The Conversion/Usability Framework: How Usability Impacts Profit

TL;DR: Usability is related to customer satisfaction and loyalty and therefore has a direct impact on profit. Yet, bad usability is abundant, especially on E-commerce websites. The Conversion/Usability Framework introduces usability as an additional lever on top of “traditional” means to increase profits, which can and should also be applied beyond E-commerce.

When businesses want to increase their profit (or simply keep it from crumbling), there are certain means that can be employed, such as restructuring processes, economies of scale, or exploring novel marketing strategies. Another lever to increase profit is the usability of products and sales channels, which is, however, surprisingly often neglected—even in E-commerce—although research and common sense suggest that it is directly related to customer satisfaction and loyalty. In this article, I analyze how usability influences revenue and costs (and therefore ultimately profit) and how the Conversion/Usability Framework can be used to improve both. Yet, to start off, let us first have a look at some basics.

Revenue, Costs, & Profit

Profit is at the heart of every business. It is determined using the profit function, which states that profit equals revenue minus costs [1]. The different parts of this function are influenced—directly as well as indirectly—by a number of factors [7]. Revenue is affected by both the price and the amount that is sold of the offered good. Then again, the amount of products a business sells as well depends on the price, which should be neither too low nor too high due to price elasticity. That is, customers will not buy an excessively expensive product, but at the other end of the spectrum it might be perceived as low-quality, thus also resulting in lower sales. Overall costs are composed of fixed costs and variable costs. The latter depend on the amount of products sold. The more you produce the higher the variable costs. Overall, this results in the following dependencies (the diagram has been adapted from [7]):

E-Commerce

In E-commerce, Amount can relate to, among other things, a product sold through a website (e.g., shoes, books, & vacation trips), or a subscription to a web service or platform (e.g., a domain, cloud storage, & movie streaming). On such websites—be it consciously or not—companies often engage “traditional” sales strategies that result in so-called dark patterns. To create a sense of urgency and scarcity, almost all travel booking websites I know constantly display warnings that “X other users are currently looking at this offer”, “only Y rooms are still available on the given date”, or “the current offer is valid for only Z more minutes”. Such warnings are usually kept in extremely bright colors, clutter the interface and distract from the actual functionality and task. To give another example, in many cases, buttons for canceling checkout processes or subscriptions are made as nonsalient as possible (or are even hidden) while purchase buttons are strongly highlighted.

The Case for Usability

Yet, evidence suggests these are bad practices. Sauro [5] found that “[p]erceptions of usability explain around 1/3 of the changes in customer loyalty” in a study that investigated correlations between the System Usability Scale and Net Promoter Scores. “Overtime,” he states, “if people think your product is usable then they are more likely to use it, more likely to recommend it and you are more likely to sell it.” [5]

In different research,  Kuan et al. [2] “found a relation between system quality and conversions, i.e., they identified three dimensions of usability that ‘explain over 70% of variance of intentions for planned purchase as well as future purchase.’ Thus, it is highly necessary for e-commerce companies to continuously evaluate their products with respect to good usability, in particular, to remain competitive.” [8]

Hence, the usability of an E-commerce website, as well as that of the product itself, directly influences sales numbers. Therefore, the above diagram must be extended to include the concept of Usability. From this results the following, which I call the Conversion/Usability Framework (due to the fact that sales in E-commerce are usually referred to as conversions):

The Profit Function w/ Usability

Usability has an impact on three of the variables of the profit function:

  • Usability → Amount: Based on what I described above, usability directly influences the amount of products a company sells. The better the usability of the E-commerce website and/or the product itself, the more products are sold due to more satisfied customers and more recommendations.
  • Usability → variable Costs: Better usability also leads to lower variable costs related to help and support. A more usable product is more intuitive to use and triggers fewer errors [3], which reduces customer support requests and replacement of defect products.
  • Usability → fixed Costs: Ensuring good usability requires to set up corresponding processes or incorporate usability testing into existing ones. This additional effort leads to increased fixed costs. However, firstly, appropriate design and QA processes should already be in place and easy to complement, and secondly, the benefits of good usability outweigh these increased costs in the long term.

