![\"Poor](\"https:\/\/cognitiveseo.com\/blog\/wp-content\/uploads\/2017\/02\/Poor-Marketing-Data-Costs-Online-Businesses-611-Billion-Per-Year.jpg\")
<\/p>\nThe data quality subject is often neglected, and poor quality business data constitutes a significant cost factor for many online companies. And, although there is much content that claims that the costs of poor data quality are significant, only very few articles are written on how to identify, categorize and measure such costs. In the following article we are going to present how poor quality data can affect your business, why is this happening and how can you prevent wasting time and money due to poor data.<\/p>\n
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It\u2019s going to be an in-depth geeky research and we advise you to keep your pen and notebook nearby and load yourself with plenty of coffee.<\/p>\n
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With no further introduction, allow us to present you the main subject we are going to tackle in the following lines:<\/p>\n
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The development of information technology during the last decades has enabled organizations to collect and store enormous amounts of data. However, as the data volumes increase, so does the complexity of managing them. Since larger and more complex information resources are being collected and managed in organizations today, this means that the risk of poor data quality increases, as the studies show<\/a>. \u00a0Data management, data governance, data profiling and data warehouse could be the\u00a0salvation for any business and not just snake oil.\u00a0<\/p>\n <\/p>\n Poor quality data can imply a multitude of negative consequences in a company.<\/p>\n<\/blockquote>\n <\/p>\n Below you can read just some of them:<\/p>\n <\/p>\n <\/p>\n In the image below you can easily see that there are not only direct costs we are talking about but hidden costs as well. Therefore, when creating your digital marketing strategy, have in mind the chart below before overlooking the data quality you are working on.<\/p>\n <\/p>\n Source: jiem.org<\/p><\/div>\n <\/p>\n 95% of new products introduced each year fail.<\/p>\n<\/blockquote>\n And yes, this statistic has much to do with the data accuracy; or with the poor marketing data quality. A McKinsey & Company study<\/a> from a couple of years ago revealed that research is a crucial part of a company\u2019s survival.<\/p>\n 80% of top performers did their research, while only 40% of the bottom performers did.\u00a0<\/p>\n<\/blockquote>\n Still, that\u2019s a lot of research done by the bottom performers. Of course, just because they do research doesn\u2019t automatically mean they do good research.<\/p>\n Trying to do research is not enough if you\u2019re not going to be thorough about it.<\/p>\n<\/blockquote>\n If you are still not convinced of the importance of having good accurate data, before running any sort of campaign, be it email marketing, SEO or mail automation, take a look at these researched<\/a> stats below:\u00a0<\/p>\n <\/p>\n <\/p>\n <\/p>\n <\/p>\n <\/p>\n <\/p>\n There are many ways in which research can lead to\u00a0poor marketing data quality, but let\u2019s look and go in-depth with just 3 particular instances. Taking\u00a0a close look and understanding them will help us identify how to prevent losing resources (time, money, people). Each of the three instances will be exhaustively detailed below.<\/p>\n <\/p>\n <\/p>\n <\/p>\n <\/p>\n Google Trends<\/a> is a pocket of gold and you might already know this. Yet, even having this instrument at your disposal, the secret stands in how you use it. \u00a0Yes, you\u2019ll find a lot of data to help you see what\u2019s trending to stay on top of your competitors. It’s a\u00a0real-time search data to gauge consumer search behaviors over time. But what happens when you don’t use it properly or when you don’t “read”\u00a0the the data correctly? Let’s say we are interested in the image search for the digital marketing techniques vs tactics. As we take a look at the screenshot below, we might be inclined to say that the “digital marketing techniques” are of higher interest.\u00a0<\/p>\n <\/p>\n <\/p>\n Yet, as mentioned before, I was going for the image search; and even if the techniques seems to be of more interest on the general search, when it comes to image search, I should probably go with tactics. Conducting research is not enough but properly understanding the environment is the key.\u00a0<\/p>\n <\/p>\n <\/p>\n On the following line we are going to offer you some nice case studies of poor understanding of the environment and customer needs, based on three dimensions: depth, context and match.