How to supercharge your marketing strategy with AI automation

The intuitive collaboration between artificial intelligence (AI) and human effort is driving innovation across a range of industries such as health sciences, supply chain management, manufacturing and research & development. In the business world, AI automation equips companies to create impactful product and marketing strategies and enhance operations to increase productivity.

AI automation powers AI marketing, which marketers, like you, can use to pivot your campaigns seamlessly based on real-time market indicators, empower your customer service teams and grow holistically. You can create highly personalized marketing campaigns that resonate with the audience, publish content at optimal times for maximum engagement and execute targeted advertising seamlessly.

In this article, we talk more about AI automation, how it benefits organizations and what its most common applications are in the business world.

What is AI automation?

AI automation is the integration of AI with the automation of repetitive, rules-based tasks. This combination enriches regular automation by embedding AI capabilities, like the ability to learn and adapt from previous tasks, into the process. It is used in several areas to enhance efficiency such as customer service, manufacturing, supply chain management, e-commerce, quality assurance, product design and more.

AI automation has revolutionized how brands approach their business and marketing strategies. AI marketing tools automate repetitive tasks with speed and efficiency and enable you to analyze vast amounts of data, such as customer or employee feedback and social conversations, at speed and scale, to surface necessary insights that can be instrumental in improving a business.

Take for example, Sprout’s AI-driven Social Media Listening solution. The tool enables you to analyze social listening data—up to 50,000+ messages a second and up to 600M+ messages a day— so you can automatically sift through billions of data points and get key brand insights within seconds. This enables you to make faster, more impactful decisions that resonate with your target audience and help you meet and exceed customer expectations.

What is intelligent automation (IA)?

Intelligent automation is the blending of AI with robotic process automation (RPA) and is often used synonymously with AI automation. By combining AI tasks like machine learning (ML) and natural language processing (NLP) with traditional rules-based automation, intelligent automation systems handle complex AI analytics to improve decision-making and workflows.

This translates into streamlined operations and improved productivity. By integrating the cognitive capabilities of AI with understanding big data, intelligent automation helps with predicting outcomes so companies can proactively improve processes.

For instance, intelligent automation can be used in multi-step email campaigns, like a promotional campaign where each step can be automated based on the recipient’s behavior. This eliminates the need for manual reading of every response for further action as it gets automated based on pre-defined conditions. Thus, making you time-efficient while enabling campaign management at scale.

How AI automation benefits businesses

AI automation enables companies to automate workflows and get actionable insights through AI tasks like sentiment analysis so they can make tangible changes to drive growth. Let’s dig in further to see the benefits of intelligent automation.

1. Enhanced productivity

Intelligent automation increases team productivity because it automates and optimizes workflows based on analytical models that ensure maximum productivity. It also reduces cumbersome, time-consuming tasks such as feedback data analysis by automating them using machine learning. This enables AI tools to automatically analyze millions of data points from numerous data sources in minutes to extract insights that would have otherwise taken several manual hours to complete. It also reduces the risk of miscalculations common when analyzing complex data manually for patterns and trends.

2. Sentiment mining

Similarly, AI automation tools use machine learning to automatically identify sentiment in data as postive, negative or neutral, and thereby give you insight into how people perceive your brand and services. Sentiment analysis can also be applied to automatically prioritize customer complaints so customer care teams know which cases to handle first.

In Sprout, you get sentiment analysis metrics of your brand automatically in the form of graphs and reports. These sentiment insights are based on social conversations around you from a certain timeline, from different demographics or on various topics, to help you understand how your product/brand is faring in the public eye.

This is a crucial advantage, considering 44% of business leaders think sentiment analysis is critical in understanding consumer behavior and are already using it to guide their strategies, according to our 2023 State of Social Media Report.

3. Cost-efficiency

AI automation doesn’t just help with productivity: it also enables you to smartly reallocate team efforts more strategically. Plus, since AI tools can take over mammoth data analysis projects in minutes (and more accurately than humans), they also reduce the costs associated with identifying and rectifying mistakes based on incorrect insights.