Applying the Conversion/Usability Framework

It is important to note that the Conversion/Usability Framework is not exclusively valid in the context of E-commerce, but can be applied regardless of the product that is being sold and the way it is sold. This is because every product is used for something by the customer, and everything that is being used has an inherent usability. A teapot, a faucet, and a microwave oven, for instance, have a certain usability just like a website has.

When a company wants to increase its profit, there are certain “traditional” levers one can make use of. One way is to reduce unnecessary fixed and variable costs, e.g., by restructuring and optimizing inefficient processes or taking advantage of economies of scale. Another way is to increase revenue by, e.g., better pricing, exploring novel marketing opportunities, or entering new markets. As described above, the Conversion/Usability Frameworks introduces the additional lever Usability that—independent of the company and product—should never be neglected and can be used in the following ways:

  • Increasing sales: In order to increase customer satisfaction and loyalty and as a result sell more products, take measures to optimize the usability of both your product and sales channels. The questions you have to ask are: Is my product easy to use for its intended purpose(s)? and Is my product easy to buy? To be able to answer these questions with “yes”, employ established design practices and conduct regular user tests.
  • Decreasing variable costs: To minimize your customers’ error rates and as a result reduce customer support efforts and the number of products that have to be replaced, ask the same questions as above. On top, you should implement a Lean Support approach based on which you can incrementally create a knowledge base that lets your customers help themselves. Here, you should apply the same usability standard as for your products and sales channels.
  • Decreasing fixed costs: Since every usability process imposes a certain amount of fixed costs, one way to bring down the latter is to review and restructure existing usability processes. Tests with representative sets of users and realistic tasks that collect behavioral and attitudinal data [6] usually yield the best results. However, they cost significant amounts of time and money. If you have limited resources and need to cut fixed costs, certain trade-offs are possible. For instance, according to Nielsen and Molich [4], a simple and inexpensive heuristic evaluation with only 3 to 5 evaluators is already enough to find the majority of usability issues in an interface.

Conclusion

Good usability is a key quality aspect of any product or website that is, however, surprisingly often neglected by companies. Especially, on today’s E-commerce websites, we can observe numerous dark patterns that try to trick the customer into purchases and subscriptions. However, this ultimately leads to dissatisfied, disloyal customers.

By applying the Conversion/Usability Framework on top of “traditional” methods, it is possible to increase customer satisfaction and loyalty, reduce variable costs and sell more and better products. This should be common sense in E-commerce settings but is also applicable beyond websites. For a successful business, it is important to develop an understanding of the importance of some sort of Usability Thinking, no matter how you sell your products and which products you sell—be it teapots, microwave ovens, cars, or SaaS subscriptions.

(This article has also been published on uxdesign.cc.)

References

[1] https://www.aatsl.lk/files/articles/Cost-Revenue-and-Profit-Functions-(English).pdf (accessed September 21, 2017).
[2] Kuan, Huei Huang, Gee-Woo Bock, and V. Vathanophas (2005). “Comparing the Effects of Usability on Customer Conversion and Retention at E-Commerce Websites”. In: Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS ’05).
[3] Laubheimer, Page (2015). “Preventing User Errors: Avoiding Conscious Mistakes”. https://www.nngroup.com/articles/user-mistakes/ (accessed October 19, 2017).
[4] Nielsen, Jakob and Rolf Molich (1990). “Heuristic Evaluation of User Interfaces”. In: Proceedings of CHI.
[5] Sauro, Jeff (2010). “Does Better Usability Increase Customer Loyalty?” https://measuringu.com/usability-loyalty/ (accessed October 12, 2017).
[6] Sauro, Jeff (2015). “5 Types of Usability Tests”. https://measuringu.com/five-types-usability/ (accessed October 19, 2017).
[7] squeaker.net (2010). Bewerbung bei Unternehmensberatungen: Consulting Cases meistern.
[8] Speicher, Maximilian (2016). Search Interaction Optimization: A Human-Centered Design Approach. Ph.D. Thesis. Technische Universtät Chemnitz.

