What if you could predict customer behavior

Artificial intelligence (AI) in marketing: application and examples

Artificial intelligence (AI) can be used effectively in marketing with simple methods. For companies, AI offers enormous optimization potential, especially in marketing. In this way, completion rates can be increased, customer behavior can be predicted and personalized advertising can be played.

Artificial intelligence has been used successfully in marketing for years, but at the same time many companies are only just beginning to use AI.

In this article I will show you what artificial intelligence is, what opportunities you have in marketing through its use and which application examples there are of artificial intelligence in marketing.

  1. What does artificial intelligence mean in marketing?
  2. What are the advantages of artificial intelligence in marketing?
  3. Which application examples for artificial intelligence in marketing are there?
  4. Conclusion: AI in marketing

Artificial intelligence in marketing - the most important things at a glance:

  • The term artificial intelligence is an abstract term that encompasses all aspects and topics of computer science, themachine learning, math and statistics covers.
  • Artificial intelligence offers a number of advantages to marketing in terms of Customer focus  
  • It can also use artificial intelligence Sales increases achieved and marketing costs are reduced
  • The fields of application of artificial intelligence in marketing are diverse and range from Price optimization using AI, over Chatbots, up to the procedure of Prevention of dismissal 

What does artificial intelligence mean in marketing?

Artificial Intelligence (AI) in marketing means using algorithms to search existing customer data for interesting patterns and relationships in order to make predictions about future customer behavior and purchasing behavior.

These predictions and recommendations can then be used in a targeted manner in order to generate more sales through personalization and also to save costs through targeted control of marketing activities.

The term "Artificial Intelligence (AI)" is abstract and covers many different aspects and topics in computer science, machine learning, mathematics and statistics. AI is a branch of computer science and is based on machine learning methods, understanding of natural language, deep learning and reinforcement learning.

What are the advantages of artificial intelligence in marketing?

A Google study shows that successful marketers invest in AI and machine learning more than twice as often as other marketers.This fact shows how important AI is in marketing. Artificial intelligence in marketing has the following advantages for companies:

  1. Customer needs along the entire customer lifecycle are better understood and individually addressed through personalized marketing. This leads to targeted marketing and stronger customer loyalty.
  2. Marketing budget is issued to the right customers who have a high customer value.
  3. Buying behavior the customer can be precisely predicted and served proactively with campaigns at the right time.
  4. Critical Pattern, such as customer churn, are recognized at an early stage. This enables you to act in good time and to retain customers in the long term.

The most important Benefits of AI in marketing are summarized in the following video:

How can AI reduce marketing costs?

The points mentioned lead to an improved customer journey for customers through relevant communication. At the same time, companies can save marketing costs by using the marketing budget for customer groups that have a higher customer value. As a result, sales can be increased with the same marketing budget.

How can AI increase sales?

Artificial intelligence benefits not only within the marketing channels. The methods of artificial intelligence can also be used in email marketing or on-site in the web shop to personalize the customer journey. This customer-centered marketing, which is precisely tailored to customer needs, can also lead to increased sales and improves customer loyalty.

So-called recommendation systems are often used, which generate the right product offers for e-mail campaigns or are displayed on-site in the web shop.

Which application examples for artificial intelligence in marketing are there?

Artificial intelligence is used in marketing for various tasks in order to optimize marketing processes, tailor targeting to the needs of customers and make the right offer at the right time. In the following, I will go into the individual application examples of artificial intelligence in marketing.

Use Case # 1: Customer Lifetime Value Forecasting

The Customer Lifetime Value (CLV) or Customer value is a very well-known concept from marketing management. The CLV includes the evaluation of customers according to their profitability and open potential.

Now you are probably thinking: How does the CLV relate to artificial intelligence? Quite simply: for a good CLV, forecasts have to be made for the future; this is based on machine learning and is part of AI. A well-modeled CLV can predict very accurately how a customer will develop over the next 12 months. Marketing budgets and campaigns can then be planned much more precisely.

