Companies are investing in these tools to get more out of their efforts. This includes handling customer support inquiries, creating personalized offers and analyzing customer data. Whether you’re looking for marketing automation or to write email copy, there’s an AI marketing tool that meets your AI marketing needs. For businesses, these investments mean faster, better decisions and a competitive advantage.

Read on to find out about the 10 best AI marketing tools you can use to speed up your workflows.

Table of contents:

• • • What is an AI marketing tool?
    • Benefits of using AI marketing tools for marketing
    • Best overall AI marketing tool
    • AI marketing tools for copywriting
    • AI marketing tools for editing
    • AI marketing tools for digital marketing
    • AI marketing tools for automation

What is an AI marketing tool?

An AI marketing tool is a software that harnesses artificial intelligence and machine learning to enhance the execution of marketing tasks and workflows. With AI being predictive based on provided data, its use within marketing tools is incredibly helpful for repetitive marketing workflows.

The nature of AI marketing tools is quite sophisticated. They intake data that humans feed them–from its built-in database and users’ direct inputs of data into the tool. From there, they form an insight based on what we are asking it to do or it’s programmed to do. Lastly, the tools will provide an action based on our request.

What’s remarkable is that some AI tools for marketing are automated, which can lift a notable workload off of marketers.

Benefits of using AI tools for marketing

AI marketing tools help marketers work faster by executing repetitive tasks based on a pattern of work that they have already accomplished. With continue collaboration with AI tools, marketers will clearly see their skill sets and marketing campaigns continue to advance.

Without making this the longest blog post you’ve ever read, here are just some of the key ways marketers are reaping the benefits of using AI marketing tools:

AI for Customer Personalization

One of the noteworthy ways that AI marketing tools are benefiting marketers is that AI is assisting marketers in producing personalized content for each customer. The benefits of this are astronomical with customers expecting personalization in every point of their buyer journey.

According to McKinsey & Co’s survey around personalization, customers are expecting brands “to know them no matter where brands interact with them.”

This is where content personalization marketing tools that utilize AI are so impactful for marketers. These tools personalize which content is being served to which buyer persona. Content personalization tools analyze a customer’s profile and leverage intent data. They predictively adjust which content is being served to them based on what best relates to their needs or preferences.

AI for Content Creation

One of the most common ways marketers are using AI is for content creation. Marketers are using it for discovering fresh content ideas, creating content outlines, content editing, and to produce written copy. A few examples of the written copy marketers are using AI writing tools for are social media posts, blog posts, and emails.

This is taking off a massive mental load for countless marketers in the creative writing process, providing them with a rough draft for content in just seconds. From there, it allows them to add their own human flair to it and adjust to their brand’s tone of voice.

AI for Data Analytics

Another incredibly advantageous way that AI is being incorporated into marketing tools is through data analytics. AI can analyze enormous amounts of data, draw insights from said data, and summarize the findings.

The uses for this are broad, but one of which is particularly helpful is for sentiment analysis. AI can read vast reservoirs of data to understand user sentiments across various online platforms. It can help marketers better understand real-time brand sentiments, evolving market trends, and the interests of their target audiences.

Using AI sentiment analysis tools set up marketer to keep their fingers on the pulse of their target consumers and be able to proactively adjust to stay relevant to them.

Now that we have covered just a breath of the benefits of AI marketing tools, let’s hop into the AI marketing tools we recommend most!

Best overall AI marketing tool

More companies are expanding their AI usage as they scale or undergo digital transformation from legacy software. And social data is at the center of many marketing transformations.

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According to our 2023 State of Social Media Report, 97% of business leaders agree that AI and machine learning (ML) will enable companies to analyze social media data and insights more effectively and use it to improve their overall marketing efforts.

Marketers are using AI in several ways, such as sentiment analysis to understand and respond to customer feedback and crises, competitive analysis to stay ahead of the market and content recommendation engines to improve engagement. AI-driven insights are playing a huge role in behavioral segmentation and other agile marketing strategies as well.

Investing in an all-in-one AI marketing tool that easily fits into your tech stack helps you scale your marketing efforts, consolidate multiple marketing functions and streamline workflows across your entire organization. Enter Sprout Social.

