KnowClick has been nominated for “Innovator / Technology of the Year” at the Web Analytics Association. 

The nomination specifically calls out one of our key innovations:  KnowClick’s ability to connect a person’s website visit to their specific offline actions/purchases – something that has been sorely lacking in the industry to date.

See all nominations here: http://www.webanalyticsassociation.org/default.asp?page=awards2011_nominees

If you are a Web Analytics Association member, please vote on deserving companies.  (hint, hint…)

Why does connecting online behavior to offline actions matter?

For those not familiar with KnowClick, the beauty of this approach is that marketers are trying to drive offline actions are finally able to close the loop between a person’s initial source of awareness, their onsite actions, and their offline actions/purchase.  With this, our clients can see the true ROI of each campaign and uncover what drives results and what doesn’t.   Marketing can finally show financial performance metrics to their senior management, rather that settling for visits, pageviews, etc.

Additionally, eCommerce and lead generation focused marketers can finally find out what the ~98% who don’t convert end up doing after their visit (e.g. did they buy from you on another site or in a store, did they buy from a competitor, and why didn’t they choose you). 

Best,

Ben

1. Total Impact of Web Marketing: on Offline Actions, Brand Perceptions, & Loyalty

Management wants to know the impact of online marketing on sales, profit, market share, brand perceptions, and loyalty; instead, our tools provide visits, referrers, pageviews, and onsite “conversions” (e.g. they don’t capture offline conversions driven by online marketing).   No wonder we have trouble getting the resources we need. 

2. Drivers of ROI.

Which content, tactics, and campaigns actually persuade your audience to take action?  Which are piggybacking on the work of others, getting more credit than they should?  (Hey, “Brand Keywords”, I’m talking to you!!)

3. What Does “Good Look Like”.

So your campaigns and site are driving 20% more business each year.  Sounds good.  But are your campaigns doing everything they can, or are you leaving wads of cash on the table due to underinvestment?   Your investors want to know.

4. People / Data-Driven Culture.

Great online marketing is part science and part art. Too many people are on one side or the other.  We have to be much more effective at training and bringing in more talent with both analytical and marketing DNA.  We also need to serve as ambassadors of web measurement, showing all levels of the company how it can be used to get closer to our customers, make decisions, and grow the business.

5. People #2.

People are coming to the website, not visitors.  We need to stop thinking “transactionally” about visitors and their clicks and look at how we are meeting the needs of the people visiting and influencing their attitudes and behavior. 

6. Stories, not Data. 

A good portion of marketing is storytelling, yet the vast majority of tools spit up data and reports.  You need to understand the people visiting and how the site impacts them to tell the best story – see #6. 

7. Surveys to Capture the Voice of the Customer. 

Today’s surveys aren’t living up to their potential.  But it isn’t surveying technology that is the problem, it is the execution. I’ll go into the details in a future blog post, but put simply, we don’t ask the right questions, we make surveys painful to take, and the results are often ridiculously biased.  This poor execution has caused many marketers to stay miles away from doing any surveys, even though this tactic can be very informative. 

8. Integration: of Multi-channel, Attitudinal, and Behavioral Data.  

Data in silos doesn’t cut it.  Enough said. 

9. Know “Why”. 

Understanding WHY something is happening is the key to understanding how to improve.  Customer behavior is too much of a mystery.  We need to get our arms around it.  BTW, knowing “why” also helps with the story telling.

10. Segmentation. 

Not just behavioral-based, but also attitudinal and others.  If we really want to understand the people visiting and the performance of our websites and campaigns, we need to look at what different types of people do on our websites, at our conversion rates by different types of visitors (such as comparing conversion rates of those people who care most about quality vs those who care most about value), and much more.

11. Predictability. 

Enough said. 

12. Be Energizing.

I really do mean this, and not in a soft, squishy way.  When you discover something that is unexpected and actionable, it is incredibly energizing.  When you have a culture that makes decisions based on solid data, it is empowering and a great environment to work in. This is what we need to strive for.

I could go on, but any more will dilute the importance of each of the ones above and more importantly, keep you from ever coming back to this blog. 

So, what else needs to be improved?

More pageviews may mean success… or failure. 

