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The relationship between web design and user engagement

As reported by Fast Company and Inc. Magazine, a new EyeQuant study has shown that there's a surprisingly strong relationship between the "visual clarity" of a website (as rated by an algorithm) and its bounce rate. In fact, the results suggest that up to one-third of a user's decision to stay or bounce comes down to a snap judgment of whether or not the page is too cluttered. In this post, we'll take a closer look at the data and the methodology behind the study. Why study the impact of visual clarity? Within the design community, there's been a definite trend towards simpler, more stripped-back design. At EyeQuant, we've seen many of our customers "de-clutter" their way to higher conversion rates, and even observed that amongst a collection of online retailers, the ones with "cleaner" design were growing the fastest. What we wanted to understand is this: does "clean" design have a positive impact on user engagement across the board, or is it limited to specific cases like overly cluttered-sites or retail? The Experiment Setup Using Amazon's Alexa service, we gathered approximate engagement stats for 300 popular websites across several website categories: from fashion, to insurance, to travel. In particular, we looked at bounce rates for the desktop homepage of those websites. Why bounce rates? First, it's one of the engagement metrics that almost all companies measure. But it's also easier to compare bounce rates across different website categories than, for example, time on site or number of page views. To measure how "clean" each of the designs were, we used the EyeQuant visual clarity algorithm, which assigns a 0-100 rating to each design. We built the algorithm by recruiting hundreds of users to participate in a study where they were shown a series of randomized pairs of designs. The participants' task was simple: to identify which of the 2 designs on the screen they felt was "more clean". This "forced-choice" approach helps to identify patterns in which kinds of designs people feel are more "clean", and helps us determine how much people tend to agree with each other (turns out, it's more often than you'd think). Using machine learning, we were able to take this data and build a predictive model that analyzes any design and rates it, with over 85% accuracy compared to a 200-person study. The Extremes: here are examples of a very low clarity score (the famous Ling's Cars), and a very high clarity score (Google).  The clarity score is driven by factors like the amount of text on the screen, layout, and the imagery used on the page (pictures with many sharp contrasts and lines tend to make the whole page feel cluttered). Finally, we calculated a Pearson Correlation between the Clarity Score and Bounce Rate for all 300 websites. Results We observed a surprisingly strong negative correlation (r= -0.57, p < 0.001) between Clarity Scores and Bounce Rates across the 300 websites, meaning that cleaner sites do tend to have lower bounce rates. Similar results were observed when we looked at individual website categories by themselves. The graph above plots each website's clarity score and bounce rate. The green line shows the trend in the data. Perhaps the most striking data point is the r-squared value of 0.327, which implies that roughly one-third of the variance in bounce rates can be explained by the variance in clarity scores - a much stronger effect than we expected to find. An open question is whether or not there's another variable at play here that tends to move in the same direction as the clarity score and magnifies the perceived impact of visual clarity. But if such a variable exists, we haven't found it yet. What should we take away from this? For anyone involved in design decisions online, this study should serve as a warning to fight the natural tendency for pages - particularly home pages - to get cluttered over time. Think about which content is really important for users and focus on (only) that content. The results also suggest that it's worthwhile to try "de-cluttering" existing designs, as improved user engagement often leads to higher conversion rates. This is especially true for companies that are running an A/B testing program, and are capable of measuring the impact of de-cluttering on their own website. Source:


The Easiest Guide to Cohort Analysis

Cohort is a group of users experiencing a common event within the same time period. An oft-repeated but very relevant example of a cohort is- a group of students joining in the same year. So the class of 2017 is a cohort and so is a class of 18, and so on and so forth. What is cohort analysis? Cohort analysis is an analytical modeling employed to study the cohorts characteristics over a period of time and the elements that influence change in those characteristics. It traces its roots to medical research where cohort studies are done to identify the cause of a disease. “In a prospective cohort study, researchers first raise a research question, forming a hypothesis about the potential causes of a disease. The researchers then observe a group of people, the cohort, over a period of time (often several years), collecting data