interactive Analysis of Web-Scale Datasets

Very Large Data Bases (VLDB)

Sergey Melnik, Andrey Gubarev, Jing Jing Long, Geoffrey Romer, Shiva Shivakumar, Matt Tolton, and Theo Vassilakis, Google Inc.

Dremel is a scalable, interactive ad-hoc query system. By combining multi-level execution trees and columnar data layout, it is capable of running aggregation queries over trillion-row tables in seconds. The system is widely used at Google and serves as the foundational technology behind BigQuery, a product launched in limited preview mode.

Systems and Infrastructure

Large-scale Incremental Processing Using Distributed Transactions and Notifications

USENIX Symposium on Operating Systems Design and Implementation (OSDI)

Daniel Peng and Frank Dabek

In the past, Google accumulated a whole day’s worth of changes to the web and ran a series of enormous MapReduces to apply this batch of changes to our index of the web. This system led to a delay of several days between crawling a document and presenting it to users in search results. To meet our goal of reducing the indexing delay to minutes, we needed to update the index as each individual document was crawled, rather than in daily batches. No existing infrastructure supported this kind of incremental transformation at web scale, so we built Percolator: a framework for transforming a large repository using small ACID transactions.

Availability in Globally Distributed Storage Systems

USENIX Symposium on Operating Systems Design and Implementation (OSDI)

Daniel Ford, Francois Labelle, Florentina Popovici, Murray Stokely, Van-Anh Truong*, Columbia University, Luiz Barroso, Carrie Grimes, and Sean Quinlan

In our paper, we characterize the availability of cloud storage systems, based on extensive monitoring of Google's main storage infrastructure, and the sources of failure which affect availability. We also present statistical models for reasoning about the impact of design choices such as data placement, recovery speed, and replication strategies, including replication across multiple data centers.

Vision

Improved Consistent Sampling, Weighted Minhash and L1 Sketching

IEEE International Conference on Data Mining (ICDM)

Sergey Ioffe

With the huge amounts of very high-dimensional data, such as images and videos, we frequently need to "sketch" the data -- that is, represent it in a much more compact form, while still allowing us to accurately determine how different any two images or videos are. In this paper, we describe a sketching method for L1, one of the most common distance measures. It works by first hashing the data with a new algorithm, and then compressing each hash to a small number of bits, which is learned from data. This method is fast and allows the distances to be estimated accurately, while reducing the storage requirements by a factor of 100.

*) work carried out while at Google