DEV Community: Arkadii

5 Machine Learning Use Cases That Transformed eCommerce

Arkadii — Thu, 29 Apr 2021 11:44:18 +0000

For the longest time, the term machine learning has resided in the domain of wild speculation. However, in the recent decade, it has made its way from incomprehensible scientific papers to real-world applications. Perhaps its biggest success story to date is e-commerce, where machine learning solutions were effectively used by large enterprises and small companies to create value for customers while at the same time spearheading growth and pushing the limits of innovation.

What is Machine Learning?

Machine learning, or ML for short, is a technology in the field of artificial intelligence that focuses on the systems capable of automatic self-improvement. In simplest terms, it works in the following way: the algorithm receives some inputs and a goal, after which it analyzes the available data to achieve the desired result. The technology is often referred to simply as artificial intelligence, although the latter is a broader concept that includes other types like deep learning.

Depending on the way the inputs are structured, machine learning falls under one of three categories:

Supervised learning: The data is selected and labeled, then expected results are outlined so that AI can compare its results with the intended ones.
Unsupervised learning: The inputs are unstructured, so the AI has to find patterns on its own.
Reinforcement learning: The AI interacts with a dynamic environment and receives positive feedback for accomplishing the desired goal.

As a rule, the more guidance is involved in machine learning, the more accurate the results are. Unguided ML, on the other hand, takes longer and requires more resources but can discover patterns that are more efficient than those suggested by “teachers.”

Influence on eCommerce Transformation

Initially, machine learning was viewed primarily as a tool for data analysis. As the technology progressed, and more resources became available for powering ML-based systems, it turned out that they can accomplish many tasks far quicker and with higher precision than humans. In many cases, such as image analysis and voice recognition, AI-driven solutions outperformed humans by a wide margin.

This opportunity was quickly recognized in the business sector, leading to the rapid adoption of the technology by companies. Digital-first services like e-commerce were a particularly suitable domain for this, where a clever idea brought to life by a mobile application development company from Singapore could topple an industry giant.

Currently, the overwhelming majority of organizations in the for-profit sector use machine learning in some form. According to one survey, 85% of employees agree that it makes their work simpler and more productive, and 75% believe that it plays a major role in the company’s digital transformation. As the technology matures, it is reasonable to expect the adoption rate to increase.

Benefits of Machine Learning for eCommerce

Commercially available ML-based solutions started as sophisticated and expensive tools available only for large enterprises. Eventually, as the technology became more accessible and eCommerce app development cost went down, smaller companies got the chance of experimenting with it, revealing a much broader range of advantages. Here are the most interesting ones:

Spot-On Marketing: Crafting a marketing campaign for a specific audience is a tricky process that needs to account for a multitude of factors. Many of these factors, such as individual values, views, and beliefs, are difficult to quantify — to a human, that is. A well-trained AI can spot patterns in consumer behavior that are buried deep under heaps of seemingly unrelated data.
Improved Performance: As mentioned above, ML-driven apps can optimize company operations. Not only does it drive performance up, but it also alleviates the load from employees who can then focus on more creative activities.
Insights and Predictions: The ability to process large volumes of information quickly opens up the possibility to provide insights on relevant operations in real-time. This adds flexibility to corporate decision-making and offers more accurate strategic predictions. ##Effective Use Cases of Machine Learning by Businesses The potential of machine learning in e-commerce is tremendous. It has already been used in the ways it was never intended for, and more creative approaches will probably be discovered in the oncoming years. Meanwhile, here are the most interesting use cases for machine learning to date.

Search

On the surface, the search couldn’t be simpler — you type a phrase and the algorithm matches it to the content on the website. So why use machine learning for it? Well, for starters, customers may not know the name of the product they need, so the AI can help them find it by analyzing the request. Taking it one step further, with machine learning it is possible to match the request with the available database and cater personalized recommendations to better match the customer expectations.

Personalization

Speaking of personalized recommendations, machine learning can make many aspects of service laser-focused on individual preferences. In fact, ML has become powerful enough to detect the intricacies of consumer behavior that people are not aware of, exceeding the expectations of visitors. In the age where a unique experience is key, this becomes a crucial advantage to build a loyal customer base on.

Service Excellence

The power of ML doesn’t end with figuring out people’s desires. Another way to enhance customer experience is through offering high-quality support that is available 24/7. Doing it the old-fashioned way — by hiring employees — is quite expensive, which is where machine learning comes in. AI-driven chatbots, for instance, are capable of basic assistance like directing the customer to a specific item in the shop, which not only reduces the waiting time but also optimizes the use of human resources.

Inventory Management

No matter how good the service is, it won’t get you far once you run out of goods. While modern inventory management tools do a good job of monitoring the supply, they cannot account for unpredictable shifts in demand. One way of making your supply chain management proactive is to implement machine learning that would track fluctuations in demand and forecast oncoming shortages.