that may be relevant to the disease. This allows the researchers to detect any changes in health in relation to the potential risk factors they have identified.” via Medical News Study So, to identify the cause of lung cancer doctors would create a hypothesis that it is caused by smoking. Then they will take two groups- smokers and non-smokers. Thereafter, both groups would be studied to identify the influence of smoking on the person’s likelihood to get lung cancer. How do we employ this in business analytics? In business applications, we compare cohorts- users sharing a common experience in a given time frame- or analyze the behavior of a single cohort, to identify a pattern that supports a growth hypothesis. That hypothesis could be anything. For instance, we may create a hypothesis that users getting acquired via display ads have higher LTV than the ones getting acquired by Facebook. To prove the hypothesis we would do the cohort analysis. Likewise, let’s suppose we want to identify the cause of the aggregate dip in your retention.So we would form a hypothesis that retention has a correlation with the first purchase of the customer. To establish the relation we shall cohortize users on the basis of their first purchase and plot their, say monthly, retention %. From the graph above it is apparent that the users who purchased marshmallows the first time displayed higher LTV than the others. This despite the fact that overall retention of the product has declined. Naturally, the intent of business now would be to get more users purchase marshmallows post acquisition. Important- That’s not to say that Marshmallows are the cause of retention. Our analysis simply told us that there is a correlation between marshmallows and retention. Correlation doesn’t amount to causation. So we have to test if Marshmallows really amount to higher retention or not. Cohort analysis gives us insight into the trend and basis for testing. Not the cause. Cohorts and Segments are not the same Most folks interchangeably use ‘Cohort’ and ‘Segment’ which is not correct. For two users to be part of the same cohort they have to be bound by the common event and time period. Eg 2017 graduates, 1990 born men. However, to create a Segment you could use almost any condition as a basis which cannot necessarily be time and event based. Eg graduates, men. Cohort is a subset of Segment. So, there can be a cohort of ‘new users this week’ and likewise, there can also ‘segment of new users this week’. Now that we have understood fundamentals of cohorts, let’s understand some business use-cases. Some powerful use-cases of Cohort Analysis To explain the use-cases start with the google sheets (linked below) where you can start with the cohort chart for every use-case. Cohort Analysis | Worksheet 1. Understanding customer retention But before we do that, a little throwback to how to read a cohort chart. We are skipping the data crunching part and jumping right into the presentation. How to read a cohort chart? Table 1 Link- Cohort by Active users- Sheet 1 | Excel Let’s go through row and column one by one. You could well see that column is for activation month and row is for the number of returning customers. Rows So, B4 represents the number of new customers we acquired in the month of Jan. C4 tells us the number of customers who were acquired in Jan but they returned in Feb. Likewise C4- number of customers acquired in Jan who returned in March. D4- the ones who returned in April And so on and so forth. Basically, as we move along the Jan’s row. we understand how the retention of new customers acquired in Jan fluctuated until Dec. Columns Column represents the number of returning or new customers. D4 represents the number of customers acquired in Jan who returned in March. D5- the number of customers acquired in Feb who returned in March. D6 is the number of new customers acquired in March. The same pattern repeats as we move along the row. Table 2 Now, let’s understand how the each cohort, retention wise, behaves over the period of time. To do that, we would slightly pivot the above table. We would change the column from the actual month to the ‘# of months since acquisition’. From Jan, Feb to 0. 1, 2 which would pull all the row data to the left. You may notice that the table changed from right aligned triangle changed to left aligned. So, in the first row, as we move along, we would know how many customers acquired in Jan returned in the succeeding months. Table 3 In this table, we changed the numbers into percentage to get better view of the data. Now looking at each row we may get the retention curve of the corresponding month. However, what if we want to understand how the retention has been over the past 12 months? So, in the final row, we have calculated the aggregate. The aggregate gives us the retention curve of the past 12 months. 