Predictive Analytics: Designing for the Future

When we discuss statistics, we often refer to probability. For example, when a student is enrolled in a college, how likely will s/he earn an honorary degree? Additionally, what is the correlation between social norms and absenteeism? If a construction worker believes his peers are not putting in enough effort, how likely will he be absent from work? The goal here is to predict future outcomes based on data collected from a sample. This process begins with forming a hypothesis, researching existing studies and findings, designing surveys or experiments, and finally interpreting the data to draw conclusions.

In today’s data-driven world, businesses are constantly looking for ways to enhance user experiences and gain a competitive advantage. Predictive analytics has emerged as a powerful tool to achieve these objectives. By utilizing historical data and statistical techniques, businesses can anticipate future outcomes and make informed decisions that cater to individual user needs.

One of the most familiar examples of predictive analytics that we encounter is Google. When using Google products like Search, Gmail, and Google Docs, we often experience Smart Compose, where the system tries to complete our sentences before we hit enter. Aside from that, predictive analytics are helpful in different areas, such as fraud and risk, as well as marketing and operations.

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What is Predictive Analytics?

Predictive analytics is a powerful field that leverages the power of data, advanced statistical algorithms, and sophisticated machine-learning techniques. By analyzing historical data, it aims to uncover patterns and trends that can help forecast future outcomes with a high degree of accuracy. Rather than just reflecting on what has happened in the past, predictive analytics seeks to provide insightful assessments about what may occur in the future, enabling organizations to make informed decisions and strategically plan for various scenarios.

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How Tech Giants Use Predictive Analytics

If you haven’t noticed it yet, Microsoft, Google, and Amazon use predictive analytics to their advantage. Because they have accumulated a lot of data, they can optimize the information for future insights. You may also notice how when you use Waze or Google Maps, it often asks if you prefer a faster route than the ones with more traffic. This fact alone indicates that the two apps have predictive analytics. Google also has experimented with machine learning, where computers can learn a picture and identify the object as a cat. It took them 16,000 computer processors to achieve this! It takes a lot to train computers to identify an object from a picture and guess correctly that it is a cat.

IBM, in particular, promotes SPSS software to calculate data analytics and IBM Watson to apply the knowledge to Artificial Intelligence. Revelwood, a client of IBM, used such tools to uncover sales trends in hundreds of thousands of products and tens of thousands of SKUs.

Harvard Business Review covered the many reasons why predictive analytics are useful to businesses. It can predict income and help us adjust our pricing model. It can also predict maintenance time and cost. Moreover, it can go even further with AI and machine learning: it can change the way things are done in an improved way (for example, in diagnosing and treating patients).

Applications in different industries

Many industries are affected by predictive analytics: finance, healthcare, video streaming, e-commerce, social networking, retail, and travel.

Finance

Detecting fraudulent transactions, assessing credit risk, and providing targeted financial advice. We see Robo Advisor in Wealthsimple, Wealthfront, and Robinhood as market transactions occur in seconds (or even nanoseconds), so keeping track of what to trade or invest in is hard. Thus, Robo Investor acts as the fund optimizer that can keep up with the speed that customers need. It considers your capital size, other investors’ actions, trending news about companies, and stock prices. Currently, it is used in ETFs and is yet to be personalized for customers.

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Health

Predicting patient readmissions, identifying high-risk individuals, and personalized treatment plans. We see Fitbit and Apple Health that try to compile customer data. Fitbit has been selected as the covered fitness benefit in 27 US states for forty-two Medicare Advantage plans. Users can benefit from access to health data when they visit doctors. Back then, people had to undergo medical check-ups to know how healthy he was. But now, with fitness trackers, patients have better access to their health data.

Retail and eCommerce

Optimizing inventory levels, predicting demand fluctuations, and offering personalized promotions. Travel, retail, and e-commerce industries are promoting their businesses through strategic sale events. With predictive analytics, you understand when your business thrives and sales are low (for example, Christmas might be the best time in business because people are buying gifts). Some e-commerce like Amazon, Alibaba and Tokopedia use this opportunity to name their own sales day: Single Day, Online Sales Day, Black Friday, and Cyber Monday.

Social media

Social media sites like Facebook, Instagram, and Pinterest use customers’ behavior to show relevant ads. Businesses pay these social media for an Ad placement and then calculate their pay-per-click. It takes some time for the user to familiarize themselves with the ads, and when they need to, they click on them.

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Biotech

We also see in Biotech that research has been done to re-engineer the structure of the protein as a recovery drug. Numerate, for example, uses AI to predict how a potential drug will behave both in the lab and body. Predictive analytics can help us discover new medicines and reduce prediction errors.