1. Sprout Social

Sprout Social’s AI capabilities make it one of the most intuitive marketing tools on the market. The all-in-one platform accelerates business impact through social, so you can strengthen collaboration between teams and put your best foot forward when you engage with audiences.

How Sprout helps marketers

Sprout handles a range of marketing functions—from social media and customer care to digital marketing and competitive intelligence—giving you more valuable insights for faster decision-making.

Use Sprout’s AI-powered social tools for:

• Business intelligence. Some 44% of business leaders are using sentiment analysis to understand consumer behavior. Sprout enables you to harness the power of your social conversations by surfacing business-critical information at the speed of social. Its AI and automation power the processing of 50,000+ messages a second in Listening and 600M+ messages per day.
• Reputation management. Sprout’s Listening capabilities set you up for early crisis detection and management by enabling you to identify spikes or unfamiliar trending words from customer conversations.
• Social media scheduling. Sprout’s AI and patented ViralPost technology puts brands in a position to streamline workflows. The latest Suggestions by AI Assist features helps you create high-quality captions in seconds, enabling you to break through writer’s block, inspire new ideas and enhance your post copy. What’s best—this feature is available on all Sprout Social plans.
• Optimizing marketing workflows. Our AI and automation doesn’t require manual setup or continuous investment, so you can start seeing value immediately.
• Customer service. About 41% of marketers are using natural language processing (NLP) to improve their customer service. Sprout enables you to build chatbots to handle customer support requests, suggest replies to customer inquiries or route messages to the right team. And the new AI Assist functionality further helps scale customer care efforts by helping you write robust customer responses quickly and effectively using AI-powered tone and length edits.

Start your free Sprout trial

AI marketing tools for copywriting

AI marketing tools help you create messages that resonate with your target audience and speed up the writing process. Here are some tools marketers are turning to for help with copywriting.

2. Keyword Insights

Keyword Insights is a powerful SEO tool with an advanced AI writing assistant designed for modern content creators. The AI writing assistant seamlessly blends content research, writing and optimization into a single platform. Keyword Insights’ writing assistant offers AI functionality with a touch of human-ai collaboration.

With the added advantage of an interactive AI chat assistant, get dynamic responses to diverse writing requests. Need a catchy introduction or specifics on cod liver oil? Ask, and you shall receive.

3. Jasper

Jasper helps you speed up the writing process using generative AI. It features content generation templates for your marketing content, such as landing pages, emails and blog posts. It offers 11,000 fonts, 2,500 categories and 25 languages you can choose from. Plus, its intuitive interface makes it easy to crank out words quickly.

Try Jasper for free or purchase one of their Creator, Team or Business plans for even more features.

4. Writer

Writer is a writer’s assistant for marketing teams, making it easy to efficiently collaborate on content. Writer’s grammar and clarity suggestions transform your writing into professional and accurate copy. Additionally, it maintains your company’s tone of voice and allows you to build your own database of approved company terminology.

Access Writer for free or purchase a Team or Enterprise plan for extended functionality.

5. Surfer SEO

Surfer SEO helps you improve the content quality of your web pages so your content ranks on search engine results pages (SERPs). It analyzes search term SERPs and compares your content against the learnings of top-ranking pages. It evaluates keywords and other ranking metrics and gives suggestions for optimizing your content for search engine optimization (SEO). Additionally, it offers an outline generator and keyword research tools to create SEO content from the get-go.

Surfer offers Essential, Advanced, Max and Enterprise plans for small teams, agencies or enterprises.

AI marketing tools for editing

Not all marketers are grammarians. Thankfully, there are AI marketing tools for editing that make sure you have error-free copy every time.

6. Grammarly

Grammarly helps anyone, from the CEO to the social media manager, churn out clear communications. It supports many of the platforms you already write on, including Google Docs, Word and Gmail. It also uses generative AI, GrammarlyGO, to help you nail your writing based on context and goals.

GrammarlyGO works in your voice with profile options for tone, formality and professional relevance. Grammarly’s free plan offers spelling and grammar suggestions. For more editing, its paid plan assists with clarity, sentence structure and tone of voice.

You can use Grammarly for free or purchase a Premium or Business plan.