<\/p>\n <\/p>\n <\/p>\n Companies have certainly come a long way from assuming they know what\u2019s best for the consumer to trying to find out what the consumer actually wants. But even when they feign interest, things can go wrong if it\u2019s not genuine or thorough enough.<\/p>\n <\/p>\n Some instances are downright hilarious, like McDonald\u2019s 2005<\/a> campaign, where it tried to emulate the youth hip lingo only for it to backfire horribly. Knowing that a certain group (young people) like something (their own jargon) doesn\u2019t mean that the group will like any version of it, especially if it doesn\u2019t fit or sounds like your dad trying to be \u201ccool\u201d and \u201cwith it.\u201d<\/p>\n <\/p>\n <\/p>\n When McDonalds\u2019 ad reads \u201cDouble Cheesburger? I\u2019d hit it – I\u2019m a dollar menu guy,\u201d it becomes obviously painful they have no idea what they\u2019re saying and they just try to sound knowledgeable (here you can find the “urban” definition of I’d hit it<\/a>). And if there\u2019s something worse than not fitting in, it has to be trying to fit in and failing. If you\u2019re not with the cool kids, but you\u2019re ok with that, you might still have a chance to earn respect.<\/p>\n If you\u2019re not with the cool kids, but you\u2019re desperate to get in, that\u2019s when you\u2019re doomed. Superficiality is definitely a mark of poor research.<\/p>\n<\/blockquote>\n Some similar misunderstanding of how youth works plagued McDonalds several years later. They tried to make something happen and \u201cgo viral\u201d when they started sharing success stories using the \u201cMcDStories<\/a>\u201d hashtag. This turned out to be another failure – not only in consumer understanding, but also in technology understanding. The \u201cMcDStories\u201d hashtag was soon reappropriated by random dissatisfied customers who decided to share their own stories. Only their recollections weren\u2019t nearly as nice and flattering as the company\u2019s. This particular example is extremely prescient to SEO, because it highlights the dangers of trying to manufacture goodwill. Something in the level of awareness of consumers and the technology tools they have at hand makes rebellion and irony very likely reactions to trend setting from the outside.<\/p>\n Market research matters. And poor data leads to poor business decisions. But you probably know that already.\u00a0<\/p>\n Even when you do research thoroughly, you can still wrong your customers by not asking the right questions.<\/p>\n<\/blockquote>\n <\/p>\n The classic example here is that of New Coke<\/a>.<\/p>\n <\/p>\n It wasn\u2019t that their research was poor in\u00a0itself: they had evidence that strongly suggested the taste was the leading cause of their market decline. They also had 200,000 blind product taste tests where more than half of respondents preferred the taste of the New Coke over both the \u201cclassic\u201d Coke and Pepsi. So all the science was rightfully pointing them towards introducing the New Coke. Which they eventually did, with disastrous results. There was public outcry and the company ended up giving up the new formula and reverting to the classic one.<\/p>\n <\/p>\n So what went wrong? Was it the consequences of poor data? The same thing, really: they didn\u2019t fully understand their customers<\/strong><\/em> (although in a slightly different way than McDonalds). Coke\u2019s mistake was to treat their customers as living in a vaccum, as consumers of soda, as opposed to consumers of Coke, specifically.<\/p>\n Blind tests are a very good instrument to use in general – except when you don\u2019t want your respondets to be blind to their options.<\/p>\n <\/p>\n So try to think about things not just in terms of functionality, but also brand recognition and emotional attachment.<\/p>\n<\/blockquote>\n <\/p>\n It might very well be that your website\u2019s design is outdated or boring. And the research reveals that youngsters are usually attracted to shiny, slick things. But maybe your particular type of outdated and boring is what works for your users. Data governance and information management are highly important; yet, so is data profiling.\u00a0<\/p>\n Try to keep a certain perspective on things and make sure you ask the right questions about your consumers.<\/p>\n<\/blockquote>\n <\/p>\n Other times, it could be more of a mismatch problem between companies and customers. Inadequate market research is a major reason why business strategies fail and the source of poor data. If you want to develop your brand, one of the most important things that you should establish is a reliable marketing research system. Customers will buy or not a product for a certain reason. And it is essential for the future of your company to understand what this reason is.