With both large real and hidden opportunity costs associated with evaluating, implementing, and learning the quirks of a new analytics tool, it only makes sense to ensure that the tool one chooses will be able to keep pace with one´s future requirements. So in order to evaluate tools correctly people need to be aware of the changes that are occurring in the online world as this will influence what data will be important to collect and leverage in the future. In this article I wanted to outline some of the top trends that are impacting the online marketing world so that when someone makes an analytics tool evaluation they are creating a framework for the future.

Part 5 of Framing the Future: Free Redefines the Market

Last but not least, we would be amiss if we did not address one of the biggest trends impacting the online analytics landscape in the last few years: the introduction of a free tool into the mix. Google is definitely re-defining the market and we will see a new role emerge for paid tools in the era of abundance of free analytics applications as free enables value to migrate to the next highest level.

Companies will now look to the paid tools to address more complex issues and demand that their quality overcomes the price hurdle. But free does not diminish the paid tools; rather it can be seen as a complement to existing paid tools as it opens the doors to new prospects that would otherwise never hear of or try out analytics. In fact, free tools should be embraced by the paid tools providers as it enables education, awareness, enlargement, and validation of online analytics. People will always pay to save time or mitigate risk, so the paid tools can address this with better set up or service or guaranteed privacy or data ownership, which can make the total cost of ownership of a paid tool even less than a free tool in the long run.

For example, if you are using a free tool but then need to invest heavily in consulting for customization or exporting of the data, your total cost could be higher than a paid tool that has intuitive capabilities or includes data exporting capabilities; additionally, if you are using a free tool but are then sued for not adhering to government required standards of privacy, then again, the free tool might also cost quite a bit more than the paid tool in the long run.

It is the responsibility of the paid tool providers now to make sure that their tool offers more value, or data privacy guarantees, or customizable service, or less risk, or more real time capabilities. In sum, there will now be a real difference between entry-level versus an enterprise level tool. The paid tools will now be required to be highly configurable and flexible in order to meet the educated demands of future users that are more aware than ever of what they should/or should not be paying for in an analytics solution.

Thus, anyone considering a new analytics provider in 2010 should be aware of the preceding trends and incorporate them into their decision making when evaluating an analytics solution.

This is Part 5: Free Redefines the Market, of a Five Part Series; See also Part 1: Data Trumps Everything, Part 2: The Real Time Web Reality, Part 3: Data Filtering and Visualization Capabilities Matter, and Part 4: Data Privacy Demands more Diligence.

Cecily Robyn Lough has over 15 years experience in pulling actionable insights from online marketing data. She is currently Director of International Sales at Webtrekk GmbH in Berlin. Please contact her through LinkedIn or at cecilyspeaks@gmail.com.

With both large real and hidden opportunity costs associated with evaluating, implementing, and learning the quirks of a new analytics tool, it only makes sense to ensure that the tool one chooses will be able to keep pace with one´s future requirements. So in order to evaluate tools correctly people need to be aware of the changes that are occurring in the online world as this will influence what data will be important to collect and leverage in the future. In this article I wanted to outline some of the top trends that are impacting the online marketing world so that when someone makes an analytics tool evaluation they are creating a framework for the future.

Part 4 of Framing the Future: Data Privacy Demands more Diligence

As personalization becomes more the norm, a lot more focus is also going to be given to the trade-off between personalization and data privacy in the near future. As each step in bringing together the open standard web creates easier opportunities for organizations to link together (and perhaps abuse) personal information, there might be more incidents like the one that happened to Google Document users last March 7th. On that day Google inadvertently shared spreadsheet information with peoples´ contacts who were never granted permission to see them. Google admitted that “we’ve identified and fixed a bug which may have caused you to share some of your documents without your knowledge,” but these types of bugs could have serious repercussions for the people affected and for legal compliance rules.

In fact, the data privacy laws are also changing in response to the technology. In Germany, there has been a lot of discussion lately on whether or not using Google analytics is even legal since tracking and storing an IP address is considered holding onto personally identifiable information, which is not allowed by German law.

