How Machine Learning enhances Personalization at scale?
If you want to learn about the miracles of the technological marvel of the 21st century, then this article is no less than a treat for you.
In today’s era, digital marketing may sound like a messy environment filled with constant change and complex systems. With the buzzword like Machine Learning (ML) that has penetrated into various aspects of our everyday life, human inputs are reduced to minimal. In other words, more freedom falls in the lap of a machine wherein the machine acts on its own by developing the required knowledge to carry out a given task.
Machine Learning holds the capability to create opportunities from data and therefore has emerged as one of the hottest fields of the industrial revolution. Such a burning need for machine learning solutions has led to high demand for it and this is probably one of the reasons why marketers have already started to change the way they manage their campaigns.
Well no need to worry, we are here to provide clarification with a deep dive into machine learning and how it can improve the world of personalization. However, before we move forward, let's take a step back and understand what personalization does and why its demand has been growing this decade.
Personalization: An Overview
Personalization is a red hot in the digital world that helps companies to target the market on a global scale with a view to carrying out the customer’s benefit spread across the world. The heart of personalization lies in goal attainment which is obviously customer-driven. The role of personalization is not limited to delivering what your business wants rather it focuses on allowing every visitor to consume the experience how they prefer and helping them achieve their goals from moment to moment.
However, the path to personalization is not a bed full of roses. That is to say, despite the fact that personalization is an increasingly hot topic, most businesses are still facing certain issues in the early stages of the personalization. One of the major issues that businesses face is that there is no single tactic that defines personalization. Moreover each class of technology comes with their respective limitations. The picture given below lists out some commonly used personalization technologies along with their limitations.
The only way to move forward is to realize the promise of personalization for which the setup needs to be changed. In other words, personalization is required to be built into the core of the experience and impact every point of interaction. For say, site search, browsing data, landing pages, product recommendations and other interaction points should work together as a unit in order to build a complete picture of each visitor across their journey. Nowadays, most of the personalization efforts are done around the fringes of the website, with one tool running product grids, another running A/B tests on banners, and another targeting content by location. When added to the experience, these solutions don’t allow data to talk and personalization is fragmented and doomed to stay on the fringes. This in turn allows you to gather data from multiple touch points and combine it in one central location.
After the process of centralizing, the data allows personalization to go beyond the fringes to deliver far more holistic experience. Further, data obtained from visitor search, page navigation, product preference, multi-channel usage, and external data from CRM systems can combine with larger picture data on product performance and user personas to gain deeper insights into how your customers prefer to interact with your brand experience.
One of the best examples of personalization can be taken from the two giants, namely Netflix and Amazon who strive hard to evolve. Since both deal with different areas, their personalization mechanism works differently to work their charm.
There is no one that understands the technique of A/B testing better than Netflix. This A/B testing helps understand the psychology of the customers which helps to personalize the user experience in the most effective way possible. The big giant also invests much in AI and machine learning to power up its recommendation engines.
On the other hand, Amazon uses the product recommendation analogy that has the prowess to skyrocket sales. Another thing that helps Amazon to rule the market is its unique ability to indulge with each of its customers on a personal level. Meaning, every time a customer lands on Amazon, they are greeted with a homepage that seems especially designed for them.
In order to become a market leader like Amazon and Netflix you have to work seriously on ramping up your personalization efforts. But it has to be done at scale (on the global market), manual marketing technology stack does not suffice. In order to deliver highly personalized experiences, you must require automation and that is where machine learning comes into the picture. Machine-learning-powered personalization helps companies to provide a scalable way that helps them to personalize any and all aspects of their website. Machine-learning algorithms work on the principle that automatically identifies the relationships between data and learn the best experiences to show based on that data.
Machine Learning offers a set of different techniques that can help in the personalization of the business services for the end user. There is no secret that user experience and conversion are the prime goals of a business. Therefore, being a marketer, you are required to keep yourself abreast of the following algorithms that will help you in achieving personalization and eventually staying ahead of the competitors present in the market.
Clustering algorithms fall under the family of unsupervised ML algorithms, which is used to analyze unlabeled data, segregate it into groups with similar traits, and assign into clusters. However, it is important to note here that the task is subjective which means you can use different algorithms to solve it, amongst which k-means algorithm is the most popular one. The algorithm starts with estimating the centroids for clusters, the number (k) of which you define in advance. After that, the second step mainly includes assigning data sets to the nearest centroid — based on the Euclidean distance. The centroids for all clusters are then calculated.
This ML method is highly useful in the areas where document classification (based on tags, topics, etc.), customer segmentation (based on their purchasing history, app behavior, etc.) is needed. Moreover, this can be applied for recommendation engine development, social media analysis, anomaly detection, and more.
Regression is a supervised ML method which is well capable of defining relationships between a dependent (target) and independent (predictor) variable. Mainly, this modelling technique can be used to fulfil the following purposes:
discover the core strengths of predictors over dependent variables
predict the end results when independent variables change
forecast future trends, for say, the price of Facebook’s shares or bitcoin’s value in a year
Some of the common forms of regression are:
This ML method is utilized to analyze various data points to define which variables are most significant predictors and to plot a trend line (disease epidemics, stock prices, etc.). Linear regression can be single or multiple depending upon the number of variables.
Logistic regression predicts data value based on prior observations of data sets. This method can be used to analyze historical data on shopping behavior for tailoring more personalized offerings.
Another yet important ML method that is capable of holding the future in its hands is association rules. This popular technique is appropriate to uncover interesting relationships that exist between different variables in huge data bases. Moreover, this method is actively harnessed to build recommendation engines, like those of Amazon or Netflix. Simply put, this method is solely responsible to rigorously analyze the items bought by different users (transactions) and further define how they are related one to another.
