6 Ways AI And Machine Learning Are Revolutionizing Marketing And Sales

6 Ways AI And Machine Learning Are Revolutionizing Marketing And Sales

Romy Catauta, Marketing Professional
Romy Catauta,
Marketing Professional

Artificial Intelligence (AI) is currently being used in marketing and sales for all sorts of processes from personalized shopping to warehouse logistics. Let’s explore how companies can leverage innovations and what algorithms work under the hood.

6 Ways AI And Machine Learning Are Revolutionizing Marketing And Sales

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1. AI and Machine Learning for Market Research

AI and machine learning enable companies to know their potential customers better, as well as their competitors and other insights about their industry.

Natural Language Processing

With the help of NLP (Natural Language Processing) businesses can analyze customer reviews and comments, social media content, and engagement and turn these raw details into valuable and insightful data. Sentiment analysis identifies positive and negative feelings in a sentence, from a customer review, it identifies mood via voice analysis and written text.

The most commonly used NLP techniques for marketing include:

  • Sentiment analysis. Used to identify positive and negative feelings in a sentence, identify customer’s mood via voice analysis and written text
  • Summarization. Allows to create a summary of a text by identifying its essential parts through the abstractive and extractive summarization
  • Named entity recognition. Can be applied to identify entities in a sentence (an organization, date, time, person, location) and classify them into categories
  • Text classification. Able to organize, structure and categorize any text
  • Language modeling. The idea behind this is to predict the next word or character in a text or document
  • Part-of-speech tagging. Enables marking up descriptors in a sentence such as verbs, adjectives, adverbs, and nouns

The goal of machine learning algorithms in marketing research is to gain valuable business insights. Here the point is not to get more data, the point is to get the “right” data. Unsupervised machine learning approaches help not only to collect data, but also to get a vision on how it’s categorized. For doing this, it utilizes algorithms, like Dimensionality reduction, Clustering, Associations mining or Anomaly detection.

The main feature of the unsupervised ML is the identification of the underlying data structures. It is valuable because sometimes marketers can not know about customer behavior’s hidden patterns, and that’s where unsupervised ML helps.

Liudmyla Taranenko - Data Science Engineer at MobiDev

Liudmyla Taranenko

Data Science Engineer at MobiDev

2. AI and Machine Learning for Marketing Campaigns

AI and machine learning are also used for marketing campaigns. This process involves NLP-based software that analyzes lists of social media comments, as well as the most frequent both positive and negative words and phrases. Thus, based on this type of algorithm it is possible to assign particular values to the output information, while this value can be considered as a negative, positive, or neutral.

This data is used by marketers to make better decisions in terms of campaigns and strategies. Machine learning can help with customer demand forecasting as well, which means that marketing campaigns and ads can be adjusted, which will have an impact on the number of sales.

3. AI and Machine Learning for Sales

AI and machine learning allow companies to make better sales decisions. Although social surveys are also useful, they do not provide as many valuable insights compared to machine learning which helps with customer demand forecasting.

Demand forecasting is a machine learning technique that predicts the number of services or products that will be sold within a certain period in the future. The availability of historical data is a must here since an ML algorithm should process these data to output the expected result. Historical data here might be either internal like sales transactions, purchase orders, inventory, or external like customer reviews, comments from social media, click streams, and others. The more data we have the more accurate forecast we can receive.

The most common ML algorithms here comprise Time Series, Linear Regression, Feature Engineering, and Random Forest. Depending on the business goal, amount of available data and their type, the demand forecasting may involve several algorithms or just a single one.

Liudmyla Taranenko - Data Science Engineer at MobiDev

Liudmyla Taranenko

Data Science Engineer at MobiDev

Additionally, demand forecasting offers the opportunity to personalize communication with customers, which becomes more personal than ever. Netflix, for example, feeds users with content that it thinks they will watch (recommendation engines). By utilizing statistical modeling and machine learning methods to improve its streaming quality, it predicts the network needs of a device, to determine what type of content to produce.

Big Data and Digital Branding

The same technology can be wielded to make marketing campaigns better. Big data is one key area for digital marketers that AI makes enticing. AI can analyze petabytes of information and give us increasingly accurate correlations and conclusions.

The datasets most important in the digital age at present are metrics related to social media. How people feel about your brand is the most crucial piece in the digital branding puzzle. User experience design is built from this model, maximizing enjoyment and minimizing pain.

Computer vision is the AI technology providing significant assistance in branding strategies. For example, in order to check the brand awareness or customers’ attitude marketers can utilize a computer vision-based algorithm which detects brand-related products on images and videos from social networks. After the analysis of visual content, the algorithm extracts user insights and categorizes them into positive or negative.

The real-life example of utilizing the computer vision for branding is the GumGum solution. The AI algorithm works in such a way to identify brand colors, concepts, and logos in the user-generated content. Moreover, the algorithm calculates brand safety threat and analyzes customers’ opinion for the contextual insights identification.

Serhii Maksymenko, Data Science Solution Architect

Serhii Maksymenko

Data Science Solution Architect

4. AI and Machine Learning in Email Marketing

AI and machine learning can also be used in email marketing as it helps businesses improve the words they use in their email copy and subject lines, customize email content on an individual level, optimize send times, clean up email lists, restructure email campaigns and decrease costs. Effective AI for email marketing has several advantages for companies such as email timing calibration, customized promotions, and rewards, increased conversion and revenues, and discovery of new user segments.

Natural Language Generation (NLG)

You might already notice text suggestions when using Gmail. Their Smart Compose service provides suggestions for text when writing emails.