Google´s terms of service do state that “Google will not associate your IP address with any other data held by Google.” However, according to paragraph 15 “Modifications of the rules” “Google reserves the right to change the terms and conditions of this agreement at any time [...] [...] [...assuming that the changes taken into account [by Google] are reasonable for you.”

In sum, Google is sitting on a treasure trove of linked data that they could potentially use in the future in ways that might violate data privacy laws. A German lawyer has even said that it is possible that the “penalties could amount up to €50,000 (about $75,000) per website that uses Google Analytics to keep track of its visitors’ usage patterns.” So it will be quite interesting to see how this will play out in the next couple of years.

The expectation for Germany is that more conservative companies may start to require that their analytics providers have a TüV certificate, which is similar to a data privacy seal of approval. This will require that the company uses only anonymous IP addresses, that they are also offering an opt-out cookie capability, and that they only use servers that are based in Germany. At the minimum, more companies may also start to require that they are the rightful owners to their own analytics data, so that they are mitigating the risk involved with passing the data through a 3rd party tool and can at any point get all of their data returned upon request. Thus, any company should probably include in their analytics evaluation process both the question of who owns their data and how compliant their current tool is with the changing legal landscape.

This is Part 4: Data Privacy Demands more Diligence, of a Five Part Series; See also Part 1: Data Trumps Everything, Part 2: The Real Time Web Reality, Part 3: Data Filtering and Visualization Capabilities Matter, and tomorrow Part 5: Free Redefines the Market.

Cecily Robyn Lough has over 15 years experience in pulling actionable insights from online marketing data. She is currently Director of International Sales at Webtrekk GmbH in Berlin. Please contact her through LinkedIn or at cecilyspeaks@gmail.com.

With both large real and hidden opportunity costs associated with evaluating, implementing, and learning the quirks of a new analytics tool, it only makes sense to ensure that the tool one chooses will be able to keep pace with one´s future requirements. So in order to evaluate tools correctly people need to be aware of the changes that are occurring in the online world as this will influence what data will be important to collect and leverage in the future. In this article I wanted to outline some of the top trends that are impacting the online marketing world so that when someone makes an analytics tool evaluation they are creating a framework for the future.

Part 3 of Framing the Future: Data Filtering and Visualization Capabilities Matter
As we are increasingly becoming more accustomed to the ease of use and nice visual displays offered by iPhones and Google Maps, we will definitely start to demand that these same types of interfaces be available from our analytics applications.

Therefore, if an analytics tool is able to present data in a visually intuitive, easy to understand format, the end users of that data are much more likely to be able to take action on the information. A good example of data visualization from an analytics tool would be the use of heatmaps, which are overlays on any webpage that can visually display where people clicked and where they did not – even in areas that are not hyperlinked. This gives any web designer immediate, clear feedback about what is attracting clicks and/or what the visitors “think” should be clickable.

Ideally, a heatmap should also be configurable to display any variety of different groups´ behavior – i.e. where purchasers vs. non-purchasers clicked, or where people clicked that came from different cities or different search engines. In addition, a nice analytics tool visualization is the website overlay view, which can also show how many clicks happened on each link and then should have the capability to drill down on these clicks to see much more detailed views (what campaign, what search word, what geography these clicks came from, etc. )

Or an analytics tool could visually show the path that the visitor took through each page before converting into a buyer, or which marketing channels the visitor clicked on before the sale. In sum, any graph that makes the data easy to understand and interpret will add value by enabling the analyst to find useful insights.

Equally important to the visual display is the filtering capability offered by your analytics tool. Since typical tools tend to aggregate and integrate information, tools that offer the ability to filter the data first and then transform it into easy to use bits of information with nice visual displays will outperform the others.

As Greg Boutin says, just like Google became the doorman to the web because it filtered things better than others. Filtering, not aggregating, is where the money is. Not more [information]…just smarter.”

As we generate and collect more data, the filtering and visual display of it enables the end user to make sense of it and leverage both the aggregate trends as well as the appropriate detailed views in order to take appropriate actions. Therefore evaluating your analytics tool to ensure that it has the ability to convey a message with useful visual displays of the data should also be on the list of factors to consider.

