5 Ways Machine Learning Is Improving Digital Marketing

The continued advancement of machine learning has significantly enhanced digital marketing by helping marketers serve targeted ads to specific audiences, collect and process data, data mine, and perform tasks that are overly time-consuming or difficult for human beings. Result? Better marketing results thanks to targeted advertising and more actionable data.

Here are five ways machine learning is improving digital marketing:

1. Customer Data Analysis and Mining

Machine learning can analyze a tremendous amount of customer data and define patterns, clusters, associations, regressions, or associations. The analysis helps marketers gain a better understanding of their customers’ behaviours, relationships with other customers, and patterns.

For example, Google Ads and Facebook Ads use their machine learning technology to identify audiences that are more probable to engage in your objective such as liking your page. Google Ads and Facebook Ads’ machine learning can analyze different datasets of online users and categorize them based on their demographics and interests and determine which audiences have the highest probability of engaging in your objective.

This technology helps businesses focus their marketing on serving ads to audiences that are most likely to help companies reach their goals so businesses can spend their marketing budget efficiently and generate healthy returns.

2. Text Analytics

With the availability of prominent social channels such as YouTube and Reddit that let online users share their thoughts about specific brands or products via text, marketers can perform text analysis on a myriad of comments gain sentiment insights. Marketers can understand how shoppers feel about specific brands or their products and use the insights to improve product features, gain competitive advantages, to enhance customer service.

For example, machine learning can crawl textual comments and determine whether the comments are positive, negative, or neutral based on specific keywords. Machine learning can also use frequency of keywords in comments to learn how shoppers view a brand or product. For example, an automaker can use text analytics to see which descriptive keywords online shoppers use when discussing the automaker’s latest vehicle release.

3. Image Recognition

Scientists train machines to distinguish different content in images. For example, scientists can train machines to distinguish a human face, an object such as an iPhone, and a brand logo such as Nike in a picture.

This image recognition capability help advertising platforms to determine different brands, products, or activities online users enjoy. For example, if a person posts hiking photos onto Facebook, Facebook can determine that the person enjoys hiking or outdoor activities. Once recognized, Facebook will categorize the person under the “outdoor enthusiasts” interests group and you can serve ads to the person if you choose to target this audience group.

4. Voice Recognition

Voice search queries are on the rise as more consumers are using voice technologies such as Siri, Alexa, and Google Home to find products or services. More consumers are conducting voice searches such as “coffee shop near me” or “sushi restaurant near me” to search for products or services based on their locations.

The advent and advancement of voice recognition technology makes online searches easy for shoppers because shoppers can speak instead of typing to find search results. Marketers benefit from this technological improvement because they can attract more qualified website traffic from voice search. Consumers are more precise with their searches using voice search than typing text which means that the search results that they generate from their voice searches are closely related to their queries. Marketers benefit from this as they can generate traffic from relevant audiences.

5. Forecasting

You can use machine learning to predict future demand of your products or services based on your input data.

For example, you can enter your previous sales performance in the last 6 years and machine learning can use the historical data to estimate the expected sales performance next year. Once you have generated the expected sales number, you can use the estimation to plan your marketing budget, inventory, and number of staff required.

Machine learning is one of the most exciting and dynamic technology in digital marketing today and it’s performing activities such as sentiment analysis and image recognition that are difficult for humans. The technology also helps marketers optimize their digital marketing by letting marketers target highly qualified audiences based on the audiences’ interests, locations, and behavioural patterns.

We’re ecstatic to see what’s in store in machine learning and how it’ll revolutionize digital marketing!

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