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How to Data Analytics (in a Start-up)

3 Lessons I Learned as the “Chief Data Analyst” of a Silicon Valley–funded Start-up

From 2015 till 2017 I helped grow HoloBuilder Inc., a start-up providing virtual reality solutions for the construction industry, as their VP of Customer & Data Analytics & Optimization, which roughly translates to “Chief Data Analyst”. The company is headquartered in San Francisco while I was a part of their R&D lab in Aachen, Germany. I was responsible for the whole data analytics* pipeline — from collecting data on the web platform using Google Analytics and own trackers to processing the data in Google BigQuery and visualizing it using tools like Power BI and Klipfolio. During my time in Aachen I learned lots of valuable lessons. Here, I want to share with you the three most important ones that are directly concerned with data analytics (please scroll down for a TL;DR).

How to Data Analytics

1. Data Analytics ≈ UX Design

Data analytics is a lot like UX design. You have specific target audiences that expect to experience what you provide them with in the most optimal way — concerning content, presentation, and possibly interactivity. For instance, providing data for C-level management and for potential investors are two completely different stories. While management requires low-level insights concerning the software itself, among other things, for VCs we usually prepared more high-level business metrics including projections and forecasts, due to the different requirements. Moreover, internal data would usually be provided through dynamic dashboards that could be adjusted and customized while data for investors would rather be delivered in the form of PowerPoint slides that matched the layout of the pitch deck. Therefore, it is crucial to have the definition of a target audience (potentially even personas) and a requirements elicitation from that audience at the beginning of every data science process. At HoloBuilder Inc., this lesson became especially clear because of the split between San Francisco and Germany and the fact that most of the (potential) VCs were residing in the Silicon Valley.

I am convinced that a data analyst without some proper UX skills — and, of course, adequate requirements and input — cannot be successful.

2. Ask the Right Questions — and Do So early

This one goes hand in hand with requirements elicitation. Don’t provide analyses just for the sake of it!

This whole “let’s just analyze everything we can get” thing doesn’t work! It’s extremely important to define the questions you intend to answer beforehand. Tracking is cheap, so you can (and should!) track more than you need (at the moment). But the processing and visualization of data that nobody ever looks at eats up a whole lot of resources that would be required for the meaningful analysis and presentation of the few nuggets that are buried in your giant pile of big data. Also, having concrete questions in mind greatly helps with tailoring data structures more precisely to your specific needs. Of course, this doesn’t mean that your infrastructure doesn’t have to be flexible enough to quickly react to changing and new questions that need to be answered. In an optimal world, the data for answering new questions is already there and you “just” have to do the processing and visualization. In general: Expect surprise on-demand questions anytime! Therefore, anticipate and be prepared!

(While the questions that need to be answered can be seen as part of the requirements elicitation, I treat them separately here, because I give requirements a more technical connotation — e.g., “possibility to toggle between line/bar charts” or “include difference to previous period in %” — compared to key questions such as “Why do we lose users?”.)

3. Data is Meaningless …

… unless you give it meaning by interpreting it. For this, it’s inherently important to not think in silos. A data analytics team has to closely cooperate with the UX team and (almost) all other teams in the company in order to find meaningful interpretations or reasons for the collected data. Yet, this is still not the norm in industry. For instance, there is still the widely believed misconception that A/B testing = usability testing.

To ensure meaningful data analytics, at HoloBuilder Inc., marketing manager Harry Handorf and I developed a boilerplate for a weekly KPI report that posed three crucial questions:

  1. Which data did we collect?
  2. What are the reasons the data looks like that?
  3. What actions must/should be taken based on the above?

That is, the first part delivered the hard facts; the second part explained these numbers (e.g., less sign-ups due to change in UI); and the third part presented concrete calls to action (e.g., undo UI change). The report looked at those questions from the platform as well as the marketing perspective. Therefore, we had to extensively collaborate with software engineers, designers, UX people, marketing and sales to find meaningful answers. According to the second learning above, the basis of the report always were higher-level questions defined beforehand, such as: “Does the new tutorial work?”, “How can we gain more customers?”, and “Have we reached our target growth?”. In general, the interpretation of data is based on the processed data and the questions to be answered, rather than on technical requirements (see infographic above).

Again, because this is really important: Your data is worth nothing without proper interpretation and input from outside the data analytics department.

