This week on Whiteboard Friday, Rand Fishkin describes the methods he recommends for outsourcing content creation. Content is extremely important for SEO and users alike so these best practices are important for those of us without the luxury of an in-house staff of copywriters.

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Rand starts this presentation by setting context with his favorite SEO diagram. You can read more about the SEO Pyramid here.

Step 1: Requirements Gathering

Decide what you are trying to accomplish. Are you doing this for sales? SEO? Engagement? Traffic? Brand awareness? Be clear and write down what you want to accomplish along with the metrics you will use to measure them.

Step 2: Locating Potential Resources

You have plenty of options for finding potential resources. You can go offshore, in-house or hire web contractors. For web contractors, you can use the traditional services like Craigslist, oDesk, Elance, Guru or tap into the world of writing communities and long tail bloggers. These last two recommendations while not as established can many times provide superior quality writing with lower budgets.

Step 3: Research Writing Quality & Voice Match

In order to do this, we highly recommend you set up a voice document (a written record of how you would like to sound in your company’s written communications and promotions). Give this to the writer before getting a sample and use this as the yardstick after they submit their first sample. This will help you gauge if this person is a good fit for your organization.

Step 4: Scale, Evaluate, Track

Now that you have established a process, you need to put checks into place to make sure the writer is hitting their targets. Look back at the goals you created in the first step and use them to track and improve upon the related metrics.

Remember, from both an SEO and from a human perspective, writing is about quality over quantity. Having one great article that engages readers and earns links far outweighs 100 poorly written articles.

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I’ve just returned from the annual useR! conference for the open source R programming language. This year the conference attracted nearly 500 individuals to the NIST campus outside Washington D.C.

The conference provided a great opportunity to meet with some of the package authors that are working on third-party extensions, including Romain Francois and Dirk Eddelbuettel who jointly gave a pair of well-attended talks on their RProtoBuf and Rcpp packages.

In addition to the 3 days of tutorials, panels, and presentations, Google sponsored a dinner for conference attendees at the National Zoo in Washington D.C. to facilitate the “hallway track” of informal discussions outside of the official conference program.

Thanks to all those presenters, sponsors, and organizers involved in putting together a successful conference. For those who weren’t able to attend, the abstracts and slides from the 168 presentations and a more limited number of videos are available from the technical sessions. Hope to see you next year…

By Murray Stokely, Software Engineering Team

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Simon Stewart, pondering

Simon Stewart started off by telling us how to measure things: using tools like Firebug and Speed Tracer to break down client-side latency.

Matt Godbolt gave us some tips for making speedy Android apps, and Tim Cox presented a “Rant at Speed” that covered everything from CPU cache latency to the speed of light, all in five short minutes!

Glyn Wintle gave a quick rundown on common exploits: “How to break into a computer — fast”, covering the top five security mistakes made by web developers. You can try out some of these attacks yourself using the Google Gruyère codelab.

Ade Oshineye gave an impromptu (and not entirely serious) plan for “Making it faster.” Mike Mahemoff talked about speeding up web applications with the new shiny features in HTML5, and chatted briefly about the ever-blurring distinction between web applications and web pages. Paul Downey gave us an overview of TiddlyWiki, and TiddlySpace, where he hosted his presentation.

Squirrel

Squirrel gave a talk about Performance Secrets, which — uniquely for a Squirrel presentation — didn’t involve a flipchart.

Matthew Wild told us about Prosody, an XMPP server written in Lua. Apart from praising Lua as a great language, he also showed us how his continuous build generated annotated performance graphs on each commit to the repository.

George Cox proposed a need for making operational changes at speed — new deployments, and so on, while Luca Colantonio discussed his experiences implementing txty.mobi, a cloud-based web application for sending SMS messages via your own Android phone.

Tom Quick talked about the open source stack he’d used to develop GlastoTag, using Redis as a fast, persistent storage layer, and how using Django had helped to speed up their development process.

