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This week’s guest was Gary Angel, Founder and CEO of Semphonic, Inc., a leader in web measurement and analytics. Gary and I first met back in 2005, when I was managing Intuit’s Online Community. At the time, I wanted to get beyond clicks, page views and links (like Lions, Tigers and Bears, oh my) and identify new and innovative ways to think about analytics in the ecommerce and social web. After meeting with many of web analytics consultants, I finally was schooled (in a good way) by Gary. He is not only one of the leading innovators and practitioners in this space, but also is one of the most effective business consultants I have worked with.

Gary started his career as working as programmer in the finance industry, which has proved invaluable in growing his analytics business. Financial institutions tend to be light years ahead of most companies when it comes to looking at customer behavior. I know this firsthand; Twenty plus years ago, I worked at American Express (AMEX), and since that time, I have worked at a number of Fortune 100 companies, and none of them have been as sophisticated as AMEX when it comes to customer data. Maybe that’s one of the reasons, Gary and I like getting together and discussing customer behavior and data. We both received our initial training at the American Express’ of the world. (By the way, we do a monthly webinar series on the topic and you can find some of our previous presentations here).

Gary leveraged his early programming experience in the financial industry: “there’s a lot of similarities (with credit cards and web behavior) that people over on the websites are showing you their interests by the way they navigate and the way they move through your pages and what they look at and what they buy and how they spend their time.” Interestingly, some of the data and predictive modeling did not carry over so well into the web space.

At Semphonic, Gary has witnessed first-hand the evolution of web analytics. He points out that in the early 2000s, people were quite skeptical of web analytics and they didn’t know how to get beyond how many page views or clicks they had. Neither of these items is very useful for building customer profiles and segments.

Since so much of Semphonic’s early work focused on how users navigated a website, Semphonic developed an approach called Functionalism, which basically breaks up a web site into its constituent pieces and then assigns one or more specific functions to each piece. These functions can be things like navigation (e.g. route visitors to a specific place), motivation (e.g. convince a user to do something) or information (e.g. provide a visitor with some piece of information). Based on the functions of the page, it is assigned a particular page type from a set of common templates that they’ve distinguished over time in the measurement of different types of sites.

As the graphic below indicates, you can build expected (and desired) use cases around people’s behavior, and then track their success rates. Or you can look at their ‘Say-Do’ ratio in your research. You can ask them how they use a page or a website and then track their behavior online to see if those two things are consistent.

The colors redgreen and yellow indicate whether or not they completed the desired task. And if they didn’t, that would be considered ‘an outage’. Or an unsuccessful task. Searching on a word, getting a search results page and leaving the site, for example, would be considered an outage. This is really important when determining how to spend your resources and to fine-tune how you lead people to a shopping cart or a desired transaction, such as downloading a white paper.

Semphonic’s evolution mirrors the maturation of web analytics. As Gary explains

“Over time we’ve become a full-service digital measurement consultancy where we work across the whole spectrum of problems around web analytics, and also around new channels like mobile and social. But we’re still in almost every case focused on really doing analysis for people because I think it’s at the analysis level that you actually start to see results from data. It’s where you actually start to take the data and make recommendations about how to improve your business; how to do better from a marketing or an operations perspective. As a company that’s what we’ve always been focused on and I think all of the other stuff is just getting you to the point where you can really do that. “

Gary calls this classic web analytics, but sees companies finally moving into a new and exciting area.

“They are (beginning to) look at customer segmentation and lifetime value, and building predictive models that help you understand which customers might attrite or which are the best candidates for retention- models and analysis that really help you understand which of your operational and marketing efforts drive incremental lift and change customer behavior.”

This is the result of ‘the maturity of the web analytics’ market and the explosion of online data on individual users. The irony in all of this is that classic direct marketers and cataloguers have been doing this type of database marketing for years. I guess this is an example of history repeating itself. Another reason for focusing more on behavioral data is that companies are relying less on third parties to house their data. This change has created a gap (or a need) in the number of data analysts and data experts companies need. A big challenge moving forward will be to find, hire, train and retain these folks. In some ways, they will achieve the rock star status that many computer engineers currently have. (Maybe I should go back to my roots and become a CDO, the Chief Data Officer in a company). Companies need to be more “audience” focused and less “traffic” focused. The current approach focuses more on campaigns, but it doesn’t give you any sense of whether the end result was a profitable customer or a customer that’s costing you money. Companies should be focusing far more on the LTV of a customer!

Besides focusing more on customer behavior, Gary believes organizations need to become less siloed. As we’ve discussed here before, different divisions or different channels (customer service and marketing) have different success metrics, different tools and rarely share learnings across an organization. I think this is a big opportunity; companies need to give customer service a seat at the product development table.