2. Correlation between category and retention A friend of mine had worked on the cohort analysis of one of the world’s largest retailer. He told me that one of the conclusions from their analysis was that the users who purchased baby products in their first visit showed higher propensity to visit again. This prompted the retailer to promote their baby section more aggressively. One can create a hypothesis that there are some categories which trigger maximum stickiness among users when they are the first purchase. To determine that category let’s cohortize users on the basis of category of their first purchase and plot their retention. Link- Cohorts by Category- Sheet 2 From the chart it is evident one can draw the following conclusions: Users buys Sportswear in the first purchase showed higher retention than the rest. Users buying Jewelleries in the first purchase showed the lowest retention rate. 5th month is critical as the churn seems to increasing beyond that. Some possible inferences can be that the marketing expense for sportswear needs to be decreased. Likewise, the retention strategies for Jewellery purchasers need to be relooked. Retention strategy for users entering 5th month since their acquisition has to be evaluated. 3. What features correspond to maximum retention A report by Quettra shows that an average app loses 77% of the DAUs within 3 days post install. Now, if your product itself isn’t deserving, then nothing can evade uninstall. However, if it is not, then apparently the first three days are critical and determinant of the user’s retention. 3 days was the average trend and your critical number could accordingly vary. You could determine your own critical number through the method that we discussed in #1. Let’s suppose it is x days for the time being then you have to do something within the first x days post install to hook users. How cohort analysis comes into picture Let’s create a hypothesis that there are some features in the app which when used increases the stickiness among users. Create an aggregate retention curve of the last 12 months like we did in #1. Note- The retention curve of the mobile app unlike a web-app is going to decrease linearly because a web-app doesn’t need to be installed on your device. A user can login any time he wishes. With mobile app, once it is uninstalled you potentially lose the user forever. Now, screen the users who have retained and jot down the features used by them on the first day. Suppose you are analysing for a e-commerce app and concluded the following traits to be common among all retained users. Let’s say “push notification clicked” and “added to wishlist” are two most common actions Now we would narrow our analysis for both of these events and do a comparison between them The result Cohort Analysis | Cohort by Features Visit the above sheet and change the value for each feature from the drop down to see how the graph changes. From the above chart, it would be clear that users who added-to-wishlist display higher propensity to retain than the rest. The ones who clicked push notification perform even worse than the average. Again, this graph gives us the correlation not the cause of retention. P.S. This is a very interesting method and extensively used by consumer businesses. I just discussed the basic framework and there are various edges that can lead you to a more definite conclusion. 4. How customers react to a new feature release Inversely the above cohort analysis could also be used to figure out what are the obsolete features that needs some rework. For instance, the cohorts curve of users who clicked on push notification fare poorly than the average retention curve. Push notification is obviously meant to complement your retention so the above chart prompts us to rethink our strategy. Creating cohorts in Mixpanel, Amplitude, Adobe- First event and Returning event If you are using Amplitude or Mixpanel, or any of the similar products, to do your cohort analysis, these are the two fields that you have to specify for creating cohort chart First event Returning event Let’s see some examples Amplitude Mixpanel Adobe Localytics First event is the primary criteria to build the cohort- the ‘experience’ element in creating cohort that we discussed in the very beginning. Returning event is the baseline that you want to track for your users. In the above charts, retention has been the baseline of our analysis. In analytics, retention could be defined as ‘any event performed by the user’ on your platform. So, if we have create cohort in Amplitude then it would somewhat look like this Conclusion Cohort analysis is a respite from vanity metrics. At any time momentary growth can be bought which may give you temporary pleasure but cohort analysis allows to be cynical. It gives a very critical view of churn and doesn’t let it get masked by growth. For instance if you are investing into acquisition there can be instant surge in the MAU but high MAU is not the indicator of growth. A cohort analysis will tell how many of those acquisitions are actually sticking with you. Similarly, a particular channel might be amounting to highest acquisition. But a cohort analysis will tell which of them contribute to maximum profit. Whatever your key metrics may be you would be able to see how it evolves over the customer lifecycle or product lifecycle. Source:


Customer Lifetime Value in Ecommerce

