Machine learning can help marketers to extract insights from data and find actionable tactics to improve their marketing campaigns. Two of the most common marketing questions marketers ask are “Where can I find quality leads?” and “How can I improve my campaigns?”
AI-based marketing helps marketers to achieve better conversion rates and sales because it is based on specific information about customers, including their behavior, purchasing patterns and much more.
Models powered by different algorithms help with all kinds of different aspects of marketing. They help to improve customer outreach, create personalized content, trigger a response, and create a great user experience.
Here are some of the many ways machine learning can help to improve marketing.
What is machine learning?
Machine learning is a subset of artificial intelligence (AI) that has pattern recognition at its core. It gives computers the ability to analyze and interpret data and offer accurate predictions without any explicit programming.
The more data points used to train algorithms, the better, as this allows for the unlocking of deeper insights and the discovering of more and more subtle patterns.
A QuanticMind survey found that almost 100% of industry experts believe that the future of digital marketing will be influenced by machine learning techniques and AI-based marketing automation.
Many of them believe that enhancing customer experience will be the area in which machine learning is most beneficial.
1. Incorporate chatbots to improve customer service
A common sight on modern websites are chatbots that pop up in the bottom corner of the screen and offer assistance soon after a visitor arrives on the site. Using chatbots enables brands to provide customers with 24-hour support.
These chatbots can answer simple customer queries and refer them on to the right people if they can’t assist. They keep learning from their interaction with visitors, and collect and interpret data to offer more accurate answers.
The eBay chatbot built for Google Assistant is an e-commerce chatbot that helps customers use voice search to find the best deal on preferred products.
Designer Shoe Warehouse (DSW) uses a Facebook Messenger bot as a shopping assistant. After customers purchase shoes, DSW makes it easy for them to track their packages and receive personalized shipping information.
Bots can be used for many other purposes too, such as sharing information about discounts or coupons and announcing new product launches.
2. Optimize content
Content optimization is one of the most important aspects of SEO which helps to increase visibility in organic search. Content that receives a lot of clicks helps to contribute to a better position in the search engines and drive more traffic to a website.
Machine learning helps reveal what content performs best, whether it is email subject lines, article headlines or images. For example, it may find that one person images work better than group images and prioritize those results.
Insights extracted from huge amounts of customer data about interests, past purchases and online behavior can help marketers to create the type of content that will most engage readers at all touchpoints in their journey, from the emails they write to the products they offer.
According to the experts at essay writing service UK, in the past, marketers would launch their advertising campaigns without really knowing their audience and would waste money on ads or promotional efforts that did not resonate with them.
Machine learning helps to eliminate this waste. It takes out the guesswork and allows marketers to reach the right audience with the type of content that offers the best chance of engagement.
3. Develop new products and services
Machine learning algorithms can help to tailor new products and services more accurately according to consumer needs. For example, it becomes possible to conduct surveys worldwide with potential customers and analyze the data to deliver a product.
This can help businesses to identify new opportunities and new products they can develop to cater to a new group of customers.
The same solution can help companies deliver different products or versions of the same product to different markets. For instance, the surveys may indicate that drivers in the U.S. prefer four-wheel drives while in Europe, hybrid vehicles are in demand.
With this type of information, a car manufacturer would be able to design a suitable vehicle for the U.S. and European markets.
4. Uncover trends
Machine learning mines unstructured data and allows insight into what customers are talking about in the public sphere. It can decipher social chatter to inspire new product or content ideas that relate directly to customer preferences.
An example of this is when Ben & Jerry’s discovered that people were talking about ice cream for breakfast in the public domain and decided to launch a range of breakfast-flavored ice cream.
5. Personalize product recommendations
There are many ways in which machine learning can improve the shopping experience of customers. It can guide the journey of buyers and make personalized product recommendations.
Amazon generates a fair percentage of its annual revenue through personalized product recommendations.
Netflix also increases its revenue by using an algorithm to offer personalized movie recommendations to customers. Machine learning helps to suggest content viewers are most likely to enjoy based on what they watched previously, rated or ignored.
Up-selling and cross-selling can have much better engagement when machine learning helps to accelerate and optimize product recommendation.
By analyzing past customer behavior and predicting demand, marketers are able to make targeted offers with better chances of conversion.
6. Improve lead generation and scoring
Leads are the lifeblood of a business and machine learning can help them to generate more highly qualified leads. Bots using AI can learn from conversations happening on a site between reps and consumers.
That information enables them to answer questions, understand more about what makes a good lead, and generate leads from visits at scale.
Knowing the probability of a lead making a purchase can help marketers who have to deal with many leads. Machine learning uses data to score leads which can increase efficiency and save time.