Some companies go one step further and calculate a CLV or sales forecast for every combination of customer and product group. In this way, cross-selling and up-selling potential can be identified and used in certain product groups.

The following questions answered a customer value:

  • How much potential does the customer have in the next year?
  • What sales do I expect from the customer in the future?
  • What marketing budget should be spent on the customer?
  • Are there customers who are not profitable and have little potential?

A Customer Lifetime Value (CLV) is made up of these Components together:

A machine-based customer value is a very useful marketing control element. Each individual customer is assigned a certain value, according to which then Marketing activities can be controlled individually.

Because customer lifetime value plays a crucial role in marketing, I wrote the following article about it:Predict customer lifetime value correctly

Use Case # 2: Prevention of Dismissal Through Artificial Intelligence

Detecting terminations at an early stage is particularly important for contract and subscription-based business models. Acquiring new customers is usually very expensive and therefore these business models are often only profitable if the customer lifetime value is high. This is achieved through long customer relationships. Predicting customer churn, i.e. customers who may churn, is an interesting use case for artificial intelligence in marketing.

AI here can be helpful for these two use cases:

  • Customer churn
  • Customer recovery.

Predicting customer churn

In detail, one tries to derive terminations based on machine learning and the resulting patterns through past behavior. Based on these patterns and relationships, the active customers are then assigned a probability of termination.

Customers with a high probability of termination, i.e. termination of the customer relationship, are provided with an attractive offer as a preventive measure. In this way, they can be tied again for the long term. This is a common use case for artificial intelligence, especially in contract business such as telecommunications, insurance, banks, etc.

Of course, there are also business models that tend to be more short-term, such as services that can be canceled on a monthly basis or prepaid-based usage. A prediction can also be made for this using a similar procedure.

Because the topic of churn is so important, I wrote a very detailed article about it: Churn Prediciton: Forecasting Customer Churn

And an article on the impact of churn rate on business profitability.

Predicting customer recovery

Also the Customer recovery, which is often associated with high costs, can be supported by artificial intelligence:

  • Who do I have to actively contact about recovery measures?
  • Which customer do I just have to send an email to in order to renew a contract?
  • Which discount leads to the extension of the contract?

The answers to these questions are data-supported and accurate thanks to AI. So you can efficiently carry out customer recovery.

Use Case # 3: Chatbots Learn to Sell

Powerful AI algorithms make it possible Chatbots as customer advisors and to use for sales preparation. Chatbots can advise customers in e-commerce and suggest suitable products through targeted queries.

On the one hand, this has the advantage that you can collect more data in the sales process and, on the other hand, they help Chatbots so the customer right product to find. If the chatbot is intelligent enough and is even combined with a recommendation system, then this can become an individual shopping experience for shop visitors.

Use case # 4: price optimization through AI

It is quite normal today that product prices on Amazon adjust according to demand or that prices on large hotel portals change within minutes. Behind it is artificial intelligencewhich not only adjusts the prices based on time, but also individually based on the willingness of users to pay.

The basis for individual pricing are large amounts of data that are stored in big data systems and are used as influencing factors in the analysis. In addition to information on price development and the individual products, customer-specific characteristics (such as purchase history, age, location, online usage, etc.) are also part of the machine learning method.

Based on these characteristics, the price acceptance of each customer is analyzed and predicted, which leads to an optimization of the sales probability.

Use case # 5: Predict buying behavior for marketing campaigns

Many companies invest an enormous amount of time in optimizing campaigns using complex sets of rules. Often this works very well to a certain extent, but at some point you can no longer achieve added value with simple rules. Artificial intelligence that helps with intelligent data analysis Targeted use of the marketing budget and thus increase efficiency.

In marketing, the artificial intelligence use for the following applications:

  • How probably is it that a customer in a particular Product group make a purchase in the next month?
  • Which one Advertising Materials is the right one for the customer?
  • When do I have to address the customer so that his Buying cycle fits?
  • In which product or contract can I involve the customer?
  • Which customer do I need a Discount give so he buys?