Media

Finally, there is also a personalization algorithm that uses predictive analytics. You might see “Based on your previous watch” and get some recommendations when you use Netflix. As the computer accumulates data about you, it recognizes a pattern of behavior. Combined with the behaviors of other like-minded customers, the personalization algorithm will predict the next products you most likely will choose.

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Why invest in predictive analytics?

The question, then, is how we will use innovation in technology in our products and services. Michael Schrage, a research fellow at MIT, wrote in Harvard Business Review about how AI can be an assistant, a guide, a consultant, a colleague, and even a boss.

Predictive analytics can be an assistant, a guide, or a consultant for you because they can perform repetitive tasks. That way, we can focus on critical thinking: how to improve the business, including design thinking, etc. For example, Tesla was automating the car factory using a fluff bot. However, it soon realized that robots are very bad at picking up fluff. They discovered that installing fiberglass didn’t reduce noise in the car, so they decided to eliminate fiberglass installation. Here, it shows that humans can focus on decision-making and critical thinking.

Business stages

Your company can either be in the Launch, Mature, or Growth stage. If you are at the latter stages, you have already compiled a huge amount of data on your customer behavior. But if you don’t, you can look at industry standards or use information from the Point-of-Sale (POS) payment platform, such as Stripe or Square.

At the core, predictive analytics will give you insights using data they analyze. IBM tried to predict the probability of white spots being cancer on an X-ray. Google Maps can predict the time to a destination based on Geo-location and our chosen route selection. IFTTT offers automation where bright lights can be turned on once you’re at home.

Benefits of Predictive Analytics for User Experience (UX)

• Personalization

  Tailoring content, products, and services to individual preferences.

• Improved Customer Service

  Proactively address customer needs and resolve issues before they escalate.

• Enhanced Product Development

  Identifying areas for improvement and developing products that meet customer expectations.

• Increased Efficiency

  Streamlining operations and reducing costs by optimizing resource allocation.

UI/UX Design

Creatives such as Gal Shir, Fabrizio, Jack, and Adobe use AI to help designers pick the right colors (or color palettes) for their art. A way to use the platform is to upload a picture, and the program automatically selects a color palette that will work in harmony.

Customer Behavior

Nir Eyal, author of Hooked: How to Build Habit-Forming Products, believes that the most successful products shape our habits and that we’re hooked because of the “rewards” given, such as Likes, Follows, etc. He mentioned that businesses should identify who their followers are, their current habits, and the common path in habit testing. Habit testing is one of the ways to use predictive analytics to better businesses.

UX design is all about interaction design and user experience. An interaction, by definition, means a compilation of different sets of actions that will result in different results. We can learn more about the customer through behavior patterns based on historical data. It can be as easy as a preference of what a customer would do to join a mailing list. Would a Pop-up be more effective? Or a side thumbnail works best? Maybe engaging news (such as a Free PDF) in a blog post would work better? Predictive analytics help because it can give insights about this behavior. People also tend to go to “Start Here” or “For You” when they first arrive at a blog website. When it’s about mobile apps, it can be about utilizing buttons and shortcuts: which ones do customers press? Which ones do they neglect? You may want to remove it if people do not use particular functions.

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Implementing Predictive Analytics

To effectively implement predictive analytics, businesses should:

1. Define clear objectives:

   Identify specific areas where predictive analytics can improve user experience.

2. Gather and prepare data:

   Collect relevant data from various sources and ensure its accuracy and consistency.

3. Choose the right tools:

   Select appropriate predictive modeling techniques and software tools.

4. Validate and refine models:

   Continuously evaluate and refine predictive models to ensure accuracy and effectiveness.

5. Take action based on insights:

   Translate predictive insights into actionable strategies to enhance user experience.

Predictive analytics in an economic downturn

While the economic downturn would pressure business owners to cut costs for efficiency, we also need to think that technology helps improve our products and services. We can create tools to help predict customers’ actions if we give incentives. In other words, predictive analytics would help us to guide customers to buy our products and services.

Internally, we can also use predictive analytics in automation. The automotive industry, for example, uses robots in car manufacturing. The operation manager then can focus on discussing strategies, designing the assembly line, and ultimately making better decisions with the help of technology.

Remember that with more advanced technology, there will be faster computers with more accuracy and cheaper unit costs. Supercomputers, for example, had a cost of one Giga FLOPS for 19 billion dollars, which was later reduced to 3 cents only in 2017.

Conclusion

Predictive analytics empowers businesses to anticipate future outcomes and make data-driven decisions that enhance user experience. By leveraging this powerful tool, businesses can personalize interactions, optimize operations, and gain a competitive advantage in today’s dynamic market.

At Designial, we offer Digital Marketing, UX Design, Digital Transformation, and Mobile App Development services. Contact us to learn more about how we can help your business.