From analyzing many websites and capturing visitor behavior, visitor satisfaction, and more, we’ve found that once you exclude “bouncers” (e.g. visitors who leave a website after only seeing one page), engagement metrics are only somewhat more reliable than flipping a coin.  Visitors who look at many pages on a website may be falling in love with your website or they may be frustrated that you aren’t able to provide them with the information they need.  Even loyalty metrics aren’t fool proof.  We’ve seen some sites where the least satisfied visitors were more likely to come back.  Customer behavior is a funny thing…

Background:  This is for a business to business website and excludes visitors who “bounced”.  For each participating visitor, we captured their website behavior “clickstream”, their satisfaction with the site, and their perceptions of the company.

These charts show a fairly common finding: visitors who are the most engaged (as measured by average pageviews) are actually the least satisfied with the site and the least likely to find what they were looking for.  In this case, their perceptions of the company and their desire to buy the product slightly diminished due to their visit to the site.  The engagement metrics were telling the company the wrong thing, confusing failure with success.   

Of course, on many other sites we find the opposite: high engagement means very satisfied customers.  My point is that you don’t know whether high engagement means happy or angry customers, unless you find out directly on your site.  High engagement is often a great thing – but it depends on your site, your visitors, and your goals.

So why does this matter?

1. By knowing the behavior of people who weren’t satisfied and knowing what they couldn’t find, we could use that to our advantage.  For example, when someone exhibits a behavior that shows that they probably aren’t finding what they are looking for, that can automatically trigger an action which prominently displays:

• Click to chat or click to call
• Contact form, with verbiage that shows the visitor that they can get their questions answered by completing the form (and becoming a lead)
• Special offer / discount

These types of offers show the visitors that you understand their needs and they give you a chance to influence them and drive them to take whatever action is important to you.

2. If you optimize campaigns and content based on visitor engagement (as many brand and informational sites do), you may be doing more harm than good.  You’d be much better off by:

• Finding out what engagement really means to you and your business.  Does high engagement mean that you are satisfying your visitors?  Does it mean that you are accomplishing your business goals and convincing visitors to take some offline action?  Or does it mean the opposite.  Behavioral data alone can’t tell you.
• In lieu of doing the above, you can simply optimize by some minimum threshold of visitor engagement.  This approach would not attribute any value to “bouncers” and also doesn’t give extra credit to visitors viewing tons of pages simply because they couldn’t find what they were looking for.  Or you could optimize by percentage of visits to “soft” conversion pages or a weighted index of these key pages.   (I say soft because in this case these conversions aren’t onsite purchases or lead generation, rather, they are simply visitor actions which are assumed to be an indicator of success).   Is this fool proof?  Far from it.   But it is a step in the right direction.

Additional Thoughts

From this simple data, it is clear that visitor engagement by itselt  is not a reliable measure of visitor satisfaction or impact.  This only scratches the surface of the limitations of behavioral data.  While incredibly interesting, onsite behavioral data can’ t tell you why a visitor did something, how it impacted their attitudes, or how it impacted their offline behavior.  We’ll cover these issues in future blog posts.

The one exception in my mind has been behavioral loyalty measures such as repeat visits.  Surely visitors who return must have had, on average, a better experience than those who don’t come back.  Or have they…

Say What?!!!

Why are visitors who are dissatisfied with the website THREE TIMES more likely to come back than those who were satisfied?  Isn’t this the exact opposite of what we’d expect?  Yes, it is.  That is what makes it so interesting. 

After double checking for any errors, I dug deeper to find out why this is occurring and what it means.  It now makes sense.  In this case, this is a company website in an emerging space that has few direct competitors.   This was the one source of information about the company and one of the few sources about this space.  Also, the people visiting had many different needs, some of which the site satisfied, and some of which it did not.

• Unsatisfied Visitors:  The visitors who couldn’t find what they were looking for after exploring many pages on their first visit gave up and left.  However, many continued to look for information elsewhere but couldn’t find it.  As they continued looking for information some came right back to this site and others found it through related searches.  Upon coming back, these unsatisfied visitors only looked at a few pages and quickly left, possibly remembering that their needs weren’t met.  Most of the unsatisfied visitors were earlier in the purchase cycle and needed extensive background information, which the site didn’t provide.
• Satisfied Visitors: This group didn’t spend as much time at the site, but they typically found what they were looking for and were more likely to be influenced by the site (we measured how the site impacted their perceptions of the company and its products).  A much smaller portion of this group came back, but unlike the unsatisfied visitors, those who came back viewed many pages.   Most satisfied visitors were later in the purchase cycle and needed more in-depth information on the product, which the site provided.

