Why You Need UX Based on Data Science to Guarantee Product Success

Created in April 2019, Tesla uploaded a 2-minute video on YouTube to showcase the full self-driving mode. All models have a 360-degree camera, 160 meters of forward-facing radar, and 12 ultrasonic sensors. Not to mention the touchscreen display and over-the-air software updates. If anything goes wrong with the car, the company will check, and if it turns out to be a software problem, Tesla will update its software. Tesla's approach to the customer experience is very different from traditional automotive companies that need to recall their cars to fix issues.

The example above is about data science. To be precise, data science combines statistics, computer science, and more. It is the process of mining data, analyzing it, modeling it, and presenting the results. Most Google searches ask about how to become a data scientist. Still, this article will talk more about utilizing data science for your products and services business.

Managing data science

Lecturers at John Hopkins University created a book titled "Executive Data Science." In the module, they mentioned that we could call a data science experiment is successful when:

1. It creates new knowledge
2. Decisions or policies are made based on the outcome of the experiment
3. It results in creating a report, presentation, or app with an impact
4. Or when the conclusion is that the data can't answer the outstanding question

Data science begins with a question built in mind, followed by data collection, formulation of the data model, and communication of the results.

Building a data science team needs integration in the company: each individual can work separately but together as a team on a big project. They also need to communicate with other teams in the company, such as the marketing team or the product team, who can be the users of their projects.

How big companies use data science in their businesses

Facebook has a research division with data scientists. They investigate many things, including how people react to Facebook Lite. One exciting piece of research was about social comparison on Facebook. A finding states that when you ask people to think when they feel worse about themselves, 1 in 5 could recall a time they felt worse after seeing a post. And yet, they didn't know you could unfollow or snooze someone to hide their jobs.

In Netflix TechBlog on Medium, they mentioned that one of the problems they are facing is determining which movies are trending based on languages. If the content is more prevalent in Language A than in Language B, then Netflix will sequence its efforts accordingly. How data science helped their decision-making is how they can turn historical viewing trends consumed across various languages into insights.

Users of data science and AI

Both customers and employees can benefit from data science and AI. Customers often utilize an app from a company to get updates on their order deliveries. From the customer's point of view, the communication steps involve browsing products, purchasing, confirmation of purchase, delivery notice, tracking update, and arrival notification. However, the task requires multi-party communication at the back end. As the customer fills in personal information, his or her profile is stored in a Customer Relationship Management program. After that, the order information is sent to the warehouse, confirming availability. This information is passed to the delivery team with trucks ready for shipment. Note that the warehouse will contact suppliers to prepare the stock if the product is unavailable.

Imagine if there's a delay in delivering a product or service due to unforeseeable circumstances (e.g., a winter storm). Airlines could potentially delay or even cancel flights. Customers now ask for a new ticket with complimentary lodging and meal coupons. Then the customer will get an update from the company with the coupons. From the employees' perspective, the back end took care of multiple things simultaneously. First, CRM pulls up a specific customer profile. There, the employee can know whether the customer is a member. The employee then arranges for new tickets and coupons to match the profiles. He or she updates the information through the airline's app or possibly to the customer service desk.

Data science is robust because it creates an integration of multiple data sources.

Launch, Mature, Growth: Your current stage of business matters

Early Stage

In Executive Data Science, the authors mentioned how startups should first focus on infrastructure: making sure that your data house is in order. This makes sense because when you're in the early stage, you are collecting customer data. For example, you can get their personal information, such as email and home addresses. You can also start collecting behavioral data that will accumulate over time as they use your product or service. Managing your data house means you consider how the computer will store inputs, how the computer will process data, and how the computer will show the results to users. The related program will show filter parameters depending on the user type (is it sales? Is it the product?).

Mature stage

As you mature, you now see the potential to enhance your product and service offerings by using existing customer data. To do this, your data science team will have to do experiments. For example, they can use machine learning to predict future consumer behavior based on past purchases. The data scientists will write an algorithm implemented in the whole system. As a manager, you want the result in crucial visualization: you should know what works and what doesn't.