<\/p>\n <\/p>\n What drives your customers? What sends them away? What makes them continue with your services?<\/p>\n<\/blockquote>\n <\/p>\n And the answer to all of these is just under your nose. Your sales and support team. They are a goldmine. Just think about it; what better way to find exactly what your users want if not from them directly, with no intermediates, no fancy feedbacks, just straightforward accurate data you can make use of. It depends, of course, on how you “master” data and how you make use of it. If, for instance, someone is asking you for a new feature you don’t have; yes, it’s frustrating as you might get dozens of similar mails. Yet, make use of them, exploit them and win customers (and therefore money). Take a look at the screenshot below and notice all the missed opportunities.\u00a0<\/p>\n <\/p>\n Source: helpscout.net<\/p><\/div>\n <\/p>\n Through all its efforts, a company\/business\/website is trying to find a way to best reach its audience. Some efforts are sort of an \u201cindirect\u201d contact with the public through online content but there are also some others which are in direct contact. A discussion with the sales and support team might be a great opportunity for you to better understand your audience\u2019s needs or challenges and adapt your strategy accordingly.\u00a0<\/p>\n <\/p>\n If you’ve\u00a0never thought of your support team as a research tool, then you might be missing a lot.<\/p>\n<\/blockquote>\n <\/p>\n Yet, let me present you what a\u00a0mismatch between companies and customers can lead to through an interesting case study<\/a> from the brand association area. \u00a0Only by looking at the screenshot below you can forsee the association issues between Tesco products and a fine wine production line.\u00a0<\/p>\n <\/p>\n <\/p>\n Yalumba, an Australian wine company was selling well into the UK through Tesco and relying heavily on their association with that supermarket chain, confident of their success because of the successful association. However, they began to be squeezed on margins, so had a close look at what was happening with Tesco, and were astounded at what they found.<\/p>\n <\/p>\n They discovered that their brand was being marketed by Tesco as a commodity – offered on promotion and regarded by consumers as a good wine, but nothing special because it was generally offered at a special price. Their market research discovered that their brand was not considered good enough for special occasions because it was a Tesco promotional wine. Great they did a market research after all yet, a bit too late.\u00a0<\/p>\n <\/p>\n It was not the consumers’ wine of choice, and there was little connection between the consumer and the brand. This is a classic commodity trap, causing margin pressure, and not sustainable on the longer term. The company had created this problem of a commodity trap caused by poor research and a link between brand loyalty and promotions that led to an expectation of lower price and hence, ultimately, unsustainable low margins for Yalumba.<\/p>\n <\/p>\n On this chapter we are going to tackle the loaded choices and questions issues but until then we are going to talk a bit about how a faulty methodology can lead you to a wrong track. And it doesn’t have to be on a large scale but even on a minimal research that you are doing. Let’s say that we are interested in the “restaurants” niche in Brasil. And we want to see everything that is out there on the web written on “restaurants” in the South American country.\u00a0I use Brand Mentions<\/a>\u00a0for that and as you can see in the image below.\u00a0<\/p>\n <\/p>\n <\/p>\n That’s indeed an impressive number of mentions. Yet, the initial search was done using the word I was targeting in english, “restaurants”. Will the search of the same word but in Portugese would make a difference? It sure does. A lot of difference. Therefore, even when doing a simple search you should always keep in mind the methodology if you want accurate data.\u00a0<\/p>\n <\/p>\n <\/p>\n Below you will find some very interesting case studies based on some common faulty methodology.\u00a0<\/p>\n <\/p>\n <\/p>\n How you phrase the questions and how much (or how little) information you decide to provide your respondents can make all the difference.<\/p>\n<\/blockquote>\n <\/p>\n For instance, in the image below, which line is longer? If you bear with us until the end of the chapter you will find out the answer if you don’t know it already.