Smart Compose Service in Gmail

Source

The core AI technology behind this service is Natural Language Generation (NLG), but in fact it also involves Natural Language Understanding (NLU) and Natural Language Processing (NLP) algorithms. The process is as follows:

  1. The NLU algorithm understands the input text by utilizing tasks as named entity recognition (NER), word sense disambiguation, news gathering, and archiving of text pieces.
  2. The NLP algorithm analyses and structures the input text by applying grammatical rules to word groups and identifying the meaning from them.
  3. The NLG algorithm generates a text on the basis of the data structured by the NLP algorithm.

Besides Gmail, a good example of NLG is Persuado service. The solution involves the generation of social media paid ads, email subject lines, and automated testing for conversions. The AI algorithm understands not only human language, but also emotions and tone. For example, it can split marketing text structure into descriptions, narrative, call-to-actions. It can also process word positioning, formatting, and emotional factors.

Persuado’s AI algorithm is self-learning. By processing every email campaign, it extends its database with new language elements and structures.

Social Data and Empirical Data

The utilization of social networks and media platforms as a data source is a common practice in AI-powered marketing. The data is generated through social media platforms, fed into customer relationship management platforms, providing marketers with models that can be utilized as a basis for their strategies.

Backtesting what marketers may know intrinsically or may see empirically is a common marketing process these days, melding data analytics and branding activities. It is possible through machine learning approaches that use advanced A/B testing and such algorithms as Linear Regression, Decision Trees, XGBoost. Thus, you can experiment with various content and messages on your paid social media ads, emails, article headlines, and other.

5. AI and Machine Learning for Content Marketing

AI and machine learning can transform content marketing by improving customer experiences and driving conversions. AI and machine learning can personalize and hyper-customize experience by analyzing their profiles. It can process huge amounts of data and make predictions based on the patterns that result from it.

Moreover, it speeds content production and helps marketers decide what content to create and when is the best time to distribute it. It will also lead to improved productivity as time-consuming and mundane tasks are being done with the help of advanced technology and intelligent automation.

Marketing texts and graphics can be created from scratch by the AI so that they can hardly be differentiated from those created by a human. It is possible using GANs – Generative Adversarial Networks used to generate texts, images, video, music, voice, and more.

GAN is a deep learning-based technique used for generative modeling. The GAN’s algorithm automatically discovers patterns in input data to generate new samples based on examples from the original dataset. Typically GANs utilize two neural networks: generator and discriminator. The generator network is trained to generate new samples, while the discriminator is trained to classify fake and real examples.

Maksym Tatariants, Data Science Engineer

Maksym Tatariants

Data Science Engineer at MobiDev

GANs - Generative Adversarial Networks

Source

6. AI and Machine Learning for B2B Marketing

AI and machine learning provide useful customer insights, help nurture a great customer relationship, personalize and improve the buying experience. Machine learning utilizes the data and social media metrics to create a clearer picture of who the ideal audience is and what they want out of a project in B2B marketing.

Most importantly, machine learning is used in retargeting efforts to find more appropriate users that are more likely to convert into ROI. AI and machine learning can turn massive amounts of data into useful insights, which predict lifetime value, lead generation, as well as scoring. Hyper-personalization is also possible by considering behavior and context into account.

Customer Service

Additionally, customer service is impacted heavily by machine learning as new AI technologies are created that take better advantage of the systems they are created in, to be as authentic as possible. Many users online can no longer accurately distinguish between chatbots and people as the technology continues to advance at a shocking speed.

AI-Powered Chatbots

According to the Reports and Data analysis, the global Chatbot market is expected to reach USD 10.08 Billion by 2026. And that was to be expected. Now the Chatbot is not just a program outputting one-word answers to simple questions – but a software system that mimics the operation of a human brain.

AI-powered chatbots stand for a software system comprising deep learning and machine learning algorithms that help recognize and match text patterns. In this case, the AI-based chatbot is trained on the repetitive patterns of text, learning to recognize them during conversations. And then, it becomes smarter by memorizing new patterns and growing the database of answers and responses used for conversations.

Machine learning CRMs such as Base, Marketo, and SugarCRM provide consistent levels of attention and better interactions than their human counterparts who are reasonably stressed individuals. Many chatbots today are capable of communicating seamlessly with customers without any complaints regarding the treatment they received.

Chatbots can also be used for alerting and granting access in biometric authentication-based security systems. For example, in the custom face recognition software we used Microsoft Bot Framework and Python-based Errbot, which allowed us to implement the alert chatbot which sends images of an unidentified person to the corresponding manager.

Serhii Maksymenko, Data Science Solution Architect

Serhii Maksymenko

Data Science Solution Architect

Image of the unidentified person is sent as a chatbot notification

Call centers and information calls can now be reasonably automated without many customers ever noticing a difference. Machine learning is revolutionizing the world one industry at a time and it all starts with marketing.

The team that is behind the technology

Marketing industry is getting deeply connected to software products and technologies. Automation, integration and even creating custom software products or components – that’s the new marketing reality.

Coming up with great ideas and using modern technology is not enough though. Finding and hiring the right designers to integrate such interesting ideas and provide business with amazing apps should carefully be analyzed as it will have a huge impact on the long-term.

Another new role to marketing departments will be a software business analyst to transform product ideas into a technical specification. And a project manager to drive the process and make the delivery happen. As with any big project, creating an AI software product has plenty of details that have to be managed.

Data Science and Machine Learning engineers are the ones who bring AI to life. And it’s another role, marketing departments are closely working with. Modern software products have to evolve constantly. And this also means that development often continues around the clock.

Many of the biggest names in technology and science believe that we are witnessing just the very beginning of AI and that in the blink of an eye we could be facing a very new world very quickly.

However, some look at what the AI industry brings into focus the question of how it could fit into this technological landscape. In that question, however, comes the most obvious answer that AI is an answer in and of itself.

6 Ways AI And Machine Learning Are Revolutionizing Marketing And Sales

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