Tesla created a 2-minute video in April 2019 to showcase its full self-driving mode on YouTube. All Tesla models have a 360-degree camera, a forward-facing radar that operates up to 160 meters, and 12 ultrasonic sensors. Additionally, the cars feature a touchscreen display and receive over-the-air software updates. If any issues arise with a vehicle, Tesla investigates the problem, and if it’s determined to be a software issue, the company can update the software. This approach contrasts sharply with traditional automotive companies, which often need to issue recalls to fix problems.

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What is data science vs data analytics?

The second part of the text discusses data science, which combines statistics, computer science, and more. Data science vs. data analytics: data science involves mining data, analyzing it, creating models, and presenting the findings, whereas data analytics focuses on analysis only. Many Google searches focus on how to become a data scientist. However, this article will specifically address leveraging data science for your business’s products and services.

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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 starts with a question, followed by data collection, developing a data model, and communicating results.

Ensuring integration within the company is essential when building a data science team. Each individual can work independently, but they should also collaborate as a team on larger projects. Additionally, they need to communicate with other departments, such as marketing and product teams, who will use the outputs of their projects.

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How big companies use data science in their businesses

Facebook has a research division that consists of 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 can unfollow or snooze someone to hide their jobs.

In Netflix TechBlog on Medium, they mentioned that one of the problems they are facing is to determine which movies are trending based on languages. If the content is more prevalent in Language A than in Language B, then Netflix will sequence their 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 a company app to get updates on their order deliveries. From the customer’s point of view, communication involves browsing products, purchasing, confirming the purchase, delivering notices, tracking updates, and receiving notifications. However, at the back end, the task requires multi-party communication. As the customer fills in personal information, his or her profile is stored at a Customer Relationship Management program. After that, the order information is sent to the warehouse to confirm availability. This information is passed to the delivery team with trucks ready for shipment. The warehouse will contact suppliers to prepare the stock if the product is unavailable.

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Now, imagine if there’s a delay in the delivery of a product or service due to unforeseeable circumstances (e.g., winter storm). Airlines could potentially delay or even cancel flights. Customers now request 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.

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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: ensuring that your data house is in order. This makes sense because when you’re in the early stage, you collect 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 differently 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 would want the result in crucial visualization: you should know what works and what doesn’t.

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Growth stage

In the growth stage, your company seeks opportunities beyond your current revenue channels. This means that you might be looking 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,” initially 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.” Regarding data science, you will need a team to experiment with many 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.

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

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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: the 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 provide digitized touchpoints for employees and customers. Given the budget, timeline, and technological constraints, we develop a digital strategy with clients to develop a conceptual solution. 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.

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The Future of AI and UX

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.

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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 do. A quick fix will be to add a system that can recognize a returning customer. Amazon offers a ‘1-click purchase’ that fastens a customer’s transaction process by seconds. Your business can provide the same feature to streamline the customer’s journey.

We often think that apps are ‘personal’ and 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 simultaneously. It is all about global-scale effects.

The other attempt you can make to increase your revenue is to improve retention. We can look at this task through emotional design. Apps should be designed to create emotional connections, which is critical to retaining 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 to advertise businesses through personalized feeds.

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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 available stock amount, report damage or false shipment addresses, 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. The manager can specify how many are in storage if the app is reliable. 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?

Conclusion

Building a robust data science team is essential for success, and it requires effective management, established protocols, and proven methodologies. By framing clear questions to address and defining success metrics from the outset, your company can unlock the full potential of data science. Start with small-scale initiatives like automating operations through email marketing and chatbots, which can yield immediate benefits. As you strengthen your data infrastructure, seize the opportunity to undertake impactful projects that will significantly enhance the customer experience and drive growth.

At Designial, we deliver a range of premium services, including UX Research and App Development. Reach out to us to learn how we can harness data science to significantly enhance the user experience for your customers. Let’s take your project to the next level!s.

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