Hollywood movies have been quite successful in showing artificial intelligence technology’s prowess in a rather dramatic fashion. From 2001: A Space Odyssey’s HAL 9000 to Iron Man’s Jarvis, films have introduced us to some interesting examples of machine learning and artificial intelligence in action. 

But what about the world we live in? The one where AI hasn’t overpowered humanity as they show in the movies. At least for now! 

A report by Mckinsey Global Institute estimates the impact of AI on businesses to be approximately in the $3.5 trillion to $5.9 trillion range annually across 19 industries and in nine business functions. The company arrived at this number after scanning 400 use case scenarios across different sectors. Mckinsey Global Institute used data from traditional analytics to compare it with deep learning techniques, and as it stands, artificial intelligence technology is growing exponentially in our contemporary business world. In addition, they found that an organization benefits the most when it uses AI in its top-line and bottom-line activities like marketing/sales and supply chain/manufacturing management. 

Let’s dive further and glance through some examples of brands that use AI in marketing to improve productivity, sales efficiency, or data/insight accuracy.

How do marketers use Artificial Intelligence to boost customer engagement in B2B?

American Marketing Association wanted to engage with its 100,000 + subscribers on a daily basis with its content. To enable this, they approached Rasa.io –  an AI-powered system that uses NLP (natural language processing) to generate newsletters. 

To tailor each newsletter sent to a subscriber, the system employs AI for curating and filtering material from AMA’s sources. This involves selecting each piece of material, placing articles, and selecting a subject line for each reader. As a result, they produced a newsletter that gives each subscriber a unique experience.

Furthermore, the platform integrated the emails with AMA’s internally created content and placed it at the top of the newsletter, thus increasing visibility. It was a game-changer for the Association’s digital presence as they became the most clicked source for newsletter referral traffic.

The results: AMA clocked an engagement rate of 42% on their newsletter compared to the industry standard of 18-21%. 

The above case study tells us the dynamic range of AI’s capabilities. It not only addresses the scale of work but also factors in the humane side of creating content. This has been possible because of the technology’s ability to read and interpret data much faster and efficiently. 

There are many more scenarios where machine learning and artificial intelligence have triumphed to help companies function smoothly. From enhancing creativity to automation of redundant tasks, AI can accommodate it all. So, let’s take a closer look at how AI technology has helped B2B marketers level up their game:

1) Predicting Consumer Insights 

Predictive analytics forecasts future outcomes using historical data, machine learning, and statistical algorithms. Marketers may use predictive analytics in various applications and solutions, but it is especially effective for campaign planning and optimization.

Predictive analytics has the potential to guide marketers and save their time, thus reducing mistakes. One such example is Marketo – Adobe’s marketing automation software. 

It scans a user’s website and discovers assets such as videos, e-books, and case studies. The technology predicts the most relevant material to present to website visitors and makes AI-powered recommendations that improve current and identify the top-performing content.

2) Increasing Lead Generation Efficiency

The capacity to collect data and extract insights from marketing and sales data using machine learning and predictive analytics is one of the critical roles of AI. Furthermore, specific AI solutions may give insights into prospects and customers to improve customer experience and conversion rates.

AI features like predictive analytics, and machine learning, are implemented into CRM solutions tools such as HubSpot and Salesforce Einstein. Einstein uses predictive analytics to find patterns and trends in consumer behavior that can assist in identifying leads and opportunities and eventually alerting staff so that they can act on it as soon as possible. Also, data input, which is one of the most repetitive and time-consuming tasks, is automated with the help of such software.

3) Automating Redundant Tasks

According to the 2021 State of Marketing study by Drift and the Marketing Artificial Intelligence Institute, AI is used to increase the efficiency and performance of repetitive jobs. By boosting a marketer’s capacity to make better forecasts, automation can help drive revenue growth. Marketing automation solutions may also improve the effectiveness and efficiency of content development and distribution.

4) Enhancing Creativity 

AI can help break down organizational barriers, link people across organizations for creative collaborations, and offer new structures that integrate business processes. Thus allowing business executives to comprehensively assess their customer experience strategy. All of this may result in daring advertising, the development of unique applications and services, and users reconsidering a brand’s position in their life. The underlying denominator is that AI-enabled enterprises have a framework that enables the company to swiftly personalize and scale projects while reducing inefficiencies that usually stifle creativity.

5) Structuring Data and Digital Asset Management

One of the key applications of artificial intelligence and deep learning at Facebook is to bring structure to unstructured data. DeepText, a text recognition engine, is used to comprehend and interpret the content automatically and the emotions behind the thousands of posts (in different languages) published every second by its users. Eventually, the social media giant can share these insights with brands wanting to reach out to a specific target audience. 

Furthermore, Digital Asset Management (DAM) solutions with AI capabilities aid in the automation and simplification of many elements of managing content such as photos, video, and other media. Hence, marketing teams benefit from using a DAM platform since it allows them to simply (and quickly) identify digital content for their campaigns. An AI-powered DAM, for example, can automate the process of labeling photographs, making them searchable throughout the business. This is useful when you have a lot of stuff to organize and tag.

6) Supporting Personalization

Marketers may utilize AI to create customized messaging for their customers during the customer lifecycle. Email marketing may be enhanced and tailored depending on user behavior using AI modules that modify experiences. At the same time, deep learning can pick up cues on specific consumer insights that can be used as the trigger points for a brand to tweak its communication strategy or product placement for better results.

In Conclusion

AI is omnipresent – with use cases varying from customer service to implementing audience segmentation and sales forecasting. AI can make marketers more efficient and productive by removing repetitive jobs, minimizing human error, and optimizing company processes. 

In addition, the ROI from the AI platforms/tools is multi-layered – one that utilizes data science, deep learning models, and predictive analytics. Marketers may expect lower data preparation, onboarding, and integration expenses, as well as faster data activation. They may also be able to achieve multi-channel conversions and greater conversion rates. Finally, B2B marketers can forecast the effectiveness of data and will be able to provide recommendations on how to improve campaign messaging for optimal ROI. 

Our constant push to innovate has created marvels in the past, and now the future looks no different. AI has the unique ability to provide key insights within a short period that allows humans to tap their creative potential and increase productivity using the same data.