For any company to be profitable, it must profit more from each customer (Customer Lifetime Value or LTV) than it spends on acquiring them (Customer Acquisition Cost or CAC). So if your average Customer Lifetime Value is lower than your Cost Per Acquisition, that should be a big point of concern for your company because it means that you are losing money. Being unable to maintain Costumer Acquisition Cost lower than Customer Lifetime Value is one of the main causes for business failure. How to calculate Customer Lifetime Value Lifetime value is how your store profits from your clients during the time they remain customers. For example, if your average client comes back to your store three times to buy something, spends on average $100 per purchase and your profit margin is 10% ($10), your Customer Lifetime Value is $30. This is important because LTV is directly linked to profitability, since a company with high LTV will be able to spend more to attract customers and will have a higher margin. To estimate LTV, you need to look into your historical data and: Forecast the average customer lifetime (or how long the customer continues to purchase your product or service); Forecast future revenues, based on estimations about future products purchased and prices paid. Estimate the costs of distributing those products. Calculate the net value of these future amounts. Famous best practices in Retention and Customer Lifetime Value Companies that have high Retention (their customers keep coming back to shop more) are more successful because their Customer Lifetime Value gets higher. For example: Zappos won against their competition by keeping their customers coming back with an excellent customer service strategy. The more often they buy, the higher Zappos’ LTV gets. Amazon’s massive product offerings helps them in upselling or cross-selling to nearly everyone using an automated and personalized email marketing system. This means users spend more, which in turn improves Amazon’s LTV. Netflix’s recommendation system keeps viewers constantly engaged in new content. Netflix’s customers keep their subscriptions for a year or more, paying every month, which increases LTV. Facebook “habit loop” keeps their users coming back to the site on a daily basis (and often, multiple times a day). When users visit Facebook more often, they tend to click more on ads. Since Facebook profits from each ad click, this greatly improves their LTV. Why the ratio between CAC and LTV is crucial for running your business Customer Acquisition Cost (CAC) is calculated based on the amount of money you spend to acquire a customer. For example, if you pay $1 for each click that a person makes on your Facebook Ad and 1 in every 10 people who click on that ad ends up buying from you, your CAC is $10. Considering the example above, of a company who’s Customer Lifetime Value is $30, if their CAC is $10, that means their profit is $20 per customer. A $20 profit is not so bad if your company has a high volume of sales. However, if that company’s clients came back to shop at an average of 10 times, their LTV would be $100. If the LTV is $100 and the CAC is $10, then the final profit would be $90. Which is obviously much better. If you’re currently running Facebook Ads or any other paid marketing channels, you’ll appreciate how difficult it is to keep Costs per Acquisition down. So it’s in your interest to keep Customer Lifetime Value as high as possible. And the secret for keeping LTV high is retention. See below: How Retention Rates impacts Customer Lifetime Value The first example is of a company struggling to retain their customers. The second shows a business with high retention rates. The graph below demonstrates the retention curve of a company with only 30% of their customers returning in the next month. You can see that they end up with almost zero customers from each cohort in less than 5 months: Low retention rates result in Customer Lifetime Value barely increasing over time. Companies with low Customer Lifetime Value can only really count on one purchase per customer to draw all of their profits. If a company has low Customer Lifetime Value, average CAC needs to be below average to profit from each customer. On the other hand, the company represented in the next graph has a subscription based model. They maintain a much higher retention rate. More than 20% of their users are still active after 18 months from their first payment. That reflects very positively in their Customer Lifetime Value, as we can see in the graph below. Even though revenue from their first purchase is low, in the long run each customer becomes extremely valuable because of the high retention rate. Whatever the case and the market you are in, a low LTV / CAC ratio is a problem that should be addressed as soon as possible. If that’s a problem you have,  we strongly encourage you to your Retention after the first transaction. Conclusion For many young businesses, keeping a healthy CAC / LTV ratio is a challenge. If that’s your case, you need to identify whether your CAC is high or your LTV low (or both). Benchmark your numbers against your competition to understand which of them is your biggest problem. Then divert all of your focus to getting it fixed. If the problem is retention, you have a few different options to test: Focus on customer satisfaction by providing an excellent experience with your product. Build a recommendations engine and an email automated system. Use them to personalize the offers to your customers based on their activity with your site or app. Work on developing a habit-forming loop to insert your product in the daily routine of your users. Or you can look at your own data and come up with your own strategy. The important thing is to focus on your CAC / LTV ratio immediately. The lifetime value of your company depends on it. Source:


