Business and organization marketing has greatly evolved since its inception back in the late nineteenth century. Marketing is the use of market or client research to promote or sell services and goods offered by different entities.

Marketing tactics have evolved through four distinct phases since their inception. These phases are:

1. Product-oriented marketing
2. Sales oriented marketing
3. Consumer-oriented marketing
4. Relationship oriented marketing

All across these four distinct marketing phases, the push for the evolution has been two factors: marketing technology and customer needs. Consumer needs entail adopting marketing tactics to ensure that they meet consumer needs, thus getting more clients.

Marketing technology entails the use of the best and high-performing technological developments for marketing. Digital marketing revolutionized the marketing industry significantly. 

It offered cheaper marketing options that were oftentimes more efficient. Digital marketing strategies also reached more people as compared to traditional marketing methods. These two benefits over traditional marketing prompted the move to digital marketing for many businesses.

Machine Learning in Digital Marketing

Further, digital marketing technologies have evolved and have adopted the use of machine learning. Machine learning is the use of already existing data to generate data models that can analyze new data and extract insights from it. 

Machine learning models can also be developed with technology that allows them to become better throughout their use. They become better in use because they get more data and information.

In digital marketing, machine learning is used for market data analysis and ad insights analysis. Machine learning has helped many businesses and organizations to forecast and predict data trends through analyzing pre-existing data. 

In this article, we shall discuss the impacts of machine learning in digital marketing.

1. Personalization of Client Recommendations

If you are a Netflix user, how many times have you felt that the site knows your movie and TV show taste very well? Many times, right? Netflix uses machine learning models to analyze all its users and understand their preferences. Upon analysis, the site can easily predict which movies or TV show a user will like.

This is an example of the use of machine learning for personalized recommendations. Personalized recommendations help build a user’s loyalty to a business. This is because the user feels that the business understands them well.

However, managing recommendation models can be a hustle because the system requires a lot of analysis on big data and algorithm designing. After data analysis, the business can end up with numerous recommendation models for different client clusters. Model management for digital marketing comes in to help businesses manage their models. 

Model management services handle the development, and deployment of machine learning models for businesses. Model management services let a business focus on its core functions, while the model management businesses work on the development of optimized machine learning models for their clients.

2. Accurate Market Predictions

A key distinguishing feature of digital marketing is the collection of data. In digital marketing, data is collected for analysis of the success rate or failure rate of the marketing tactic and other metrics. However, the collected data can be too much for manual data analysis. 

Machine learning can help in the analysis of collected and gain meaningful insights from it. These days, the use of machine learning models for market predictions is a common tactic used by businesses.

Market predictions help businesses and organizations to make necessary strategic decisions. The strategic decisions help to ensure that the businesses overcome the market changes. 

3. Automation of Marketing Processes

Source: Pixabay

Contrary to popular opinion, the sole purpose of machine learning is not to observe and strategically target users. Machine learning was developed for the sole purpose of enabling process automation without requiring human intervention.

Digital marketing campaigns consist of numerous steps. Of these steps, many of them are mundane and/or repetitive. Machine learning models can be deployed to perform such functions, thus ensuring an optimized and successful marketing campaign. 

This is because when humans perform repetitive or mundane tasks, they often get bored and tired therefore reducing their efficiency. However, this is not the case with machine learning models. They can perform 1000 iterations of the task without reducing their efficiency.

Automation of digital marketing processes allows marketers to focus on tasks that require human intervention. This automation creates an efficient marketing campaign process.

4. Ad Fraud Detection

Since the inception of internet technologies, fraud has been a great plague to the service. Reports state that in 2020, there were a total of 2.2 million fraud cases reported. These statistical figures depict the worrying state of internet services abuse. 

Ad fraud is the attempt by criminals to rob businesses of their digital advertising efforts. Ad fraud cases generally involve criminals stealing online identity and pretending to be a business, then scamming all the willing buyers.

Digital ad fraud detection is a lengthy process that involves analyzing data from various sources to identify sketchy trends on ads. This process is often too hefty to be performed by humans. Recently, machine learning models have been used to identify and flag ad fraud.

The machine learning models simultaneously analyze and compare data, such as digital ad engagement trends, ad timing trends, etc. The models critically analyze the data to identify any unscrupulous activity.

Once machine learning detects ad fraud, it automatically blocks the ads from the digital marketing platform. These models can also be integrated with deep learning technology that allows them to gain more knowledge about ad fraud detection. This will improve the model’s efficiency at ad fraud detection.

5. Dynamic Product and Service Pricing

Machine learning models can be deployed to analyze the regional pricing for products and services and give a detailed report. This allows businesses to price their products and services at par with their competition, without overpricing or undercharging. 

This service can also be used to identify clients who looked up the product and did not buy, or added it to the cart and left it. The model offers timely discount codes to such clients to coerce them into completing the purchase.

Conclusion

The deployment of machine learning models in digital marketing paints a bright future for the service. With better integration of deep learning, online ad fraud cases can be significantly reduced. They can be reduced with the use of ad fraud detection models equipped with deep learning technology.

With machine learning, the efficiency of digital marketing strategies will improve significantly, thereby improving profit margins for companies.