How Machine Learning Improves Your Digital Marketing Efforts
There is no doubt that Artificial Intelligence has changed the digital marketing landscape. Machine learning, in particular, has done a great deal to change the way that audiences and brands interact. Each year, technology continues to adapt and change, paving the way for highly personalised digital campaigns that target customers on a deeper level than ever before.
As a digital marketing tool, machine learning has the ability to fine-tune campaigns, making it easier to see results in the process. In this guide, we take a look at some of the biggest advantages offered by machine learning.
How Machine Learning Empowers Digital Marketing
There are many ways that machine learning adds value to your digital marketing campaigns. For starters, here are some of the biggest advantages.
Better lead scoring accuracy.
The entire process of lead scoring uses machine learning. When you can rank potential customers using a scale that assigns value for various actions and behaviours, it becomes far easier to nurture leads effectively. Improved lead scoring accuracy means a more effective lead generation strategy. Rather than trying to score leads manually, you can use machine learning to monitor consumer behaviour to track everything from the websites that are visited to emails that have been opened, files that have been downloaded and clicks that have been made.
You can also use machine learning to monitor and analyse behaviour such as the social media accounts that are followed by leads, posts that have been likes and ads that have been engaged with on any level. This allows you to create deeper, more accurate profiles of your customers, which, in turn, makes your marketing far more targeted in the process.
Enhanced customer experience.
Customers also benefit from machine learning. Machine learning offers a way to improve customer experience in ecommerce, helping to guide customers through the buying journey, making personalised recommendations of products that help customers find what they need more quickly, making it easier to keep your store in stock (or offering alternatives when stock is getting low) and offering 24-hour support in the form of Live Chat or other chatbots.
If your business uses drop shipping, machine learning can also help in this way, helping you provide an enhanced customer experience remotely.
Easier prediction of customer churn.
Customer churn or turnover focuses on the volume of customers who no longer use your business. For service-based businesses, this could be customers who cancel a service or membership. In product-based businesses, this could be customers who no longer purchase from your store. Churn rates are determined by the number of customers that leave your business over a set time frame. In order to manage churn rates, customer satisfaction is paramount. You cannot afford to have a high churn rate and a low customer acquisition rate. In fact, seeing as how it costs more to acquire new customers than it does to retain new customers, you cannot afford to have a high churn rate if you have a low customer retention rate.
What this means is that you need to be able to predict and manage customer churn. You need to know how satisfied your customers are and find ways of reducing churn. You need to monitor engagement and track purchase history easily. Machine learning makes it easier to track this data and analyse it in a way that helps you plan ahead to avoid losing customers.
New revenue stream opportunities.
Another way that machine learning helps is the introduction of additional revenue stream opportunities. Thanks to the large amount of data that is available through digital marketing, businesses have a far better way of boosting revenue streams through upselling and cross-selling and other similar strategies. Machine learning helps in this regard by allowing you to easily determine customer preferences, interests, recently viewed products, wishlists and other similar data.
When you use AI to gain insight into your customers’ needs and wants, it becomes far easier to expand your revenue streams to new markets. This helps you grow your business, year on year.
Easier sentiment analysis.
It’s far easier to determine how people are feeling when talking face-to-face, where you can easily view facial expressions and body language and notice tone and other subtle signals. In the online world, it is seldom that simple. AI has paved the way to ‘read’ customers more easily, to get a better idea of what they might be thinking or feeling.
Sentiment analysis offers a way for businesses to understand their online reputation on a deeper level, using online data in the form of social media comments, reactions, favourited website pages and other clues. Negative content can be flagged easily, along with positive reviews and recommendation, allowing you to better read sentiment online, even without face-to-face interaction.
Enhanced content optimisation.
Another way that machine learning and AI helps in digital marketing is the ability to optimise content on a deeper level. While AI and machine learning are very similar, the one difference is that the latter focuses purely on analysing information and finding ways to optimise in the simplest way.
A good example of this in practice is split testing. Email subject lines, social media ad copy and graphics, article headlines and various other things can be A/B tested to determine which version resonates the best with audiences. This data can then be used to provide far better content delivery, optimising content in a way that adds genuine value to audiences.
Finally, machine learning can also be an excellent tool for personalisation across digital marketing campaigns. Personalisation itself offers a highly useful tool, providing a way to engage audiences on a deeper level. Customers that feel as though brands they use are listening and authentically showing interest rather than sending out generic messages that have no real value are customers who will be more likely to stay loyal and recommend their favourite brands to friends and family.
Personalising messages according to data on online behaviour, interests, previous purchases and other actions offers a way to provide a greatly improved experience. A good example of this is Amazon, who frequently sends messages that show items that have been viewed as well as items that are similar to those that have been purchased in the past. When offers, as well as the messages themselves, are personalised, there is a far better chance of customer retention and long-term loyalty.
Investing in a powerful platform such as Grapevine’s marketing automation platform is the best way to see the true value of machine learning. Contact us today to learn more about getting started with our automated digital marketing tool.