These questions (and many more) can be answered with relatively simple approaches to artificial intelligence answer.

Through the use of artificial intelligence in marketing I have marketing campaigns around 200-300% increase in completion rate (previously no use of artificial intelligence).

More on the subject under Next Best Offer (NBO).

Use case # 6: Personalization in Marketing

The personalization in e-commerce is a known use case for artificial intelligence. The trend of personalized customer approach with the right offer will continue in 2020 and serve as a growth factor for companies.

Also the Entry into Artificial Intelligence in Marketing often happens via recommendation systems as a use case.

The advantages of personalization are obvious and can be easily measured using simple tests:

  • Stronger customer loyalty
  • More cross-selling
  • Increase in long-tail sales
  • Increase in activity

The implementation is often a bit more complex, as the processes have to be highly automated.

We believe the combined effect of personalized referrals will save us more than a billion dollars a year.

Carlos A. Gomez-Uribe

Companies like Netflix, Spotify, Amazon and Facebook are among the absolute thought leaders in the field of personalization.

Use case # 7: customer segmentation for marketing audiences

Customer segmentation through cluster algorithms are great application examples of artificial intelligence in marketing (here you can find more information on customer segmentation).

A cluster method is an unsupervised machine learning method; it is not trained on the basis of a target variable, but forms independent groups and thus subdivides the data set.

The Cluster process divides the customer base into a certain number of possible, based on various (behavioral) variables homogeneous subgroups. These subgroups can be distinguished from one another by characteristic patterns. So can a Customer base in e.g. 10-20 easily explainable groups be subdivided.

Described exactly Customer segments give marketing deep insights into the properties and behavior patterns of the target group groups, so these can be used well for campaign optimization.

I have described customer segmentation using an example in Python in this post.

Use case # 8: sentiment analysis

Sentiment analysis is the evaluation of unstructured data such as text, images or audio. In marketing, sentiment analysis (or text mining) is often used to analyze comments, posts, e-mails and reviews from the Internet.

In marketing, sentiment analysis is also known as sentiment analysis. Frequently occurring problems of individual products can thus be identified more quickly, or customer complaints can be dealt with more quickly in service.

The basis for this artificial intelligence is the understanding of natural language (English: Natural Language Processing). Here the AI ​​is trained to understand and interpret our language. Not only are single words interpreted, but complete text components are brought into context.

Use case # 9: Returns optimization with artificial intelligence

Another use case for artificial intelligence is returns optimization. Returns are a big problem, especially in retail. Often they lie Return rates over 30-40%which of course causes high costs.

How helps artificial intelligence to reduce returns?

Reduce returns in e-commerce through machine learning

Through the use of machine learning, the customer is given precise recommendations on suitable sizes, preferred colors, styles and cuts. in the Sales process is intervened in order to reduce the return rate.

With the help of transaction data from web shop, ERP and checkout systems, machine learning can be used to identify clear patterns that determine an individual return probability per product. The subsequent optimization of the sales process through alternative offers for the customer enables the provider to sustainably reduce returns.

Ultimately, you want to offer customers good service. Such an approach can be used to give appropriate recommendations to the customer. This has in addition to the obvious Reduction of returns, also one positive effect on the shopping experience.

Conclusion: AI in marketing

Marketing experts around the world use AI in marketing to better understand, serve and retain their customers. In particular, the further development of products, the customer journey and marketing campaigns lead to increased sales through improvements.

Artificial intelligence is developing extremely quickly. However, many possibilities only arise through an intelligent collection of customer data. Customer data is the fuel for AI. The more efficiently marketing uses this data with the help of artificial intelligence, the better customer needs can be understood and acted on in a customer-centered manner.

If you have any help with planning and implementing AI in marketing, then get in touch with me. I am happy to hear from you.