4 Steps for Effective Customer Acquisition in the Digital Era

It’s no secret that acquiring new customers is difficult. While most companies work to derive as much value as possible from existing customers–which they should—your business will have a tough time reaching its growth goals if new customers are never brought into the fold. In the digital era, customer interactions occur online, and in shorter, more frequent stints, rather than the longer in-person, but less frequent, interactions of old. Likewise, traditional marketing meant focusing on customer segmentation and campaign performance measurement. That no longer works. Instead, the focus needs to be on individual preferences and intentions. Not doing so can lead to missed opportunities. There are several factors to consider in your customer acquisition strategy, and they all come down to an explicit focus on the customer: details such as understanding how (and why) individuals interact with each channel differently, recognizing how to leverage multi-channel data to connect with the right customers at the right time, and respecting that customers want to be treated as individuals. To win the battle for new customers, companies must continuously leverage digital technologies to attract new customers and connect in a relevant, meaningful way based on the prospect’s individual preferences. Here are four key steps to acquire new customers in the digital era. 1. End the Siloes  Your business can’t be effective with your consumer interactions if you’re working with data that is in siloes. Marketing teams must know and understand information around sales calls, online behavior, marketing program feedback, etc., to make the most of each marketing campaign and next best offer. These make up an ongoing cycle of events that contribute to a personalized understanding of the prospect or consumer. Having a holistic, real-time view is the only way to be relevant and effective in your marketing efforts. 2. Detect Opportunities Pinpointing new opportunities at the prospecting stage will allow your teams to allocate resources in the areas that will have the most impact. Signals of intent – like multiple visits to your website – need to be merged and used during the acquisition process as quickly as possible. Typical prospect journeys will become visible and can be mapped to other prospects, all the while improving acquisition. You want to be able to compare past activities of the customers you have acquired and apply those behavioral patterns to new prospects to gain a better understanding of predictive behavior. This can help you make the right offers to transition the prospect into a customer. 3. Turn Insight into Action  Detecting an opportunity is not enough on its own to improve customer acquisition; you need to process that opportunity as quickly as possible and use the most appropriate channel to connect with that prospect while the opportunity is still there. That process can be a call by the sales team, but it can also be a digital interaction – an email, online banner offer or other digital conversation, depending on the profile of that customer. The key is delivering the right message that will resonate with that particular prospect based on the behavioral, contextual data mentioned above. 4. Test Multiple Strategies  If you’re in a rut, it’s beneficial to engage prospects with different marketing messages and track response rates to learn what’s working and what’s not. Being able to track and change based on individual behavior patterns will allow you to improve your customer acquisition tactics. Delivering the right message via the right channel at just the right time is crucial, so despite the number of ways in which we can reach prospects, if we haven’t carefully considered what it is they want or need, or how they want to hear that message, we likely won’t get very far. Instead, companies that embrace a customer-centric approach include customer-focused concepts in their entire makeup. “Personal,” “thoughtful,” “anytime, “anywhere” – these are requirements for growth in the digital era and impacting the customer acquisition process. It may take some trial and error, but a successful acquisition strategy is all about committing to better understanding your customer—at all stages of engagement and via a variety of digital channels—and building personalized relationships with each. Source:


7 Predictions For The Shape Of Content Marketing In 2020

