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		<title>Keep An Infinite Memory for your Metrics</title>
		<link>http://www.johnquarto.com/2013/05/keep-an-infinite-memory-for-your-metrics/</link>
		<comments>http://www.johnquarto.com/2013/05/keep-an-infinite-memory-for-your-metrics/#comments</comments>
		<pubDate>Wed, 15 May 2013 12:34:04 +0000</pubDate>
		<dc:creator>John Quarto-vonTivadar</dc:creator>
				<category><![CDATA[Featured]]></category>
		<category><![CDATA[Headline]]></category>
		<category><![CDATA[Metrics]]></category>

		<guid isPermaLink="false">http://www.johnquarto.com/?p=5844</guid>
		<description><![CDATA[The Exponential Moving Average (“EMA”) is a very useful tool in your metrics arsenal because in the EMA the memory of past metrics values is never forgotten]]></description>
				<content:encoded><![CDATA[<p>Often, you want to look at your metrics over a longer time period than usual. This is especially true if your metrics are &#8220;jagged&#8221; and move around a lot. You have two choices:</p>
<p>The Exponential Moving Average (&#8220;EMA&#8221;) is a very useful tool in your metrics arsenal because in the EMA the memory of past metrics values is never forgotten, though it is gradually given less and less weight over time. Further since the EMA is based on what the value of your metric is today combined with what the EMA was yesterday, you don&#8217;t have to keep large amounts of past data on hand to update each day&#8217;s new values.</p>
<p>Google AdWords almost certainly uses an EMA to track your keyword Quality Score.</p>
<p><a href="http://marketingland.com/?p=43411‎">Read this entire article over at my MarketingLand.com column</a><br />
<!--<br />
First, you could use a larger time period. Weekly, say, if you usually watch daily metrics. Or monthly, or quarterly. This will smooth out your data but will also smooth out directional trends emerging from your data right at the time when you want to catch such changes.</p>
<p>Second, you could use an average of values of your metrics. For example, instead of Weekly period, you could examine the average over the last seven days. Since the past data examined changes every day, this is often refered to as a "moving average" since the days looked move forward in time as you collect more and more data.</p>
<p>Now, you could use a simple moving average ("SMA"), where the weights of each day of data is given equal weight. You might even do something slightly more sophisticated by using varying weights to the n-th day, referred to as a weighted moving average ("WMA"). But either solution leaves you with two issues:</p>
<p>1) a loss of memory: if you're looking back N periods to calculate your average, you've effectively forgotten what happened more than X+1 days ago. If something important occured any earlier than that, too bad.<br />
2) a data-scale problem: you have to keep the data around back far enough to re-calculate the average each day forward. Not a big deal if you're looking at shorter periods (daily, weely, even monthly) but this can be cumbersome if you're examining longer trends.</p>
<p>Enter the exponential moving average ("EMA"). A very useful tool in your metrics arsenal, because in the EMA the memory of past metrics values is never forgotten, though it is gradually given less and less weight over time. Further since the EMA is based on what the value of your metric is today combined with what the EMA was yesterday, you don't have to keep large amounts of past data on hand to update each day's new values.</p>
<p>I'll show you a quick example in a moment. But if you do anything with Google AdWords, you are almost certainly concerned with something that is based on an exponential moving average: the Quality Score ("QS"). As any AdWords advertiser knows, Google reports QS scores only at a keyword level, though it's commonly believed that Google has QS numbers for AdGroups, Ads, as well as on an Acccount level for you. </p>
<p>That's a lot of number crunching, even for Google. And you may have noticed when Google reports your QS to you it's presented as a single number, not as a time series. Thus, your QS is not itself a metric, but rather a property of your account. It's also well known that the history of your account plays a great part in determining your QS. </p>
<p>Sounds familiar, right? Because those are exactly the characteristics of an exponential moving average discussed above. I don't think I'm going far out on a limb to assert that Google is using an EMA (or equivalent) to store, score and report your Quality Score.</p>
<p>Why not use this same technique when examining your own metrics? Here's how the EMA is calculated. </p>
<p>Let's say you want your past data to have a strong memory and thus have yesterday's value of the EMA be pretty important when calculating today's value. In an EMA one uses a factor, usually called alpha, which represents a contstant smoothing of the previous values of the metric. The value of alpha is anywhere between [0,1]; the higher the number, the quicker past data is discounted. </p>
<p>In our example, we want to discount past data slowly, so we'll sent alpha at, say 1/3 (33%). That's the weight of the current data. So, normalizing, that means the weight of the past data is (1 - 1/3) = 2/3.   If the value of the EMA yesterday was, say 27, and the value of today's metric is, say, 30, then the value of the EMA today is:</p>
<p>EMA (today) = (2/3)*27 + (1/3)*30 = 18 + 10 = 28</p>
<p>If you want an EMA that responds faster to changes in trend, then set alpha higher, so that past values of the EMA are discounted faster. For example, set alpha to 80%. Now:</p>
<p>EMA (today) = (20%)*27 + (80%)*30 = 5.4 + 24 = 29.4</p>
<p>This may give you a much easier way to track important changes to your metrics while at the same time smoothing out any spurious noise in the data as it occurs. Your mileage may vary, but if Google is doing it with your Quality Score, it certainly is a trick I'd want in my bag o' metric wizardry!<br />
--></p>
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		<title>Setting Up Your Negative Metrics</title>
		<link>http://www.johnquarto.com/2013/04/setting-up-your-negative-metrics/</link>
		<comments>http://www.johnquarto.com/2013/04/setting-up-your-negative-metrics/#comments</comments>
		<pubDate>Wed, 17 Apr 2013 06:04:41 +0000</pubDate>
		<dc:creator>John Quarto-vonTivadar</dc:creator>
				<category><![CDATA[Featured]]></category>

		<guid isPermaLink="false">http://www.johnquarto.com/?p=5817</guid>
		<description><![CDATA[This is a continuation of  my exploration of negative metrics thread which covers how to think about your negative metrics and how to set them up<br /><span class="excerpt_more"><a href="http://www.johnquarto.com/2013/04/setting-up-your-negative-metrics/">[continue reading...]</a></span>]]></description>
				<content:encoded><![CDATA[<p>This is a continuation of  <a href="http://www.johnquarto.com/2013/04/thinking-about-your-negative-metrics/">my exploration of negative metrics thread</a> which covers how to think about your negative metrics and how to set them up as part of your testing and optimization process. It&#8217;s based on earlier work I&#8217;ve published on the <a href="http://www.johnquarto.com/2012/08/the-value-of-nothing/">value of nothing</a> and <a href="http://www.johnquarto.com/2013/01/treating-your-conversion-symptoms-but-not-the-disease/">treating your conversion symptoms but not the disease</a>.</p>
<p>You can <a href="http://www.johnquarto.com/2013/04/thinking-about-your-negative-metrics/">read Part 1 dealing with thinking through negative metrics here</a>.</p>
<p>Now, let us turn attention to setting up negative metrics for any of your tests. This, too, I&#8217;ll cover by discussing a specific example from real life.</p>
<p>One of my larger clients earns a significant percentage of its revenue from ad impressions on its site. It&#8217;s considered a &#8220;trusted source&#8221; in its niche, and people visit this site all the time for information, reviews, and fair-market price comparisons.</p>
<p>Because of the important revenue coming from ad impressions, one of the success metrics they use is &#8220;pages per session&#8221;. One of the metrics they try to minimize is &#8220;bounce rate&#8221;. Both of these are completely rational ways to measure success. In a recent test they were able to substantially reduce bounce rate on a given landing page, and apparently page views per session was approximately constant, so on first blush this seems like a success. &#8220;But what about the negative metric?&#8221;, I asked.  Reader, can you think of what the negative metric might be? Or at least think of how it would manifest?</p>
<p>What if solving the bounce rate problem simply causes the visitor to &#8220;pogo stick&#8221; around on ensuing pages? Pogo sticking means more page views but very short, very quick changes of pages, indicating non-engagement. This isn&#8217;t good, <a href="http://www.johnquarto.com/2013/01/treating-your-conversion-symptoms-but-not-the-disease/">you&#8217;ve simply moved the problem to another place</a> as I mentioned in my earlier post on conversion symptoms versus conversion disease. So in this test, bounce went down, which is good, but the effect for the company isn&#8217;t good.</p>
<p>What about page views per session, this test&#8217;s other success metric? If someone is pogo sticking then these should be expected to actually go up . Great for ad impressions (maybe), but probably not  for ad viewing (surely not what the person who bought the ad impressions wants!). And pogo sticking means the visitor isn&#8217;t achieving what they want, and that&#8217;s not an effect the company wants either.</p>
<p>So what  we&#8217;re actually looking for is an additional metric, this time negative metric, to indicate if our success metrics of bounce rate and page views per session are fooling us. In this case a time component would help, so perhaps &#8220;avg time spent on page per session&#8221;. Right? If the visitor is pogo sticking, this metric will go down strongly relative to the control, even if bounce on the landing pages improves and page views go up. It can help indicate to us if the test is truly a success or not. If this number stays relatively constant, and we improve bounce and page views at the same time, then we really do know we have a success.</p>
<p>Take-away: every time you define a success metric for your testing and optimization efforts, consider what the negative metric will be. What ways could your test have a false success &#8230;  and how would you know?</p>
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		<title>Thinking About Your Negative Metrics</title>
		<link>http://www.johnquarto.com/2013/04/thinking-about-your-negative-metrics/</link>
