6 examples of using AI, machine learning in digital marketing

“A.I. is more important than fire or electricity,” Sundar Pichai, Google CEO

“97% of marketing influencers believe the future of digital marketing will involve human marketers working with machine learning-powered automation” QuanticMind Survey

It is becoming increasingly clear to most digital marketers that machine learning and artificial intelligence will play a crucial role not only in most other industries but in digital marketing as well. Indeed, as digital marketing field regularly produces extreme quantities of data, which is the main input of AI algorithms, it is expected that digital marketing will be one of the industries where AI will bring many benefits and will be most transformed by AI introduction.

Although digital marketing practitioners mostly agree on its importance, there is often less information on how machine learning is already used in this field, what are its main benefits and what will be the main trends of its use in the future.

We are presenting 6 examples of AI use in digital marketing today.

Search engines

Machine learning has become an important part of Google search engine in recent years. In 2015, Bloomberg reported that Google uses machine learning algorithm called RankBrain as part of its search engine Hummingbird. According to Google sources in Bloomberg article, “RankBrain has become the third-most important signal contributing to the result of a search query”. The other two are content and links, as revealed by Google in 2016

Note that Hummingbird encompasses many other algorithms, addressing other specific areas. So-called Panda and Penguin are meant to combat spam, Pigeon is addressing local search, while Mobile Friendly is rewarding websites that are mobile friendly.

Rankbrain machine learning algorithm was developed by Google to address the problem of ranking websites on previously unseen queries. In 2015, queries that Google normally never sees represented 15% of all queries. in 2019, Gary Illyes clarified another important feature of RankBrain, namely that it uses historical data on how users interact with search results thus tempering some speculations that interaction with content plays an important role for Rankbrain.

Voice search and speech recognition

Voice search allows users to search by using a voice command rather than typing them. To achieve that, it uses speech recognition technology to understand the search queries.

Voice search can be used for various purposes:

  • querying search engines for information
  • dialing contacts
  • searching photos or audio
  • starting programs
  • selection of options

Popularity of voice search is being driven by three major trends:

  • proliferation of mobile phones and other web-connected devices such as Amazon Alexa, Siri, Microsoft Cortana and Google Assistant.
  • improvements in speech recognition technologies due to advances in deep learning and availability of larger amounts of data on which AI models are trained
  • better ability of search engines to understand what we are searching for or so-called natural language understanding (NLU)

Predictive analytics

Predictive analytics is the use of data, statistical algorithms and machine learning methods to predict future events, results or behaviors based on historical data.

There are two types of predictive analytics models, classification models predict whether something belongs to a certain class, for example, is a particular email spam or not. Regression models predict a number, for example what are expected company sales in next month.

Predictive analytics can be applied in a wide range of problems typical for digital marketing firms.

One application of predictive analytics is for lead scoring which is the process of assigning numerical scores to each lead or prospect according to different actions they take in the sales funnel. By using machine learning models based on demographic, behavioral, and other data, the company can more accurately determine which leads should be top priority by sales.

Another important application of predictive analytics is understanding the drivers of churn. By analyzing historical data associated with customers who left the company, one can identify sets of attributes which lead to higher probabilities of flight risk to competitors giving the company ability to proactively contact such customer and prevent churning processes. 

Sentiment analysis

Companies can use social media to track wealth of information about their products and brands. By using natural language processing (NLP) methods, they can infer which topics, themes and complaints are discussed in relation with their brands or specific products and services.

Machine learning models are excellent at assessing the sentiment of texts, making it possible to track sentiment of brands as discussed on social media or news. The companies can thus flag certain topics of conversations when negative and react on social media proactively.

Visual search and image recognition

Image recognition programs have made vast advances in recent years due to application of deep learning algorithms, use of GPU for training them and availability of vast amount of images due to rise of mobile devices.

One of the results is the rise of visual search, using image instead of text as query for the search engines. Pinterest Lens, one of the leaders in visual search, has recorded a 140% rise in visual searches on its platform, reaching 600 million monthly searches in February 2018.

Google Lens, another major visual search products allows its users a large amount of tasks that they can perform on their platform:

  • look up a meal from the menu,
  • add events to calendar,
  • get directions,
  • make a phone call
  • take a picture of clothes, furniture, home decor and other products
  • look up information about places or locations by taking its picture
  • identify plants and animals

Retailers are increasingly integrating visual search technologies in their apps and websites to reduce friction in the process from search to conversion and thus improve shopping experience for their users.

Chatbots

Chatbots or Virtual Assistants are the latest tools with the goal of making interaction between humans and computers simpler and more joy to use. Chatbots are artificial intelligence programs (primarily natural language processing – NLP models) that are capable of conducting a conversation or chat with user either on websites, mobile or messaging apps or through telephones.

Increased usage of chatbots is partly driven by the lowering of technological requirements as it is being increasingly easier to construct and launch a chatbot.

Company that implements a chatbot can obtain numerous benefits from it.

A recent survey showed that 83% of shoppers need some form of support during shopping. A chatbot solution can greatly assist and improve customer support service, by providing answers to typical questions, offer product pages, tutorials and other rich content. It can even do complex tasks such as take an appointment or return user-specific information such as bank account balance.

Additional benefits of chatbots:

  • they save time
  • they save costs
  • are available 24/7, 365 days a year

Machine learning is powering an increasing number of key digital marketing products as well as being used in many areas with significant benefits for digital marketers. As the AI algorithms improve and digital marketing field learns the best ways to incorporate AI in their work processes, it is expected that machine learning will play an even more crucial role in digital marketing in the years ahead.

 

 

 

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