4. Competitive advantage

AI automation uses advanced tasks like NLP to analyze and filter text with pre-defined topics and keywords so you can keep a tab on what people are saying about your brand and its competitors. These algorithms let you dig into the identified data further to investigate potential market drivers.

In Sprout, you can do this by applying Listening Alerts to notify you of shifts in conversation around your preferred Listening Topics. This functionality ensures you never miss out on relevant conversations happening around your brand or competitors.

And because intelligent automation enables you to process big data from disparate sources simultaneously, you get valuable insights in real-time so you can alter your approach promptly.

5. Scalability

Unlike traditional systems that require an increase in team size to manage higher volumes of tasks and their complexities as a business grows, intelligent automation handles those same tasks while maintaining efficiency. This enables you to scale quickly and efficiently while ensuring flexibility in your work environment.

6. Risk management and compliance

AI automation helps you build an effective risk management strategy by identifying inflammatory or sensitive comments about your brand. This also enables you to manage your influencer marketing more effectively to ensure your influencers are following brand guidelines. Similarly, AI automation identifies financial and legal fraud by scanning outliers in data patterns to support compliance.

How AI automation works

Several AI tasks work behind the scenes to power AI automation—including technologies such as named entity recognition (NER) and sentiment analysis, which are pivotal in managing brand reputation and quality assurance. For example, these technologies can automatically identify anomalies in data patterns such as potentially provocative comments based on pre-determined entities and keywords and alert the end-user. This capability helps PR, social and customer service teams to immediately address such comments and take appropriate action.

Here’s a brief look at what happens behind the scenes in an AI automation process and how data engineers build a model.

Step 1: Data collection

Data is collected from different sources such as social networks, customer forums, news articles and survey responses. If we’re building a model to identify sentiment in data and send alerts and notifications when an inflammatory response is detected, data from a wide range of social media conversations is collected. The bigger the data, the more accurate the model will ultimately be.

Step 2: Data preparation

The gathered data is cleaned by removing outliers and inconsistencies and then formatted to suit the AI tool that will be used for data analysis.

Step 3: Training the model

The tool is now trained with this clean data, which is annotated or labeled so machine learning algorithms can identify and understand word patterns and associations. NLP and part-of-speech taggers help in text analysis while deep learning algorithms ensure the model remembers these patterns so it can apply to similar data analysis in the future.

The results are validated against the testing data, and the cycle is repeated until the results are optimal.

Step 4: Data processing

The AI tool can now process data like social listening or customer feedback and will swiftly pick up @mentions and keywords it has been trained to identify. The model is further refined so it is specific to your business and industry.

For example, if the model picks up a post that violates social media guidelines by detecting negative words built into its vocabulary, it will automatically take pre-defined actions such as hiding the post, curbing its reach and alerting the admin through notifications.

Step 5: Continuous learning

Neural networks help the AI tool be in continual learning mode so it remembers the results and notable data points from each data analysis cycle. It will also automatically add new words and @mentions to its vocabulary from any fresh data it analyzes. This makes it smarter, faster and more efficient with time.

What are the applications of AI automation?

From influencing the content we watch on our favorite streaming channels to enriching patient care and hospitality management, applications of AI automation are abound.

Let’s dive into some of them in detail.

Marketing and advertising

One of the most significant contributions of AI automation to marketing is in drawing insights from conversational analytics and enhancing business efficiency through software like RPA and business process management (BPM). Companies today have access to innovative market research methods that can bring key insights within minutes, compared to traditional time-consuming approaches like written surveys and focus groups.

These insights help brands personalize content for marketing and advertising. They also serve to influence other departments, including product or PR, to enhance brand engagement and ensure growth.

Robotic process automation

RPA software is used to automate rules-based tasks (such as report generation) and to power marketing automation like setting up automatic workflows for publishing content. This ability is pivotal in helping remote teams collaborate better with greater transparency in workflows.

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This article was written by Sprout Social and originally published here.

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