		<comments>http://www.johnquarto.com/2013/04/thinking-about-your-negative-metrics/#comments</comments>
		<pubDate>Tue, 16 Apr 2013 21:11:44 +0000</pubDate>
		<dc:creator>John Quarto-vonTivadar</dc:creator>
				<category><![CDATA[Headline]]></category>
		<category><![CDATA[Metrics]]></category>
		<category><![CDATA[Optimization & Testing]]></category>

		<guid isPermaLink="false">http://www.johnquarto.com/?p=5808</guid>
		<description><![CDATA[&#8220;Negative&#8221; metrics &#8212; You might prefer the term &#8220;Deoptimization Metrics&#8221;. Either way they can be just as important  to your continuous optimization efforts as your positive ones. The purpose of a negative metric  is to isolate for you the deleterious effect you may be having on your positive metric. A negative metric is not necessarily<br /><span class="excerpt_more"><a href="http://www.johnquarto.com/2013/04/thinking-about-your-negative-metrics/">[continue reading...]</a></span>]]></description>
				<content:encoded><![CDATA[<p>&#8220;Negative&#8221; metrics &#8212; You might prefer the term &#8220;Deoptimization Metrics&#8221;. Either way they can be just as important  to your continuous optimization efforts as your positive ones. The purpose of a negative metric  is to isolate for you the deleterious effect you may be having on your positive metric.</p>
<p>A negative metric is not necessarily something you want to get less of &#8212; for example, &#8220;reduce bounce rate&#8221;. That&#8217;s just an easier way to state the positive inverse of &#8220;increasing the un-bounce rate&#8221;.  Rather, a negative metric is something you look at to ensure that when you have success with your positive metric, you aren&#8217;t penalizing yourself somewhere else. I covered this when I discussed the <a href="http://www.johnquarto.com/2012/08/the-value-of-nothing/">value of nothing</a> earlier.</p>
<p><a href="http://marketingland.com/?p=39649‎">Read this entire article over at my MarketingLand.com column</a><br />
<!--</p>
<p>If conversions go up (a positive metric) but qualified leads go down, you want to know this. If the qualified leads go down with lesser impact on the company than the conversion rate going up is worth, then the company might decide that's okay. However, if the quality of the leads drops such that you're losing more revenue than the increased conversion rate is worth -- well, then the company might decide that the increased conversions weren't worth it.</p>
<p>Or perhaps your company is trying to decrease bounce rate (a positive metric, as described above) with an eye toward boosting ad views on your pages. You might achieve that, but inadvertently cause other problems that detract from your ultimate goal. You can't know these things if you're not measuring them, right?</p>
<p>Real-Life Lessons In Identifying Negative Metrics</p>
<p>This post focuses on thinking about  negative metrics in real-life situations: what to look for, what direction they should move in if you're doing well or doing poorly, etc.</p>
<p>A great way to practice thinking about negative metrics is when you yourself have a negative customer experience. After all, you might as well as turn it into something positive, even if you're not the one in charge of measuring. I'll use the anecdotal story that inspired this article. This one is easy since it's about the cable company, and cable companies are always great places to learn what not to do.</p>
<p>As we go along, think about your own customers and their experiences with your website, your marketing outreach, your call center and your retail presence (if any). Can you identify negative metrics you should be tracking?</p>
<p>I'm betting most readers will recognize the pain point in my anecdote: the other day, the cable company managed to call me -- get this -- seven times. Seven. Each call was about getting me into a different package of services. Negative metric #0 (so obvious it isn't even worth counting as #1): "How many times per day/week/month do we contact the customer without his contacting us first?"</p>
<p>Negative Metric #1</p>
<p>The first time, I was happy, because I watch very little broadcast TV and wouldn't mind hearing about a way to save money. Who wouldn't? After confirming that I am who I am -- which begs the question, "Why are you calling me if you don't know who I am?" -- the agent asked if she could put me on hold "for a few seconds" so she could look up my account and find what offers I'd be eligible for.</p>
<p>Now, my first reaction was, "Why are you calling me and then looking up offers for me? Why not have the offers ready to go as soon as you reach me?" But a few seconds seemed trivial enough, so I said yes. After 60 of those "few" seconds, I hung up.  Negative metric #1: "Of the total time spent with a customer on the phone, what percentage of that represents hold time?" That'd be an interesting metric, wouldn't it?</p>
<p>Here's a more subtle one: "What percentage of hold time comes from getting a response to a customer question, versus what percentage comes from us (the company) asking to put the customer on hold?"</p>
<p>You don't want to penalize your call center staff for answering customer questions, but you do want to penalize the call center (systemically; not the agents) if the system itself causes the agents to put a customer on hold for information that should be readily available. So you're looking at these negative metrics in light of the positive metric: "customers who took advantage of an offer we contacted them about."</p>
<p>Negative Metric #2</p>
<p>An hour later, another agent called me and started the same spiel. This time I interrupted and asked to be put on the no-call list. No problem, apparently -- except that the astute reader will note, "Wait, you said they called you seven times!" You needn't be a genius to realize that being added to the no-call list didn't take. So, negative metric #2: "How long does it take you to stop contacting customers after they tell you to leave them alone?" If your answer is "up to 30 days to take effect" then, congratulations, your company is a corporate stalker.</p>
<p>If I can ask them to shut off my cable and they can do that in an hour or less, surely they can flag my account for no calls in approximately the same period of time.  What's the negative metric here? "After a customer requests us not to call them, how many times do we do so anyway?" And, "Of that group who received calls after asking to but put on the no-call list, what's our churn rate for those who got one extra unwanted call? Two? Three?"</p>
<p>Measuring this results in a learning: "What's the correlation between stalking our customers and their complaining/stopping/reducing their avg $ value to us per month?" Everyone reading this knows the individual pieces making up these metrics exist -- good companies put it together so they understand their negative metrics and work to reduce them; bad companies never even put the puzzle together.</p>
<p>Negative Metric #3</p>
<p>Third call, same thing. Except this time the agent was someone who spoke two different languages, but was fluent in neither. Negative metric #3: "What ratio of hang-ups or customer-didn't-do-what-we-wanted's occurs for native speaker agents calling our customers versus our 'multi-lingual' agents?" If you don't have a problem, then this metric should be approximately equal across both groups.</p>
<p>Fourth call. Now I'm getting in the rhythm and really start to think up ways to torture this process. I wait through the "several seconds" (apparently in 2013, "several" and "78" are equivalent) it takes for the fellow to put me on hold and return with offers, and  he tries to get me into a Triple Play (this is a package in which they provide cable + internet + digital phone service). As a bundle, they can offer this at  lesser price. I don't need a home phone (mobile only, here!) so I'm not really interested in this package, even though it's maybe $10 cheaper per month than what I'm currently paying.</p>
<p>The agent seems surprised I'm not interested in saving $10 and rattles off to me the "value" of the services which, if purchased separately, would be far in excess of what I'm currently paying. Nice try, but all you're doing is convincing me that your prices are already inflated, since you can discount them so dramatically. Kind of like the Persian rug business down the street that has been going out of business for 12 or more months. Jack up the price and then discount it heavily so people think they're getting a bargain. It also inspires a test: "if we offer customers less of a discount, do fewer of them convert?" You'd think so, but if the discount is so large that even Grandma realizes she was over-paying to begin with, this test might turn out to be a surprise.</p>
<p>Negative Metric #4</p>
<p>"What about packages just for internet + cable?" I ask. Oh no, no specials available for me since those are the services I already get. Negative metric #4: "What churn rate do we have for customers who find out that they have been paying far more for staying with the company than they would as a new customer?" This sort of bad impact on brand is poisonous and hard to measure.</p>
<p>I don't know how you'd keep me from finding out, but surely you don't need to throw the negative consequences of my loyalty in my face. Perhaps you could set up a test on a (very) small percentage of the customers where you explicitly say this to them and then look at the churn rate of that group compared to the control -- where you don't mention on a sales call with an existing customer that new sign-ups always get a better deal.</p>
<p>Wouldn't it be an important thing to know? What if it turned out that churn among existing customers were 2-3-5x higher when they were told it was cheaper for them to discontinue service and then call back in a month and get a sweetheart deal? Wouldn't understanding this impact how you'd go about future pricing, or at least modifying the sales script of call center agents?</p>
<p>Calls 5+6+7 were close repeats of the above, and I did get more negative metrics identified -- which would be great if I worked at the cable company and gave a crap. I will say the final call convinced me to drop cable tv altogether, so now I just have internet.  I'm pretty sure that wasn't the positive success metric were going for when they first started calling me. Their negative metrics completely obscured their positive metrics. But are they aware of this? Are you aware when this is happening at your company?<br />
--><br />
<a href="http://www.johnquarto.com/2013/04/setting-up-your-negative-metrics/">I do have an article post on setting up negative metrics for your tests, along with an example</a> from a completely different direction.</p>
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		<title>I Before E, Except After C</title>
