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Foreword

Machine learning marketing presents a sub-area of artificial intelligence and refers to the IT systems' ability to recognize the patterns in data fed via marketing channels. Nowadays, marketers heavily rely on these outputs as they provide accurate results based on studying specific data points for a deeper understanding of the marketing trends and influencing factors.

The growing importance of implementing machine learning techniques in marketing is fuelled by the success of such e-commerce leaders as Amazon and Alibaba that actively use AI applications and machine learning for more accurate product recommendations.  

Analysis of historical data is directly linked to creating more successful business interactions with customers, deepening their engagement, and forecasting their behavior with precision. Overall, 81% of global market players admit that machine learning capabilities are essential when it comes to delivering personalized experiences to the customer. machine learning in marketing

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Machine Learning Marketing Use Cases

Targeting and Personalisation

The direct impact of applying machine learning techniques can be clearly visible in targeting and personalization: mining data allows marketers to achieve peak relevance in customer reach. 

For example, Facebook, as one of the best examples of machine learning marketing use cases, gathers data to allow you to create audiences that can predictably respond to the offer that you bid for and, thus, build a proper strategy of predictive targeting for the guaranteed realization of the specific ad goals.

What is important to know?  Sifting the huge amount of data enables marketers to perform high precision segmentation and bring personalization to a new level, thus, creating the grounds for upscaling the delivery of personalized content.

Customer Churn Prediction

Customer churn costs businesses losses of revenue and time allocated on expensive marketing campaigns. Minimizing the customer churn is one of the most important objectives of businesses that want to maintain their high competitiveness both locally and globally. 

With the implementation of machine learning, predicting customer churn gets simplified. It brings transparency in spotting triggers and general behavior patterns in regards to actions leading to user churn. Hence, analysis of the specific metrics can assist in driving the deeper engagement of customers and build the strategy that stops the churn.  

What is important to know? The majority of predictive churn models are based on logistic regression, applying classifiers and binary models, which makes it possible to enable a higher accuracy of forecasting customer behavior at all stages.

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Customer Lifetime Value

About 80% of the effects come from 20% of the causes: this famous equation known as the Pareto principle can be easily applied to sales, which means that 20% of customers generate 80% of income. 

Machine learning techniques work well for categorizing customers according to their ‘lifetime value potential’. 

Machine learning enables businesses to quantify such important metrics as recent customers’ purchases, general frequency of purchases, and expenses the customer regularly afford. It helps create modeling for fail-proof identifying and predicting customer lifetime value, which is of great importance both tactically and strategically. 

What is important to know? It has been proved that 81% of companies that invest in measuring customer lifetime value increase their sales. 

Recommendation Engines

Machine learning is widely used in any recommendation engine that, broadly speaking, computes ‘overlapping’ data gathered from the history of customers’ browsing actions. For creating relevant recommendations, machine learning creates the list of suggestions based on item-item model or item-user model.

Applying machine learning, Amazon initially became a favorite search and selling platform for readers satisfied with offered recommendations. Other giants building their businesses around recommendation systems include Google Play Store, YouTube, Spotify. 

Their highly advanced recommendation systems spot relevant services and products, which translates into saving customers tons of time on the one hand and generating revenue for companies on the other hand.

What is important to know? With machine learning, the recommendation engines discover data patterns based on consumers' historic and current choices and introduce the outcomes catering to customers’ specific needs. 

 machine learning marketing use cases

Marketing Mix Modeling

Machine learning is an effective means of leveraging technologies to digital environment needs and is widely applied for evaluating ROI against marketing spends and optimizing the return on media spends.  

It helps estimate the prospective impact of most marketing decisions and operationalizes marketing tactics (by identifying the target audience expectations on each of the advertising platforms) for increasing the monetary value of presence on multiple channels during the machine learning marketing campaigns.  

What is important to know? Marketing Mix Modelling paves the way to connecting such parameters as marketing expenses against sales numbers. 

Customer Attribution

With implementing the machine learning model into evaluating the marketing touchpoints a consumer experiences when taking a decision to purchase, customer attribution gets successfully operationalized. The model learns with feeding customer data and, thus, allows detailed observation of revenue streams and lead generation in terms of spotting all the touchpoints along the customer journey.

By creating the historic matrix of buying patterns, it is easy to identify the recurring factors that influence buyers and build strategies that would guarantee to improve customer engagement. 

The idea behind the implementation of machine learning in customer attribution is to start with the analysis of the outcome and breaking down customer experience along the journey backward.

What is important to know? Customer attribution methodology based on machine learning compared to the application of rule-based models gives marketers better visibility of funnel marketing.

