Successful companies address customer needs better than the competition. However before you can address customer needs, you must first identify them.
“What are my customers’ needs? And how can I address them better than the competition?” are the ultimate strategic questions every CEO should ask daily. A close attention to user experience (UX) will help answer the first question, and a clever use of artificial intelligence (AI) will help answer the second.
Today, there are no excuse not to invest into understanding your customer needs. I’m not talking about paying a few bucks to some random study groups. I’m talking about investing in actionable data points — the ones that are in immediate contact with your customers.
If you can understand exactly why someone is not completing a key-step in your conversion process, then you have identified your customers’ unmet needs and can proceed to solving them. Understanding this user behavior is the craft of UX — defined as “the study of the experience of consumers using a product, system, or service.”
I facetiously mention study groups because, too often, business owners, entrepreneurs and “wantrepreneurs” will conduct organized “market research” sessions to give themselves the illusion that they are steering their strategy in the right direction. What is more valuable, however, is understanding exactly how people are physically interacting with your product, system, or service in a completely unbiased setting.
To get these insights, you need to set up a data structure to record and analyze this information. For example, in eCommerce, you need to track the number of users who clicked the “Add to Cart” button and at what rate. In publishing, you need to track how many people are reading the entirety of your articles and which topics are most popular. There is an infinite amount of good data points to capture, as long as it helps you better understand your customers’ needs.
With enough information, you can create computer systems and algorithms to adapt your solutions to individual customers, at mass scale. This is AI. With enough data, companies can use AI to remove painful decision-making from their customers, reduce friction in customer journeys, and ultimately deliver a better user experience.
Apple vs. Spotify
Consider the example of Spotify and Apple iTunes. Before Apple Music, iTunes knew exactly which songs we listened to and the times at which we were in the mood for funk, rock, R&B or jazz. But management never put that data to good use, failing to provide relevant and timely recommendations to increase user engagement.
Conversely, Spotify was poised to find out what listeners wanted. Spotify realized it was in the entertainment business, not the music cataloging business. It understood that the biggest point of friction in getting the average Joe to consume music is to recommend him songs.
Joe doesn’t want to research, organize, and store music. Joe simply wants to listen to Beyonce’s new track. In removing pain-points and addressing that need, Spotify’s “Discover Weekly” skyrocketed the service into mass adoption.
The UX angle comes first, then comes AI to answer the unmet needs. Interestingly, Pandora shared similar views and insights to Spotify. In spitting out radio playlists – based off of one song or artist – they remove the guesswork of searching for the next song. Unfortunately, Pandora didn’t focus enough on increasing the conversion rate from “User opens Pandora” to “User listens to a song.” Even today, users are forced to use brain-power in determining which station to create.
Focus on the UX, then push your findings by applying AI to solve the problems identified in the UX.
Another prolific case is that of Internet giant Google. They won over the search market by focusing on the user experience and de-cluttering the Google homepage and search results page. Google now processes over 40,000 search queries per second.
They maintain their first position in search by betting on the same strategy: making sure that their search experience is the best one out there. With their colossal amount of user data, Google uses AI and deep learning tools to determine which questions you want answered, as you are typing in the search box.
This is labeled as AI, but really, it’s not much more than using accessible data to improve UX. The “guessing” algorithms make perfect business sense too. With more people satisfying their search queries faster, Google keeps users coming back and satisfies advertisers with high-quality impressions, clicks, and conversions.
Lastly, let’s consider “Destiny,” Amazon’s tremendous AI tool that pools millions of data point to give impeccably related and recommended products to the user.
First let’s go back and consider Amazon’s business strategy, which manifests itself with their innovations in UX. Amazon’s strategy is twofold: “sell cheap products; deliver them fast.” The overarching theme around their strategy is to make online shopping incredibly easy.
When Amazon patented the “One-click purchase” checkout system, it was created – once again – with the user experience in mind. Amazon had identified friction in the checkout process and created a tool to address it perfectly. Today, the patent is one of Amazon’s biggest advantages over competitors in eCommerce.
Note: Amazon’s one-click purchase patent expires on September 11th, 2017. Soon, Amazon’s breakthrough in UX will become ubiquitous in the eCommerce world, revolutionizing startups and large brands worldwide.
Similarly, Amazon’s product recommendation AI, “Destiny” is born out of this mission to make online shopping easy. To stay ahead of the competition, Amazon remains religiously focused on improving their UX. AI enables Amazon to improve UX for their billions of shoppers, at the individual level.
Marketing + AI / UX
For the scrappy entrepreneurs, the aspiring startups, and the savvy marketers, you don’t need to invest billions of dollars into AI. Though the examples listed above are initiatives taken on by tech’s biggest players, the insights are extremely simple and are just as applicable to any business.
The main action item here is:
Know your customer need, and invest in tracking and analytics.
Tracking systems from Google Analytics, to Mixpanel, Intercom, Heap, or Localytics will either be free or inexpensive. Use these tools to get real insights into the pain-points in user journeys. Once you understand those needs better via a smart tracking system, then offer customized solutions to those needs.
Here are 2 down and dirty examples of this:
If you are a SaaS business, and you notice a large drop-off from one key conversion step to the next. If you have significant data to support this assumption and data on the type of business the consumer is in, use Intercom to popup a personalized message for those users; say something along the lines of “Hey – Are you still figuring out if this is the right solution for you? We’ve worked with a similar XXX client last year, here’s the case study: LINK”.
If you are an eCommerce business that sells ties and cufflinks, and you have data to support that a user has entered your newsletter and never purchased before. That user is coming back 3-4 times a month to check out your tie collection. Send them an email along the lines of “Hey – I see you’ve been eyeing this specific tie. If you were wondering what it looks like on people, here are a few pictures of people wearing it, from Instagram :)”
You can’t solve a need if you can’t identify it. That is the ta-dah point of this post. Invest in data and address the needs in a customized fashion with low barrier tools.
If you think of AI, not as a thing out of iRobot, but instead as a really smart spreadsheet, then you begin to understand how basic uses of data can be used to improve UX and in turn, win over more customers.