Should Machine Learning be in Your Marketing Strategy?

The digital age is seeing a paradigm shift in consumerism, where a great deal of sales are closed via eCommerce transactions. This means that marketing companies are in the midst of evolving data sciences and engagement strategies to fulfill the changing demands of the modern customer. 

At the heart of this major change lies the revolutionary technology of machine learning. Machine learning enables marketers to predict consumer trends based on big data, which is processed to create hyper personalized services. 

There are many reasons why machine learning is all the hype these days. For starters, the process grants marketing executives and agencies unprecedented information through user analytics, which improves customer experiences and support.

What is Machine Learning?

Machine learning is a form of data analysis that enables AI or software to interpret consumer trends and human behavior toward creating effective and accurate solutions. Machine learning involves applying mathematical formulations to big data that have been accrued by organizations in various industries. The process essentially condenses overwhelming data into bite-sized information and patterns that provides a 360-degree view of the customer.  

Preparing Quality Data

Machine learning utilizes valuable centralized data collected from customers, which serves as the foundation of analytical technology. From the gathered data, marketing companies can better understand customer trends and behaviors to cater personalized messages and recommendation engines based on customer segments, sales periods and other data projections.    

However, machine learning is only applicable if the data pool is accumulated from a reliable CRM system. Outdated data from sources such as excel data lakes and siloed data sets will only lead to inaccuracies within the metrics that will affect market predictions. Hence, the first major point for any marketing organization is to ensure that all collected data originates from an accurate sources like customer data platforms

The Rise of Chatbots

Chatbots have become a common sight on many business websites. These businesses may range from tuition centers, to furniture e-stores and online grocers. Chatbots are expected to eventually replace human-controlled virtual assistants. Chatbots are essentially programs that have the capacity to conduct a conversation in a natural language through auditory or text-based means. 

These user-based programs can improve online customer services as well as ongoing marketing strategies. 

According to statistics, about 85% of customer services will be handled by a chatbot by 2020. It has also been researched that the personality of a chatbot greatly affects the perception and experience of customers. 

Machine learning is one of the cardinal concepts behind the success of chatbots, since it interprets consumer trends and needs from chat logs – which in turn, refines the database of preset bot answers. The ultimate goal is for a seamless communication between online users and chatbots where key consumer needs are handled without the need for human intervention. 

This will improve the user experience for site visitors and provide quality consumer data for future marketing campaigns. Additionally, this helps marketing efforts by reducing costs by eliminating the need for human involvement through surveys and feedback channels.  

Railway operation company Amtrak and language-learning platform Duolingo, feature chatbots that have effectively supported marketing initiatives and customer relations. 

Providing Personalized Marketing

Modern consumers are looking for a personalized touch to their shopping experiences, and machine learning ranks high in that regard. 

Traditional marketing campaigns are known to be mired by hits-and-misses due to a poor understanding of consumers’ needs and inaccurate guesswork. Or what is commonly defined as marketing waste. Machine learning, on the other hand, leaves little to doubt.  

Machine learning is the reason behind incredibly specific recommendation lists that appear as consumers browse through an online store (since the built-in algorithm keeps a reliable record of preferred products, brands and shopping category among other information).  

Identifying the needs of a target crowd can be a herculean feat for most marketers. Machine learning can help avoid pitfalls by personalizing suggestions for each individual. Marketing companies been known to benefit from a marked rise in CTA engagements and site visits with the integration of machine learning tools. 

This technological advantage can substantially reduce marketing losses by guiding businesses through effective campaigns that resonate with the needs, trends and desires of consumers.  

Provides Additional Revenue

Effective recommendation lists can improve the number of recurring customers. Additionally, customers may consider adding items to the cart before checking out. This concept functions similarly to display shelves that are strategically positioned near the cashier at a retail outlet. Machine learning can help provide the appealing last minute additions for your online patrons.  

Improving Lead Scoring

Machine learning can also improve the accuracy of how marketing companies perceive and categorize their prospects. Traditionally, marketing agencies are known to rely on guesswork and analysis based on first impressions from call quality and query tickets. 

However, machine learning provides greater reliability, with specialized algorithms that tap on detailed information such as browsing history and social media scores. This provides a wider assessment of the prospect (and also determines if a query is a dud) through followed ads clicked sites, followed accounts, etc. 

Accurate lead scoring can help marketing companies focus on the strategies that matter and prioritize on effective lead generation processes for improved results.  

Better Prediction Models for Churn Data

Customer churn remains a nagging problem for marketing efforts. It represents the number of customers who have dropped out of a business within a financial period. In order to stay profitable, businesses must be prepared to replenish (and top) churn numbers through a steady flow of new customers.

Churn value is often difficult to assess due to the erratic nature of consumer behavior and many companies find themselves ill-prepared when faced with unexpected numbers.  

Machine learning makes it easier for marketing agencies to predict churn rates, through interpreting consumer data in online interactions, such as the frequency of logins and shopping activities. The process can help identify individuals who are at a high risk of becoming a part of the churn list. 

The analysis of data on a large scale allows marketing teams to take emergency measures in anticipation of the upcoming “churn storm” and reduce losses. 

Providing Sentiment Analysis

The algorithm of machine learning enables users to decode the mood of a customer through textual analysis. This can provide an estimation of customer satisfaction and inform marketers about the general perception of a product, service or marketing campaign.

Algorithms focus on three major aspects during the course of the analysis: the polarity, subject and opinion holder. 

The “polarity” refers to the nature of the feedback – between a negative or positive response. The “subject” relates to the topic or issue being raised during communication, while the “opinion holder” is the identity of the customer who submitted the case. 

Sentiment analysis allows marketers to understand more about their online reputation and plan creative ways to raise their position according to the information derived from analysed sentiments. This is a deep learning method that machine learning can bring to the table in revolutionizing marketing initiatives. 

 Predicting Customer Behavior

Machine learning allows marketers to predict seasonal trends, popular purchases and restock periods. Known for being one of the leading pioneers in predictive marketing, Amazon can achieve through machine learning.

Amazon interprets the purchase data of their products, which are aggregated and analyzed to create an accurate forecast of unique customer needs. The company first began its foray into machine learning through an effective delivery drone system and went on to create the Amazon Web System (AWS), which disseminated machine learning solutions to companies around the globe. 

Predictive marketing has enabled Amazon and other machine learning-supported businesses to thrive by optimizing inventories and recommending the most suitable deals for each customer. 

Paving the Way for Innovation

By understanding customers on a deeper level, marketing companies are provided with insights on their preferences as well as unfulfilled needs. Machine learning can be highly suggestive of ways to improve the business by developing new products and services. 

Machine learning will help by constantly revamping your business to fulfill the latest demands in your target crowd. The unrivaled speed of identifying and catering to the needs of customers can help businesses become innovative leaders in their respective fields.  

Enhancing A/B Tests

A/B tests are popular among businesses as a means of extrapolating the probability of a marketing campaign through statistical analysis. Achieved by comparing a control group (A) with a treatment group (B). Business owners and marketers apply the A/B method in testing the customer response rates toward landing pages, keywords and other digital marketing factors. 

However, these tests do not provide clarity on the drives behind a market due to the innate complexities of consumer behavior. 

Machine learning can greatly enhance the results of A/B tests by identifying the social factors and general direction or patterns (customer retention levels) within a case study, providing marketing teams with clearer and more actionable plans. 

Calculating Customer LTV (Lifetime Value)

Lifetime value predictions of customers are vital to any business. A well-integrated machine learning system will help in calculating the ROI of each customer according to purchase histories and general consumer behavior.

An accurate breakdown of LTV will calculate the value of incentives spent per customer and assesses the monetary returns that come with each investment. This will help businesses to make well-informed decisions that target the most profitable crowd before finalizing a marketing campaign.  

Effective Customer Segmentation

A well-established LTV can contribute to the segmentation process of your marketing efforts. The most important thing for a marketer is the calculation of the future impacts that a client will have on your business. After all, the main goal of a marketer is to achieve maximum revenue and profits while minimizing losses for the business.  

LTV calculates the aggregate value that a client has within a business though this also takes past spending into consideration, which may be a poor indicator of future purchases and customer loyalty. Machine learning will examine the entire customer profile – through interpreting past data and estimating future profits.

Effective customer segmentation enables marketers and business owners to optimize the use of their resources toward targeting the specific needs of each customer segment.   

Machine Learning, the Future of Marketing

Machine learning continues to supply marketers with vital information and insights toward optimizing their campaigns. Amazon, Google, and Netflix are just some of the big names that have enhanced their marketing campaigns through the applications of machine learning. 

According to Forbes, machine learning’s expected to rake in global value of 4.6 trillion by 2022. Machine learning continues to push the boundaries of various industries and alongside digital innovations such as the IoT and other forms of A.I., consumerism is entering a paradigm shift – and with it, the standards of marketing.     


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