Ultimately, to conclude this article, I don’t want to withhold from you Harry’s take on the topic:

You might have heard of the metaphor for life feeling like a tornado. It perfectly applies to working with data of a young business — it spins you around with all of its metrics, data points and things you COULD measure. It’s noisy and wild. A good data scientist figures out how to step out of it. But that does not mean getting out of the tornado completely, letting it do its thing and becoming a passive spectator. It means getting inward, to the eye. Where silence and clarity allow for a better picture of what’s going on around you, defining appropriate KPIs and asking the right, well thought-out questions.”
—Harry Handorf (tornado tamer)

TL;DR

  1. Data analytics is a lot like UX design! As a data analyst, you have to define target audiences and elicit requirements. Tailor content & presentation of your analyses to those.
  2. Define the questions to be answered beforehand, then process and interpret the data necessary to answer those questions. Don’t analyze everything you can just for the sake of it.
  3. Data is meaningless without interpretation. Extensively collaborate with other departments — especially UX — to ensure meaningful data analytics.

(This article has also been published in Startups.co on Medium.)

Footnotes

* What we did at HoloBuilder Inc. was clearly a mix of data analytics and data science. But since it was closer to the analytics part, I refer to it as data analytics in this article. In case you are interested in the specific differences between the two (and how difficult it is to tell them apart), I recommend reading the Wikipedia articles about data science and data analytics, as well as “Data Analytics vs Data Science: Two Separate, but Interconnected Disciplines” by Jerry A. Smith.

Acknowledgments

Special thanks go to Harry for proofreading the article & his valuable input.

Schrödinger’s Website

Before you receive feedback from users, the user experience and usability of your website are both ‘good’ and ‘bad’ at the same time.* Through a good design process you can only raise the chances of user experience and usability manifesting as ‘good’** once the feedback arrives.

* That is, the factors x and y of a linear combination U = x\ \mathrm{good} + y\ \mathrm{bad} with x + y = 1 are unknown.
** Subject to definition. For instance, x > y or x \geq \frac{2}{3}.

The U Score: Redesigning Usability Testing

logo-bigUsability testing is often perceived as cumbersome and time-consuming and therefore not thoroughly applied. This was one of the key observations leading to the topic of my PhD thesis. Particularly conducting tests with actual users is often omitted, which results in the release of suboptimal products and websites. In my thesis, I tackle this problem through more automatic evaluation and optimization, however, in the specific context of search engines. Yet, every type of website—no matter if private or professional—should undergo at least one usability test before its release. Therefore, we need to redesign usability testing itself:

  • It must be quicker.
  • It must be cheaper.
  • It must be easier to understand.
  • Still, the result must be as precise as possible.

the-u-score_screenshot

The U Score is a more general derivative of the findings of my PhD project that provides quick and precise usability evaluation for everyone based on actual research. Any designer or developer who isn’t able to conduct a regular usability test can answer a minimal but exhaustive set of yes/no questions and receives a single usability score for their website or web app. The questions have been designed to be as objective as possible and are based on established research findings. Also, for time reasons I try to minimize the need to involve other people, which, however, cannot be completely eliminated (still, you can receive a complete U Score with the help of only three friends who have a look at your site).

In this way, the U Score provides an approach to usability testing that is as precise as possible given the minimal effort it requires. It’s intended for situations in which designers/developers don’t have the chance to conduct a traditional usability test. Also, it addresses everyone who needs a quick assessment, has never tested the usability of a website before or is new to usability testing. However, please note that the U Score can only be an approximation and is not a complete substitute for established usability testing methods. Still, it gives you a very good baseline according to the motto: Any usability test is better than no usability test!

The current version of the U Score is still in beta development status. Therefore, I highly appreciate your feedback, which you can add to this public Trello board.