7. Hemingway Editor

Hemingway Editor uses machine learning (ML) to identify editing opportunities at a sentence level to make your writing stronger. It analyzes your content and suggests where to make it clear, more concise or improve your diction. Additionally, it gives you a readability score and grade level to assess the overall performance of your content. Hemingway also plans a new beta version of its solution where AI can fix issues for you.

Hemingway is free to use.

AI marketing tools for digital marketing

A lot goes into digital marketing. Here are some tools that help with targeted emailing, campaign management and conversion rate optimization.

8. Seventh Sense

Seventh Sense is an email marketing tool that uses behavior profiling to attract customers’ attention. It creates predictive models that analyze the perfect time and day to send email campaigns. The selling point? It does this individually for each contact based on their activity. Marketers can increase email deliverability and improve the sender score. Seventh Sense also seamlessly integrates with HubSpot and Marketo.

Seventh Sense offers flexible pricing that works with HubSpot and Marketo’s subscription model.

9. Optimove

Optimove is a customer data platform providing a unified view of customer behavior and insights, campaign performance, hyper-segmentation, A/B testing and multi-channel tracking. These features give you insights into which campaigns to drop or which customers have been exposed to too many marketing emails.

Optimove offers custom pricing solutions based on your company’s needs.

10. Tidio

Tidio is an AI-powered chatbot platform. It helps you focus on conversion rate optimization (CRO) through customer interactions on your website. Use Tidio to improve web conversions and generate leads with live chat to reduce churn rates and build customer loyalty.

Get started for free or purchase a plan starting at $29.

AI marketing tools for automation

If you have a lot of robust workflows or if you are a team of one, consider automating your tasks and processes with AI marketing tools.

11. Zapier

Zapier is the tool on the market for AI-driven automation. Automate time-intensive tasks and turn text-based inputs into data to answer simple questions to help your team cut down on manual work and scale your business with valuable insights.

Use a free version or purchase one of Zapier’s plans for more features.

Test AI marketing tools today

If you’re looking to scale your marketing efforts or transition from a legacy tool, it’s worth trying out AI marketing tools. These tools enable you to take back your time, extract insights from customer data and manage your marketing tasks more efficiently.

No matter your marketing needs, there is an AI marketing tool for you. But, if you’re looking for a centralized AI marketing tool that handles a range of marketing functions, consider Sprout Social. Sign up for a free 30-day trial today.

The post 11 AI marketing tools your team should be using in 2024 appeared first on Sprout Social.

Why this surge? At the heart of this AI revolution is the ability to segment behaviors and target specific customer groups with precision. It’s about making smarter decisions, crafting more personalized strategies and understanding customer needs on a deeper level, all of which are crucial across various business units.

This article will explore the diverse roles AI plays in enhancing business functions like marketing, operations, product development, human resources, customer support and security. Each domain benefits from AI’s ability to streamline processes, improve efficiency, and provide actionable insights, making every interaction and decision more meaningful and effective.

What is AI?

Artificial Intelligence refers to the simulation of human cognitive functions by machines. It includes a wide range of technologies that enable machines to perform tasks traditionally requiring human intelligence, such as reasoning, problem-solving, decision-making, and learning from experience.

Today, AI is an umbrella term that includes various subfields, such as machine learning, deep learning and natural language processing.

The development of AI has led to the creation of numerous applications, including chatbots, virtual personal assistants (e.g., Siri, Alexa), recommendation systems, autonomous vehicles and medical diagnosis systems. These applications demonstrate the practical implementation of AI in performing specific tasks with high accuracy and efficiency.

Which functions of AI are used in business?

Let’s explore the core AI functionalities transforming businesses today, from machine learning and natural language processing to deep learning for AI automation. Each plays a critical role in enhancing decision-making, optimizing operations and driving innovation.

Machine learning (ML)

Machine learning algorithms analyze data, identify patterns and make predictions based on their results. They power AI tools that enable businesses to gain valuable insights from disparate data to help decision-making. While ML models learn independently as they process data, they can also be updated manually based on your specific needs.

