It’s no secret that advancements in technology are constantly changing our world. As a marketer, you need to have a good understanding of the changing technology trends.
Let’s look at the stats here – Around 84% of marketing organizations implementing and expanded in AI and machine learning in the year 2018. It is seen that 75% of enterprises using AI and machine learning enhance customer satisfaction by more than 10%. According to a survey done by Capgemini, three in four organizations implementing AI and machine learning increase sales of new products and services by more than 10%.
Some of you might be thinking you’re not currently working on AI or Machine Learning, so the information isn’t relevant to you. That couldn’t be farther from the truth.
But the reality is that your competitors might already be using this technology to help improve their marketing campaigns and increase revenue by optimizing the customer experience. And if you don’t have your finger on the pulse, you could fall behind your competition. Even if you’re not ready to implement machine learning today, you should be prepared to do it in the future.
To build my case, I’ll explain the top 5 ways machine learning is reshaping marketing world –
1. Improved lead scoring accuracy
Lead scoring helps in ranking prospective customers on a scale in order to represent their value to your company. Improving on your lead scoring accuracy helps you prioritize your lead generation strategies.
Marketing professionals do not have the highest confidence in their lead scoring methods but as they embrace machine learning, their confidence will definitely increase. This is because many factors go into these calculations, and machine learning can be your solution to them.
Marketers use machine learning mainly to monitor customer behaviour and write algorithms to track:
2. Content Optimization
A significant way that many marketers practice this is through A/B testing.
A/B tests allow marketing departments to try out various options for content like email subject lines, Facebook ad graphics, or an article headline and garner the results to determine which connects best with the audience.
These methods of implementing machine learning in marketing are valuable for marketing campaigns. Companies can use the feedback to provide more targeted content, ultimately collaborating with machines in order to optimize.
3. Chatbots for Engaging Customers
An increasingly common sight on many modern websites is the friendly chatbot that pops up in the bottom corner of the screen to say hello and offer assistance to the customer.
Machine learning is fundamental to the success of chatbots, as it allows the chatbots to continuously learn from its interaction with customers, collecting data and interpreting it to provide more accurate answers.
4. Profitable dynamic pricing models
A dynamic pricing strategy allows a business to offer flexible prices for the services they offer.
This strategy helps you to segment prices based on customer choices.
Dynamic pricing is also related to real-time pricing, that is when the value of a product is based on market conditions.
For instance, purchasing an airline ticket. The price of the ticket depends on how far in advance you book it.
Machine learning makes it easier for companies to implement and improve their dynamic pricing models. Thereby, helping you generate more profits by focusing on your pricing strategy.
5. Improve Personalization
People want brands to care about them and they are likely to switch if they don’t feel a company is making enough effort to personalize their messaging.
Amazon’s success with e-commerce personalization is built upon machine learning. They harvest the huge reams of data on their customer’s online behaviours, interests, and past purchases to improve the online shopping experience for the user. Everything from the emails to the product offers is personalized.
E-commerce personalization makes customers feel special with the experience carefully crafted to cater to their needs and interests which helps breed loyalty.
Research indicates that 44% of customers return to make future purchases after having a personalized shopping experience.