Exploiting the immense potential of artificial intelligence to excel in business is no longer a far-fetched pipe dream. Many companies have already discovered this, and as technology advances at a rapid rate, it’s clear now that machine learning and marketing go hand-in-hand.
In this age, to do one without the other is a mistake no business can afford, not if they want to remain competitive.
Data insights are more valuable than ever before, enabling for better customer engagement. It comes as no surprise that there is an increased reliance on data. Gartner research projects that more than 75% of companies will invest in big data in the next two years.
For businesses today, being able to anticipate customer behavior is key to optimizing marketing campaigns. In this article, we’ll explore just how machine learning can help companies improve and enhance their marketing efforts.
Studies indicate, that 57% of major executives believe this is the area where machine learning can be most beneficial.
Machine learning can improve the customer’s online shopping experience in many ways, such as:
The rising popularity of drop shipping in the past decade has paved the way for many e-commerce companies and solo entrepreneurs to put machine learning to good use in improving the customer experience.
An example is Kate Somerville, who has combined a Magento e-commerce platform with nChannel to great effect. They used machine learning to create a more personalized shopping experience by responding to real-time data. This has boosted traffic, conversions and of course, revenue.
Digital marketing in the modern era is all about data. Due to the huge amounts of data available, it’s increasingly common to see marketing become the priority for many companies, as it has a direct link to increasing revenue.
The retail giant, Amazon, has harnessed the power of machine learning quite unlike any other, with 35% of their annual revenue generated through personalized product recommendations.
Their cloud computing service, Amazon Web Services (AWS), provides opportunities for other companies to benefit from AI by using data insights to tailor their services to the customers’ needs. This in turn, will allow Amazon to gain additional revenue streams in innovative, new market areas.
This so-called marketing prophecy is something that many marketers have been working towards for years before machine learning came to the fore. With data insights in hand, decision-makers have much more insight and ability to predict what customers want, before they even are aware they want it.
In the digital age, people have quickly become accustomed to shopping in innovative and streamlined ways. As a result, their expectations are higher.
This provides more opportunity for companies to tailor their marketing specifically for the niche groups in their industry, or even with their own customer base.
Many businesses are already well on their way in this regard, developing new products and services based on the findings from machine learning software.
Baidu are developing a service known as Deep Voice, which can reportedly generate entirely synthetic human voices. This software learns from human speakers, modifying the pitch, tone, and pronunciation to create accurate – and eerie – imitations.
In terms of marketing, this project may well change the landscape of voice search applications, which is expected to grow considerably in the near future.
Artificial intelligence and machine learning are often mentioned in the same breath, but there is a significant difference. Machine learning doesn’t try to outsmart and usurp human intellect. Instead, it focuses on analyzing problems and processes and finding a way to optimize them.
A popular way that many marketers practice this is through A/B testing.
Whether it’s email subject lines, Facebook ad graphics, or an article headline, A/B tests allow marketing departments to try out various options and garner the results to determine which connects best with the audience.
This method of using machine learning in marketing proves valuable with segmented marketing campaigns. Companies can use the feedback to provide more targeted content, ultimately collaborating with machines to optimize content and services.
Possibly the best example of this is Google Rank Brain. Its ability to learn from the searcher intent has made the search engine giant an incredibly efficient service, consistently improving in the accuracy of its results depending on the context of each query.
People want brands to care about them. So much so, that 52% of customers are likely to switch brands if they don’t feel a company is making enough effort to personalize their messaging.
Amazon’s aforementioned success with e-commerce personalization is built upon machine learning. They harvest the huge reams of data on their customer’s online behaviors, interests, and past purchases to tailor the online shopping experience.
Everything from the emails to the product offers is personalized, along with every touchpoint in the buying journey.
It may seem somewhat ironic, but machine learning helps to create a more human experience.
E-commerce personalization makes customers feel more important, with the experience carefully crafted to cater to their needs and interests.
This helps breed loyalty. Customers will trust a brand that makes them feel like they are being listened to. Research indicates that 44% of customers will return to make future purchases after having a personalized shopping experience.
When it comes to marketing, it is incredibly useful to have a system that can quickly identify trends and actions in real-time, and then respond accordingly without any human input. This ability to “learn” on the go is what makes machine learnin g so important in marketing today, and in the years to come.
In the past, many marketers launched advertising campaigns on little more than guesswork. Without truly knowing their audience, a lot of money was wasted on ads or promotional efforts that didn’t resonate with their target customers.
Machine learning eliminates this marketing waste.
Taking a scattershot approach to marketing in the digital age is not only unnecessary but mere folly. Machine learning takes the guesswork out of the process, allowing marketers to reach their audience with content and product offers that stand the best chance of engagement – and ultimately, conversions.
An increasingly common sight on many modern websites is the friendly chatbot that pops up in the bottom corner of the screen, offering assistance or advice soon after a visitor arrives on the site.
Machine learning is fundamental to the success of chatbots, as it allows the chatbots to continually learn from interaction with visitors, collecting data and interpreting it to provide more accurate answers over time.
Not only will chatbots inevitably phase out human virtual assistants in time, but they provide companies with the means of revolutionizing marketing activities.
We can already see how machine learning is being used in many different industries today, from helping to calculate risk in financial companies, to providing personalized healthcare through the Internet of Things (IoT).
With the insights that machine learning provides, businesses can tailor their marketing efforts, providing a better service for their customers, and ultimately, delivers a more personalized experience. This will help to build a loyal audience that trusts your brand and will come back to purchase more products and services.
In the end, this is great news for the bottom line of any business. With more optimized content and astute analysis of the data insights available to them, companies who use machine learning in their marketing strategy stand to gain a lot going forward.
When it comes to digital transformation, there is no doubt machine learning and AI are already massively important to the future of business.