For many years, product targeting has been practiced in the online world. Customers are targeted with products based on their preferences and history. As technology advanced, so did product targeting.
From displaying the static product recommendations as a part of the shopping process to predictive analysis of every shopper action in real-time and then targeting them with a personalized product recommendation.
The main objective here is to push a customer further into your sales funnel, regardless of which sales funnel builder you use, and increase the conversion rate by recommending or showing products that a consumer is interested in buying. Although it can’t provide 100% conversion rates, real-time product targeting is proven to improve conversions compared to traditional product targeting.
One of the hot solutions for real-time product targeting is machine learning.
What is Machine Learning?
Machine learning is a product of AI that has up to 63% adoption level. This technology provides systems with the capacity to learn automatically and improve user experience without being programmed. It focuses on the development and advancement of programs that can access data and use it to learn on its own.
The process of learning starts with data or observations, such as direct experience, examples, or instructions to look for patterns in data and making better decisions in the future based on the example provided. Its main objective is to allow computer programs to automatically learn without any human assistance or intervention, and then adjust responses or actions accordingly.
In marketing and advertising, machine learning for eCommerce starts by accepting all historical shopper data and ingesting customer clicks in real-time. Over time, it will learn from the shoppers purchasing and browsing behavior and understanding what they might be looking for or the kinds of products that interest them. It can then fine-tune product recommendations by processing these data in real-time.
Machine learning powers numerous services that we use today— search engines like Google; recommendation systems like YouTube or Netflix; voice assistance technologies like Alexa and Siri; social media feeds like Twitter and Facebook; and so much more!
Types of Machine Learning— A Quick Review of 3 types
1. Supervised Learning
Supervised learning is when an algorithm learns from an example data as well as the linked target responses that may consist of string labels or numeric values, such as tags or classes. The data is then used to sooner predict the proper response when posed with a new set of examples.
In short, supervised learning is somewhat similar to a student-teacher relationship, human learning under supervision.
The teacher will provide good examples for the students to learn and the students then derive the general rules from the examples.
2. Unsupervised Learning
This is when an algorithm learns from an example without the associated response. The algorithm is left to establish data patterns on its own. This kind of algorithm usually restructures the data into a new series of uncorrelated vales or new features that may represent a class.
Unsupervised learning is useful in providing people with specific insights into the meaning of data and fresh helpful inputs to supervised learning algorithms.
Unsupervised learning mimics the methods and approaches that people use in order to figure out that particular actions or objects are from the same class, like examining the similarity between them. Some of the recommendation systems in the form of marketing automation you see today are through this kind of learning.
Its suggestions are derived from what you have brought in the past. It is based on what group of customers you belong to or resembles the most, then figuring out your possible preferences based on that group.
3. Reinforcement Learning
A reinforcement learning is presenting the algorithm with examples that don’t have enough labels just like unsupervised learning. You can, however, include an example with a negative or positive response according to the solution this algorithm proposes.
Reinforcement learning is associated with applications for which the algorithm makes decisions so that the product is descriptive and prescriptive. The decisions made will then have consequences. Such an example is a trial and error for humans.
Errors can help you learn since they have penalties such as pain, regret, loss of time, or cost. This teaches you that a specific course of action does have a lesser chance to succeed than the others.
One example application of this type is when computers learn to play online games on their own. This application then presents the algorithm with examples of situations. The application allows the algorithm to know the result of the actions done. Learning happens while trying to avoid what the system learns to be threatening and then survive.
How Machine Learning Impacts Businesses
Machine learning solutions have taken the business industry by storm. Emerging almost 2 decades ago, it has since evolved and morphed numerous times to meet the ever-increasing demands for increased speed, Google in-depth analysis, and easier-to-digest outputs.
Every new evolution gives birth to new use cases and the resulting business value booms. It is clear that machine learning really had a huge impact on businesses but in which ways?
The ability to offer customers more personalized offers and services can help in generating loyalty and boosting conversion and sales. Machine learning allows businesses to determine the patterns in their customers’ purchasing behaviors and create a more personalized and accurate offer for each customer.
Most customer communication needs to be done manually such as social media conversations, emails, or instant messaging. Thanks to machine learning, however, communication becomes much easier by automating these kinds of communication and making responses easier, quicker, and less time-consuming.
With machine learning, you can create a complex structured conversation program that learns over time. This means a more pleasant and satisfactory experience for your customers.
The marketing today is very fast-paced and has high requirements. And the numerous communication channels popping as well as the frequent interactions with customers have made it harder to obtain and understand the interests of each individual. And as we all know, a personalized experience is what marketing should be about to attract the attention of potential customers.