		<link>http://www.johnquarto.com/2013/03/i-before-e-except-after-c/</link>
		<comments>http://www.johnquarto.com/2013/03/i-before-e-except-after-c/#comments</comments>
		<pubDate>Tue, 19 Mar 2013 15:11:03 +0000</pubDate>
		<dc:creator>John Quarto-vonTivadar</dc:creator>
				<category><![CDATA[Featured]]></category>
		<category><![CDATA[Metrics]]></category>
		<category><![CDATA[Optimization & Testing]]></category>

		<guid isPermaLink="false">http://www.johnquarto.com/?p=5792</guid>
		<description><![CDATA[&#8220;I Before E, Except After C&#8221; &#8212; do you remember that from 4th grade spelling? Well, it works just as well when you&#8217;re concentrating on<br /><span class="excerpt_more"><a href="http://www.johnquarto.com/2013/03/i-before-e-except-after-c/">[continue reading...]</a></span>]]></description>
				<content:encoded><![CDATA[<p><a href="http://www.johnquarto.com/wp-content/uploads/2013/03/Screen-Shot-2013-03-19-at-10.07.19-AM.png"><img class="alignright  wp-image-5805" alt="Screen Shot 2013-03-19 at 10.07.19 AM" src="http://www.johnquarto.com/wp-content/uploads/2013/03/Screen-Shot-2013-03-19-at-10.07.19-AM-290x300.png" width="174" height="180" /></a>&#8220;I Before E, Except After C&#8221; &#8212; do you remember that from 4th grade spelling?</p>
<p>Well, it works just as well when you&#8217;re concentrating on your metrics and optimization program.</p>
<p>What do the letters mean?</p>
<p><a href="http://marketingland.com/?p=35980" target="_blank">Read this entire article over at my MarketingLand.com column</a></p>
<p><!--</p>
<ul>
<li><strong>I</strong> is for Insight &#8212; learnings you derive from your continuous optimization efforts.</li>
<li><strong>E</strong> is for Earnings &#8212; which is presumably why you&#8217;re going through the optimization process.</li>
<li><strong>C</strong> is for Conversion &#8212; which is how you&#8217;re measuring the efficacy of your past <strong>I</strong> on your current <strong>E</strong>.</li>
</ul>
<p>Think about it this way: every time you perform a test &#8212; heck, it doesn&#8217;t even have to be a formal test &#8212; you&#8217;re using your past experience, learnings and insight to guide your decisions as to what you should be testing. And this is not a science lab, where one might do a what-if to nudge out deep metaphysical rules of the universe; the reason you&#8217;re doing the test at all is because there&#8217;s something about what you&#8217;re proposing to test that your experience tells you might be better than what you currently have. No one on the business side does an experiment to try to make things worse (though from some of the testing schedule proposals I&#8217;ve seen at companies, I&#8217;m starting to question that!).</p>
<p>And the actual improvement your company nets from your optimization efforts, your Earnings, come after you&#8217;ve applied your Insight. Fair enough, especially on the net&#8230;because everyone takes credit when there&#8217;s a net increase in revenue. You know the old adage, &#8220;success has many fathers, but failure is an orphan&#8221;. So when you propose a test, perform it well, and it results in a net positive to the company, anyone who&#8217;s touched any part of effort is surely going to be in line for some of the credit.</p>
<p>Note: If you&#8217;re at a big enough company, you may well notice that the total amount of credit claimed is often far in excess than the total amount of effort put in. Ah, but I digress.</p>
<p>What happens when your testing efforts don&#8217;t result in a net gain for the company &#8212; the test fails? Well, that precisely where you actually drive future Insight. Your learnings always come from your failings. And even better, after a failure there isn&#8217;t a whole line of cousins thrice-removed claiming they were instrumental to the test, competing with you for the new learnings. You often have this insight all to yourself. This is a very human sort of occurence, going back to the first caveman who put his hand in a fire. At several of the large companies I consult with, I actually encourage them to publically reward folks who propose tests that don&#8217;t pan out &#8212; assuming the test ideas weren&#8217;t idiotic in the first place &#8212; because the insight that comes from great test ideas that fail is the entire basis for the great test ideas that succeed.</p>
<p>In other words, &#8221;I Before E, Except After C&#8221; = &#8220;Insight comes before Earnings, Except after Conversion improvements&#8221;.</p>
<p>Great companies embrace failure because it makes them smarter. How does your comapny reward smart failure?</p>
<p>&#8211;></p>
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		<title>Treating Your Conversion Symptoms But Not The Disease</title>
		<link>http://www.johnquarto.com/2013/01/treating-your-conversion-symptoms-but-not-the-disease/</link>
		<comments>http://www.johnquarto.com/2013/01/treating-your-conversion-symptoms-but-not-the-disease/#comments</comments>
		<pubDate>Mon, 21 Jan 2013 19:30:14 +0000</pubDate>
		<dc:creator>John Quarto-vonTivadar</dc:creator>
				<category><![CDATA[Conversion and Persuasion]]></category>
		<category><![CDATA[Headline]]></category>
		<category><![CDATA[Landing Page Optimization]]></category>
		<category><![CDATA[Metrics]]></category>
		<category><![CDATA[Optimization & Testing]]></category>

		<guid isPermaLink="false">http://www.johnquarto.com/?p=5779</guid>
		<description><![CDATA[Many organizations get fixated on metrics data, without thinking of where it comes from. But what if your metrics indicate a problem which originates somewhere else?]]></description>
				<content:encoded><![CDATA[<p><a href="http://www.johnquarto.com/2013/01/treating-your-conversion-symptoms-but-not-the-disease/symptomatic-treatment/" rel="attachment wp-att-5784"><img class="alignright  wp-image-5784" style="margin: 5px;" alt="symptomatic-treatment" src="http://www.johnquarto.com/wp-content/uploads/2013/01/symptomatic-treatment.jpg" width="270" height="180" /></a>Many organizations get fixated on metrics data, without thinking of where it comes from. But what if your metrics indicate a problem which originates somewhere else?</p>
<p>In other words, why should we believe that the place at which we measure our metrics is also the location of the problem?  Perhaps the metric is simply reporting a symptom, but not the malady itself?</p>
<p>Take, for example, a rather common fixation on Exit Pages. These are often bumped up the organizational ladder as &#8220;oh, these pages have to be fixed! They have high Exit rates, therefore people are leaving! Let&#8217;s re-factor, or AB test, or etc&#8221;.</p>
<p>Certainly a high Exit rate indicates <em>something</em> is amiss, and deserves attention. What I&#8217;m suggesting, however, is that a high Exit rate page is often not a problem with the Exit page itself at all, but rather a manifestation of a problem which may have occured much earlier in the process.</p>
<p>Do we really want to be treating Symptoms, rather than the Disease? You have Weight Loss? Eat More. You&#8217;re Thirsty all the time? Drink more. Feel tired or run-down? Get a full nights sleep.  Yet all three of those symptoms are correlated with Diabetes, for which eat more, drink more, sleep more are hardly the best pieces of advice. You may well cure a symptom (act locally) but have little impact on the disease (globally a problem)</p>
<p>The Exit page can be thought of the place where the visitor &#8220;gave up&#8221;. Something occurred on previous pages or interaction points, and the reported Exit page is simply the final divorce decree your customer is serving on you. Yes, it is much like a divorce, where the marriage has ended <em>de facto</em> long before it&#8217;s ended <em>de jura</em>.</p>
<p>Many analysts get caught up in this conundrum. They are tasked with reporting metrics and (usually) making suggestions for improvement. But unless they are looking at the bigger picture, they have a built-in incentive to treat the symptomatic problem. Don&#8217;t you fall into that.</p>
<p>To be sure, there are plenty of cases where the Exit page is the problem. And this is my point; simply being a page with a high Exit rate isn&#8217;t sufficient in and of itself to diagnose the problem. So, in line with taking a broader view of continuous optimization at your organization, join me in this thought experiment: what would it mean if the Exit page itself were a problem, versus those instances where the Exit page is simply the place where the problem is measured?  How would we expect the metrics of Exit pages to act in this context?</p>
<p>Here&#8217;s one approach:</p>
<p>If we think of Exit page as &#8220;end of conversation&#8221; or &#8220;not interested&#8221; etc &#8212;  then you might expect that the time spent on this page to be of approximately average or even above average of time spent  compared to all other Exit pages. The visitor has continued down a conversational path with you, and has come to a point where, in some context, you&#8217;re no longer relevant to her. If this is that point, then she&#8217;ll finish up with this page and look around for more info or move on. Of course, she doesn&#8217;t move to another page on your site at this point (since we measured THIS page as the Exit page). Fair enough, we can investigate the various factors on that page that may have gone awry, and fix those we are capable of fixing.</p>
<p>However, what about when the visitor loses her way long before the Exit page? Obviously, she hasn&#8217;t exited yet (otherwise one of the earlier pages would have been analytically reported as the Exit page for this visitor). But from the moment of her dis-engagement, what we might expect a human to do is to flitter around a bit in an attempt to get back on track or find what she is looking for. Visitors have goals on your site and they will put in (at least a little) effort in getting to those goals. Maybe hit the Back button. Or go to the home page. She might even start using the Primary navigation (you may be surprised, but Primary Nav is one of the least used parts of a site among visitors who are getting what they want, and one of the most used parts of a site among visitors that are having a &#8220;disconnect&#8221; from you).</p>
<p>So what might we expect to see in the metrics in this case? We should see such Exit pages to having a lower time spent on this page compared to the average Exit page. And likely the pages just before getting to the Exit page also have lower-than-average Time Spent as she jumps around trying to rediscover the scent of her intended trail.</p>