\u00a0<\/p>\n <\/p>\n <\/p>\n Giving them too little information can obviously skew their ability to give a useful answer. But it turns out that giving too much information can have the same effect. In his post on market research failures, Philip Graves<\/a> provides two excellent examples of this phenomenon at work:<\/p>\n <\/p>\n 1. One is of a bank-related survey, where people are first asked if they have changed their bank lately and second if they are happy with their bank. While this line of questioning doesn\u2019t necessarily provide unwanted information, it does create it implicitly in the mind of the respondent.<\/p>\n <\/p>\n Just imagine: if you\u2019ve answered \u201cNo\u201d to the question about changing banks, once you get the second question, your brain is tempted to give an answer that is coherent with the answer to the first question. You can\u2019t answer \u201cNo\u201d to the second question, or else your answer to the first question would seem weird in retrospect. Have you changed your bank lately? No! Are you happy with your bank? The “Yes” comes automatically almost.\u00a0Why would you not change your bank if you\u2019re unhappy?<\/p>\n <\/p>\n But of course, that\u2019s not something you can do on the spot. What you can do on the spot, however, is find an answer that is consistent with the previous one, which, in this case, is \u201cYes.\u201d You haven\u2019t changed your bank because your are happy with it. There doesn\u2019t need to be a causal link between the two, but the two answers are easier to accept if there is. We like order, symmetry and harmony.<\/p>\n <\/p>\n When conducting a research, make sure the questions are consistent, logical and easy to understand.\u00a0<\/p>\n<\/blockquote>\n <\/p>\n 2. The other example in Graves\u2019 article is more glaring. In a survey that asked \u201cAs the world\u2019s highest paid cabin staff, should BA cabin crew go on strike?\u201d 97% of respondents said \u201cNo.\u201d To nobody\u2019s surprise, we should add. This a classic example of a loaded question. The way it is worded makes it very unlikely that someone might say \u201cYes,\u201d since we\u2019re talking about the world\u2019s highest paid cabin staff. In fact, the mechanism at work here is very similar to the previous one – it\u2019s just more explicit. Our brains find it hard to hold two opposing views, so we try to adjust one of them to fit the other.<\/p>\n <\/p>\n It’s like asking: being the best tool in the digital marketing with 80% success rate, do you think that the pros should choose us?<\/p>\n<\/blockquote>\n <\/p>\n In this case, the two implicit views at work are \u201cpeople who are well-paid should be happy with their work\u201d and \u201cpeople who are happy with their work shouldn\u2019t go on strikes.\u201d Or, to further reduce them to equation-like mantras, \u201cmoney = satisfaction\u201d and \u201csatisfaction \u21cf manifesting dissatisfaction.\u201d<\/p>\n <\/p>\n <\/p>\n This kind of thinking – of which we are often not aware – is something that Nobel laureate Daniel Kaheman talks about at length in his \u201cThinking, Fast and Slow\u201d book. It\u2019s easier and faster to think in terms of simple equations and binary relationships between things, so we usually do. We are also prone to take the path of least resistance, so depending on how the question is phrased, we are more likely to give the answer that requires the least mental effort.<\/p>\n <\/p>\n Just like in the case of the lines from the image above; even if you know, at a cognitive level that the lines are equal, you still have the tendency to consider the first one as being longer.\u00a0<\/p>\n <\/p>\n Phrasing questions in a survey is such hard work.<\/p>\n<\/blockquote>\n <\/p>\n Loaded questions are just one example of asking the right questions in the wrong way.<\/p>\n<\/blockquote>\n <\/p>\n An inherent bias in the language you use can also have a significant effect on the answers you get.<\/p>\n<\/blockquote>\n <\/p>\n For instance, there is a huge difference between asking people \u201cHow satisfied are you?\u201d and \u201cHow dissatisfied are you?\u201d even when the answer options are exactly the same (for instance: \u201cVery safisfied,\u201d \u201cSatisfied,\u201d \u201cRather satisfied,\u201d \u201cRather dissatisfied,\u201d \u201cDissatisfied,\u201d \u201cVery dissatisfied\u201d). Priming can have a very strong effect even if people don\u2019t realize they are susceptible to it and even when they downright deny it.<\/p>\n <\/p>\n <\/p>\n But getting the question right isn\u2019t enough – you have to also get the choices right. Have open-ended questions and you might get answers that aren\u2019t comparable and end up not being useful.<\/p>\n <\/p>\n Have closed-ended questions and you might end up not including some very relevant answers, which in turn will force respondents to provide answers that do not represent them.