Companies today are using machine learning for automating their marketing campaigns— target new prospects, find new customers, and communicate with consumers. Before, these tasks were done manually. Thanks to machine learning, these tasks can be completed quickly and more efficiently. Human error is also eliminated.
With machine learning, it’s no longer people who set rules in establishing which product can be related to a particular type of person. The platform itself does this, collecting data as much as possible from the user. From there, it can analyze the user actions and define in self-learning the degree of affinity of a person with the products or services and adjust accordingly.
For instance, when an abandoned cart happens, machine learning will decide and implement on its own the most effective lead nurturing strategy in order to keep that customer.
These are just some of the seemingly endless ways in which machine learning can enhance the efficiency of business operations and deliver a more satisfying consumer experience.
Best Machine Learning Algorithms for Real-Time Product Targeting
1. Exponential Regression
Under supervised learning is the regression algorithms that can help in predicting continuous values. One key regression algorithm is linear regression. It is an approach that models the relationship between output and input features. The goal here is to predict the value of the output based on the input features, multiplying it with the ideal coefficients.
For real-time product targeting, exponential regression, a branch of linear regression can be used. It is used to model situations where growth starts slowly and then accelerates rapidly without bound or when decay starts rapidly and slows down to get closer to zero.
Some of the real-life examples of exponential growth learning algorithm are:
- Compound interest
- Human population
- Pandemics like Covid-19
- Smartphone uptake and sales
- Spoilage of food
Applications of Exponential Regression Machine Learning Algorithm
1. If you’re buying services or items such as stock prices, raw materials costs, and labor costs you can make use of a linear regression in predicting what the costs of such items are going to be in the forthcoming.
2. Another application of this algorithm is that it helps in assessing the risks involved in the financial or insurance domain. For instance, a health insurance firm can perform a linear regression analysis in terms of the number of claims for every client against age. Such an analysis will help insurance companies find out that older clients usually make more claims. Moreover, these results of analysis play a crucial role in necessary business decisions and are created to account for risks.
2. Decision Tree
A decision tree is amongst the most popular machine learning algorithms, thanks to its simplicity and intelligibility. It is a class of powerful machine learning models suitable for real-time product targeting because it is capable of achieving high accuracy in several tasks while also being highly interpretable.
What makes it special in the realm of machine learning algorithms is the clarity of information representation that it provides.
This algorithm is one of the best predictive modeling approaches for machine learning. The decision tree starts from observations of certain items that are represented as branches and then followed by conclusions about the target values of the items which are represented as leaves.
Applications of Decision Tree Algorithm
1. A famous baby product company, known as Gerber Products used this machine learning algorithm to decide if they should keep on utilizing the plastic poly vinyl chloride (PVC) in making their products.
2. When it comes to the management of customers’ relationships one of the most frequently used approaches is to investigate how people access online services. This kind of investigation is primarily performed by collecting and analyzing the people’s usage data and giving recommendations depending on the extracted information. In investigating the relationship between the preferences and needs of the customers as well as the success of online shopping one will need to apply decision trees.
3. Hierarchical clustering
The hierarchical clustering algorithm is a part of unsupervised learning. It is mainly used to group together unlabeled data points that have the same characteristics. This algorithm is based on the union between the 2 nearest clusters. The starting condition is realized by setting every data point as a cluster. Once a few iterations are achieved, the final clusters are reached.
Hierarchical clustering falls into 2 categories:
- Agglomerative Hierarchical Algorithm: Each data point is treated as a single cluster which merges or agglomerates (a bottom-up method) the pairs of clusters. Cluster hierarchy is represented as a tree structure or a dendrogram.
- Divisive Hierarchical Algorithm: All data points are treated as 1 big cluster. This process of clustering includes dividing (a top-down method) the cluster into various smaller clusters.
Applications of Hierarchical Machine Learning Algorithm
With this machine learning algorithm, you will be able to classify your product-level data to a retailer hierarchy. In fact, this will produce a clearer structure specified from the segment, subcategory, category, department, to product levels.
A hierarchical machine learning algorithm is helpful when making floor plans, product assortments, as well as shelf plans. It will also involve an understanding of the customers’ decision-making process through the production of customer decision trees.
Machine learning may be relatively new and still have a long way to go before it is perfected. However, it is safe to say that it already has a huge positive impact on businesses and the way products are marketed.
It can provide efficient and fast algorithms for real-time data processing with the primary goal of delivering highly-scalable, accurate, and predictive analysis of various kinds. Its applications are vast and are a must for every business that wants to respond to the growing tech needs of the modern business environment.