NER and semantic search

Named Entity Recognition (NER) identifies entities defined in the ML model as important to a business, such as geographic locations, brand names, famous people, etc. And semantic search helps provide a contextual understanding of a query input by a user. Together, they help process and analyze large volumes of unstructured data to help you improve search accuracy, automate data processing and extract meaningful insights for informed decisions.

NLP and sentiment analysis

Natural Language Processing (NLP) and sentiment analysis enable businesses to understand and interpret human language, making them essential tools for analyzing customer feedback, social media content and other textual data while automating report generation.

Deep learning (DL) for AI automation

Machine learning uses algorithms and learns independently but may need human intervention to correct errors. Deep learning, on the other hand, uses neural networks to learn and adapt to new data patterns with little to no human input. It’s utilized to automate complex data analysis tasks and processes, including image and speech recognition, enabling businesses to streamline operations and improve efficiency.

How artificial intelligence helps in business functions

AI has revolutionized various business functions, including marketing strategies, product development, sales efficiency, customer support, human resources, operations and security. Marketers and business professionals leverage AI to create stronger campaigns, make smarter decisions and streamline workflows. AI is changing the game and setting new benchmarks for business excellence.

Artificial intelligence in marketing

With marketing, AI transforms how businesses interact with their customers in several ways, offering tangible benefits that directly impact a company’s bottom line.

Social listening

AI-powered social media management tools like Sprout Social integrate AI into social listening to help you analyze social media conversations, translating vast amounts of data into actionable insights. This helps you understand customer sentiment in real time, monitor brand health and respond swiftly to market trends, ultimately fostering stronger customer relationships and brand loyalty.

Content personalization

AI personalizes content based on customer behavior, preferences and demographics. For example, Netflix uses AI to provide personalized movie and show recommendations, enhancing the user experience and engagement for its audience.

Sprout Social’s Enhance by AI Assist uses AI to personalize customer interactions at scale. It tailors responses based on the tone of the incoming messages, ensuring that replies are relevant and resonate with the customer’s emotional state. This capability enhances customer engagement by delivering personalized experiences at scale.

Market research

AI tools dissect large datasets, revealing market trends, customer preferences and competitive landscapes, enabling businesses to make data-driven decisions.

Content generation

AI is also impacting content creation and the way we write. For instance, Sprout’s Message Ideas by AI Assist generates engaging content suggestions, helping marketers quickly craft messages that align with their brand voice and audience interests. This tool streamlines the content creation process, allowing users to focus on the strategic aspects of their campaigns.

Competitor analysis

Competitor analysis using AI offers detailed insights into competitors’ strategies and customer perceptions. By tracking essential metrics such as audience growth, engagement and post volume across various networks, marketers can benchmark their social presence against competitors. This comprehensive analysis, including share of voice, engagement, sentiment and impressions, helps identify key areas where a brand stands out or needs improvement.

Sprout’s powerful competitor analysis features aid in discerning which content types are most effective, helping to refine strategies to resonate with target audiences. Regular use of these AI-powered tools for competitive analysis empowers businesses to stay agile, adapt strategies and maintain a competitive edge in their industry.

International marketing

AI’s ability to analyze and interpret multilingual sentiment plays a crucial role in helping businesses navigate the complexities of international marketing by providing language support, cultural adaptation, market analysis and customer insights. This enables businesses to create more effective and targeted marketing strategies for diverse international audiences.

Predictive analytics

AI’s predictive analytics are pivotal in forecasting future customer behavior, a crucial factor in marketing decision-making. By analyzing customer data, AI predicts trends and preferences, enabling businesses to tailor their marketing efforts more effectively. This foresight leads to smarter, data-informed choices, ensuring that marketing strategies are relevant and timely, catering to evolving customer needs and preferences.

Targeting decisions

AI enhances targeting decisions by sifting through extensive customer data to pinpoint the most appropriate audiences. It identifies patterns and preferences within customer interactions, allowing businesses to focus their products or services on the groups most likely to engage. This targeted approach, driven by AI’s deep learning capabilities, ensures that marketing efforts are concentrated where they have the highest potential for impact and conversion.

Media buying

Companies like Volkswagen utilize AI to optimize their advertising strategies. AI analyzes vast amounts of data to determine the most effective channels and placements for advertising, enhancing the efficiency and effectiveness of media spend. AI’s application in media buying improves campaign performance and provides valuable insights into consumer behavior and media consumption patterns.