Jag

Last but not least, OS Jam favourite Jag gave us an overview of some of the performance decisions he’d made while developing Din.

As always we retired to the pub afterwards to continue our discussions. If you’re around London you’re welcome to join us for the next Jam. Join the mailing list or keep an eye out on the Jam site to find out more.

By Malcolm Rowe, Software Engineering Team

Tags: London OS Jam

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We have a lot of changes going on at SEOmoz (feel free to get excited, we sure are!) and with all of these changes to the site comes the need to focus on tracking. Internally we have spent the last few months redirecting our attention to not only the best practices regarding analytics and data mining, but really pushing ourselves to revisit our analytical processes.

You know what we realized? There sure is a lot of data. While I have always appreciated the reporting features in GA, I find that too often people take the reports at face value and fail to go deeper. It’s unfortunate since it is in those deep dives that you usually discover the data that can change your current course of action. So this post is going to tackle an approach to analytics that is often overlooked and (thanks to Google and their silly naming convention decisions) is rarely used to its fullest capacity. Get excited folks we are going to talk about benchmarking {Woohoo! Insert audience applause here}.

All of you excel spreadsheet lovers out there know plenty of ways to extract data and pinpoint specific red flags or recent successes. In fact, most people use analytics to simply analyze the current state of their account. While this is certainly a priority, it really is one dimensional. Instead of stopping there, why not go further? Why not better understand where your data was, and how you are measuring up? In fact, why not use this data to help inform your internal decisions as a company? It’s like an analytical epiphany—“using past and current data to help guide you moving forward.” Glorious.

While many of the analytics platforms out there have given us a number of ways to compare historical data to current data, we are still limited to two distinct time ranges (for the most part). It’s great to see those two ranges stack up against each other, but that still leaves a lot to be desired. Without going further you miss the "interaction" between those two distinct time ranges.

Benchmarking your data is a great way to discover more about this, often overlooked, gray area. Benchmarking simply means you set a standard at which you compare something else to. When used for data mining, it means you plot two distinct variables (time ranges, metrics, dimensions, etc.) over a period of time and then use these “benchmarks” to infer conclusions when making decisions.

You can then see  a more complete picture of your site’s momentum. In my opinion, understanding your site’s momentum is one of the most powerful metrics an analyst can calculate. If you can say with authority that you know how your site is doing and how it will likely be doing in the next week, month, few months, etc., you are in an ideal place. With data like that you can take more calculated risks.

*First, I want to throw out a disclaimer—a little over a year ago Google decided to integrate “Benchmarking” into their Visitors tab in GA. This just made things confusing in my opinion. The GA feature actually shows your site in comparison to a {very very very limited} industry pool of similarly {not really} sized sites. There is a lot wrong with the assumptions of this feature, but for our purposes here, when I say “benchmarking” I mean the act of plotting two distinct variables over time to extract insight…not the {ridiculous-I-can’t-believe-they-took-it-out-of-beta} GA feature.

The "benchmarking" feature in GA on SEOmoz

Okay now that we got that out of the way, let’s talk about how you can benchmark your data to hopefully gather some insight into your site’s performance.

Know your bottom-line (and your "high-line" –yes, I just made that word up)

This is probably the most common approach to benchmarking. It’s a pretty simple way to analyze the current state of your account. You should know your extremes for every metric. For example if you are a company that sells a seasonally successful product, you should know what your lowest conversion rate is for the year, as well as your peak conversion performance. In understanding the extremes you can make better assumptions on how your off season stats are trending. While not the most accurate approach to data mining, benchmarking the extremes of your account enables you to speak intelligently, at any given moment, on how your site is currently performing.