<\/p>\n<\/blockquote>\n <\/p>\n Even if you go for a mixed approach, you can still mess up by not making the choices comparable or symmetrical at both ends.<\/p>\n <\/p>\n Let’s say that you are asked about your assessment of a mailing automation\u00a0service and the options were: \u201cExcellent,\u201d \u201cVery good,\u201d \u201cGood,\u201d Very poor,\u201d \u201cDisastrous.\u201d If you were just mildly dissatisfied with the service you received, you\u2019re not likely to signal that because unless you\u2019re up in arms about it, there\u2019s no answer that fits your level of dissatisfaction. And even if you were seriously dissatisfied, you\u2019d more likely go for \u201cVery poor\u201d than for anything else, because \u201cdisastrous\u201d is generally a very rare occurrence. Moreover, the fact that there are 3 positive options and only 2 negative ones makes the least positive option (which is still quite positive) appear as more of a middle-ground than it should. So, if you\u2019re just slightly dissatisfied, you are more likely to keep it to yourself and choose \u201cGood\u201d rather than choose \u201cVery poor.\u201d This is, obviously, an exaggerated example, but there are plenty, more subtle – but equally problematic – research design flaws out there.<\/p>\n <\/p>\n Here we are talking about useless data actually. Not quality issues, inaccurate data, business process, data managements, cost associated or process quality issues like we’ve discussed so far.\u00a0\u00a0We’ve concluded by now that the quality of online data is a major concern\u2014not only for the market research industry but also for business leaders who rely on\u00a0accurate data to make decisions.\u00a0<\/p>\n <\/p>\n Sometimes companies are so focused on the mousetrap they tend to lose touch with the emotions and needs of the people they actually work for: consumers.<\/p>\n<\/blockquote>\n <\/p>\n Let’s say for instance that you are planning on boosting your website through SEO. And one day you realize that you have around 3 million links. That’s amazing, right? A few more and you are catching Microsoft’s link profile. Yet, is there anything you can do with this info alone? The 3 million links? Not really. You need to correlate it with the number of referring domains (which in our case is incredibly low reported to the high amount of links), the link influence, their naturalness, etc. Otherwise, there is no much use in having just the number of links. You cannot set up a strategy based on it nor take a digital marketing decision.\u00a0<\/p>\n <\/p>\n <\/p>\n Yet, these things\u00a0happen to the big guys as well. Let’s take Google Glasses for instance. \u00a0Not even 2 years have passed from the great launch and the producers announced that they are discontinuing the product. Didn’t Google have the means to conduct a proper research? Of course they did. Then what happened?\u00a0<\/p>\n <\/p>\n What did they overlook? \u00a0Everything, some would\u00a0say. Well, not quite everything but it’s clear that they made use of the data they had but not in an efficient way (we can say that these are still consequences of poor data). After researches, the producer came up with one conclusion: if high tech can be shrunk to fit in the hand, as with smartphones and tablets, and have such a great success, then why not shrunk technology to be close to a part of the human body? And their market research sample seemed thrilled about the idea. This was the info they’ve provided to the company. The product is created, it gets launch and no glory at all. Even more, it\u00a0unceremoniously fails within months. \u00a0So what happened? Was it the consequences of poor data? The producers made their potential customers’ dreams come true; why did they not sell?<\/p>\n <\/p>\n Privacy and price<\/strong> concerns alienated<\/a> much of the public\u2014and the fact Google released early copies only to rich geeks in the west coast, thereby branding the product as elitist instead of the \u201caffordable luxury\u201d philosophy. Even if they had a lot of data, it was quite useless. \u00a0According to CNN<\/a>, before launch, Google didn’t anticipate that few would want to pay (or could afford) $1,500 to parade in public with a device slung around their eyes that others might perceive as either a threat to others’ privacy or a pretentious geeky gadget.<\/p>\n \u201cGoogle\u2019s fast retreat exposes the most fundamental sin that companies make with the \u201cbuild it and they will come\u201d approach. It\u2019s a process that tech companies rely on, referred to as public beta testing.\u201d<\/p>\n<\/blockquote>\n <\/p>\n There are blind spots in marketing research that are costly and variables that no market research studies can predict.