Effects of machine learning in marketing
In this article, we want to highlight some key data science use cases in marketing.
Artificial Intelligence and Machine Learning have changed the way businesses interact with customers. AI and ML in marketing help in analyzing and interpreting customer data leading to more significant conversions. Companies like Google, Amazon, and Microsoft Azure have launched Machine learning platforms on cloud that have made AI and ML prominent in recent years.
Some Application of Machine Learning in Marketing
1. Forecast Targeting and Decision Making
Forecasting is one of the most common use cases for machine learning. It allows you to predict your future revenue, what your costs will be, or even commodity prices. This helps you make better decisions about inventory, predict campaign responses, and more.
Example: In the retail industry, machine learning is being used for pricing. By understanding how demand fluctuates and which products are selling at what price, retailers can make better decisions about pricing their inventory. This helps them stay competitive, while also maximizing profits.
2. Better Customer Retention Activities
When a lead becomes a customer, the marketing activities should focus on retaining them by offering more specific services that keep them loyal to your brand. And AI does precisely this by understanding the customer touch points well and delivering the results.
3. Customer Segmentation
Customer segmentation is the process of dividing customers into groups based on shared characteristics so you can market to them more effectively. With machine learning, you can automate this process so it’s more accurate and efficient.
4. Predicts Customer Behavior
Every business has internal systems that collect customer data point that help them understand how the customer perceives their brand. AI helps in predicting the future actions of the customer based on the past data collected. It forecasts customer behavior and predicts if he/she will stay or leave. It even predicts how a customer reacts to new trends or events based on historical data.
5. Churn rate forecasting
In marketing, the concept of churn or outflow refers to customers who have left the company and the associated loss of revenue and is usually expressed in percentage or monetary terms. Churn rate forecasting allows you to respond to a customer’s intention to abandon your product or service before they actually do.
Segments can be uploaded to email or push notification services as well as to Google Ads, Facebook Ads, and other advertising systems. You can also pass this information to the retention department so they can personally reach out to customers with a high probability of leaving.
6. Chatbots
Chatbots can be a powerful customer service tool, yet they also have a role to play for marketers. A chatbot gathers information about customers from every interaction, and it learns what the best answers are for questions.
Marketers can periodically analyse data that chatbots have gathered. That information can tell them what customers and prospects want to know more about, which helps them hone their marketing materials.
Conclusion
Marketing that incorporates Artificial Intelligence is based on the use of the latest technologies for the benefit of consumers and improving the customer journey.
Artificial intelligence and machine learning are improving the performance and productivity of companies by automating processes. We will be able to use customer data to capture their attention and solve their needs, as well as increase your revenue and synchronize your marketing and sales teams.
Thank You…
References:
https://www.akkio.com/post/7-examples-of-machine-learning-for-marketing
https://www.proxzar.ai/blog/top-8-use-cases-for-machine-learning-ai-in-marketing/
