How Machine Learning is Transforming Marketing
Machine learning is transforming several industries. From agriculture to banking, aviation to banking, machine learning is a technology that is showing no signs of stopping.
Businesses are also greatly benefiting from modern advances in technology. A significant number of companies have started gathering data regarding their business, but only a few of these companies are using the data available to them and using it to make their businesses better.
Giants like Amazon, Google, Facebook, etc., are using data and machine learning to come up with innovative solutions to the problems they face.
Here is how machine learning is transforming marketing:
Chatbots.
Chatbots have been around for some time now, but what has changed is their effectiveness.
Chatbots allowed visitors to a website to ask about a problem they were facing. The chatbot would give a response with respect to the question asked.
Previously, chatbots had a number of programmed responses that they could give, which didn’t help people out a lot.
With the aid of machine learning and natural language processing, chatbots have become more advanced.
The chatbots of today are better able to understand a customer’s intent and give appropriate responses. The level of accuracy of chatbots will only continue to increase as time goes by.
Chatbots are improved by collecting data, analyzing the data, and interpreting it correctly. As the amount of data available to chatbots increase, so will their accuracy.
Chatbots help marketers by engaging customers. The longer a visitor stays on a website, the higher the chance of him making a conversion and ultimately becoming a customer.
A benefit of using chatbots is that they are becoming more cost-effective with time. Businesses and marketers can use the data available to them and make
More and more people are using social media. As a marketer, you need to make sure that the brand you’re marketing for is adequately represented on social media.
By using chatbots on social media platforms, you’re facilitating customers to ask any questions or place an order, all the while scrolling through a platform of their choice.
Previously, if a visitor wanted to buy something, he would have been directed to another website, which resulted in a bad customer experience.
Another alternative for chatbots is to use a live chat app. This will help give you time to transition and set you up to use chatbots in tandem later on.
Discovering trends.
Modern marketers need to stay on top of each and every trend. You never know what the next big thing is going to be.
Thanks to social media, trends are changing so frequently that a top trend will be replaced the very next day.
Another thing of importance is that a lot of trends seem to be recurring. If something goes out of fashion, it’ll likely pop up after some time.
Machine learning is making it easier for marketers to discover and predict trends.
By analyzing tweets on Twitter using machine learning, marketers can identify what the next big trend is going to be.
By knowing what’s the new hot topic among your target audience, you’ll be in a better position to take advantage.
Ben & Jerry’s was used machine learning to identify what the public was interested in and came out with a new product line of breakfast ice creams that was made from cereal milk. This was back in 2017, and we can only assume that machine learning and artificial intelligence will increase with time.
Google Trends is another free tool that you can use to identify the latest trends in a particular region.
You can also use Google Trends to determine how your marketing campaign is doing concerning promoting your brand image and popularity.
Google Trends is also beneficial in identifying how well your competitors are doing. By having this insight, you’ll be better able to make decisions that will put you on top of your competition.
More personalized content.
The days of making generic advertisements are gone. People want to see ads that are relevant to them and their interests.
According to a study, 52% of customers will consider changing a brand if they don’t see enough effort for personalization.
Amazon is using data and machine learning to give you personalized recommendations. The Amazon recommendation engine is responsible for generating 35% of all revenue for the company.
According to an article by the Royal Statistical Society, companies like Amazon and Walmart have become so skilled at predicting results that they now need to dilute their results. This dilution in results is to make sure that customers aren’t threatened by the accurate results.
People want to have a personalized experience, but that doesn’t mean that they want to ignore the human element of things altogether. Maintaining a delicate balance between these things will put you in a better position for conversions and sales.
Marketing has fundamentally changed over the years. Blasting ads won’t do you any good if you ignore your audience. Keeping your audience first and foremost while designing your marketing efforts will make sure that you are able to retain them for a long time.
Better propensity models.
A propensity model correlates customer characteristics with predicted customer behaviors.
Propensity models are a thing of importance for marketers because marketers can use these models to estimate what behaviors they can expect from customers.
Modern marketers who follow a data-driven approach to decision-making can use propensity models to identify which products have the highest chance of being purchased by a potential customer.
Machine learning is helping to modernize the propensity model pipeline, making the whole process more efficient.
There are three significant types of propensity models that will help you in your marketing endeavors:
- Propensity to Buy. This model focuses on the likeness of a customer to make a purchase.
- Propensity to Churn. This model focuses on the customers that are at risk of leaving your brand for another.
- Propensity to Unsubscribe. This model focuses on the customers who have seen one too many of your advertisements and are deciding if they should try another brand.
As you can imagine, every marketer would need to use something like these models to evaluate a campaign’s performance.
Propensity models can significantly increase the revenue generated through a marketer’s efforts. These models can also aid in decreasing the costs associated with appealing to a particular type of customer.
Improved offers that grab people’s attention.
A person is more likely to click on an offer that has value to him.
Let’s consider an example.
You are a part of a biker community that loves to go on long rides on the weekend. Each member of the biker community even has matching kits and helmets. Riding a bike has become a part of your identity, and there is no good reason for you to stop riding your bike.
The biker community also has an active social media presence. You also love to share blog posts related to bikes and other relevant articles.
While using social media, you see two kinds of ads:
The first ad shows that there is a 70% discount on truck tires with free shipping.
The second ad tells you that there is a 15% discount on seat covers explicitly designed for the bike you have.
Which one do you think is the better ad?
By offering people what they would want, you increase your chances of making a conversion and ultimately a sale.
Machine learning helps marketers understand what a particular customer would be interested in.
Marketers can use their marketing budgets more effectively if they can better determine the type of customers that are most likely to click on a particular ad.
Significantly reduce customer churn.
Customer churn is something that marketers need to be particularly careful about when deploying campaigns.
Customer churn is the phenomenon that occurs when a loyal customer stops doing business with a particular company.
There can be many reasons for customer churn that include a poor customer experience, negative reviews on social media, lack of communication, low value, etc.
Marketers can use machine learning to curb customer churn.
By employing a model known as the intervention model, marketers can precisely identify at which point they need to intervene with a customer to make sure that he doesn’t go away.
Machine learning can also be used to predict risk. Marketers can use the data available to them and make decisions that protect them from any potential losses.
Conclusion:
Machine learning will continue to play an integral role in the overall business strategy for businesses. Companies that are reluctant to adopt machine learning into their business processes make it harder for themselves to stay competitive.
Marketing has transformed into science with recent technology and must be gotten down to it.
Proactive marketers have already been using machine learning in their marketing efforts to make their lives easier. Still, in the near future, all marketers will need to rely on machine learning to keep up with their goals and objectives.
Author Bio:
Hamzah Adil is a digital marketing executive currently working at SwiftChat- a live chat app designed to aid your customer support and sales teams. Find him on Twitter.
Website: https://www.swiftchat.io
