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  • Writer's pictureCraig Risi

Top Use Cases for Machine Learning in Web Applications

This article first appeared in Snapt.

So, you have a web application, and you are considering exploring some machine learning (ML) solutions to see where they can be of value for your business. Well, the industry has already seen a lot of development and research in this space, so there are already many successful use cases that we can look at to see how ML might benefit your particular application.

Needless to say, each business has different purposes and goals for its web applications, so when looking at these top use cases, it’s important that you analyze the core needs of your own system and then see whether any of these use cases apply to you. Just because an ML model has proven to be successful elsewhere or could even add value to your site doesn’t mean that it is vital to your core business to justify an investment in it—at least not until alternative solutions have proven unsuccessful. Learn more about when to use machine learning, and when not to.

Bot Protection

We design our web applications around human users. Yet, globally, bots contribute to approximately 25% of overall web traffic. Most of these bots are malicious and can cause cybersecurity attacks, such as data breaches, malware attacks, or other threats. Bots can also take control of application users and perform malicious activities. And with bots becoming more intelligent and behaving more like human users, it is no longer possible to use traditional security techniques to deal with them.

Machine learning algorithms can be effective in fighting against bots. Many social media sites are already using them to combat misinformation and abuse. Financial institutions are also increasingly relying on ML to identify bots looking for gaps and vulnerabilities in their security or looking to take advantage of unsuspecting users. Bot detection using ML technologies utilizes numerous factors, such as temporal patterns, message variability, and response rate, to identify bot behavior. The benefit that ML brings to this sphere is that as bots evolve, so do the ML models in detecting unusual behavior—so even if it’s not guaranteed, using ML for bot detection is likely to provide you with the best overall protection.

User Experience Optimization

Perhaps the most important thing for product owners and developers is to understand their application's users and to meet their needs. Machine learning techniques are effective in identifying customer journeys and then helping teams design their sites to optimize the overall user experience.

One objective is to optimize the customer acquisition cost at specific conversion points. The typical UX approach is to consider customer objectives, such as purchase patterns, pricing, and competitor comparisons, and map these objectives to customer touchpoints. This approach, however, does not always lead to significant revenue generation due to the absence of data insights, which is where ML can play such a valuable role in providing this feedback to companies.

Machine learning can be used in multiple ways. The first is simply to collect data logged from user movements on a site and identify patterns in their behavior to suggest an improvement.

Besides being used in pure web application design, ML can also be fine-tuned to focus on specific data points and user behavior across all applications to understand the different paths taken and decisions made by customers, which has greater benefits to the entire organization. Machine learning algorithms determine all customer paths and provide a score for each of them.

This approach can also consider the customer acquisition costs and customer lifetime value as factors, all of which are valuable to marketers and UX designers.

Reinforcement ML is another useful technique to predict/forecast the next touchpoint to enhance the possibility of a specific outcome. Machine learning algorithms can determine real-time points of interest in the customer journey to develop realistic, data-driven recommendations. This can significantly bring down the costs and also work toward customer retention as you cater more to consumers’ individual needs.

Content Recommendation

With the increased focus on the user experience, it makes sense that we need to ensure that all different user needs are catered to equally. Content development and curation can be an extremely time-consuming process. They are, however, essential components of the current digital marketing era.

Machine learning curation tools make these tasks easier for teams. Machine learning tools extract information and content from web sources, such as buying and search patterns (those that are available), blogs, social media platforms, etc., and customize the content according to the customer’s likings and preferences. Deep learning, natural language processing, and clustering are some of the ML techniques in use. These ML tools effectively annotate and organize the content for better customer engagement.

This applies to the marketing information that is communicated to clients: ML models can select only the information that is most relevant to them and allows the information they see directly on the site to be more suited to them.

Dynamic Pricing

Machine learning techniques can be effective in dynamic pricing. Supervised ML models can learn new patterns as per the input data. These algorithms can then continuously update the results according to trends.

Online retailers use ML algorithms and techniques to dynamically price a product or service. These techniques make it possible to map the best prices for each product and alter them across different regions.

The idea behind the process is to enhance sales while optimizing the inventory and catering to different customer needs by offering appropriate discounts based on their personal interests, which increases the likelihood of customers making a purchase on the site.

Dynamic pricing is something that you see many online retailers use, with each unique user getting different deals and specials—something that the likes of Amazon have used to great effect.