This is Part 3: Data Filtering and Visualization Capabilities Matter, of a Five Part Series; See also Part 1: Data Trumps Everything, Part 2: The Real Time Web Reality, and tomorrow – Part 4: Data Privacy Demands more Diligence.

Cecily Robyn Lough has over 15 years experience in pulling actionable insights from online marketing data. She is currently Director of International Sales at Webtrekk GmbH, a leading Web Analytics company based in Berlin, Germany. Please contact her through LinkedIn or at cecilyspeaks@gmail.com.

With both large real and hidden opportunity costs associated with evaluating, implementing, and learning the quirks of a new analytics tool, it only makes sense to ensure that the tool one chooses will be able to keep pace with one´s future requirements. So in order to evaluate tools correctly people need to be aware of the changes that are occurring in the online world as this will influence what data will be important to collect and leverage in the future. In this article I wanted to outline some of the top trends that are impacting the online marketing world so that when someone makes an analytics tool evaluation they are creating a framework for the future.

Part 2 of Framing the Future: the Real Time Web Reality

The most important trend this year is the advance of the “real-time” web, because this concept has changed the products and strategies of almost every major Internet company in 2009. In fact, it has become a core part of many Internet products this year: Twitter, FriendFeed (now Facebook), Facebook itself, Google, Delicious, WordPress, and many more.

However, the “real-time” web in 2009 implies both real time immediacy as well as the social graph associated with it. Real time is immediate in terms of “what are you doing right now?” updates, but the social graph portion also makes it a great indicator of the trends, interests, topics, and intentions that will ultimately lead to additional revenue streams as companies learn to react.

Think of the rapid changes that create additional revenue streams in e-commerce, travel, news, and retail sites today. Now think of the flood of additional real time feeds and applications that may impact your ability to sell or disseminate PR now or in the near future. Even if you do not have a real time web oriented business, your current and potential customers are using “real-time” communication devices to talk about your product or services. Ideally an analytics tool should also be able to scan social media for positive and negative sentiments and then incorporate this data into your analytics dashboard. So it makes sense to find a tool that also has the functionality to track and present social graph data in order to position your company to be able to respond appropriately.

As for the benefits of real time immediacy, the time when management used to fear expensive mistakes is over because when resources are cheap and digital you can take more risks and fail fast. So even though some may argue that real time data might be messier, the pivoting ability for your company and your marketing channels can be made much faster. For example, you will be able to tweak features, ads, and marketing campaigns in a faster time frame. In other words, if each person can monitor in real time how certain segments are reacting and then make the appropriate adjustments the repercussions can be more beneficial than doing the same adjustments in a lagged time frame.

Thus, as the internet eco-system changes around us, the real time web combined with social media will become increasingly important. So one should evaluate an analytics tool on whether or not it can enable (now or in the future) your employees to have access to real time data so that they then can at least have the possibility to create more revenue-enhancing responses.

This is Part 2: The Real Time Web Reality, of a Five Part Series; See also Part 1: Data Trumps Everything, and tomorrow – Part 3: Data Filtering and Visualization Capabilities Matter.

Cecily Robyn Lough has over 15 years experience in pulling actionable insights from online marketing data. She is currently Director of International Sales at Webtrekk GmbH, a leading Web Analytics company based in Berlin, Germany. Please contact her through LinkedIn or at cecilyspeaks@gmail.com.

With both large real and hidden opportunity costs associated with evaluating, implementing, and learning the quirks of a new analytics tool, it only makes sense to ensure that the tool one chooses will be able to keep pace with one´s future requirements. So in order to evaluate tools correctly people need to be aware of the changes that are occurring in the online world as this will influence what data will be important to collect and leverage in the future. In this article I wanted to outline some of the top trends that are impacting the online marketing world so that when someone makes an analytics tool evaluation they are creating a framework for the future.

Part 1 Setting the Scene: Data Trumps Everything

We can even take a step back before this evaluation process to ask on a high level whether the benefits in general of implementing an analytics solution outweigh the costs. The answer becomes evident if we look at the events that have happened in the last year; in 2009 more people than ever agreed that data driven organizations are much more effective than those based on HiPPO (Highest Paid Person´s Opinion) or other strategies.