Artificial intelligence in product development

AI in product development helps teams move beyond traditional design and customer preferences. Its capabilities extend to creating more intuitive and customer-centric products driven by data and innovation.

Product design

AI modernizes product design by analyzing market trends, customer feedback and historical data, leading to products that meet current and future market needs. For example, AI prompts can guide designers in creating products that satisfy market demands and predict future trends. This approach enables exploring a broader range of possibilities, ensuring products are both innovative and relevant.

Project management

In project management, AI automates routine tasks, optimizes resource allocation and provides real-time progress insights. By analyzing historical project data, AI enhances risk assessment and mitigation strategies, leading to more productive and proficient project management.

Predictive maintenance

AI’s role in predictive maintenance is pivotal, using data-driven methods to analyze historical data, identify patterns and anomalies, and generate proactive maintenance recommendations. This approach significantly reduces downtime and maintenance costs, increasing overall efficiency.

Integrating AI into product development marks a new era of innovation, where products are functionally superior and deeply aligned with customer expectations and market dynamics.

Artificial intelligence in sales

AI is creating a new paradigm of efficiency and customer engagement. From email campaigns and lead scoring to proposal writing, AI is enhancing both the sales process and the outcomes.

Email campaigns

AI-driven analytics optimize email strategies by creating narratives that strike a chord with customers. By analyzing customer data and behavior, AI crafts engaging and contextual content. It automates the drafting and sending of personalized emails at scale, ensuring each communication is tailored to the recipient’s interests and needs.

Lead scoring

In lead scoring, AI processes huge quantities of customer data to improve accuracy in identifying potential customers. It considers various factors, including website behavior, demographics, firmographics, job title, purchase history and social media engagement. This results in a ranking system that prioritizes leads based on their conversion likelihood, streamlining the sales process.

Proposal compilation

AI also plays a key role in generating personalized, evidence-based sales proposals. By utilizing AI tools, sales teams can create compelling visuals, presentation slides and text that directly address customers’ needs and aspirations, significantly increasing the chances of closing deals.

Artificial intelligence in customer support

AI enhances customer support by enabling businesses to offer more personalized and optimized service. AI enables teams to customize customer interactions, automate the ticketing process and leverage trend analysis to provide deeper insights into customer preferences and behaviors. These applications streamline operations and elevate the overall customer experience.

Personalize customer support

AI empowers businesses to offer personalized support by analyzing customer data and interaction history. This leads to tailored support experiences and recommendations. For example, KFC China’s introduction of facial recognition technology to predict customer orders based on age and mood is a testament to AI’s capability to enhance customer service.

Customer Care by Sprout equips teams to create authentic customer connections at scale. With tools like AI-powered sentiment analysis, Sprout uses this technology to turn customer interactions into insightful data, helping businesses fine-tune their care strategies and content.

Sprout’s social listening solutions further automate FAQs and identify customer sentiment, respectively, supporting a comprehensive and personalized customer service approach. This AI integration is not just about responding faster; it’s about understanding customers better and providing a consistently excellent experience across platforms.

Automate ticketing process

AI streamlines the ticketing process by efficiently routing customer queries to the appropriate agent or department and providing standardized responses for common queries. It also offers self-service portals for customers to find solutions independently, boosting efficiency in customer service.

Trend analysis

AI analytics tools gather and examine large amounts of customer data, offering valuable insights into customer behaviors, preferences and trends. These insights are crucial for businesses to refine their service strategies and product offerings, ensuring they stay aligned with customer needs.

Artificial intelligence in human resources

AI is transforming the HR landscape by streamlining recruitment, enhancing employee engagement and understanding employee sentiment.

Understand the voice of the employee

AI analyzes employee feedback from various sources, such as surveys, performance reviews and social media. By providing insights into employee sentiment, engagement, and satisfaction, AI helps businesses understand and respond to their workforce’s needs more effectively.

Simplify recruitment process

AI automates several recruitment tasks, including resume screening, candidate sourcing and interview scheduling, thereby saving time and enhancing recruitment efficiency.