We could never find this out simply from looking at web analytics alone.  We could see the repeat visits, but we’d know nothing about why they came back or why some repeat visitors spent so little time on the site.  We could also never get this from surveys alone, as we’d never see their behaviors on the site – and we’d wrongly assume that those who were more satisfied would be more likely to come back.

Why this matters

In this specific case, my client was using repeat visits as a Key Performance Indicator (KPI).  Alongside a few other metrics, they used it to determine which campaigns to expand and which to kill.  This data showed them that, while  repeat visits in themselves are good, they are poor measures of website and campaign success for THIS particular website.   This relatively simple information helped them to stop wasting money and time on poor performing campaigns (which had high repeat visits but unsatisfied visitors and low business impact) and refocus on those which really perform best.  

This information doesn’t answer whether repeats visits are a good proxy of success for your website, but if you currently optimize your campaigns in part to drive repeat visits (and you don’t make money via selling online advertising), I’d recommend that you find out what is really meeting visitor needs and accomplishing your objectives.

What do you think?

From its humble beginnings as server logs used by IT, web analytics has advanced over the years to become the defacto standard of online measurement, used by every serious online marketer (and quite a few unserious ones…)   

I recently returned from an eMetrics Marketing Optimization Summit.  Since I start going to these summits in 2003, they have morphed from interesting, geeky technical debates among analysts, consultants, and CEOs of fledgling web analytics companies to international events where companies are tripping over themselves to announce major product launches.  This got me thinking about what has changed (and what hasn’t) in web measurement over the past decade.  I thought that I’d share my thoughts on the most important improvements in web measurement.  I’ll save the most of criticisms and needs for improvement for a later post…

Here is my “top six list” of the most significant advances in web measurement. 

1. Democratization of Web Analytics. 

Google Analytics, the first robust, easy to implement, and most importantly free version of web analytics, has made web analytics available to everyone. (It also has made intimate details about your company’s marketing performance available to the same search company who is often the recipient of a large share of your ad dollars – but that is another story). Not only has Google Analytics driven much greater adoption of web analytics, it also has forced the other web analytics players to offer more value or be steam rolled.

2. Industry “Ecosystem”. 

As the industry has matured, we now have the institutions, vendors, consultants, “best practices”, and more to help advance web measurement. This has been the greatest, albeit most predictable, advancement.  Note that I use the term “best practices” very loosely. 

3. Testing. 

Testing tools have made tremendous progress, moving from simple A/B tests to multivariate tests of tens of thousands of permutations of content.  But more importantly than the actual tools is the adoption of testing as a core tactic used by companies.  Got a great idea but your company is skeptical?  Test it.  Trying to choose between two different messages?   Test them.    This mindset states that we don’t know what is going to work best until we test it.  With customer behavior as odd as it is, this is one approach that is almost guaranteed to improve results. We still need to get much better at determining what to test and how to measure the results, but we are on the right track.

4. Marketing Optimization (particularly for eCommerce and Lead Gen). 

Here, I am referring more to the idea of automatically taking behavioral data (clicks)  and using software and tools to automate tactical improvements to the campaigns and content (usually through testing and behavioral targeting).  Of all the tools out there, paid search software has probably made the most progress here due to the innate measurability of search.  Other tools still need much more development, but it is great to see this idea achieve widespread adoption and so much attention from software providers.

5. Targeting Based on Behavior

This refers to the delivery of targeted content on the website, in online ads, and in email ads based on a visitors’ behavior and what will likely be most effective at getting them to convert.  We are still far from the original vision of the web as a highly personalized medium, but we are getting there.  Slowly.

6. Voice of Customer (VOC). 

While these tools still need dramatic improvement and greater integration with behavioral data, capturing visitor opinions and feedback is now a fairly common practice.  It is a good complement to the behavioral data that still dominates web measurement.

“Sounds great but I still can’t convince my management to spend more online”

So am I saying that I am happy with where we are?  Not a chance.  If I was, I wouldn’t have started KnowClick to address many of the issues with web measurement.  While the industry has made progress, it is woefully inadequate in so many other ways.  Much of the industry lives and breathes on onsite behavioral data (clicks) and that data alone.  We often know nothing about how a site impacted brand perceptions, how it impacted offline actions, and sometimes we know little about the actual people who are visiting.  Without these, we are only seeing one piece of the puzzle, and we struggle to convince others of the value of our online marketing and therefore struggle to get the resources we need to get more from the web.  I’ll cover this in a semi-rant in a future blog post.