<p>What else might we expect? Well, in those cases where the problem cases of the Exit pages being of only one type of problem or the other (that is, &#8220;we have a problem with Exit Pages&#8221; versus &#8220;we have a problem somewhere earlier in the process&#8221;), the spread of the average metrics for this page such as Time Spent, etc should be fairly narrow and static over time. The std deviation of the metric will be fairly tight compared to its average.</p>
<p>In contrast, if you have both types of Exit page problems on your site, then you&#8217;d expect the standard deviation of Exit page metrics to be much wider, because really you&#8217;re measuring two diff&#8217;t populations of problems. This in itself suggests an occasional &#8220;binning&#8221; of the Exit pages in some visual way so you can diagnose if you have anything other than a bell curve distribution of Exit page problems.</p>
<p>Once you start thinking about your problem with Exit pages this way, you can come up with better ways to isolate Symptoms from Disease, and you&#8217;re that further along in treating both effectively. Your Patient-Visitor will thank you because she&#8217;ll get more done at your site.</p>
<p>By the way, this sort of shift in your thinking will point you towards a similar approach to other problems on your site. For example, Bounce Rate.</p>
<p>[As an aside, I'll make the distinction here that Exit Page is the last page the visitor was on in a session, and Bounce page is a special kind of Exit page where the visitor was only ever on that one page before leaving]</p>
<p>For years, people have made a lot out of Bounce Rate &#8212; as they should &#8212; but without considering that the Bounce page, typically a landing page or home page, may not really have any problem with it all.</p>
<p>Again, this doesn&#8217;t mean that all Bounce Rates are ignorable. Just the opposite, because what I&#8217;m asserting is that there is as strong a possibility that the Bounce Page is being bounced off of because of something wrong with the Ad or the referring Search Engine result, or etc which had set up an expectation of relevancy &#8212; a contract with the visitor if you will &#8212; that the Bounce page isn&#8217;t prepared to handle. Perhaps someone in charge of PPC efforts has changed something in the Ad &#8212; all with good intent &#8212; but if this scent isn&#8217;t followed through from the Ad onto the ensuing pages it manifests as an increase to the Bounce rate when visitors get to the site.</p>
<p>This comes about far more often than you would think because so many organizations are set up as silos. You&#8217;ve got the analysts on one side trying to measure as much of it as they can get done, people responsible for the website tweaking and optimizing away, and PPC folks add driving click-thrus but possibly without interacting with the team managing the site. All of which create symptoms that there&#8217;s something wrong with the site when the disease may well be the lack of co-ordinated effort cross-company.</p>
<p>That should give us all something to think about, right? I&#8217;m curious, what percentage (rough estimate) would <em>you</em> put on the ratio of Exit Pages that have problems with the page itself versus problems that occured much earlier in the process? My experience is that it&#8217;s far closer to 50:50 (meaning: &#8220;it&#8217;s a coin flip!And I can&#8217;t treat this problem until I know more!&#8221;) than any organization would like to admit.</p>
<p>[This article is Cross-posted to my monthly column at <a href="http://marketingland.com/author/john-quarto-vontivadar">MarketingLand</a>, which is a great place to read all sorts of interesting content.]</p>
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		<title>Polls and Testing: When Is Close No Cigar?</title>
		<link>http://www.johnquarto.com/2012/11/polls-and-testing-when-is-close-no-cigar/</link>
		<comments>http://www.johnquarto.com/2012/11/polls-and-testing-when-is-close-no-cigar/#comments</comments>
		<pubDate>Tue, 20 Nov 2012 21:22:12 +0000</pubDate>
		<dc:creator>John Quarto-vonTivadar</dc:creator>
				<category><![CDATA[Headline]]></category>
		<category><![CDATA[Optimization & Testing]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[Statistics]]></category>

		<guid isPermaLink="false">http://www.johnquarto.com/?p=5760</guid>
		<description><![CDATA[Now that the 2012 U.S. Presidential elections are over, there&#8217;s a bit of a buzz around why some folks thought the election would be close and others who, using some solid statistical techniques, predicted a definitive (and it turns out, accurate) outcome. I want to try to explain the latter, especially for marketers. Why? Because<br /><span class="excerpt_more"><a href="http://www.johnquarto.com/2012/11/polls-and-testing-when-is-close-no-cigar/">[continue reading...]</a></span>]]></description>
				<content:encoded><![CDATA[<p><a href="http://www.johnquarto.com/wp-content/uploads/2012/11/election-results.jpg"><img class="alignright  wp-image-5768" style="margin: 5px;" title="election-results" src="http://www.johnquarto.com/wp-content/uploads/2012/11/election-results-150x150.jpg" alt="" width="200" height="150" /></a>Now that the 2012 U.S. Presidential elections are over, there&#8217;s a bit of a buzz around why some folks thought the election would be close and others who, using some solid statistical techniques, predicted a definitive (and it turns out, accurate) outcome.</p>
<p>I want to try to explain the latter, especially for marketers. Why? Because political elections are in many ways like optimization testing: you&#8217;ve got two (or more) candidates and the &#8216;market&#8217; is choosing between them. So insight we gain from understanding political polling is valuable to us when we look at our testing efforts and the metrics we use to judge them.</p>
<p>And, I&#8217;m going to attempt this without using much math. Why? Because many readers are marketers, and numbers can be &#8230; a challenge.  You can always go dig up your local &#8220;small data&#8221; geek when you need to run such numbers yourself &#8212; so let&#8217;s focus on the concepts.</p>
<p>First off, to avoid the inevitable arguments of party affiliation, so let us consider two fictional candidates, Mr. Smith and Ms. Jones. In the latest polls, Jones leads Smith 49 to 47 in a poll where the tiny print at the bottom of the results says there&#8217;s a margin of error of +/- 2 points.</p>
<p>First off, what do the above numbers means? Based on a sampling of the voters, the simple answer is &#8220;Jones is slightly ahead (apparently)&#8221;. You didn&#8217;t need a math whiz to derive that, although the &#8220;apparently&#8221; might seem odd.</p>
<p>As always, start with defining what metric we&#8217;re looking for. What we would really like are the &#8220;true&#8221; numbers &#8212; what the results will be on election day. That&#8217;s what we&#8217;re trying to predict or at least get a sense of. We are looking for which candidate is ahead at the time the poll is conducted as a means of guessing what the election outcome might be like. And again, for simplicity, we&#8217;ll leave out the discussion of polling bias, systemic bias, turn-out bias, etc., anything that might cause the people responding in the poll (the &#8220;sample&#8221; voters) to be anything other than completely representative of folks on election day (the &#8220;population&#8221; voters).</p>
<p>Now a poll is just a snapshot in time; it&#8217;s not really a prediction for the (future) election day. But you can well imagine that as the polls get closer and closer to election day that they should in principle reflect closer and closer to what the end results turn out to be, at least as compared with polls taken many months ahead.</p>
<p>In the same way, if you&#8217;re running an AB test on your site, you are hoping that the people responding to the test are approximately the same sort of customers as who buy from you &#8212; usually true since the &#8220;control&#8221; variation is often the same as &#8220;how we currently do things&#8221; on your site. And, the equivalent of a &#8220;poll&#8221; is the snapshot of your AB test when you&#8217;re part-way through the test.</p>
<p>Back to Jones and Smith. What we know is this: a poll was conducted. Jones leads by two points. And there&#8217;s this odd &#8220;margin of error&#8221; number being thrown at us.</p>
<p>Now since we&#8217;re really interested in is the outcome on election day, what we get from the margin of error is an estimate of where the True number for Jones and for Smith might be. A very common way to quote margin of error is at the 95% level&#8230;From the Jones-Smith poll, this means that Jones&#8217; &#8220;true&#8221; number is somewhere in the range of 49% +/- 2% &#8212; that is, the True Jones number is somewhere between 47% and 51% (by the way, you will often see that range referred to as the &#8220;confidence interval&#8221;). Likewise, Smith is somewhere in the range 45% to 49% (47% +/- 2%).</p>
<p>And again, these are qualified as being &#8220;with 95% confidence&#8221;&#8230; all else being equal, if you did this poll a bunch of times you&#8217;d certainly get slightly different numbers for Jones and Smith, but their True numbers would be outside the range above only about 1 time out of 20 (5%).</p>
<p>This is where most people stop. Hmm, wait a second, most people stop way before this! But most folks who are interested stop right about at this point. But then you&#8217;d be missing all the interesting stuff to follow:</p>
<p>1) One important thing to keep in mind is that the margin of error, expressed as a percentage like +/- 2% above, is dependent on the number of people responding in the poll, and how close the results are&#8230;the same results conducted over a larger number of people will mean a tighter margin of error. A larger difference in the candidates (for example, say one candidate is way ahead) over the same number of people will mean a tighter margin of error (although in this case it&#8217;s often more accurate to calculate margins of error for each candidate, but I digress). In races that are relatively close as in our Jones-Smith example or in a country whose electorate is somewhat evening split between two candidates, the margin begins to increase.  In U.S. politics the numbers for each side of the major party candidates are often in the 40&#8242;s almost the entire election cycle, so that a particular poll&#8217;s margin of error is often just a function of the number of respondents in the poll &#8212; in this example, in the Jones-Smith poll there would have to have been a couple thousand people in the poll to get a m.o.e. of 2%</p>
<p>2) More importantly, just because Jone&#8217;s True number is somewhere in the range 49% +/- 2%, doesn&#8217;t mean it&#8217;s equally likely within that range. It&#8217;s much more likely for Jones to be at, say 50% than for her to be at 51%. After all the entire analysis is based on the assumption that we&#8217;re randomly sampling people when we do our poll. But also keep in mind that for Smith to be at 50% is way less likely than for Jones. This is an important part of what most people miss.</p>
