Artificial intelligence in marketing strategy — 5 examples of use

Artificial intelligence is more and more often introduced wherever possible because it holds leverage over human analytical work and traditional analytical tools. Marketing, in particular, is commonly integrated with AI due to the vast advantages coming from this and relative simplicity of this implementation. You don’t need to have an extensive business to gain the benefits arising from incorporating AI into your marketing strategy.

Originally published at https://websensa.com on April 7, 2020.

AI and ML systems in companies of all sizes

This revelation is becoming more common because of the increasing availability of machine learning-based tools and the growing simplicity of using them. Of course, to develop an entire software and train neural networks basing on big data, it’s still needed to have a whole team of data scientists, coders, and software developers.

However, it is possible to start with a modest amount of data and relatively uncomplicated (and available) analytical tools. The implementation of simple AI technology might still vastly improve your marketing strategy. It can also prepare a stable ground to integrate more complex AI software in a more distant future. So, what are the advantages coming from the introduction of AI into your marketing strategy?

Advantages of implementing AI into marketing strategy:

1. Perfectly specified target groups

Data-driven decisions allow you to accurately specify target groups that are interested in your product. These people are more likely to begin an interaction with your company. Getting real-time data about the interaction with your customers enables you to make real-time decisions.

This way you can adjust your marketing strategy for the current situation. After all, adapting to the current situation is vital in marketing to maximize the number of customers/users.

2. Sales forecasting

In sales forecasting, analytical tools once again are irreplaceable. By predicting sales using AI, all the decisions can be data-based and thus are significantly more reliable. Predicting sales and company’s outputs enables us to decide about marketing strategy.

Whether it’s vital to take on an aggressive marketing strategy to quickly increase sales? Or is it possible to lay back and focus the energy elsewhere? It allows determining the importance of actions that need to be taken and suggest where the main focus should be directed at.

3. Product recommendation

AI is introducing a highly personal product recommendation. This proves to be an extremely valuable aspect, as it’s highly effective. Its effectiveness is confirmed by the success of the corporate giants such as Netflix, Spotify, or Amazon. They all create a personalised customer experience and succeed in this approach. They collect data, learn users’ preferences, and suggest appropriate recommendations.

To put this simply, AI and machine learning-based mechanisms base on similarities.

Product recommendation using ML is based on:

  • similarities between products

Firstly, the systems use the alikeness of products. This means that software knows that a customer is likely to buy complementary products. Thus ML-systems are programmed to identify the similarities, and then recommendation engines suggest products based on that.

  • similarities between clients

Secondly, AI software bases on the fact that similar persons might like comparable products. In this case, ML software needs to identify the similarities between customers; for example, their age, sex, ethnicity, etc. Recommendation engines will suggest a product for someone based on the preferences of another, the akin consumer.

4. A deeper relation with your customer

AI allows you to ‘see’ into your customers’ minds. You know what they’re thinking and feeling. It means that you are able to adjust your marketing strategy and modify the message to improve the interaction and overall maximize the effects.

You will be able to develop a deeper relationship with your consumers as a result of personalised user experience and accurate recommendations.

5. Customer behaviour

Another aspect that is influenced by the introduction of AI to marketing is the customer’s behaviour. Recommendation engines are used to limit the amount of options presented to customers. Why do people like to attend shops with their preferred style of clothes? It’s more challenging to search for something we might find suitable if we have a lot of choices available, out of which most are utterly unfit for us.

We do like to choose, but we want to choose among the things that we already like. It’s simply more work otherwise, and most of us prefer the easy way. Therefore, there is no point in doing all that work by oneself. The software can get to know all the preferences and present to users a list of choices that are suited ideally to their preferences.

Summary

All in all, the main point is to show you that implementing AI analytical tools into your marketing strategy will vastly improve it. This improvement comes from the fact that it will enable you to make real-time data-driven decisions. This way your message will be presented to those people who are interested in it. Furthermore, creating a personalised user experience will improve the relationships with your customers.

If you want to learn more about the usage of artificial intelligence in modern business, read how we implemented AI in three of our projects: Artificial intelligence — our means of work at WEBSENSA.

Originally published at https://websensa.com on April 7, 2020.

IT company providing technology solutions for Publishing, E-commerce & Financial industries, but not limiting itself just to them. https://websensa.com

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WEBSENSA

WEBSENSA

IT company providing technology solutions for Publishing, E-commerce & Financial industries, but not limiting itself just to them. https://websensa.com

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