Wait a minute, isn’t it only 2017? You’re right, and 2017 is shaping up to be a big year for content marketing, but as fast as technology develops, it still takes a few years for trends to really take form. Google Glass seemed like a big deal at the time—until it wasn’t, and smart watches never grew to become the market dominators they were once forecasted to be. At the same time, I remember seeing the flurry of posts calling for the death of SEO at the arrival of the Panda and Penguin updates, which played a major role in shaping SEO (but never came close to killing it). So rather than taking a stab at the immediate repercussions and developments that may tweak your content marketing strategy this year, I want to look further into the future, where these trends and technologies will have had more time to manifest, so you can prepare for the bigger disruptions to come: 1. Augmented reality interactions. Augmented reality had a big year in 2016, with Oculus Rift, Pokemon Go, and the announcement of Snapchat Spectacles (among other tech developments). But it’s still not popular or widespread enough for it to be categorized as a viable medium for content marketing. But now, all doubts about the technology’s future have been squashed, and brands will be racing to be among the first to leverage this new medium for their own purposes, whether that’s interactive advertising or new experiences for in-person customers. 2. A reshaping of SEO. Unless you’ve been centering your business on an Amazon store or a similar eCommerce platform, most of your SEO efforts revolve around your website. This seems both intuitive and obvious; search engine results pages (SERPs) are basically giant lists of web pages, so the more visibility you get there, the better. However, we’re starting to see different kinds of entries in SERPs, and less exposure for websites in general. Knowledge Graph entries and rich answers are replacing traditional site entries, apps (including streaming app content) are rising in relevance, and of course, our digital assistants are parroting answers to us, eliminating the need to review an SERP. As these trends develop, users will still rely on search, but they’ll use it in entirely new ways—and the importance of website-specific optimization will begin to decline in favor of things like app SEO and optimization for rich answers. 3. Live video dominance. Live video’s popularity isn’t exactly a secret, but there’s one thing holding it back from being a dominant form of content on the web: participation. Live videos, when available, attract a lot of user attention, but not enough brands have jumped on the trend. Part of this is due to the amount of planning necessary for a “successful” feed, and mobile data plans and Wi-Fi reliability may also enter into the equation. But by 2020, my guess is live video will stabilize as an available means of communication, and we’ll see it in higher demand and in more places—including search results. 4. A native advertising surge. People hate advertisements. They’re tired of being bombarded with ad messages, they don’t like the idea of being persuaded, and they resent the big businesses that are trying to take their money. That’s why native advertising, which I view as a hybrid of traditional advertising and content marketing, is likely to constitute the majority of ad revenue online by 2020. Even traditional forms of advertising will work harder to “blend in” with the type of content that users expect to see in a given medium. 5. Content length extremes. Currently, there’s a wide range of different-length content that can become popular. Short, medium, and long posts all have advantages and disadvantages, with long posts attracting more links, and short posts spreading faster and requiring less investment. By 2020, I imagine we’ll see more polarization toward content extremes; people who want deep, long content will want the deepest, longest content they can find, while anyone who wants a fast read will only consume content in bite-sized chunks. This will force most content marketers to rethink their direction, optimizing for one style over the other. 6. Higher social value. We’ll also see a spike in the social value associated with the content we produce and share. Authorship is currently important, and influencer marketing yields fantastic results, but as corporate distrust grows and internet accessibility widens, it’s going to be even more important to know—personally—who you’re getting your content from. Individual personalities are going to make or break brands, and the value of a post can increase exponentially based on who writes or shares it. 7. Personal device interactions. Voice search has exploded in popularity over the past five years or so, mostly because algorithms became good enough to actually understand what we’re saying. But we’re now starting to interact with our devices in new and uncharted ways; we’re having real, back-and-forth conversations with them, eliminating the need for screen-based or type-based interactions. By 2020, I believe this will give rise to new types of content that aren’t screen-based; podcasts are an interesting start, but in the future, more conversational, interactive forms of content will be in demand. Though some of these predictions are speculative, the majority of them are end-game visions of trends that have already begun. If you have a good rhythm, it’s a good idea to maintain it; there’s no use scrapping your strategy and rebuilding from scratch for concepts that are only now coming into fruition. Still, it pays to think ahead; the most successful content marketers tend to be the ones who beat their competitors to market, so there’s definitely a value in early adoption. Source: