The use of machine learning (ML) has significantly transformed the field of marketing. Previously, companies relied on general information to decide their marketing strategies. Now, ML assists them in analyzing a large amount of data to gain more insight into their consumers and therefore make better decisions.
Moreover, ML is beneficial in processing large volumes of data in a short time. It can recognize specific sequences and make a reasonable assumption about the potential future behavior. This assists companies in understanding aspects such as who to display ads to, what products a person may be interested in, and how to use their marketing dollars efficiently. Marketing experts at McKinsey & Company revealed that implementing ML into marketing can help companies increase their sales by 10-15%.
This article will discuss how ML can enhance marketing in the following five ways. It will describe how ML can assist businesses in learning more about their consumers, predict the effectiveness of a marketing campaign, and even generate some content. Through this, companies can increase their marketing efforts and make it more personalized thus enabling them to outdo their competitors.
Enhanced Customer Segmentation
Machine learning (ML) has redefined how companies categorize their customers. Unlike the traditional classification where data is grouped into categories like age or location, ML uses a lot of data to subdivide into more specific categories. It examines the products that consumers purchase, their online activities, and their statements on social networks. This enables businesses to have a better insight into their clients.
ML employs a technique known as clustering to categorize customers into different groups. These tools can identify trends that a human may not be able to see. They also can make assumptions about what a customer will do in the future. This means businesses can now predict what a customer is likely to buy next or how they will respond to a sale.
With the help of ML, businesses can design highly targeted experiences for their customers. Rather than copying the same email to all of them, you can send messages that are relevant to the recipient. For example, a clothing store may see that certain customers only purchase items during the sales. You can offer these customers special discount coupons. This kind of personalized approach helps businesses to make their customers happy and make them purchase more products.
How to Implement ML-Driven Customer Segmentation
Data Collection and Preparation: Begin with the compilation of all customer data from different sources such as CRM, social media, transactions, and web analytics. Arrange the collected data in such a way as to make it easier to analyze.
Choosing the Right Algorithms: Understand which clustering algorithms and predictive models are most appropriate for your industry and objectives. K-means clustering can be used as a first step, whereas for more intricate patterns, neural networks or deep learning algorithms can be employed.
Segment and Analyze: Apply the chosen models to create the customer segments. Check the segmentation to see if it is feasible and consistent with your business model.
Personalized Marketing Execution: Apply these segments to design and execute targeted campaigns across different media. Revise the strategy as often as possible depending on the behavior of customers and their feedback.
Spotify divides its customers into categories according to their listening habits, preferences for specific genres of music and moods to offer tailored playlists. Their ‘Discover Weekly playlist is a perfect example of how they use ML to offer highly targeted recommendations.
1. Predictive Analytics for Personalized Campaigns
Predictive analytics is a relatively new concept in marketing and has become one of the biggest trends. Instead of employing the same strategy for everyone, it allows the companies to try to understand what each customer might be interested in. 48% of businesses use Machine Learning and predictive analytics which help them make data-driven decisions.This is done by collecting a vast amount of data regarding what customers have been doing in the past and what they are doing in the present.
Now, let’s discuss the various ways through which businesses leverage predictive analytics. It applies mathematics to ascertain what attracts customers to buy certain products. It recommends products that a particular customer may be interested in based on the products that other similar customers have purchased. It can also predict the probability of a customer taking an action such as subscribing to a service or making a purchase.
This way, businesses can make their marketing much more personalized and as a result, you can reap the benefits of this approach. You can send special deals and offers that are most relevant to each customer, according to his or her preferences. This makes customers happy because they receive what they want and need. With the help of ML, you can also assist companies in cutting costs because they are not spending their time and resources on ineffective marketing strategies.
Research by Boston Consulting Group has indicated that firms that employ personalization strategies gain a lot of profits besides maintaining customer loyalty.
Steps to Start Using Predictive Analytics for Your Marketing Strategy
Data Collection and Integration: Gather information from different sources such as websites, social media, CRM, and transaction history. Data quality and data completeness are extremely important in developing better models.
Choosing the Right Models: Choose the predictive models that are relevant to your campaign goals. Regression analysis can help to determine which factors influence conversions most, whereas recommendations are useful for suggesting products or content.
Test and Optimize: Launch pilot campaigns to determine how accurate the model is and adjust your approaches. Also, constantly monitor the results, modify the parameters, and make the necessary changes to the model to improve the prediction.
Implement and Scale: After ensuring the model is accurate, expand the personalized campaign across various channels including email marketing, social media, and direct messages.
2. Optimizing Ad Spend with ML Algorithms
Adopting machine learning (ML) is making advertising better and more targeted. Instead of guessing where to place the ad budget, ML examines a large number of factors to determine the most effective strategy. It can easily determine which websites, social media, or search engines are providing the best returns for ads. This means that companies can invest more resources in the advertisements that are effective and less in those that are not.
Another strength of ML is that it is also effective in adjusting ad prices in real time. It tracks things such as the time, the device being used and what other companies are charging for their advertisements. This enables companies to negotiate for the best prices on their ads. It can also help to identify which segments of consumers are most likely to be receptive to a particular product. This means that the ads can be targeted to the right audience and this makes the ads more effective.
However, some other ML strategies enhance spending on ads. The first one is called reinforcement learning where the computer learns through trial and error. Another one is the multi-armed bandit problem that aims at finding an optimal solution between exploration and exploitation. These techniques assist businesses in getting more value for their ad money by enhancing how they use it.
3. Implementing Automated Ad Bidding Using ML
Choose the Right Ad Platforms: Begin with platforms that offer ML-based bidding, including Google Ads’ Smart Bidding and Facebook’s automated bidding options. These tools use ML to adjust bids according to conversion rates.