Growth stage

In the Growth stage, your company is looking for opportunities beyond your current revenue channels. This means that you might look at growth in specific demographics of your users and decide to expand your business to cater to their needs. An example would be the Indonesian unicorn startup "GoJek," which initially was a third-party platform connecting motorcycle riders with customers. They grow their business to deliver food "GoFood," and even more, they integrate a mobile payment system called "GoPay." In data science, you will need a team that can experiment with many different trial projects. And in doing so, you will need a dedicated data science team that overviews the results and successes.

Data science during a downturn

McKinsey wrote an article about leveraging analytics in times of downturn. They encourage leaders to strategize using AI in the company, move the AI topics to priority, reskill workers, hire when others don't, validate data and models, and establish methodologies or protocols. The key here is to view AI as an investment in the longer term.

Keep in mind that technology is becoming cheaper, more accessible, and more advanced. When considering investing in technology, you can consider it a long-term investment. You can use Cloud services, such as Amazon AWS, Google Cloud, or IBM Cloud Pak & Data.

The key is the willingness to learn

Tesla didn't start with self-driving mode right away. Their first goal was to defy the myth that electric vehicles are slow and ugly, and they proved it with a prototype: Roadster. It was in 2012 when Model S was launched, and only then they started to develop autopilot features. Tesla consistently upgrades its hardware and software to improve its service quality.

In a sense, any business needs to adopt data science and AI slowly but surely. You can start by focusing on doing small things: automating emails, utilizing Google business hours, or personalizing your customer's experience.

At Designial, we offer Digital Transformation to upgrade your business. Our approach to achieving business goals involves researching, analyzing, and documenting current processes. Then, we offer digitized touchpoints for employees and customers. We develop a digital strategy with clients to devise a conceptual solution given the budget, timeline, and technological constraints. In our case study with a company with more than 1,000 stores across 49 states that generates annual sales of roughly $19 billion, we increase efficiency by six times.

How to build UX Design to cater to customers' needs

Now, how can data science be applied in building business apps? Often, we forget to address critical issues during the development process.

To begin with, we need to acknowledge the purpose of integrating technology into your business. Ultimately, the goal is to grow the business. You can achieve that either by increasing revenue or decreasing costs. Apps can be useful tools to do both.

Customer-facing app

Knowing your customer's pain points is essential in building a UX Strategy. They are the points when customers find difficulties getting what they want. For example, an e-commerce customer has to input his personal information to purchase a product repeatedly. Although he can still buy the product, the task feels like an extra effort he needs to make. A quick fix will be to add a system that can recognize a returning customer. Amazon offers a '1-click purchase' that speeds up a customer's transaction process in seconds. Your business can provide the same feature to streamline the customer's journey.

We often think apps are 'personal', so we focus on how the app can help one person. But in reality, there are millions of people using apps. In that sense, improving and upgrading your business apps would help many users. It is all about global-scale effects.

The other attempt you can make to increase your revenue is to increase retention. We can look at this task through emotional design. Apps should be designed to create emotional connections, which is critical to retain customers. You can use UX research to get to their mind better, building a customer base that fits the customer persona. A good example would be Instagram and Facebook. They use extensive data science for advertising businesses through personalized feeds.

Employee-facing app

Meanwhile, an app that reduces cost is an employee-facing one. One way is to increase efficiency; an example is an app for warehouse management. Employees should be able to easily navigate the app: know the amount of stock currently available, report damage or false shipment address, and track delivery to retail stores. For example, a customer comes up to a furniture store manager, asking if there's remaining stock of a new black office chair. The store manager would pull up his apps and check immediately. If the app is reliable, the manager can mention how many are in the storage. If none, he can suggest the next delivery date. The manager could refer to the nearest store chain with a stock if it's still the next week.

The questions you want to ask and answer through the brainstorming process are: What are the challenges the customers and employees face now? How can apps and data science help solve the problems?

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Conclusion

Building a data science team requires proper management, protocols, methodologies, clearly defined questions to answer, and a definition of success. Your company can benefit from data science, starting from a small-scale experiment: automation in operations, such as email marketing and Chatbots. As you build data infrastructure in your growing company, you can experiment with different projects to create a better customer experience.

At Designial, we provide different services, such as UX Research and UX Design. Contact us to see how we can leverage data science to improve the user experience for your customers.

Editor's note: This post was originally published in August 2020 and has been updated for comprehensiveness.