<\/p>\n<\/blockquote>\n <\/p>\n Bad data can lead to bad analysis, which can lead to bad business decisions, some really bad revenue and not only. The first step is made. If you are aware\u00a0of poor data quality impact today, it\u00a0will not affect your business tomorrow. One should always be aware that bad data has long time consequences not only on your overall data management but also on all your future business decisions.\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":" The data quality subject is often neglected, and poor quality business data constitutes a significant cost factor for many online companies. And, although there is much content that claims that the costs of poor data quality are significant, only very few articles are written on how to identify, categorize and measure such costs. In the […]<\/p>\n","protected":false},"author":5,"featured_media":13137,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6,4],"tags":[],"class_list":["post-13094","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-seo","category-social-media"],"_links":{"self":[{"href":"https:\/\/cognitiveseo.com\/blog\/wp-json\/wp\/v2\/posts\/13094","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cognitiveseo.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cognitiveseo.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cognitiveseo.com\/blog\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/cognitiveseo.com\/blog\/wp-json\/wp\/v2\/comments?post=13094"}],"version-history":[{"count":0,"href":"https:\/\/cognitiveseo.com\/blog\/wp-json\/wp\/v2\/posts\/13094\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/cognitiveseo.com\/blog\/wp-json\/wp\/v2\/media\/13137"}],"wp:attachment":[{"href":"https:\/\/cognitiveseo.com\/blog\/wp-json\/wp\/v2\/media?parent=13094"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cognitiveseo.com\/blog\/wp-json\/wp\/v2\/categories?post=13094"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cognitiveseo.com\/blog\/wp-json\/wp\/v2\/tags?post=13094"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
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![\"poor](\"https:\/\/cognitiveseo.com\/blog\/wp-content\/uploads\/2017\/02\/poor-seo-data.png\")
<\/div>\nDrowning in Dirty Data? It’s Time to Sink or Swim<\/strong><\/h3>\n
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<\/div>\n1. Poor Understanding of\u00a0the Digital Marketing Environment<\/strong><\/h3>\n
![\"Google](\"https:\/\/cognitiveseo.com\/blog\/wp-content\/uploads\/2017\/02\/Google-Trends2-1024x548.png\")
<\/p>\n![\"google](\"https:\/\/cognitiveseo.com\/blog\/wp-content\/uploads\/2017\/02\/google-trends-image-1024x540.png\")
<\/p>\n <\/div>\nA) No Depth<\/strong><\/h4>\n
![\"McDonalds](\"https:\/\/cognitiveseo.com\/blog\/wp-content\/uploads\/2017\/02\/McDonalds-2005-campaign.jpeg\")
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<\/div>\nB) No Context<\/strong><\/h4>\n
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<\/div>\nC) No Match<\/strong><\/h4>\n
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![\"Source:](\"https:\/\/cognitiveseo.com\/blog\/wp-content\/uploads\/2017\/02\/support-team.png\")
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![\"tesco](\"https:\/\/cognitiveseo.com\/blog\/wp-content\/uploads\/2017\/02\/tesco-and-yalumba-wine-1024x466.jpg\")
<\/p>\n <\/div>\n2. Faulty Methodology Being Used to Collect Digital Marketing Data\u00a0<\/strong><\/h3>\n
![\"BM](\"https:\/\/cognitiveseo.com\/blog\/wp-content\/uploads\/2017\/02\/BM-restaurants-1024x519.png\")
<\/a><\/p>\n![\"BM](\"https:\/\/cognitiveseo.com\/blog\/wp-content\/uploads\/2017\/02\/BM-restaurants-portugese-1024x519.png\")
<\/a><\/p>\n <\/div>\nA) Loaded Questions<\/strong><\/h4>\n
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![\"muller-lyer\"](\"https:\/\/cognitiveseo.com\/blog\/wp-content\/uploads\/2017\/02\/muller-lyer.gif\")
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![\"think](\"https:\/\/cognitiveseo.com\/blog\/wp-content\/uploads\/2017\/02\/think-fast-think-slow.png\")
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<\/div>\nB) Loaded Choices<\/strong><\/h4>\n
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![\"dissatisfaction\"](\"https:\/\/cognitiveseo.com\/blog\/wp-content\/uploads\/2017\/02\/dissatisfaction1-1024x604.png\")
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<\/div>\n3. Data That Cannot Be Translated Into\u00a0Conversion<\/strong><\/h3>\n
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![\"cognitiveSEO](\"https:\/\/cognitiveseo.com\/blog\/wp-content\/uploads\/2017\/02\/cognitiveSEO-site-explorer-1024x547.png\")
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<\/div>\nConclusion<\/strong><\/h3>\n