<p><a href="http://www.johnquarto.com/wp-content/uploads/2012/11/graph-2-02.jpg"><img class="aligncenter size-medium wp-image-5770" title="graph-2-02" src="http://www.johnquarto.com/wp-content/uploads/2012/11/graph-2-02-300x166.jpg" alt="" width="300" height="166" /></a>Yes, it&#8217;s absolutely possible for Smith to really be at 49%, the upper end of his confidence range, and for Jones to be at 47%, the lower end of her confidence range, but that&#8217;s not nearly as likely than for Jones to be at 50 and Smith at 48. You might find it useful to think of it visually with Jones having a bell curve centered at 49%, and Smith having one centered at 47% (with the spread of each curve determined approximately by 2%/2). For Jones to win, her curve needs to be above Smith, which it is most of the time. For Smith to win, he not only needs to get more of the vote than he is currently but he must also get more of the vote than Jones, which is more of a challenge. So in this way, Jones really is further ahead than the spread between her and Smith, 49-47, superficially indicates.</p>
<p>3) So how would we go about estimating Jones chances to win? We know that voters in the poll are choosing her 49% to 47% for Smith. But 49% isn&#8217;t her probability of winning. That is determined by all the cases wherein she gets the most votes. This is handled mathematically by something called a Monte Carlo method: we run the election based on these poll results and generate a random number, though of course, as mentioned earlier, this is not equally random &#8212; you can roll snake eyes on a pair of dice but the chances of rolling snake eyes is not the same as rolling, say, an eight. But if you do this enough times you start to build up a probability curve for the expected results.</p>
<p>So we repeat this simulation of running an election a hundred, or a thousand, or ten thousand times and look at all the results where Jones wins compared to where Smith wins. Professionals that number crunch like this would typically use at least 100,000 runs. Given the numbers from our fictitious poll, what do you suppose the winning probability would be for Jones?</p>
<p>Pick a number in your head. even odds of 50%? 60%? 75%? Write it down.</p>
<p>This is where the human part of the brain can lead one astray. If you saw numbers like that on your TV screen, one candidate ahead 49%-47%, you&#8217;d think &#8220;hmm, that&#8217;s somewhat of a close race; maybe the underdog can pull ahead&#8221;.</p>
<p>Turns out Jones is expected to win upwards upwards of &#8230;wait for it &#8230; 96% of the time! Of course, the election is not run umpteen thousands of times; it&#8217;s only run once. So there&#8217;s definitely a chance for underdog Smith to win (about 4%, 1 in 25). And there&#8217;s always the probability that more of Smith people&#8217;s people will turn on on election day, etc., but according to the snapshot poll in our example, Jones looks pretty darn strong to take it.</p>
<p>So, many times things that are close are not really so. And it&#8217;s <a href="http://www.johnquarto.com/2012/10/oh-what-a-tangled-web-we-weave/" target="_blank">extraordinarily easy to deceive oneself</a> on these occasions. When you&#8217;re doing your AB tests keep this in mind!</p>
<p>By the way, this use of the Monte Carlo technique is what Google Analytics Content Experiments (previously called Google Website Optimizer) used as a technique for &#8220;chance to beat&#8221; calculations.</p>
<p>p.s. I ran the simulations for our fictitious poll using a nifty <a href="http://www.r-project.org/" target="_blank">program called R</a> (which numbers people love but which might confuse the average marketer &#8212; use with adult supervision!)</p>
<p>[This article is Cross-posted to my monthly column at <a href="http://marketingland.com/author/john-quarto-vontivadar">MarketingLand</a>, which is a great place to read all sorts of interesting content.]</p>
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		<title>I, Deceiver</title>
		<link>http://www.johnquarto.com/2012/10/i-deceiver/</link>
		<comments>http://www.johnquarto.com/2012/10/i-deceiver/#comments</comments>
		<pubDate>Tue, 02 Oct 2012 14:36:05 +0000</pubDate>
		<dc:creator>John Quarto-vonTivadar</dc:creator>
				<category><![CDATA[Featured]]></category>

		<guid isPermaLink="false">http://www.johnquarto.com/?p=5745</guid>
		<description><![CDATA[This is a follow up to my previous article &#8220;Oh, What A Tangled Web We Weave&#8221; which discusses 8 common ways we practice self-deception on<br /><span class="excerpt_more"><a href="http://www.johnquarto.com/2012/10/i-deceiver/">[continue reading...]</a></span>]]></description>
				<content:encoded><![CDATA[<p><a href="http://www.johnquarto.com/wp-content/uploads/2012/10/images.jpeg"><img class="alignright size-full wp-image-5748" style="margin: 5px;" title="images" src="http://www.johnquarto.com/wp-content/uploads/2012/10/images.jpeg" alt="" width="220" height="224" /></a>This is a follow up to my previous article &#8220;<a href="http://www.johnquarto.com/2012/10/oh-what-a-tangled-web-we-weave">Oh, What A Tangled Web We Weave</a>&#8221; which discusses 8 common ways we practice self-deception on ourselves, particularly in the context of metrics, analytics, and continuous improvement testing.</p>
<p>Continuing with our earlier list, here are four more ways a marketer might self-deceive:</p>
<h4>#5: Confuse Accuracy With Precision</h4>
<p>Example: &#8220;Our Average Customer is 32.47 years old&#8221;</p>
<p>Apparently, your average customer was born June 21. Ok, and your point is&#8230;.? Do you honestly think you can measure average age of your customers to within three days (i.e., hundredths of a year)? And is there any difference between your customers that are 28 or 37? Maybe, for actuarial reasons, and if you&#8217;re in the insurance business. Maybe not, if you sell sweaters.</p>
<p>Precision &#8212; particularly for those who are also subject to Self-Deception #1 (<a href="http://www.johnquarto.com/2012/10/oh-what-a-tangled-web-we-weave">&#8220;Innumeracy&#8221;. See previous article.</a>) &#8212; is often a fetish, giving a false sense of accuracy. In other words, if you can measure it as 32.47, then by golly, it must not be 31 or 33 so it <em>must</em> be right!</p>
<p>Instead consider the difference in the meanings of the words:  Accuracy measures how close the data are to the True number (in this example, it would be the average age of the your customer, rounded to the nearest year). Precision, however, measures how close the data are to each other, without regard to how close they are to the truth.</p>
<p>If you&#8217;re more of a visual person, imagine a typical bell curve. In this case, a bell curve of the frequency of customer ages. Accuracy is a measure of how close a number is to the top of the bell curve; Precision is a measure of how spread out the bell curve is.</p>
<p>While this may seem like a little too prissy in insisting on what the words mean, this self-deception #5 is a fairly common mistake even among people who are pretty smart! So the better you get at avoiding #1-#4, the more likely you are to end up committing #5 ! Oh, we humans <em>are</em> an amusing lot!</p>
<p>General rule of thumb: you can&#8217;t be more precise with your calculated metric that you are with the least precise data. If you are measuring customer age in years (not months), then your average age will be in years. In fact, your math teacher likely told you to quote instead a median value, rather than the average, in such cases specifically to avoid this sort of self-delusion. (That was probably the time cut class and got a detention for smoking under the bleachers)</p>
<h4>#6: Confuse a Local Maximum For A Global Maximum</h4>
<p>Example: &#8220;We&#8217;ve tested zillions of variations on product images for our shoes &#8212; everyone prefers a human model wearing them! Everything else we tried lowered conversion!&#8221;</p>
<p>It&#8217;s easy to get caught up testing too narrow a set of variations once one factor displays its importance. What about multiple images? What about shoe treads/soles (often not seen in primary images). Call to Action buttons in proximity to the image? Ancillary data (&#8220;returned shoe rate&#8221;) that has little to do with imagery but imbues trust? One can go on and on.</p>
<p>Testing is about <em>continuous</em> improvement; you&#8217;re not guaranteed <em>perpetual</em> improvement. And you&#8217;re not even guaranteed to come up with the important factors (though I hope you do!).  Get over it, and get on with it.</p>
<p>And, although the following comment has little to do with self-deception, I think it bears repeating since I first said it 2004: when you engage in continuous testing and you win (trans: &#8220;you improve the desired outcome&#8221;) then you make money. When you test and you lose (trans: &#8220;you don&#8217;t improve the desired outcome&#8221;) then you learn something, and that something that will give you insight into testing more efficiently in the future. So you are ahead either way, and most especially ahead of competitors who do nothing.</p>
<h4>#7: Intermix False Positives with False Negatives</h4>
<p>A false positive: you are looking for a particular type of outcome and you identify one falsely. A false negative: you are looking for a particular type of outcome and you identify one as not occuring when, in fact, it really does. To be sure, this is less about self-deception and more about experimental bias risk &#8212; but the outcomes are usually so important to the company that not knowing about false positives or false negatives can lead you down a bad path.</p>
<p>This time I&#8217;ll use a non-business example to get the point across and then circle back to a business situation. (No doubt this will roll the comments in, from both sides.)</p>
<p>Example: You&#8217;re in charge of security at  your airport. You&#8217;re trying to determine when one of the crazy dangerous people are trying to get on the aircraft. You&#8217;ve decided for whatever reason (or, <a href="http://cdn.cagle.com/working/100428/sack.jpg">if you live in Arizona</a>, apparently) that you have this power to &#8220;tell&#8221; who is the crazy, dangerous person<em> just by looking, </em>so you decide to cavity search anyone at the airport wearing, say, a turban or burqa. I didn&#8217;t say it was a great idea, I&#8217;m just setting up an example to make a point.</p>
