Machine learning can be a powerful ally in brand marketing especially considering the sheer amount of consumer data floating around. Every day marketers are exposed to large volumes of data focusing on consumer preferences which should be processed accordingly.

For the most part, this information should make it easier to segment users and create interesting, relevant content but it is not always the case. In many instances, the more data presented to marketers will only result in more time needed to process and take action. So how can machine learning help brands improve their marketing efforts?

What Is Machine Learning?

Machine learning forms part of artificial intelligence (AI) and is a method of data analysis that automates the analytical process. It helps computers successfully manage, analyse and interpret data and thanks to the advanced algorithm, it offers more accurate predictions without using specific programming.

The more data is fed into the algorithm, the more it learns for improved performance and accurate results. Brands can create more meaningful marketing campaigns aimed at optimised target audiences to boost engagement.

Machine learning is a handy tool to identify patterns and determine the right tactics which could be hidden deep within the vast amounts of data. Here are just four of the many ways that machine learning can help improve marketing campaigns.

Taking Out The Guesswork By Identifying Online Trends

Before the digital age, brands launched ad campaigns based on no more than ‘calculated’ guesswork. Without really knowing their target audience, there was always a risk of wasting advertising budget on promotional efforts that weren’t relevant or interesting to their primary consumers. Machine learning has helped eliminate marketing waste with a more dynamic and targeted approach.

A shotgun approach to marketing in the digital age is not recommended as it only creates inaccurate data sets. Machine learning removes the guesswork from the equation while allowing brands to reach the right audience with the right content to maximise engagement and lead to more conversions.

Case Study: Ben & Jerry’s Breakfast Flavoured Ice Cream

A great example of how companies have used machine learning is the ice cream giant, Ben & Jerry’s. In 2017, they launched breakfast-flavoured ice creams using “cereal milk” with names like Fruit Loot, Frozen Flakes and Cocoa Loco.

They discovered that artificial intelligence and machine learning could help improve customer insight by listening to online public sentiment. Using machine learning, they managed to sift through loads of unstructured data to create the new flavour range. This was achieved by identifying at least 50 songs mentioning “ice cream for breakfast”.

Through this process, they discovered how popular the phrase was across various platforms which helped them uncover emerging trends. Machine learning can analyse and decipher social and cultural mentions to inspire new product designs or content that will appeal more to consumers’ preferences.

Enhanced Customer Personalisation

People want to feel as if brands truly care about them and speak to them directly instead of as a group. We’ve touched on personalisation in email marketing but this is so much more. In fact, 52% of customers would change brands if they feel that the company is not doing enough to personalise their message.

Amazon is a fantastic example of how to maximise machine learning as 35% of their annual revenue is generated through personalised product recommendations. They collect and analyse large amounts of data on their customer’s online behaviours, interests and purchase history to personalise the online shopping experience.

Everything in the buying journey is personalised including email communications and product offers. In an e-commerce setting, this helps customers feel more important and relevant as their online experience caters to their unique preferences.

Find And Target The Right Influencers

In today’s digital world, influencers often play an influential role across all industry types. Finding the right one can be challenging but machine learning has helped streamline the process. Japanese car manufacturer, Mazda, is another good example. They employed IBM Watson to find influencers for the launch of their new Mazda CX-5 at the SXSW 2017 festival in Texas.

Using machine learning tools, they searched various social media posts for specific indicators that aligned with Mazda’s brand values. They found the right influencers to connect with SXSW fans based on artistic interests, extraversion and excitement. These brand ambassadors would later visit the city and post about their experiences on social media using a targeted campaign, #MazdaSXSW.

Advanced Campaign Analytics And Customer Insights

While these examples indicate how effective machine learning is at tapping into a customer base, it’s important to note how cost-effective it can be as well. Machine learning can help identify valuable information about your customers for future campaigns. It will provide you with more accurate data to build personas to help improve personalisation and target the right people.

Many people may buy from your brand but that doesn’t mean they are all the same. This is where market segmentation comes into the equation as you need to group your customers accordingly. This will help improve engagement and conversions when you do target them. One example of software using machine learning that can help with this is Affinio.

Case Study: Sephora

Cosmetics giant, Sephora, has shown a formidable email marketing strategy over the last few years thanks to predictive modelling.  We define this as the process of creating, testing, and validating a model to best predict the most likely outcome.

Sephora used predictive modelling to send customised emails with product recommendations based on purchase behaviour from several loyal consumers. This data-centric tactic resulted in a 70% increase in productivity and five times less time spend on campaign analysis. They also reported no measurable increase in spending.

Final Thoughts

As online data continues to grow, machine learning in marketing will become an even bigger talking point. It will be particularly handy in terms of starting engaging conversations with consumers based on real data. According to the International Data Corporation, global spending on machine learning and AI systems could reach $77.6 billion by 2022.

Many companies have already recognised the massive potential of machine learning on better engagement rates and increased ROI. With this kind of data insights, brands have more information at their disposal to accurately predict what customers want before they even know.

Get in touch with WSI eMarketing if you need any help establishing a lasting online brand impression. We specialise in several areas including marketing automation, social media marketing, online reputation management, SEO and web development.

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