It becomes much easier to focus energies on trying to convert such leads. When looking at the profiles of customers who purchase the most, marketers are able to identify common traits that they can keep in mind while marketing.
7. Optimize advertising
Traditionally advertising required making decisions about which advertising channel to choose, how much ad space to buy, when to place an ad and how long a campaign should last.
Advertising is a major cost for companies and using machine learning can help to optimize its performance.
Previous decisions that had to be made by marketers are now informed by machine learning. For example, using a Facebook Lookalike Audience will help marketers to find and target potential customers with similar attributes to their existing customers.
Smart Bidding is a strategy that uses machine learning to make PPC campaigns more effective. It combines machine learning and contextual signals to optimize bids. Billions of data points are used to estimate the likelihood that a prospect will convert.
8. Automate marketing
Automation takes marketing to the next level. Machine learning crunches the numbers, learns from past outcomes and offers actionable insights.
It helps with all aspects of marketing such as customer segmentation, making recommendations, personalizing content and customer service.
This helps to simplify decision-making for marketers and as it keeps learning, it keeps improving. Brands that manage user experience by using marketing automation achieve a much higher rate of qualified leads and experience an increase in revenue.
Machine learning-powered email marketing helps marketers to segment customers and highly personalize their email campaigns. They can write personalized email subject lines and messages designed to foster customer engagement.
They can use previous responses to determine the optimal time and way to send messages. Building split-testing into their email marketing can help to keep driving up ROI.
9. Optimize prices
Dynamic pricing has already been around for some time and is often used in the hospitality and travel industries. These industries offer flexible pricing based on market conditions and customer demand.
Increasingly retail businesses are also employing flexible pricing thanks to having the data they need and machine learning to analyze it.
Pricing elasticity is determined for each product by factoring in elements such as the sales period, the customer segment, product positioning and more. Machine learning algorithms can also help to identify which customers are likely to respond to the offer of a discount.
10. Predict customer churn
Being able to predict customer churn enables businesses to reach out to them before they leave. It is possible to train a machine learning model with examples of customers who did or didn’t churn to discover patterns and identify those not likely to churn.
Urban Airship, a digital grown company, uses a machine learning algorithm to analyze the behavior of mobile customers to help app publishers identify loyal users and predict those most likely to churn.
Marketers can then take action to deepen customer engagement or invest more in retaining certain customer segments.
11. Target the right influencers
More and more brands are using influencers today. They know better than to blindly use them and want those that align with their brand values. This can help them to reach and engage with a wider audience and promote brand credibility.
A machine learning tool can help to search social media posts for various indicators and recommend influencers that would best connect with an audience.
Machine learning helps to fight one of the biggest problems when using influencers which is that of influencers with fake followers and those inflating their performance.
Natural Language Processing (NLP) machine learning-based tools can make sense of video content posted by influencers and help brands choose the right brand advocates. It also helps them to understand how brand messaging is done by the influencer.
Mazda used IBM Watson to choose influencers for a launch of one of their new vehicles at a festival in Austin, Texas They rode around the city in the vehicle and then posted about their experiences using the hashtag #MazdaSXSW.
12. Manage social media
Machine learning helps marketers to use the power of data to optimize their social media presence. For instance, it can help them to identify the reviews or complaints that need a response straight away in order to manage the brand’s reputation.
Social listening tools powered by machine learning can track hashtags, keywords and brand mentions across all social media platforms.
The insights obtained from analyzing this data can help brands to create the right type of content for each platform that engages an audience at a deep level.
Machine learning does not replace the role of the marketer
Good marketers are still as important as ever, and machine learning does not replace their role. It is ironic that machine learning helps to humanize their marketing efforts. = They don’t have to waste time on valueless content or ignore relevance.
They have the ability to reach customers effectively at every touch-point in their journey.
It is possible to predict which customers are most loyal and spend time focusing on them. They can also predict which customers are about to churn and intervene before it is too late. They understand which content is most effective and brings the best results.
Having to reach out to a generic audience and hope for the best is a thing of the past. They now have enough insights from customer data to be able to plan campaigns effectively and achieve the best results because they are not shooting in the dark.
A final word
Machine learning in marketing is becoming a game-changer as a wave of new technologies leveraging it put power into the hands of marketers. This is enabling a new era of being able to understand consumers better and enhance customer experience.
Over the next few years, it is likely to become even more evident how machine learning changes the way brands interact with customers and offer a more authentic experience in attracting, selling to them and serving them.
This is likely to have a great impact on the business bottom line.
Author Bio
Charlie Svensson works as a senior academic writer for a leading college essay writing service in the UK. He has rich experience in writing thesis, essays, lab reports and coursework. He is currently developing an online course to make learning easier for the non-English speaking students studying in the US and UK.