A March 2009 Accenture Survey of executives working at U.S. companies with annual revenues of $500 million or more found that high performance businesses are five times more likely to use analytics than lower performers. Additionally, 70 percent of high performers identify analytics as a significant decision support tool.

The overarching example is Google: Marissa Mayer, EVP, has said that “we let the math and data govern how things look and feel” and this policy has made them extremely successful. The extent to which this is true can be seen by an article on the front page of the business section of the New York Times on May 9, 2009, that quotes employee Douglass Brown saying that he could not even decide whether a line on a web page should be 3, 4, or 5 pixels wide until he had tested all 3 versions. In the end, Brown left Google since the engineering culture was “not friendly to designers” since all of his decisions were asked to be backed up with data. This was only newsworthy because Brown argues against what has become acceptable practice at Google as well as other high performing businesses: data trumps everything.

In fact, in the last few years, data driven decision making has been validated for all size organizations. For a larger organization with significant amounts of traffic, making even the smallest changes can have quite a large positive impact on revenue. “More is different” due to the large volumes of traffic involved. Therefore, it is important for a high traffic site to find an analytics tool that can handle billions of transactions while presenting granular data in order to make decisions and analysis based on more detailed reporting. For example, it might be interesting to have more detailed knowledge of all of the marketing initiatives that contributed to a sale besides what happened during the last click in the last session before a purchase. Or it might be interesting to know exactly which customers put which items into their shopping carts and then abandoned them, in order to send follow up email marketing campaigns.

On the other hand, a small, start up organization can also use an analytics tool with equal effectiveness. One way would be for the product manager to measure how much additional traffic is gained by adding different features and later doing some comparative testing to understand which feature sets are optimal. Then as one works to grow traffic, the marketing manager might also use analytics for tracking the effectiveness of email campaigns and ad inventory. In fact, Andrew Chen (a former Entrepreneur-in-Residence at Mohr Davidow Ventures) recommends viewing analytics as a necessary tool to be used during your start up product development process, no matter how small your company might be, in order to confirm or deny assumptions you have made on why people stay engaged on your site. He asks, “in reality, is it better to build 10 features where you do not know what worked and what did not, or is it better to build LESS features but have a clear sense of why it worked?”

Stay tuned for Part 2 tomorrow: The Real Time Web Reality.

Cecily Robyn Lough has over 15 years experience in pulling actionable insights from online marketing data. She is currently Director of International Sales at Webtrekk GmbH, a leading Web Analytics company based in Berlin, Germany. Please contact her through LinkedIn or at cecilyspeaks@gmail.com.

Guest Post: Cecily Robyn Lough

Web Analytics Guru Avinash Kaushik has outlined in his book that the ROI you can get from leveraging real-time, raw data is so low it does not make sense for most companies to implement it or care about it. His conclusions are not based on the inherent inutility of using real-time, raw data but rather the cost and complexity hurdles that have to be surmounted in order to get the kind of ROI that would make an analytics solutions justifiable.

And since most of us are fans and students of his book Web Analytics: An Hour a Day (I include myself in both of these categories) we do not take issue with this idea; also we do not take issue with it because his book is so chock full of pearls of wisdom that everyone gleans some insights from it regardless of their level of experience.

However I want to open up for discussion on how working with real time raw data can be cost effective. And, it can provide greater ROI if and only if the analytics product´s surrounding eco-system is set up in a way that enables the data to be leveraged effectively. In fact, my theory is that:

• Real Time Data Can be Leveraged Effectively
• Both Real Time and Raw Data have Beneficial Repercussions throughout every Analysis
• Both Real Time and Raw Data Create Actionable Metrics
• Real Time Raw Data Ensures Greater Accuracy (no caching) and Timeliness
• Real Time Raw Data Future-Proofs Your Analytics Solution

I absolutely agree with Avinash Kaushik that real-time data does not matter to a company unless they will actually take action on the reports; that is what in the long run drives the ROI of the tool.

