How to Build Machine-Learning-Driven Interfaces
From the shows we stream every day to the phones we can’t be without, machine-learning-driven interfaces are growing increasingly prevalent across nearly every industry.
While many technologies have come and gone, if one thing’s for certain, it’s that the popularity of machine learning (ML) is no passing trend. The global artificial intelligence software market is forecasted to grow rapidly in the coming years, reaching around $126 billion by 2025, as reported by data aggregator Statista. Overall, the ML market includes a wide array of applications, just some of which include natural language processing, robotic process automation and machine learning.
As consumers continue to place a heavy priority on personalized and user-friendly experiences, leveraging these technologies will prove essential for organizations looking to remain competitive in 2021 and beyond.
Dispelling Common Misconceptions About Machine Learning
The widespread adoption of these technologies is due in part to ML’s ability to solve complex problems quickly using data-driven frameworks. However, the growing activity in this sector also stems from the one-of-a-kind user experience these systems can offer consumers.
That being said, for customers and business leaders alike, the phrases “artificial intelligence” and “machine learning” inherently invoke feelings of unease and ethical concern. However, users can rest assured that this technology is already being used to enhance human interactions — not replace them.
Although the all-too-common theme of a robot takeover seen in pop culture may convince users otherwise, the reality is that with many ML applications, we as consumers don’t even notice their presence in our daily life.
More than 90% of companies with world-leading brand recognition are already using ML-enabled solutions to increase customer satisfaction, according to a report from MIT Technology Review and Genesys. Additionally, by mixing the human experience with automated recommendations and ML-driven technologies, these organizations have seen success in optimizing nearly every phase of the customer journey.
From streaming giants to technology leaders, below is a sampling of some of the ML-driven interfaces most consumers likely come into contact with almost every day:
- Shopping recommendations: Based on users’ interests and previous transactions, websites can offer a streamlined path to purchase featuring personalized item suggestions.
- Suggested apps: Using the time of day, a user’s location or previous in-app activity, smartphones can recommend shortcuts to the path it assumes an individual is about to take.
- Content recommendation: One of the most common examples of ML-driven interfaces are media suggestions provided by streaming platforms. This can include songs based on users’ listening habits and shows similar to those they’ve already watched.
The most important thing to remember is that rather than asking a user to take action, an ML interface simply offers the information needed to make a choice easier, providing value to the end-user. There is no pressure to make a decision — unlike what the ML of science fiction may lead consumers to believe.
Despite much of the uncertainty surrounding the application of ML, it’s clear that these interfaces are already entrenched in the modern consumer’s day-to-day experience with technology. Additionally, users have come to discover they deeply enjoy the degree of personalization these interfaces can offer.
In one survey by Accenture, 91% of consumers said they were considerably more likely to shop with brands that provide real-time suggestions and recommendations that are relevant to them. Comparatively, when their experience wasn’t tailored to their unique context, customers became disengaged with a company.
The customer experience has become the final frontier when it comes to capturing a competitive advantage. As businesses begin to leverage personalization as a means of obtaining this edge, ML-driven interfaces will play an essential role in doing so.
However, to fully unlock the potential value of machine-learning-driven interfaces, developers will need to create an interface that’s built by and for users’ needs.
The Process of Developing Your Own Interface
As an organization looks toward developing its own ML-driven interface, the most important thing to remember is that the system should be driven by your users’ unique behaviors. As the field continues to mature, this will be key to creating an interface that generates the most relevant and personalized results possible.
While every team’s process may vary slightly, the general steps to developing and implementing an ML-driven interface are as follows:
Step 1: Product Exercise — Define the Use Case
What solution will the end product provide to customers? Before beginning the actual development process, it’s important to have a clear understanding of the product’s purpose, as well as any points of frustration along the user experience that it will need to address.
In addition to defining the use case, teams will also want to identify any potential variables or uncontrollable factors that the algorithm will need to account for.
Step 2: Collect Data
There is no tried-and-true formula when it comes to the volume of data a team should collect before creating the interface’s algorithm. That being said, the information collected should be as representative of the complete user experience as possible. As a general rule of thumb, just remember the more data, the better.
Step 3: Create Algorithm
Once the interface’s use case has been defined and a representative data sample has been collected, a development team can begin to create and train their algorithm.
Step 4: Design Interfaces
The highest risk an organization can run is wasting a powerful algorithm on a poorly designed interface. The fewer time customers spend learning an interface, the better.
The faster users are able to “pick up” on the language of the interface, the sooner they’ll enjoy interacting with it and begin leveraging its recommendations. Consider the end user’s perspective throughout every stage, but especially as the interface is being designed.
Step 5: Test and Refine the Algorithm
Once the product is available, it’s important to continuously collect data and refine the user experience based on user activity as well as inactivity. Which recommendations are customers following? And — maybe even more important — which do they ignore? By incorporating these insights into the algorithm over time, teams can further refine their ML-driven interface to provide more relevant and useful suggestions to users.
Product timelines typically vary by use case — sometimes it may only take days, whereas others can expect development to last for years. Just remember: At the end of the day, the outcome of any ML-driven interface should always be conversion.
Best Practices and Tips for Building an Intuitive Interface
The process for creating an algorithm that supports a machine-learning-driven interface is relatively simple — the true challenge is making it user-friendly enough to drive value for customers.
As mentioned previously, the two biggest obstacles teams run into stem from this innate difficulty. First, defining the confidence level of their recommendations and second, making the interface understandable for real-life users.
With these concerns in mind, testing is essential. Developers will have to iterate continuously and throw out some drafts along the way while addressing and incorporating user feedback. Once data is being collected, teams will still need validations to continue to refine the experience for users. Someone will need to go back, input these results, update the user profile, and keep training the algorithm.
Generally, the overall approach is more relevant than the toolset used. Real-world user interactions should power an ML-driven interface as much as possible. One advantage B2C companies benefit from is the ability to test and train the algorithm used continuously once the interface is live. However, if a company operates within the B2B marketplace or its product needs to be fully operational at launch, this practice won’t be as applicable. Instead, it will be critical to leverage simulated test environments that mimic the real-world conditions the technology will be used within as closely as possible.
During the development process, the most important thing to keep in mind is maintaining a high degree of intuitiveness. The best ML-driven interface is one that allows users to pick up and understand how it works and what its suggestions mean to their real-time context as quickly as possible.
Looking Ahead to the Future of ML-Driven Interfaces
Looking toward the future of ML, there’s truly no limit for innovation.
As the technology and algorithms that run these systems continue to evolve, as will the opportunities for commercial application across every sector. Developing, implementing and continually improving upon ML-driven interfaces will prove essential in offering consumers personalized and user-friendly experiences.
As an experienced development partner, the team at Trascenda has the technical know-how and industry-spanning experience to help you get your ML-driven interface off the ground. If your team is interested in using ML as a way to further develop your user experience, learn how we can help.