Know your ratios & relationships

Am I the only one that always reads “ratio” as “radio”? I digress. Knowing your metric ratios and how they relate to each other, is a great way to quickly detect when things are headed south. Often, as analysts, we don’t realize something has gone wrong until we see sales are down. While that is an effective method of pinpointing mistakes, it certainly isn’t ideal. Wouldn’t it be nice to quickly identify issues as they actually become issues? Crazy, I know. Well this is exactly what benchmarking the ratios of your site’s metrics can do. At SEOmoz, we use ratio/relationship benchmarking to keep our traffic stats in check. We don’t just plot out how many visitors each section of the site brings in out of the total visitors; we compare those percentages against each other. This gives us a ballpark value to guide us. An example; “the X part of the site brings in roughly twice as much as Y, which brings in about 1/3 of the traffic as Z.”

The great part about this method of benchmarking is you can easily turn it into a visual representation of the different pieces of the pie, and isolate out when things start to shift. Below is an actual example Rand pulled together earlier this week (yes he does that sort of thing for fun! A true data-head!). In this chart we have graphed out the top trafficked pages on our site, and then plotted them against each other to show how they are performing in relation to each other.

Also see a larger, detailed version

You can see the significant drop in the blue segment (our Tools page), which was due to a redirect mistake we made (oops…Rand talks more about that here). By visually representing these sections, we can easily identify shifts in the relationships, which can guide us on where we should focus our attentions (aka fix our silly SEO mistake ASAP!).

Know the norm

Okay I know, I know…I talked a whole lot of trash above on the GA benchmarking feature, and here I am talking about “knowing the norm,” but approaching data analysis this way can be insightful. Knowing and using industry standards in benchmarking can efficiently identify low hanging fruit.

However, the actual GA benchmarking tab is a poor example of this. Keep in mind that sites have to opt into the benchmarking, so (a.) this feature might not even have your industry represented and (b.) you have no way of knowing how many sites these “standards” are calculated on. Also keep in mind there are only three buckets for website “size” in this feature—small, medium, and large. WTF right? Yeah, since when do all websites fit into those three sizes? What am I ordering a latte over here?

With that said, it’s worth knowing the vital metric standards for your industry. If you see that similar sites to your own have a bounce rate of around 40% and you are chilling around 65%, while all the other metrics look closer in range, then you can assume this metric is where you should direct your optimization efforts. This approach isn’t as scalable or as accurate as other benchmarking methods, but it’s definitely worth a mention, if only for peace of mind.

Know the limits

While benchmarking is incredibly effective for things like trending, projecting, and exploring the data, it’s important to know the limits of the process. It is meant to be a discovery process, not a scientific formula. Just like anything else you take away from the data, it is just an insight, not a guarantee. You are making assumptions based on past performances, and performances change. So one word of caution to all of you data-heads out there—benchmarking is a great tool to add to your bag of tricks, but it is only one of many you should be using. Don’t get so caught up in forming relationships between the metrics and dimensions of your site that you lose perspective on the independent variables themselves.

In conclusion

Get in there. I mean it, seriously. I know we are all crazy busy, but that shouldn’t translate into a two minute GA log-in, a quick glance at the vital metrics and a few automated reports. Our analytics are meant to be explored. Benchmarking is one of those processes that may take an extra hour or two, but discoveries made during those few hours can be instrumental in guiding your company’s decisions.

Confession: At SEOmoz we haven’t always been the best with analytics and tracking, but in the past half a year we have refocused our energies on truly knowing what our users are doing, how our site is performing, and finding opportunities within the data. It’s time consuming, and tricky, and what you discover is not always fun to find out, but it has certainly helped us redirect resources where they are needed.

Over the next few months we are rolling out all sorts of good stuff, {the Chrome toolbar launch was just a teaser my friends }. We are using processes like benchmarking to better prepare us for these changes. Taking on new challenges as a company is an awesome thing, but doing it with a little data to steer you, makes the ride even more fun.

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There are also notes available from multiple OSCON sessions on Google Wave – check out the full listing if you want to catch up!

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For consumer product businesses on the Web, managing data can often be difficult because of all the data constituents involved (Marketing, IT, Operations and all the sub-managers under each).