When asked about mobile and smart phone platforms, Gary stated the fixed web (what we think of as regular websites) and mobile currently are tracked in similar ways. The opportunity, he believes, is to treat mobile analytics differently, especially when it comes to mobile apps: how are people using the app, how does it fit into a broader customer journey, etc.. “You know, one of the challenges to measuring mobile apps is they do not look like websites. So it’s not page-by-page situation. That’s a paradigm that does not work very well when it comes to smartphones and other devices. If you try to fit your mobile app into a page based paradigm, you’ll find, I think, that your measurement isn’t very interesting.”

Big Data: No analytics discussion these days happens without touching upon big data. I think there is a technology barrier that companies have to cross. It’s very difficult to do customer level analytics within the web analytics solutions that are on the market. One interesting reason for this phenomena is that companies realize they can control their destiny more, relying less on third parties to manage and manipulate their data. This has created new titles, such as Data Scientists, hot new areas, such as business intelligence and a gap of well qualified analytics practitioners. As data management has moved in house, IT has gotten more involved and closer to marketing. IT can help determine what sort of technologies are needed for the amount of data a company has. For example, it might not make sense to build the Stealth Bomber warehouse, when a simple prop plane infrastructure might suffice (My analogy).

Gary emphasized the importance of growing important of analyzing qualitative information verses just looking at the numbers when transitioning to customer analytics. He believes “the biggest opportunity at the enterprise level right now is to really consistently and aggressively exploit attitudinal, social, textual data that’s collected. Most enterprises we work with collect vast amounts of data at the call center level. They collect vast amounts of social media data. They collect opinion lab data on their sites. They do attitudinal surveys, both on and off-line. But all those efforts tend to be completely siloed. And not only are they siloed, they’re non-standardized and the distribution of the information is poor. And so what we find is that every research effort tends to be a one-off. And that’s really ineffective.” This is a key organizational challenge. How to get different parts of the organization to share data on a consistent basis and to establish a common language and approach.

Gary continues “creating a set of standards for how we talk about and think about those customers, making sure that the data quality is consistent, that we categorize customers the same way; all the things that have become standard practice around structured behavioral data are critical to using attitudinal information effectively. But none of those things are done in the world of attitudinal and unstructured data. I think there’s a tremendous opportunity for competitive advantage to organizations who are willing to put the effort into effectively build a voice of customer warehouse; to consolidate all that information; to put standards around it; to make sure that they’re doing it on a consistent basis and to distribute it out to everyone in the organization in a consistent fashion. I don’t see many of our clients doing any of those things and I think there’s tremendous benefit to a voice-of-customer warehouse relative to cost; probably more than anything we’re doing on the behavioral side. “

I love the term “voice of the customer warehouse.” It might not be easy to say, but it is definitely something that most organizations need. Unfortunately, when it comes to looking at what customers are actually saying, companies usually just focus on word frequency or sentiment analysis. We both believe it is important to understand the use of language – how people describe things. Gary continues “it’s much more understandable and actionable when I can understand whether the sentiment is related to issues around customer support or issues around product functionality or issues around my advertising campaign. Those are three fundamentally different things, and frankly, to understand overall that my brand sentiment on Twitter is going up or down doesn’t really tell me very much because Twitter’s not a representative sample. And so I have to be able to contextualize it relative to what that sentiment’s about before I can actually understand what it means and what to do about it.”

When asked about exciting trends, Gary discussed some of the work the Financial Times is doing when integrating their mobile data with their web data. Most companies, as we discussed earlier, tend to keep these two areas separate. One of the last topics we discussed was what industries have done a good job in web analytics. Not surprisingly, he highlight the success traditional direct marketing (catalog) companies have had vs. other companies that have historically relied more on a direct sales force or a unique product. In Gary’s opinion, those companies have a culture pre-built to focus on and consume analytics.

Since Gary touched up on many different areas, I will use my next post to discuss the Web and Mobile analytics impact on the organization.

Insights shared:

  • Functionalism: An approach for understanding issues/outages with your website
  • Focus on Lifetime value (even with websites and mobile platforms)
  • More companies are managing their own analytics software vs. relying on a vendor
  • Organizations starting to move data in house and are looking at big data solutions—so IT needs to get more involved
  • Companies face real organizational issues because their analytics / data initiatives are very silo’
  • Think about combining, for example, your call center learnings/verbatim with the website info
  • Focus less site metrics on traffic, conversion rates and need to rethink the approach and make it more audience focus
  • Go beyond just being attribution focused (more than just how each channel is doing)
  • Don’t think of mobile analytics in the same way as mobile analytics — there are differences
  • But you can use similar approaches fro mobile and web – tagging, reporting
  • But differences too: resulting from types of websites, screen size and device dependences.. and mobile apps are different
  • Opportunity to measure web apps and mobile apps better
  • When it comes to behavior analysis, consider building a (big data) Voice-of-the-customer warehouse
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Scott Wilder

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