The algorithms uses various metrics to understand the strength of associations among these transactions, such as:
Support assists to make a choice from the most important and interesting item sets for further analysis.
Confidence informs how likely an effect is when the antecedent has occurred.
Lift manages the consequent frequency, keeping negative dependence or a substitution effect at bay.
Lastly, Markov chains are widely used to statistically demonstrate random processes. Rather than depending on the historical information, this method is commonly used to describe a possible sequence of events (transitions) based on the process present state.
Let’s assume that there are two possible states, namely A and B. According to Markov chains, there are four potential transactions comprising different probabilities of transitioning from one state to any other. This is an implication that the more current states you have, the more sequences of events are possible. Furthermore, it is suggested to build a ‘’transition matrix’’ to tally all transition probabilities
Since Markov chains make use of just real-time data, this method is not one-size-fits-all. For example, PageRank- Google’s algorithm that is used to determine the order of search results.
Improving web personalization With Machine Learning
Machine learning has come to end to futuristic hype and has finally become ever more commonplace in the technology world. Machine learning for personalization can sound even overwhelming when you set up your own custom strategy – or ‘’recipes” that can be easy with the right solution. There are four main components that add to each recipe-
Base algorithms set out the foundation for delivering recommendations and individualized experiences. When you choose to work with base algorithms, you navigate the system on where to begin when selecting items to recommend or experiences to show. Base algorithms are generally divided into two, basic or advanced.
Basic algorithms work on the principle of specific criteria or the “wisdom of the crowd.” For instance, “trending,” “recently published” and “co-browse” are popular basic algorithms that help you display products or content popular on the site or new on the site, or based on what others have also viewed.
On the contrary, advanced algorithms are more sophisticated in nature. They continue to learn as visitors engage with the site and produce personalized experiences without necessarily adding boosters. One popular advanced algorithm is collaborative filtering which is primarily used by Netflix.
Once you get done with choosing the one or more base algorithms, you can customize them with the help of filters. Filters empower the visitors to exercise control over the categories, brands, price ranges, locations and other attributes that are shown in the recommendations. In other words, filters provide you the access to add human guidance to your machine learning-driven experiences.
After that, boosting allows you to absorb as well as prioritize the specific preferences including brand, category, price range, content type, keyword, etc., of each individual on your site. Further, these preferences can be easily tracked down with deep behavioral tracking on each individual, which not only includes what a visitor clicks on, but also tracks the mouse movement, scrolling, inactivity and time spent per page. Doing so gives a clear indication of preference and level of interest of the visitors. Boosting can help you provide individualized experiences even with the basic algorithms.
Finally, you can also decide to include certain variations in your own custom strategy (aka recipe). These variations are capable of taking several forms. For instance, you can set your homepage recommendations based on random arrangement (while still being relevant to each individual) so they stay fresh. Just like all the other elements of a machine-learning algorithm, the power to include or exclude variation is vested in the hands of the visitors depending on what you think will be most effective for the needs of your particular site visitors.
Besides these aforementioned components, an array of companies are currently capitalizing on ML to quickly adapt to tectonic shifts in clients’ expectations and craft more personalized offerings. The reason being, machine learning-based personalization provides a more scalable and accurate way to achieve unique experiences for individual users. Therefore, for your consideration, we have listed down a few major improvements that can be done when market leaders incorporate ML technologies:
Strengthen customer team support: Customers are the king of a website. The whole concept to build a website revolves around the fact that the customer feels delighted every single time they come on your website. To strengthen customer support experience, setting up an AI chatbot on your website can help you provide your users much better assistance. Furthermore, implementing a chatbot helps you build natural communication with the user. Some of the common examples of chatbot are Capital one’s eno, MedWhat’s virtual medical assistant, and dominos’ dom.
Maximize user experience (UX): A website built with machine learning features helps you understand your customer’s preferences. ML features help you keep track of your customer search history and even location. This way you are able to design websites as per your customer requirements. Moreover, you can also give them a better customer experience by updating your UI accordingly. For example- Amazon,com uses AI and ML features for its product recommendations.
Fast Access to information: In today’s contemporary era , voice-based search functions are becoming more popular than ever. As a result, business owners are looking to add apps like Google Assistant, Siri, and Cortana in order to support voice-enabled search functions that can surely give users fast access to information. For example, if you run an online clothing website, then you may want to use voiced-based search systems that can help your customers find the clothing types they want with complete product information, including the price of the item, fabric type, size, color, and length.
Streamline Market Strategy: Marketing teams have been engaged in machine learning technology for making major marketing decisions and market predictions based on the current demand in the marketplace. The ML technology analyzes a user’s behavior by finding out what type of products a user likes and other preferences. Further, this data helps marketing teams to decide what changes should be done in order to increase sales and improve conversion rate. And for this reason, the potential of machine learning in web personalization has not escaped the attention of top companies such as Google, Facebook, IBM, and Microsoft Launch.
However, the bottom line comes down to how effectively business leaders implement machine learning technology into their web development processes.
To conclude, the advent of machine learning technology has totally changed the dynamics of business. In this contemporary world, companies have become more aware than ever as to who their prospective customers could be and how to market products and services with the help of personalization, thereby making it more captivating and interesting for the user. Moreover, machine learning algorithms are highly used to process and analyze data in order to obtain insights into the visitor’s behavior pattern and their past purchase history.
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Shalini is a former Content Associate at OpenSense Labs. She has a background in mass communication and shows great enthusiasm for writing. With zeal towards technological research, she dedicates her powerful words to communicate complex information more easily. Besides this, she has a great interest in fashion design and loves watching TV series.