In general however the ROI on any analytics tool will be greater if each person on every team that touches the website has the power and the understanding to create little frequent changes; i.e the power of crowd-sourcing for managing a large, dispersed multi-national website is immensely greater than having one smart analyst in one location interpreting all the data. No matter how smart they are, one analyst can never react in a timely enough manner, nor in a localized enough manner, nor even be able to digest all the complex statistics fast enough to glean the insights that create the actions that drive the ROI.

Therefore, if each person on each team can react daily to even just one real time data point, you will be creating a competitive advantage over those that have one analyst in a lagged time frame. The actions will be created by those that are familiar with the data and in a time frame that provides real monetary value (think of the rapid changes that create additional revenue in eCommerce, Travel, News Content, etc.)

Therefore the tool you choose needs to complement the real time data with an easy to use interface and unique user management so that each marketing person can have the metrics that matter to them provided to them on a daily basis. If each person is responsible for certain KPIs (Key Performance Indicators) and the tool provides an intuitive enough interface or an export capability so that these people will potentially not even have to learn how to use the tool, then you have a winning combination. Real time data with unique user management and easy to understand reporting is very effective and can drive the ROI required. However, you have to have the complete eco-system in one tool and at a reasonable price point in order to make implementing it worthwhile.

And, even more importantly, I would agree with Avinash that you need to also ask yourself whether your website gets enough visitors exhibiting the right behavior to ensure that the real time data choices you make will result in statistically significant outcomes. He does confirm that “..statistical significance is not just about raw numbers..” but more about the impact of the changes you can make in real time. However, if it is clear that your business can impact the ROI by leveraging real time data, then you should also be looking for a tool that can handle significant volumes of data in real time with ease (i.e. as much as 1B page impressions a month) If you have a large amount of data that you are processing then even smaller changes you make can have quite a large monetary impact.

The same idea is pervasive about leveraging raw data for analytics – i.e. it might be great in theory, but provides poor ROI because it is too costly.

However, having the raw data enables complete flexibility in every analysis, as well as future proofs your analytics solution – increasing the tools´ long term value and again providing more effective ROI.

When all data can be correlated in any way in all time periods, this enables much more detailed views, segmentation, and most importantly, retro-active on-the-fly queries. In other tools you need to know ahead of time what sort of analysis you think will be important in the future; with the raw data you can slice and dice the data any way you would like at any moment. You can add any special unique metrics in any time frame. Therefore leveraging raw data becomes imperative as you compare and contrast different campaign success metrics in different time periods ( Even Avinash Kaushik cites that as your “..strategic objectives evolve [y]ou should expect a 20% churn in your main KPIs every six months…if they are not changing at least that much, either not a single dimension of your business has changed on the Web in that time (highly unlikely) or your KPI’s are stale.. ” p.349.)

In addition, raw data´s ability to provide a granular enough view so that you will get user level data also makes the ROI on the tool much higher than those tools giving you aggregated information. Who wouldn´t want to receive a timely email with a special discount coupon for a pair of pants that you had just put in your shopping cart but then abandoned because they were too expensive?

Thus, the level of detail that raw data provides creates action oriented metrics – not vanity metrics. For example, knowing that your conversion rate is increasing is great and makes a website owner proud (vanity metrics) but what are the next steps that one should take to continue to help increase this traffic? High conversion rates could be the result of non-reproduce- able actions, such as moving out a deeply discounted product line. With a raw data based analytics solution, you can drill down to find out exactly how this conversion rate was increased and by which visitor segments on which search engines, thus enabling the online marketer to know exactly what action to take next ( buy more keywords on certain search engines, reach out to certain publishers, etc.)

Thus, granular, extensive, sophisticated data analysis along with the ability to perform any retro-active analysis on the fly is the key to providing better ROI and getting longer term value from your analytics tool.

In sum, real-time raw data can be cost effective if the attributes of the tool enable your team to leverage action-oriented metrics now and any unknown metrics that they may want to leverage in the future at a reasonable price point.

Cecily Robyn Lough is currently Director of International Sales at Webtrekk GmbH in Berlin. She believes that Webtrekk´s current analytics solution does have the ability to make raw, real-time data cost effective for web sites that need up to a billion pieces of data segmented on-the-fly.

Please contact her at Cecily@webtrekk.com or +49 (0) 30 755 415 440 for more information