Search engine optimization needs to be accounted for as part of your data architecture planning. Whether you are dealing with inventory, classifieds, information, etc., there are ways to manage your product data to better optimize your SEO efforts. Managing product data is often at the intersection of Marketing and IT Infrastructure.

I often refer to this as your “Markestructure.” Your Markestructure can be evaluated in more ways than just enhancing SEO efforts, such as CRM, customer acquisition, data hygiene, etc. But for this post, I will focus on four major ways to manage your product data (as part of your Markestructure) for SEO.

1. Creation of unique product data

2. Online Distribution

3. Customer Segmentation

4. Data conversion

Creation of Unique Product Data

We all know how Google loves content to be unique. Over the past few years, social media has facilitated unique content as user generated reviews appended to product information has helped grow companies like Yelp, TripAdvisor and others. However, outside of user-generated content, how do you make your product data more unique? Retail businesses will often struggle with having to merchandise the same products as competitors — whether it is refrigerators, cars, apparel, etc. A great way to manage differentiation is the utilization of third party data or branded content on your product details pages.

For example, if you are selling appliances online, you can utilize great data from data.gov to mash-up government benefits attached to each type of appliance. Let consumers be aware of the benefits to buying an energy efficient appliance. In addition, you can utilize content staff to enhance the messaging around the product details of the appliances to match of the voice of your brand. For example, if you are selling automobiles online, it is often difficult to differentiate yourself from competition on anything other than price. To a search engine, you can add content on the product page associated with your brand such as testimonials, statistics about your dealership customer service or quirky facts about the make/model of the car using strong keywords.

Online Distribution

Your website is merely one Web page or a group of Web pages from which to market your product data. Depending upon your industry, make sure you have a viable syndication and RSS program in place to proliferate and your product data to the highest degree possible. Once syndicated, you can create SEO programs around your data.

For example, back to the automotive example, there are great syndication outlets for your product data such as Googlebase, Oodle, Vast, etc. Once deployed, you need to assess the meta data associated with each data feed that is impacting search results for that channel. As an example with Googlebase, you may need to have stronger ALT tags on your images, increase the updates (freshness) of your data or merely add more content description with keywords in order to differentiate your product data.

SEO is not only about managing your product data results directly to high SERPs but also your marketing channels that indirectly impact SERPs.

Customer Segmentation

The great thing about SEO is how it is now converging with consumer marketing. The search engines now want to not only consume content like a reader, but also evaluate content like a reader. As such, it’s better if you you can work with marketing to create customer segmentation and integrate strategic keywords into your segment product data pages.

For example, back to the automobiles example, it would be great to utilize the same product data for a Make/Model (say, BMW) against several customer segments in order to generate several unique content pages. You may have a luxury buyer, a mid-life crisis buyer, a speed racer type, etc. For each segment, you can generate a specific landing page to filter to your product data in order to merchandise to that particular customer segment. I know my wife will not be buying a mini-van any time soon for our family because she refuses to be labeled a ‘Soccer Mom.’ Perhaps have a “Cool Mom Car” selection instead?

Data Conversion

Data associated with your products — whether it is retail commerce, classifieds, etc. — should not be tied to a specific format when it comes to generating SEO. At first, you should cover your bases and create an XML file to include all pages with your product data. However, you should explore options on how to convert your data into different formats, such as video, in order to create new unique content for the search engines using the same data. Perhaps take images and convert them to video files. There are sites like Animoto or companies like Liquidus Marketing that can generate great content with static jpeg images. You may also explore text to speech options where you can convert your data into audio files. Whether you create video or audio files, you can syndicate and index these new file types and generate user content against them over time to further enhance the uniqueness of the data.

Overall, these are merely four ways to evaluate product data as part of your Markestructure in order to better manage SEO. The process of search optimization is ongoing and requires constant attention. However, this post is a reminder to evaluate the origins, architecture and uses of your product data as part of your overall SEO strategy.

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