Fraud Detection for Secure Transactions

A significant amount of money is lost annually in fraudulent transactions. If your application deals with financial transactions and payments in any way, then you need to identify fraudulent transactions and hopefully prevent them on your system. This is even more true when you consider the financial impact that fraud can have on a company, as it now needs to deal with the loss of income due to fraudulent activity.

So, if you process a lot of money through online transactions, it’s definitely worthwhile investing in some form of ML modeling that can help you analyze the different transaction data that is gathered on your site and enable you to identify potentially fraudulent ones. Furthermore, following up on fraudulent transactions may also uncover larger issues that could lead to substantial financial losses if left unchecked.

Customer Communication

Using ML is not just about trying to optimize the individual customer journey and experience but also about improving how you communicate directly to customers. Machine learning can assist in the enhancement of customer communication in several ways.

The first is through chatbots using ML and AI technologies. Chatbots allow applications to communicate with customers 24/7 even when human support personnel are not available. This helps engage customers and hopefully address their questions and needs immediately (though I’m sure we can all agree that chatbots still have a long way to go before replicating human interaction).

Customer communication can also be tracked through the customer journey across chatbot interactions, search history, and email communication. This enables companies to better understand exactly what customers are looking for and to provide better responses while also using ML to follow up on clients to ensure that their needs have been addressed.

Additionally, ML allows for tailored marketing messages that ensure that customers are sent information and recommendations that are useful to them. This prevents users from feeling like they are endlessly spammed by a site and instead makes them feel that their needs are actually catered to. This also leads to increased engagement and potential purchases through the marketing material.

Email Monitoring

While emails might not form part of our web applications directly, chances are that customers are reaching out to you as an organization through emails. If you have a large user base, it can be incredibly difficult to keep up-to-date with all the incoming communication and be able to identify which emails are relevant or require immediate attention and which are perhaps spam or don’t present a level of urgency.

Emails are also the most vulnerable channel for malware or other cybersecurity attacks. Phishing emails, for instance, extract sensitive information by tricking end-users. Machine learning techniques can be instrumental in the detection of cybersecurity attacks by conducting real-time email monitoring. Natural language processing is one of the technologies used to analyze email content and determine the possibility of phishing attempts. Anomaly detection using ML technologies is also effective in performing email monitoring.

Demand Forecasting and Stocking

It is important for e-commerce and other online stores to maintain a balance between demand and inventory. Procurement of products higher than the market demand can result in huge losses if the products expire or are damaged over time. Similarly, running out of stock at a time of demand can have a negative impact on the customer experience and brand reputation.

Machine learning techniques can be effective in demand forecasting and stocking. Regression and time series techniques can help predict the expected sales for a specific time frame. AutoRegressive Integrated Moving Average (ARIMA) and exponential smoothing are two effective time series models used extensively in retail. The former can make accurate predictions when making short-term forecasts, while the latter combines components such as error, trend, and seasonality for precise forecasts.

Strategic Recommendations

Besides curating content for customers, ML can also be used at the corporate level to better understand customer behavior and provide companies with strategic recommendations to improve their own businesses and product offerings.

Recommendation engines are generally run by independent companies in conjunction with big firms; using ML, data science, and AI technologies can provide retail firms with a competitive edge. Machine learning tools can simultaneously analyze the online activities of millions of customers in real-time to provide product/service/price recommendations, which companies can then use to provide a better service to clients.

These engines leverage ML and deep learning techniques on the large volumes of data collected, can quickly determine marketing trends across the industry, and help provide organizations tailor their customer experience to the broader market.

Many of these engines—which are currently largely utilized by e-commerce giants—are also able to track user behavior across all the different sites, thereby allowing businesses to cross-sell to clients based on their behavior elsewhere and providing sellers with better opportunities to cater to a user’s demands even when they are new to the site. We see this on many e-commerce sites that conveniently apply a special offer to products that a person was searching for elsewhere.


Obviously, many of these ML techniques are only useful because of the data afforded to them to analyze and make certain decisions—data that many customers are likely not even aware is being gathered. Over time, as we become more aware and protective of our personal online data, we will see companies needing to be far more sensitive and proactive toward what they do with user data. However, this will also impact how they can leverage ML techniques or certain benefits.

Machine learning is making a massive impact on online applications—especially in the retail space—but it’s clear that how data is collected and used needs to also be taken more seriously.

Therefore, we are likely to see a lot of fluctuation in this space and in how companies use data and ML to find better ways of improving customer experiences and marketability while still being sensitive to ethics and consumers’ data concerns. And the companies that can learn to leverage that balance the best may be the ones that will make the biggest impact in the online space in the future.


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