Integrate High-Quality Data: The efficiency of the ML algorithms depends on the quality of the data fed into the algorithms. Check that your tracking tools, such as Google Analytics or your CRM, gather reliable and detailed insights about every touchpoint.
Set Clear Objectives and KPIs: Identify your campaign objectives, which could be reducing CPA, enhancing ROAS, or boosting conversions, among others, and then put in place the right metrics for evaluating outcomes.
Test and Optimize Regularly: Even though ML can help in the automation of this process, there is a need to monitor and make changes periodically. Occasionally experiment with new strategies, creatives, and target audiences to fine-tune the model and improve outcomes.
Image source Google Blog
Kellogg’s adopted a computer system known as DoubleClick Digital Marketing to present advertisements to individuals who may likely be interested in its products. This system employed artificial intelligence (AI) to determine when and where these ads would be displayed. Before using AI, only 56 percent of the people were exposed to the ads of Kellogg’s. With AI, over 70 out of every 100 people viewed the ads.
4. Boosting Content Marketing through Natural Language Processing
Natural Language Processing (NLP) is a branch of artificial intelligence that can assist companies in producing quality content for their marketing. This is like having an intelligent personal assistant that knows how to process and interpret language. NLP helps companies to understand the preferences of their audience, enhance the visibility of their pages in the search results, and even generate some content.
One way NLP helps is through the analysis of what people are posting on social media. It can crawl through posts, comments, and reviews to determine what people are discussing and their sentiments toward the topics. This makes it easier for businesses to develop content that will be of interest to their target market. NLP is also useful for determining the best words to use in content to increase the chances of getting ranked on search engines. It can provide search terms that are connected to the queries that people are making.
Another good thing about NLP is that it can generate some texts on its own. AI still cannot fully replace human beings and their creativity, but NLP can assist in tasks such as writing descriptions for a product or answering frequently asked questions. This helps in saving time and frees up the human writers to work on other aspects of the content. Through NLP, organizations can generate more content that people would like to consume, and they can do so in less time and with greater accuracy.
How to Write Better Content Using NLP Tools
Sentiment Analysis for Audience Insights: Employ NLP APIs and tools such as MonkeyLearn or Lexalytics to gather audience sentiment from social media and reviews. Integrate the researched data into your content plan and aim for topics that have positive connotations for your target audience.
Keyword Optimization with NLP: Tools like MarketMuse or Clearscope use NLP to suggest keywords and phrases that are relevant to the content and the competition. These tools offer a set of primary keywords as well as related terms to ensure that the content created is comprehensive and optimized for search engines.
Content Automation for Scale: Tools like Jasper or Writesonic use NLP to create content outlines based on your parameters. These tools help create draft or base content that needs to be edited and tailored further.
Image source Coca-Cola Twitter
Coca-Cola employs NLP-based sentiment analysis tools to assess brand sentiments in real-time. As a result of this analysis, they adjust their content strategy to ensure that they can change direction as soon as the audience’s sentiment changes. This has led to increased customer interaction and more loyalty to the brand.
5. Customer Journey Optimization with ML
ML is already being used by organizations to map and enhance the entire customer journey. Instead of relying on stale data or assumptions about customer needs and behavior, ML can uncover what customers are doing in real-time and across all channels. This provides much more detailed insights into how customers progress from the awareness stage to the purchase stage and even the post-purchase stage.
Another key strength of ML is the ability to forecast customer behavior. Through customer behavior and other data, ML can make assumptions such as the likelihood of a customer making a purchase or the probability of the customer ceasing to be a customer. This enables the companies to communicate with the customers precisely at the right time to provide assistance or special deals.
ML is also quite useful in identifying issues in the customer journey. It can identify where consumers have difficulties or issues, such as if a website layout is uninterpretable or if there are too many steps in the checkout process. Thus, businesses can identify these problems and solve them so that their customers do not encounter difficulties. This results in more people purchasing products and services and being satisfied with the company in general.
Integrating ML Tools for Customer Journey Analytics
Data Integration and Collection: First, collect all data from all customer interactions and place it into one system. Tools like Salesforce’s Einstein Analytics or Adobe Experience Cloud offer end-to-end solutions for capturing, processing, and displaying customer journey data.
Use Predictive Analytics to Anticipate Behavior: Use predictive analysis to pinpoint the events that need attention in the journey. For instance, Google Analytics 360 and IBM Watson have predictive analytics that can predict the actions of customers and recommend ways to improve the journey.
Map Dynamic Journeys: Use ML journey mapping tools such as Pointillist or Kitewheel to map and analyze the customer’s journey in real time. These tools enable you to track the flow of customers across various touch points and can also assist in the identification of areas for improvement.
Image source Tredence
Amazon has a well-orchestrated customer journey where every touch point is recorded and analyzed with the help of ML algorithms. From product recommendations to the different pricing options, Amazon adjusts the journey as per the customer’s need resulting in higher customer retention and repeat purchases.
Is Machine Learning the Future of Marketing?
Marketing has been greatly enhanced by machine learning (ML). It assists in helping enterprises learn more about their consumers and develop advertisements that people would like to view. Unlike humans, ML can process a vast amount of data and make reasonable assumptions regarding the most effective action. This means that companies can now use their money more effectively on marketing and can offer their customers a much better experience. Machine Learning is especially useful for changing strategies frequently based on current customer behavior, which is much better than using stale data or making assumptions.
With the advancement in marketing, it is high time that businesses started to incorporate ML. Businesses that have integrated ML in their marketing strategy are realizing improvements such as increased revenue and satisfied customers. Machine learning helps companies to give the right message to the right customer at the right time even if the number of customers is large. It assists companies in expanding and sustaining their operations in a world where strategic application of data is crucial.
