By Anna Anisin, founder at DataScience.Salon, overseeing community and business development.
Machine learning has been inducted into various domains for automation and insights. It has helped businesses grow by aiding decision-making based on data. Organizations create and deploy machine learning applications as Software-as-a-Service or use them for streamlining internal processes such as data entry operations, sales and marketing. This article will discuss how machine learning has evolved the marketing industry and how companies leverage it for profits and growth.
What Is Machine Learning?
Machine learning is a branch of artificial intelligence that deals with predictive modeling and analysis based on historical data. In simple terms, it uses complex mathematical algorithms to extract useful, encoded information from structured data to make predictions regarding trends and behaviors.
Machine Learning Applications In Marketing
Market leaders have been using data and machine learning to improve their sales for years. This is done by understanding the customer base and making data-driven decisions. Machine learning has several uses in marketing—let’s discuss some of them below.
• Customer Segmentation: Create divisions among customers based on their usage patterns, purchase histories, demographics or all of these. These divisions can be used to create personalized marketing campaigns for increased customer satisfaction and marketing success rate.
• Recommendation Systems: Machine learning-based recommendation systems are designed to keep your customers hooked on new products. These systems use models that contain information about the purchase history of each customer and know what would appeal to them. Such systems help boost sales and increase customer interaction.
• Customer Churn Prediction: Machine learning can help predict which of your customers might be on the verge of leaving. Marketing teams can use this information to create targeted strategies and campaigns for improving retention.
• Life-Time Value Forecasting: ML models can forecast the total benefit (in terms of revenue) a particular customer could bring to your business. Marketing departments use this information to plan marketing budgets. A higher budget is associated with a high-value client and vice versa to increase the efficiency of the campaigns.
Many known organizations opt for machine learning-based marketing strategies to reach success and growth. Some of the use cases include:
• JPMorgan Chase: JPMorgan uses machine learning to create written content such as marketing emails and advertisements targeted toward specific users.
• CBS Interactive: CBS uses machine learning and natural language processing techniques to analyze customer feedback. By automatically extracting knowledge from data, they use the insights gained to improve their products and services.
• Stitch Fix: Machine learning is embraced at Stitch Fix. In fact, they have one of the most advanced algorithm repositories in the retail and e-commerce industry. They use data science to understand what products are more popular at which locations, to match the best outfits to customers’ needs and to replenish and reorder their inventory in the most efficient way possible.
• Dstillery: Dstillery analyzes company data to understand potential customers and build customer-specific profiles. They offer prebuilt models as well as custom training on personalized data sets.
• Sephora: Sephora has adopted AI in the form of a chatbot. This bot helps customers narrow their makeup choices by asking them the correct questions and reviewing the answers.
Marketing with machine learning has proven to be highly productive for business owners, but it is not easy to get started. Diving into machine learning has some prerequisites that must be fulfilled.
Requirements For Using Machine Learning In Marketing
The first step in every machine learning journey is to explore the data. Data exploration and analysis provide a broader view of the database’s information and how it can be utilized. Depending on the quantity and structure of your data, you might need to hire data analysts and scientists for this purpose. Some may even employ experienced Ph.D. scholars and research scientists to lay out the road map for machine learning projects.
The next step is to gather and process data accordingly. Data processing requires ETL pipelines to be built with preprocessing and cleaning steps and finally dumped into an easily accessible destination. Data is generated every second for marketing use cases, so pipelines are required to ingest and process data in real time. This job involves data engineers with expertise in modern tools such as those for data lakes and data streaming.
Once data preparations are complete, machine learning engineers move toward creating complex models. AI models require high-end hardware (CPU, GPU, RAM, etc.) for training and deployment infrastructure for quick validation and CI/CD. Machine learning engineers need to have a diverse set of skills to successfully deliver a model to production.
Machine Learning Out-Of-The-Box
The above-mentioned procedure seems quite complex and expensive for most organizations. Luckily, there is no need to reinvent the wheel. Many cloud providers, such as AWS and Azure, offer machine learning model endpoints. These services encompass pretrained models that perform well on general data.
Additionally, these providers offer to retrain their models using a company’s data. This saves smaller organizations a lot of effort and funds as these services are relatively cheap and mostly ready to serve.
Some common, readily available marketing ML services include:
• Chatbots: Chatbots are available on an organization’s website and serve as customer support. Intelligent bots are able to deduce customer queries by asking a strategic set of questions. These are available as plug-and-play packages that fit right into the web page via an endpoint.
• Recommender Systems: Recommendation systems use clever statistics to profile customers and provide them with relevant recommendations. These may take some time to adjust to your customers’ data but can be easily built.
• Sentiment Analysis: Marketers use these systems to predict customer sentiments regarding advertisements and products. These analyses can be used to improve experience and customer retention.
Most of these services are trained for general use. However, slight tweaks may be required for enhanced performance. For example, if your organization deals with clothing, a chatbot would have to understand apparel-specific terms. These slight tweaks are nothing compared to building models from scratch and a ready-to-use ML service still stands out.