Fraud Prevention

One of the scary properties of AI is its ability to generate imagery that is indistinguishable from real-life photographs. The fidelity of these images is so high that only AI can spot fakes generated by AI. This opens up interesting possibilities for detecting forged documents and fraudulent transactions that still plague the eCommerce industry. Not only that, the process is nearly instantaneous, so no customer experience is sacrificed to security.

Final Thoughts

The power of machine learning has captured the imagination of entrepreneurs for a long time. Now that it has become accessible and mature enough, we are seeing more and more of its creative applications that bring value to both businesses and customers. Time will tell how much of its potential still remains undiscovered, though it’s probably reasonable to say that what we see now is just the tip of the iceberg.

All You Need To Understand About App Scalability

Arkadii — Tue, 20 Oct 2020 08:59:39 +0000

Business growth is complicated by many factors, from the size of the company’s staff to the app’s usage capacity. In the digital-first market, the latter becomes especially important. It is not uncommon to see companies trying to address the issue by throwing more hardware at the task – often to no effect. In reality, making an app scalable takes both strategic vision and the right tools.

What Is App Scalability?

Simply put, scalability is the app’s ability to grow in scope. Depending on the app's type, it may involve the ability to remain responsive, handle uneven loads, or store large volumes of data. In the end, it can be boiled down to the balance between performance and resource availability. As long as adding new resources provides enough of an increase in performance, the app can be considered scalable. The problem is, the rate of demand often outpaces the benefits of resource addition.

Scalability is particularly important for digital businesses like m-commerce startups that are expected to grow in popularity at an increasing rate. Suppose you are a new Amazon, processing a modest $7 billion worth of transactions annually. Every three years, you need to double your app’s throughput, which, in the best-case scenario, involves buying more and more servers, building new infrastructure, and pouring money into custom mobile application development.

Let’s say you’re okay with constantly mounting expenses and write it into your business plan. Now imagine something happens that brings in an influx of customers. Not only will it stretch the budget thin, but it will also probably deal a blow to performance. Research shows that modern customers are not the most patient kind and will happily bounce off even at the slightest slowdown. So, scalability is essential for any kind of strategic perspective in the digital-first economy.

Common Barriers To Scalability

Making an app scalable involves a whole host of factors. For a simple application, it may be achieved by adding new hardware to increase throughput. More complex products like enterprise solutions would require a systemic approach integrated with the app’s strategy to address all possible problems, including:

Low computing power: The app is taking too long to perform the operation due to insufficient resources
Lack of integration: Different components of the app are having trouble communicating with each other
Inefficient code: The codebase is poorly organized and difficult to work with
Inefficient database handling: The app overloads the database with queries
Poor server configuration: The servers cannot put all requests through
Poorly organized testing process: The issues are difficult to spot and trace

As you can see, some of the problems can be solved by application developers simply by following the best practices in the field, while others require estimating the rate of growth and adding the scalability considerations to the deployment strategy as early as possible.

How To Develop a Scalable App

A successful commercial app is expected to tick these four boxes in order to be scalable:

Performance: Running smoothly no matter the load
Availability: Working at all times
Data integrity: Nothing is lost due to malfunction
Monitoring: Issues are identified and addressed

While there is no single way to meet all these requirements in every case, several recommendations can make the task easier.

Choose the Right Tools

As the industry of app development is catching up with the demand, more and more tools acquire the features that can alleviate the problem. Some programming languages are built with scalability in mind – Elixir, for instance, boasts its process organization that is scalable both vertically and horizontally. Other languages introduce elements that enable scalability, like Python’s Flask framework. The same can be said about databases – the development of NoSQL, for example, was partially driven by the need to circumvent the limitations of scale in relational databases.

Maintain Robustness

There is probably at least one element in any app’s architecture that, once removed, will render the entire service unusable. In technical terms, this is known as SPOF (single point of failure), and, in the case of the digital-first market, it can easily become a decisive blow to the business. The audience of digital products is extremely sensitive to subpar experience, so be sure to have a backup of everything through replication.

Utilize Caching

Once your app becomes popular, you’ll have to deal with uneven loads that can easily overwhelm your database with queries. One way to reduce the load is by caching the data. Of course, caching means additional expenses on memory, so the trick here is to cache only what is most often requested. Another trade-off is in that cached data is not recalculated each time, so apply it only where data freshness is not critical.

Wrapping Up

It is tempting to think that an app can be made scalable simply by throwing more computing power in the firebox to keep it running. In reality, this is like trying to cure obesity by loosening the belt. To truly address the issue, the principles of scalability have to be taken into account as early as possible and integrated with other aspects of app development.

Originally published at DZone