Predictive Analytics

Predictive Analytics is a form of data mining, machine learning, and statistical modeling to make forecasts based on historical data. It has already found its application in banking as well as other areas, and marketing is no exception. 

It is a well-known fact that Netflix and Amazon apply predictive analytics for titles and products that consumers are offered upon identifying the browsing patterns in individual searches. Thus, these platforms use machine learning in advertising to serve more relevant ads to their customers. 

What is important to know? With predictive analytics, marketers get data enabling the marketing teams to focus on leads that bring the highest ROI. Moreover, based on customers’ historical behavior and data on consumption, retailers can estimate demand with precision and, hence, make sure they have the right level of stock for each of the products. 

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Using Machine Learning in Digital Marketing

Marketing Automation 

Implementing machine learning in marketing can literally fuel the brand you create by leveraging the capabilities of marketing automation. 

Machine learning and marketing automation help set a ‘catching’ ad funnel once you launch the marketing campaign and respond to customers’ reactions fast by ‘dripping’ the campaign (offering diverse types of ads while directing the prospective customers to multi-channel). 

With machine learning in marketing and sales, marketers can automate the identification of the successful communications within the copy, call to action, offer, targeting, or frequency, and apply them in the coming campaigns for guaranteed results.

Email marketing

In addition to applying autoresponders in email marketing when marketers collect diverse data to generate business insights for future campaigns, there is an opportunity of implementing machine learning for optimizing sending frequency and identify the days most suitable for new and follow-up marketing messages. 

Moreover, machine learning enables marketers to divide email lists in accurate segments and personalize the sending frequencies for individual recipients.   

Content Marketing 

Content promotion is one of the best examples of machine learning in marketing. Machine-learning technology in content marketing helps forecast the consumers’ reactions and behavior models (probability of sharing, channels of sharing, depth of engaging with content). For instance, Adobe’s Smart Tag technology assists in automating metadata insertion for obtaining better search results while reducing the time you spend on this task to the bare minimum.

Machine learning also helps create the ‘working’ content, i.e., engaging for your target audience: for enabling this operation, you create the content, then gather data and generate insights to be able to expand on applying the proven keywords, format, content curation, etc.  

SEO

Machine learning in sales and marketing isn't limited to usual marketing areas. Marketing mix modeling machine learning also unlocks a wide range of automated options for performing time-consuming SEO tasks. Machine learning presenting self-driving technology shows excellent results of application in self-connecting the website pages, optimization, and managing the content in the view of SEO relevance.

The famous Campbell soup brand uses AI-based SEO automation for compressing up to 75000 images per day and comes with a set of up to 4000 precise keywords in weeks. The application of machine learning in SEO practices lets Campbell brand feel confident enough to state they ‘earn consumer’s trust instead of buying it.

Another prominent example is a recent Google algorithm BERT (Bidirectional Encoder Representations from Transformers) that made a breakthrough in the use of NLP when generating the language models.

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With that said, it can be stated that machine learning implementation is a proven method making a definitely positive impact on marketing in regards to: 

  • Enhancement of customer experience
  • Creation of diversified revenue streams
  • Development of products and services
  • Content optimization
  • Better personalization
  • Minimizing ‘Marketing waste’. 

Speaking of applying machine learning marketing in the future, we can be positively sure of the leading role of machine learning. 

In particular, building a digital marketing strategy nowadays is impossible to imagine without the implication of chatbots that are powered to perform lots of functions (from answering customers’ questions to gathering potentially insightful information for forecasting consumer behavior) due to their AI-based capabilities. This is why machine learning and marketing are so closely related.

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In Summary 

Apparently, to ensure that your business gets relevant marketing insights, you will need to implement machine learning in your marketing operations on an everyday basis. This way, you will have the competitive advantage in delivering the best results to your customers and have the tools to turn them from occasional purchasers into clients loyal to your service. 

With the wide domain expertise that our in-house teams of marketers and data scientists demonstrate, the customers of our company get the full spectrum of services pertaining to the successful performance of full-fledged machine learning marketing projects.  

How is machine learning used in marketing?

Machine Learning in marketing is used in the following areas: automation of marketing processes, acceleration of email marketing, content analysis, and automation of SEO practices.

What are the four types of machine learning?

ML algorithms are usually divided into the following types: Supervised learning, Unsupervised learning, Semi-supervised learning, and Reinforcement learning.

Is machine learning required for digital marketin?

Today, there are still companies that dispense with machine learning solutions in their marketing processes, based only on manual data processing. Nevertheless, it is already clear today that a company that doesn't use ML in marketing won't be able to reach a higher level, since it will eventually fail to process content so quickly and give users what they want.

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