<p>So a false positive is: &#8220;someone who appears crazy dangerous but turns out to be just someone who got a stylish burqa from LandsEnd&#8221;. The rule we used to indentify dangerous turns out to not apply to this person, thus a false positive.</p>
<p>A false negative is &#8220;someone who appears completely safe &#8212; in this case all one has to do is wear jeans and a t-shirt to satisfy the cavity search rule &#8212; and then promptly hijacks the plane at 30,000 ft.&#8221;</p>
<p>In both cases, we got it wrong. One was in to incorrectly find something we were looking for, the other was to incorrectly <em>not</em> find something we were looking for. [By the way, for those readers who did not skip out to go smoke under the bleachers that day, the false positives are referred to by the quants as "Type II errors" and the false negatives as "Type I errors". Just FYI. ]</p>
<p>Now back to a business example to you can be ready for false positives and false negatives at your job:</p>
<p>Example: We&#8217;re running a test. Do Green Add-to-Cart buttons convert better than Blue Add-To-Cart buttons. The false positive: We run the test and it apparently shows that Green converts better, but in reality the Blue really is the winner. The false negative: we run the test and it apparently shows our original Blue button remained the winner, but in reality the Green really is the winner.</p>
<p>Two different risks that have to be handled separately. Some readers will like to know what to do in such a case, to which I&#8217;d advise: use my earlier rule of &#8220;do the opposite&#8221; and &#8220;look for evidence against what your wished-for outcome is&#8221;. In this case, which ever test result you end up declaring the winner, consider re-running the test again. You may not need to run nearly as much traffic through it, though you might run it longer just to make up for the lessened traffic. See if this second test confirms the first test&#8217;s results. If you&#8217;re really keen, don&#8217;t run one test across 10,000 customers, but run 10 different test across 1,000 customers. And then try a lesser test a month or a quarter from now. Always be on the look out for &#8220;how can I challenge my presumptions &#8212; especially those I got from testing a while ago&#8221;. This is often a great exercise in July or August when you&#8217;re not quite ready for the Christmas season to start but you&#8217;ve got extra cycles to try a few extra tests.</p>
<h4>#8: Confuse Correlation with Causation</h4>
<p>First off, a quick little <a href="http://blog.lib.umn.edu/nich0185/myblog2/Stivers%202-10-03%20Pavlov's%20dogs.gif">illustration that hilariously spoofs this point</a>.</p>
<p>Example: &#8220;I changed our Green Add-to-Cart Buttons to Blue in November. Out December sales figures were way up. Ahha! Blue converts better than Green!&#8221;</p>
<p>Not necessarily. Maybe sales were just up because of Christmas. Maybe our new marketing guy is doing miracles on Facebook. Maybe the new Button color actually <em>decreased</em> sales but the Facebook efforts more than made up for that, leading to a net plus for us. Maybe a lot of things. We just don&#8217;t know enough to be assigning credit.</p>
<p>Causation is about one thing causing another. And since our everyday experience of time flows in only one direction, that means the causer happened before the, hmm, causee. In the example above, if the changing of the Button color really caused sales to go up, then the change must&#8217;ve occured first. In fact, there&#8217;s a old logic fallacy &#8220;post hoc ergo proptor hoc&#8221; &#8212; so old that they came up with it in Latin when Latin was a live language (or the <a href="http://www.csmonitor.com/USA/Politics/2011/0603/Political-misquotes-The-10-most-famous-things-never-actually-said/I-was-recently-on-a-tour-of-Latin-America-and-the-only-regret-I-have-was-that-I-didn-t-study-Latin-harder-in-school-so-I-could-converse-with-those-people.-Dan-Quayle">urban myth about Dan Quayle</a>) &#8212; which means roughly &#8220;after, therefore because of&#8221;. Just because something comes after something else, doesn&#8217;t mean the first caused the second.</p>
<p>Correlation is much looser. Two things are correlated when they tend to move in the same direction of change somewhat in accordance with each other. This doesn&#8217;t mean that one causes the other. Nor does it means that it doesn&#8217;t. It&#8217;s just that there&#8217;s some sort of relationship in how they tend to move. For example: <a href="http://user.cloudfront.goodinc.com/community/patrick/rs-500-us-oil-production11.jpg">Rock Music Quality correlates well with US Crude Oil Production</a>. Or US <a href="http://3.bp.blogspot.com/-Oy_WFlzXIPo/UEio1Zh7OEI/AAAAAAAAnUY/B2Wvd41o1nc/s1600/blog-correlation-291209.gif">Highway Fatalities correlate well with Fresh Lemon Imports from Mexico</a>. These are correlated, but there&#8217;s no causation. (Unless you think the highway deaths are caused by slippage on lemon juice!).</p>
<p>But here&#8217;s another one: <a href="http://upload.wikimedia.org/wikipedia/commons/thumb/9/94/Cancer_smoking_lung_cancer_correlation_from_NIH.svg/326px-Cancer_smoking_lung_cancer_correlation_from_NIH.svg.png">Men who smoke cigarettes and the incidence of lung cancer among men</a>. Turns out these are fairly well correlated, with a lag of about 20 years from when the guy starts smoking to when the cancer rates start going up. Do you think there&#8217;s possibly a causation here? Yeah, probably.</p>
<p>Now it&#8217;s true that the phrase &#8220;correlation doesn&#8217;t imply causation&#8221; is an oft-bandied about one. In fact, <a href="http://www.slate.com/articles/health_and_science/science/2012/10/correlation_does_not_imply_causation_how_the_internet_fell_in_love_with_a_stats_class_clich_.single.html">it&#8217;s over-used in the extreme</a>.  But an important point to keep in mind is that all causation implies some sort of decent correlation (if you&#8217;re measuring what counts), whereas high correlation in and of itself just means, well, that there&#8217;s a high level of correlation. Causation and Correlation are not opposites, causation simply includes a way to &#8220;give credit&#8221; to a causal event. If you keep the distinction in mind and ask youself &#8220;is there anyting, <em>other than the correlation</em>, that causes me to believe that A caused B?&#8221; you&#8217;ve got a powerful way to come up with new ideas for testing more broadly or more deeply.</p>
<p>Well, that&#8217;s it. It&#8217;s been a long two articles! But I hope you&#8217;ve learned something that will help you keep sharp in your testing efforts, the metrics you use to measure success, and your self-assessment of what you think you &#8220;know&#8221; for sure.</p>
<p>Which ones of the Self-Delusions do you feel you&#8217;ve perpetuated on yourself in the last 90 days?</p>
<p>[This article is Cross-posted to my monthly column at <a href="http://marketingland.com/author/john-quarto-vontivadar">MarketingLand</a>, which is a great place to read all sorts of interesting content.]</p>
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		<title>Oh, What A Tangled Web We Weave &#8230;</title>
		<link>http://www.johnquarto.com/2012/10/oh-what-a-tangled-web-we-weave/</link>
		<comments>http://www.johnquarto.com/2012/10/oh-what-a-tangled-web-we-weave/#comments</comments>
		<pubDate>Mon, 01 Oct 2012 11:07:43 +0000</pubDate>
		<dc:creator>John Quarto-vonTivadar</dc:creator>
				<category><![CDATA[Featured]]></category>
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		<description><![CDATA[&#8220;Oh, What A Tangled Web We Weave, When First We Practice To {Self} Deceive&#8221; With apologies to Sir Walter Scott and my mangling of his<br /><span class="excerpt_more"><a href="http://www.johnquarto.com/2012/10/oh-what-a-tangled-web-we-weave/">[continue reading...]</a></span>]]></description>
				<content:encoded><![CDATA[<h4>&#8220;Oh, What A Tangled Web We Weave, When First We Practice To {Self} Deceive&#8221;</h4>
<p><a href="http://www.johnquarto.com/wp-content/uploads/2012/10/Screen-shot-2012-10-02-at-2.42.47-PM.png"><img class="alignright size-thumbnail wp-image-5732" style="margin: 5px;" title="Screen shot 2012-10-02 at 2.42.47 PM" src="http://www.johnquarto.com/wp-content/uploads/2012/10/Screen-shot-2012-10-02-at-2.42.47-PM-150x150.png" alt="" width="150" height="150" /></a>With apologies to Sir Walter Scott and my mangling of his most popular turn of phrase, I&#8217;d like to take some time now to discuss how people &#8212; and particularly people involved in metrics and evaluation of &#8220;how did we do this week/month/quarter?&#8221; &#8212; are prone to self-delusion. Along the way, I run the risk of insulting you but I see that as a necessary risk when confronting a topic like this. It takes a healthy sense of self-worth to say &#8220;holy crap! I&#8217;m a dufus!&#8221;</p>
<p>Now, let&#8217;s start off with some assumptions. First, that (most) people are honest, and don&#8217;t really intend to deceive themselves or others. [If you're doing it intentionally, then bad karma on you, but I leave your fate to another writer.] This leaves with us two groups: the honest self-deceivers who do so consciously and unconsciously.</p>
<p>Conscious self-deception? Surely not!  Yet, how else do we explain common phrases for just this very thing:</p>
<ul>
<li>&#8220;I don&#8217;t want to know&#8221;</li>
<li>&#8220;It&#8217;s got nothing to do with me&#8221;</li>
<li>&#8220;Don&#8217;t make waves&#8221;</li>
<li>&#8220;Look the other way&#8221;</li>
<li>&#8220;Nothing I can do about it!&#8221;</li>
<li>&#8220;Let sleeping dogs lie&#8221; (personally I love the double entendre here)</li>
<li>&#8220;Ignorance is bliss&#8221;</li>
<li>&#8220;Brush it under the carpet&#8221;</li>
<li>&#8220;Why didn&#8217;t I listen to my intuition?&#8221;</li>
</ul>
<p>All of these phrases are used in situations where the facts are uncomfortable and we <em>consciously</em> choose to ignore them. We make believe the situation is different, all the while knowing it isn&#8217;t so. I like to call this &#8220;quantum shifting&#8221; into an alternate universe where the unpleasant is no longer so.</p>
<p>Many of the situations where you&#8217;d use a phrase like the above are ones in which you feel you may have little or no control on things. So the natural human reaction is to ignore and hope that things change.</p>
<p>Is there a way to minimize self-delusion? Actually, yes, there is somewhat of a process you can use, though with varying levels of success.</p>
<ul>
<li>admit that it occurs; realize, as a human, you <em>will</em> do it again</li>
<li>recognize when it&#8217;s happening</li>
<li>look for the opposite</li>
<li>friends don&#8217;t let friends self-delude: have a buddy who is willing to level with you</li>
</ul>