For implementation, I’ve relied on a number of well-known technologies and frameworks in combination with some that were new to me (the ones marked with an asterisk):

I hope the U Score can help to reduce the number of websites who’ve never been tested at all and particularly help you to conduct more and quicker usability tests. Enjoy!  🙂

Lean Support: The Case of HoloBuilder

SupportWhen I started working on HoloBuilder.com over a year ago, there was no support. Of course, we would’ve immediately helped anyone who sent us a question via e-mail or Twitter, but those options weren’t communicated anywhere. Users accessing HoloBuilder ended up directly in our augmented/virtual reality creator, the only way for communication with us being a “leave feedback” option indicated by a line chart icon. HoloBuilder menu (old)However, “leaving feedback” is definitely not the same as “getting support” or “getting help”. Thus, following my UX rule #1, support functionality was de facto nonexistent. Also, we had neither a knowledge base nor a collection of FAQs, no support personnel and user feedback was still pretty rare.

Hence, due to the limited resources in a start-up, we decided for a lean support approach. That is, we rolled out HoloBuilder support in small pieces, treating every stage like a minimum viable product (this is also what Nate Munger describes on Quora).

Making Support Visible

First, we added a “?” icon next to the feedback option, which was visible at any time and clearly showed the user that they could get help. Since there were no FAQs available yet, clicking the new support option simply instructed the user to send their question via e-mail or Twitter. As you can see, we changed nothing about our support back-end, but we finally made the possibility to get help visible to the user, which is already a huge gain. This was confirmed by an increasing number of relevant support request that reached us in terms of e-mails and tweets.

Growing a Knowledge Base on Demand

Second, to continually grow a knowledge base, we created a blog for development news and tutorials, which can be found at createholo.com. In that blog, we publish solutions to (potential) problems on demand, i.e., when users get back to us with questions or when new HoloBuilder features are released. CreateHolo was then linked as “Tutorials” on our new landing pages, which we created to provide users with some introductory information and help before being confronted with the AR/VR creator itself. However, through heat map analyses, we found that the tutorials gained more attention when changing the link text to “Help & Tutorials”. This indicates that “help” is still the major keyword when it comes to support.

Adding Channels

In the next step, we integrated a tawk.to live chat into our HoloBuilder pricing page as soon as it went live in January 2016. In this way, we established a third feedback channel in our support back-end with almost all of our employees acting as support agents, thus providing a more direct and personal connection to users with urgent questions. Since our experiences with the live chat feature have been consistently positive so far, we plan to extend it to our different landing pages as well in the future.

Collecting & Organizing Feedback

Finally, all questions and pieces of feedback we receive through our three support channels—e-mail, Twitter and live chat—are collected and organized in a dedicated Trello board. Based on that board, on a regular basis, we decide on feature requests, tutorials to be written and continuously grow the aforementioned knowledge base. The FAQs collected in that knowledge base are at the same time treated as a list of to-dos for our internal UX team. Some particularly crucial FAQs are already featured on the HoloBuilder pricing page. Interaction with those FAQs is tracked anonymously to find out what users struggle with most. Interestingly, interactions seem to be not overly influenced by position bias since FAQ #8 at the very bottom receives the second-highest attention in terms of clicks.

To conclude, by following a lean support approach, we have established a well-working process and a convincing customer success rate within a year, without additional resources or personnel. The next big milestone of our process of implementing lean support for HoloBuilder will be the release of our knowledge base, so that we can provide an on-page support experience that’s just as awesome as MailChimp’s (disclaimer: I’m a fan).

This article is dedicated to Anna, our awesome support ninja.

Little MOO, the Friendly Print Robot – That’s Customer Experience!

Little MOO, the friendly print robot

Recently, I’ve ordered a set of new business cards online and shortly thereafter, I received a confirmation e-mail from Little MOO, the friendly print robot. He told me that he had received my order and was forwarding it to Big MOO, the loyal print machine. Immediately, I had to think of a little robot looking a bit like WALL·E doing some paperwork concerned with my order—and let’s be honest: that one’s pretty cute.

The point I wanna get across here is: A confirmation e-mail (or similar) might seem like a trivial piece of interaction in the process of a purchase. But still, you can do a lot wrong and a lot right there. If your confirmation e-mail is actually able to make me smile instead of immediately forgetting what I just read and then at some point deleting it, that’s a fantastic piece of customer experience! Good customer experience in turn leads to a satisfied and loyal customer. So really, well done, MOO.com! (By the way, I also like the very clean layout and typography of the e-mail—but that’s only a side note.)