<p>Again, I&#8217;d stress that the more compelling the bad news is, the more likely one is to steadfastly hold onto one&#8217;s convenient alternate reality. Let&#8217;s all group-self-deceive right now and chalk that up as &#8220;part of the charm of being human&#8221;<em>.</em></p>
<h4>Eight Common Self-Deceptions</h4>
<p>Now, I can&#8217;t help you with that last buddy part. Nevertheless here are eight common ways we <em>unconsciously</em> self-deceive using stats, ignorance, bad logic, or other wily human tricks. I&#8217;ll present them as encouragements &#8212; makes them funnier that way &#8212; so you can recognize when they might occur and be prepared to challenge yourself.</p>
<p>To encourage your own self-deception, it helps if you let yourself:</p>
<h4>#1: Be Innumerate</h4>
<p>Example: &#8220;what&#8217;s the big deal going from 2% conversion to 3%?? That&#8217;s only a 1% lift! We want more!&#8221;</p>
<p>Uh, no. Going from 2% to 3% is a 50% lift.</p>
<p>As saying goes, &#8220;Innumeracy is a serious problem which affects 8 out of every 5 people.&#8221; I&#8217;m listing this as #1 on the self-delusion hit parade, because I see it the most often and the people who practice innumeracy really do believe they &#8220;get math&#8221;. In some ways, you can argue this is easy to fix. In other ways, you can argue &#8220;you can&#8217;t fix stupid.&#8221; You decide which group you see yourself in.</p>
<h4>#2: Start With your Wished-For Outcome &#8230; Then Look For Supporting Proof</h4>
<p>Example: &#8220;Our Customers love us! We had 317 positive reviews on Yelp last month&#8221; (conveniently ignoring the 2,182 negative reviews and a poor Net-Promoter score)</p>
<p>I call this one the &#8220;Creationist Ostrich&#8221; technique. (oh boy, I can see the comment box filling up now!) You start off in a comfy zone, and you really, really, <em>really</em> want to stay there, so you ignore all the evidence that contracts your view. Usually this is because the alternative suggested by the evidence is bothersome in some way, and the more deeply bothersome the easier it is to just dismiss, hold out with your head in the sand till 5pm, then go grab dinner. Problem solved!</p>
<p>Instead under the part of the process mentioned above as &#8220;look for the opposite&#8221;, consider when you have a wished-for outcome that the best thing you can do for yourself is to try to find evidence against what you&#8217;d like the outcome to be. At least, if you&#8217;re goal is to uncover truth. Virtualy everyone involved in metrics, analytics, and testing-driven improvement online does, in fact, have a wished-for outcome: &#8220;get higher conversion&#8221;, &#8220;increase revenue&#8221;, &#8220;decrease cart abandonment&#8221;. Every metric you have for success implies you&#8217;d like more or less of that metric, so you always have a wished-for outcome at hand. So you&#8217;re subjecting yourself regularly to this sort of self-deception. Cut it off from the start by always trying to prove yourself wrong. Give yourself permission to be wrong, by constructing your tests in such a way that before you even run the test you can say &#8220;heck if I see such and such from the test results, we know we&#8217;ve completely missed the mark&#8221; &#8212; and then look for such-and-such.</p>
<h4>#3: Take Credit For Forces Beyond Your Control</h4>
<div>Example: &#8220;Our sales were up in December!&#8221;</div>
<div></div>
<div>Yes, well, <em>everyone</em>&#8216;s sales are up in December, especially in Retail. If you&#8217;re up by 20% yet everyone else is up by 30%, aren&#8217;t you actually down? This self-deception, ranked #3 on my list, is a close relative of #1, but illustrates how you can get the math right, yet still self-delude. Commodity traders have a saying for this, by the way: &#8220;don&#8217;t confuse brains with a bull-market&#8221;. It&#8217;s very easy to make money &#8212; or in the cases of metric and analytics to increase the positive metrics you want more of &#8212; when market conditions are moving in the direction that gives rise to that in the first place. The real artistry comes from those who do more with less. Current economic conditions are a great example of that. There are a lot of active testers and optimizers right now who are getting so-so lift from their experiments but who are gaining valuable insight as to how to test and continuously improve. Then there are those who are just going through the motions dismissing key learnings, especially those that disagree with their pre-conceived notions or the corporate hoped-for storyline. Wanna make a guess as to which group is gonna look like all-stars when the next boom in their industry occurs?</div>
<div></div>
<h4>#4 Settle for Industry Averages</h4>
<div>Example: &#8220;We convert at 2.5% and industry average is 2.4%.  Yay us!&#8221;</div>
<div></div>
<div>A better measure is &#8220;how much did we leave on the table?&#8221; Or &#8220;how high could we get our Conversion rate?&#8221; Or &#8220;what does the market leader do that we do not?&#8221;</div>
<div></div>
<div>This one can be hard to spot when you&#8217;re imposing this self-delusion on yourself. Often times, your industry average is the only other data you may have access to, due to proprietary info at your competitors. And the industry average is often comprised of self-reported stats, which do have a level of uncertainty or, shall we say, &#8220;hopefullness&#8221; or &#8220;putting your best foot forward&#8221; attached.</div>
<div></div>
<div>Nevertheless, do you consider your company akin to Garrison Keillore&#8217;s Lake Woebegone, where &#8220;all the women are strong, all the men are good looking, and all the children are above average&#8221;? Do you really want to be average? Don&#8217;t you want to excel?</div>
<div></div>
<div>What makes In-N-Out Burger customers so nuts about their fast food? What about the Apple &#8220;fan boys&#8221;? Do these companies sit around figuring out how to meet the industry average , or are they setting the industry average bar higher by striving for excellence above and beyond?</div>
<div></div>
<div>The biggest danger to this delusion #4 is when it combines with #3 as well as decent profit margins. For example, if industry averages for conversion rates were 2.5% and you are hitting a number seemingly much higher (let&#8217; say, 5%) <em>and</em> you&#8217;ve got a very healthy profit margin. In such cases, almost everyone at the company will be patting themselves on the back, and the tendency will be to forget questions like &#8220;how much more could we really squeeze out of this?&#8221; It&#8217;s in such situations that complacency sets in and eventually (who knows when?) some disruptive competitor comes along and completely blindsides you. Always, always, <em>always</em> be wary when your organization is doing too well, for that is the time you&#8217;ll most likely self-deceive with combination of #4, #3, #2. Celebrate the good times, yes, but keep a designated driver on hand.</div>
<h4>More To Come</h4>
<div>It looks like my space for this month&#8217;s column is filling up. Tune back in next time, when I&#8217;ll finish up with four more self-delusions on topics like Precision, Maxima, Causation&#8230;and Falsies. Yes, you too can become expert self-deludatrix!</div>
<div></div>
<div>[This article is Cross-posted to my monthly column at <a href="http://marketingland.com/author/john-quarto-vontivadar">MarketingLand</a>, which is a great place to read all sorts of interesting content.]</div>
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		<title>The Value of Nothing</title>
		<link>http://www.johnquarto.com/2012/08/the-value-of-nothing/</link>
		<comments>http://www.johnquarto.com/2012/08/the-value-of-nothing/#comments</comments>
		<pubDate>Thu, 30 Aug 2012 17:26:29 +0000</pubDate>
		<dc:creator>John Quarto-vonTivadar</dc:creator>
				<category><![CDATA[Analytics]]></category>
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		<description><![CDATA[When is nothing worth something? Turns out, just about all the time. Whenever you look at your metrics consider that a metric is, in virtually<br /><span class="excerpt_more"><a href="http://www.johnquarto.com/2012/08/the-value-of-nothing/">[continue reading...]</a></span>]]></description>
				<content:encoded><![CDATA[<p>When is nothing worth something?<a href="http://www.johnquarto.com/wp-content/uploads/2012/08/Bohr_1920.jpg"><img class="alignright size-medium wp-image-5719" style="margin: 5px;" title="Bohr_1920" src="http://www.johnquarto.com/wp-content/uploads/2012/08/Bohr_1920-190x300.jpg" alt="" width="190" height="300" /></a></p>
<p>Turns out, <em>just about all the time</em>. Whenever you look at your metrics consider that a metric is, in virtually every case, designed to measure a specific outcome. It may be a positive outcome, one that is beneficial to your company: more sales, more customers, less inventory shrink, etc. Or it may be a negative effect, something that impedes your ability to do business: more churn, fewer repeat customers, higher costs. It doesn&#8217;t take a rocket scientist to figure out you want &#8220;more&#8221; of the former and &#8220;less&#8221; of the latter.</p>
<p>Either way, the most insightful metrics are the ones that lead to actionability and that comes from isolating one end of the the above spectrum. A metric that is good at indicating positive benefit pretty much is not going to be useful for indentifying detrimental effects. And vice versa. In fact, a single metric that attempts to imply anything about both ends of such a spectrum is almost always useless. Oh, you might get a few nuggets now and then, but those nuggets will only be nuggets because some other metric(s) were involved in isolating them.</p>
<p>Why should this be?</p>
<p>Of course part of the answer lies in our human and never-ending fetish that &#8220;more numbers mean more insight&#8221; &#8212; the current buzzword for this is &#8220;big data&#8221;, by the way. But from more of a mathematical perspective, a metric that&#8217;s designed to measure one sort of outcome cannot tell you much about the anti-outcome. Particularly when what you&#8217;re looking at is based on human behavior, rather than a physical system.</p>
<p>Knowing this, you can learn a lot more from your metrics when you cast them as 1&#8242;s and 0&#8242;s. For every positive outcome you want more of (conversely, every negative outcome you want to minimize), consider inputs to the metric that result in what you want more (less) of as one, and everything else as zero. There&#8217;s a good reason to do so, because in a system based on human responses and behavior, zero now takes on a more useful meaning: &#8220;I don&#8217;t know&#8221;. It&#8217;s not a binary physical system, where 0 is the opposite of 1; instead the zero indicates an absence of any knowledge <em>in the context of the behavioral outcome you measured</em> (the &#8220;one&#8221;).</p>
<p>Consider  direct mail. The common average of response to direct mail is an expectation of about 2%. Which, of course, means 98% of people didn&#8217;t respond. You&#8217;d probably be ecstatic if you could get your DM response rate up to, say, 2.5%. You&#8217;d scrub your mailing list of all sorts of characteristics, demographics, previous behavior, psychographics, etc to figure out how to do I better indentify more of the sort of people who were like the 2%.  If you were building a model for this (perhaps for predictive purposes), you&#8217;d want to have available as fat a data stream about people on the list as possible, among those that responded positively. Not those other folks.</p>
<p>In other words, if I&#8217;m building a model or even a simple metric upon which to act, I don&#8217;t need to do a hell of a lot of work to predict what most people on the list will do&#8230; I have the remarkable well-correlated insight already to know what 98% of them will do! Which is, &#8220;not respond&#8221;. This in itself is fairly valuable. But I&#8217;m not looking for anti-responders. Instead I want to be looking at characteristics of those that complete the action I want and to derive what goes into getting more of those. Once I&#8217;ve done that, then I acquire more traffic through that filter and my response rate goes up.</p>
<p>Of course, this doesn&#8217;t mean this is the only way to skin this cat. It&#8217;s simply the best way to do it,<em> in the context of the metric we set up,</em> which was to look at the response rate, and the characteristics of people who exhibit that behavior, and proactively identify it in others. But you can also approach the same goal by looking at the opposite behavior, which is &#8220;those that didn&#8217;t respond&#8221;. Now, the &#8220;one&#8221; becomes how we indentify non-responders and our zero becomes not positive responders but &#8220;any behavior that doesn&#8217;t inform non-responsiveness&#8221;. Our goal remains the same (more cutomers from a higher response rate) but our strategy has changed to indentifying characteristics of those who do not respond and to cut those sorts of people out of our future mailings. Having culled out the non-responders, our response rate will naturally go up, without even having acted to better indentify positive responders.</p>
<p>This sort of &#8220;opposite pole&#8221; approach might remind you of the old joke about the fellow who goes to the doctor, moves his arm up and down, and complains &#8220;doc, my arm hurts when I do this!&#8221;. To which the doctor responds, &#8220;Well, don&#8217;t do that&#8221;. The take-away from the joke is to do the anti-behavior. The real take-away is that sometimes it&#8217;s easier to get more of what you want by getting less of what you don&#8217;t want.</p>
<p>Of course, you can combine both of the above techniques. You should keep in mind, however, that you cannot combine both techniques if you cannot be each technique separately. Start off with the wrong metrics, and you can create a phenomenally well-fitted model that predicts a behavior you&#8217;re not even looking for.</p>
<p>More on this topic in the future, but I&#8217;d like to leave you with a quote from Neils Bohr, the famous Danish physicist:</p>
<p>&#8220;The opposite of a correct statement is a false statement. But the opposite of a profound truth may well be another profound truth.”</p>
<p>Don&#8217;t muddy your insight with a mishmash of metrics that muddy up the profound truths in your analytics data. Aim for simple, focused metrics that measure one thing very well.</p>
<p>[This article is Cross-posted to my monthly column at <a href="http://marketingland.com/author/john-quarto-vontivadar">MarketingLand</a>, which is a great place to read all sorts of interesting content.]</p>
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		<title>Would You Tip You?</title>
		<link>http://www.johnquarto.com/2012/07/would-you-tip-you/</link>
		<comments>http://www.johnquarto.com/2012/07/would-you-tip-you/#comments</comments>
		<pubDate>Mon, 09 Jul 2012 22:00:52 +0000</pubDate>
		<dc:creator>John Quarto-vonTivadar</dc:creator>
				<category><![CDATA[Conversion and Persuasion]]></category>
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		<description><![CDATA[In many ways, being effective online means paying attention to the things that matter. That recipe includes conversion, and analytics, and metrics &#8212; important ingredients,<br /><span class="excerpt_more"><a href="http://www.johnquarto.com/2012/07/would-you-tip-you/">[continue reading...]</a></span>]]></description>
				<content:encoded><![CDATA[<p><a href="http://www.johnquarto.com/wp-content/uploads/2012/07/diner-waitress.jpg"><img class="alignright size-thumbnail wp-image-5710" title="diner waitress" src="http://www.johnquarto.com/wp-content/uploads/2012/07/diner-waitress-150x150.jpg" alt="" width="150" height="150" /></a>In many ways, being effective online means paying attention to the things that matter. That recipe includes conversion, and analytics, and metrics &#8212; important ingredients, all &#8212; but it also means using your brain to pay attention, as a human, to what your customer, another human, needs.</p>
<p>Waitresses are great at this. And as far as I can tell, the over-worked ones at diners and dives often have the best 6th-sense for how to handle a customer. When it comes to your restaurant (your website) and your hungry patrons (your customers), and your waitress (uh, YOU!), answer me this: &#8220;Would you tip you?&#8221;</p>
<p>Bryan Eisenberg often writes about the &#8220;<a href="http://www.clickz.com/clickz/column/2079320/conversion-trinity">Conversion Trinity&#8221; as the effective formula</a> for improving conversion from when we were at our consulting agency (back in the day). I&#8217;ve always shied away from that nomenclature since I feel it has too much religious overtones to it (IMHO).  I prefer instead to think of the three core concepts as the very same ones that you&#8217;d expect from a great service experience at a restaurant: Relevance, Value, and Call To Action.</p>
<p>Of course, that&#8217;s not the parlance used at a great eating establishment. (&#8220;86 Those  Words, Chef!&#8221;). But the principles are the same:</p>
<ul>
<li><strong>Relevance</strong>: From a Conversion standpoint, we talk about about being relevant to the customer&#8217;s wants, desires, search (explicit or implicit), and the concept of maintaining the scent trail for the customer.</li>
</ul>
<p>From a waitress&#8217; perspective, if I walk in and ask &#8220;what&#8217;s good?&#8221; a great waitress immediately has something to recommend. And she&#8217;s familiar enough with the menu, that if I get a no-no-on-carbs look in my eye when she says &#8220;pasta&#8221; , she immediately  pivots to a steak or salad. If I walk in and say &#8220;I&#8217;d like a steak&#8221;, then a great waitress doesn&#8217;t spend anytime singing the praises of the risotto, fine as it may be.</p>
<ul>
<li><strong>Value</strong>: From a Conversion standpoint, we talk about if the customer knows why this is exactly the right restaurant to be getting what I wanted from my Relevance query. Has the value prop been explained well?</li>
</ul>
<p>From a waitress&#8217; perspective, why is &#8220;what&#8217;s good&#8221;, well, good? Maybe it&#8217;s &#8220;we&#8217;re known for this throughout the South&#8221;, or &#8220;we&#8217;ve been making it the same way since 1912&#8243; or &#8220;we use special mushrooms gathered under the light of a full moon&#8221;. Or perhaps we&#8217;re Value in the literal sense: &#8220;we serve Prime cuts you can&#8217;t get anywhere else, and we do it for prices that make our owners weep&#8221;. However the waitress sells it, a great waitress implicitly knows the Value has to match the Relevance.</p>
<p>[Personally, this is why I immediately tune out a waitress who tells me she's never had dish X, but "has been told" it's really excellent. This is like the Vegan animal rights activist who wears a leather belt. Just say "I've never had it, but I get the portobello burger all the time  and I love it!"</p>
<p>I don't have to like the same stuff as you but I want authenticity, not a blog aggregator of what others have said. To that end, I want a waitress who likes to eat! Never trust a skinny waitress! Ah, but I digress...]</p>
<ul>
<li><strong>Call to Action</strong>: From a Conversion standpoint, we talk about whether it&#8217;s obvious what to do next, and whether the customer has the confidence to take the next action.</li>
</ul>
<p>From a waitresses perspective, this means helping me narrow down my choices (&#8220;yes, we have half a dozen different kinds of pasta, but the only two to consider are&#8230;&#8221;), addressing concerns I might have (&#8220;this is a great dish except if you&#8217;re allergic to peanuts&#8221;), and then asking for the sale (&#8220;Can I bring you that salad you have your eye on? You can always get something else if you&#8217;re still hungry after&#8221;). In most cases, she needn&#8217;t even ask for the sale at all, since the customer will sell himself (with her help, &#8216;natch).</p>
<p>If you get Relevance and Value correct, you&#8217;ll have to go out of your way to goof up Call to Action &#8212; and even if you do, it&#8217;s easy to spot and correct later. But goof up the Relevance/Value marriage, and the best Call to Action in the world will sound hollow and forced. The waitress does the same thing by establishing rapport with you a.s.a.p. to help you get to Action.</p>
<p>And don&#8217;t ask if I want to see the dessert tray: Just bring the damn thing over, and while we ooh and aah, <em>then</em> ask &#8220;may I entice you with a dessert?&#8221; That&#8217;s a damn fine combination of Call to Action (&#8220;Yes&#8221;) and up-sell through creation of new Relevance and Scent.</p>
<p>Think about this the next time you&#8217;re at a restaurant and you get great (or horrible) service. You&#8217;ll soon see the parallels. It&#8217;s easy to tip a great waitress, because she is a joy to work with.</p>
<p>Shouldn&#8217;t your website be this way? All those metrics and analytics &#8230; it&#8217;s nothing more than supporting materials  to help you consider what you&#8217;re doing online from the customer&#8217;s perspective of Relevance, Value, and Call To Action.</p>
<p>Given how you currently handle this on your site, would you tip you?</p>
<p>[This article is Cross-posted to my monthly column at <a href="http://marketingland.com/author/john-quarto-vontivadar">MarketingLand</a>, which is a great place to read all sorts of interesting content.]</p>
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