For example, Unilever uses AI to screen video interviews and analyze candidates’ body language, tone of voice and word choice. Thanks to AI’s ability to eliminate bias, Unilever saw a significant increase in new hires from various gender, racial and socioeconomic backgrounds.

Trend analysis

AI tools gather and analyze employee data, offering insights into behavior, preferences and trends. These insights help refine HR strategies and boost employee engagement, fostering a more satisfied and productive workforce.

Artificial intelligence in operations

AI enhances operational processes by increasing efficiency and supporting innovation via automated processes and optimized asset management.

Automate processes

Intelligent automation blends AI with robotic process automation (RPA) to enhance decision-making and streamline workflows. AI’s cognitive capabilities and big data understanding enable predictive outcomes, allowing companies to proactively refine processes.

AI-powered automation is critical for Business Process Management (BPM). BPM automatically analyzes optimal methods at different stages, creating replicable models. An example of BPM in procurement is automating the entire purchasing process. Advanced automation increases profitability, accountability, productivity and flexibility. It saves time and reduces errors.

Inventory management

AI plays a critical role in optimizing inventory management. A notable example is Heineken, which uses machine learning algorithms to forecast demand and maintain optimal inventory levels. AI-driven inventory management lowers storage costs and increases profitability and customer satisfaction by ensuring products are readily available when needed.

AI is also shaping a more proactive, data-driven approach to business management. This sets a new standard for business agility and customer responsiveness, where operational excellence is intrinsically linked to intelligent technology.

Artificial intelligence in fraud detection and security

The global cybersecurity AI market is poised to reach $38.2 billion by 2026. Utilizing advanced algorithms, AI proactively detects, counters and minimizes potential risks, processing extensive amounts of information in real time to spot patterns and anomalies that could signal breaches or fraudulent activities.

Monitoring media threats

AI helps monitor media dangers by continuously scanning digital spaces for potential security threats to brands. This capability is vital in today’s digital-first landscape, where threats can emerge from numerous online channels.

Identifying physical anomalies

AI algorithms are also adept at detecting unusual patterns, significantly contributing to sectors like retail, banking and public safety. Swift recognition and response to physical threats are made possible by AI’s keen pattern recognition, enhancing security measures in these critical areas.

For example, Mastercard is helping banks predict scams in real time and before any money leaves a victim’s account. This enables the organization to thwart fraudulent attempts swiftly and effectively, minimizing potential damage.

What does the use of AI in business mean for different stakeholders

Integrating AI in business encompasses a spectrum of benefits and use cases for diverse stakeholders.

For employees

AI’s role in business processes translates to an enhanced employee work experience. By taking over routine tasks, AI frees employees to focus on strategic, creative activities, increasing job satisfaction and opening up avenues for career development. This shift towards high-value work fosters a dynamic and innovative work environment.

Education and training in AI ethics and application enable businesses to inform their workforce about AI’s potential and limitations and how to leverage it responsibly.

For businesses

AI equips businesses with tools for enhanced efficiency, deeper customer insights and innovative product development. It drives a competitive edge and lays the groundwork for future growth, enabling businesses to make informed decisions, create stronger marketing campaigns and develop more effective workflows.

Defining an organizational structure for AI governance is key. This includes assigning roles and responsibilities and introducing an independent body to oversee AI compliance for responsible and ethical usage.

For investors

Investors should focus on AI solutions that enhance a company’s existing strengths. AI should drive long-term impact and act as an ‘exoskeleton’ to business processes. It’s crucial to select strategic AI partners who understand the nuances of control, ownership and accountability. Look beyond the immediate allure of AI and focus on sustainable, value-driven integration.

For the public

As AI integrates deeper into our lives, it becomes vital to digital literacy. Ethical AI frameworks set clear guidelines and principles for AI development and deployment. This ensures AI systems prioritize transparency, fairness, privacy and accountability, serving the greater good without harming individuals or communities.

Continuous monitoring and auditing are critical to upholding these ethical standards. Regular audits by trained experts are necessary to assess AI performance and identify potential issues like biases that may emerge over time. Informed consent is another crucial aspect, where the public’s voluntary and informed agreement is obtained for AI-related processes, and individuals are fully aware of how their data is used.

Adopting these measures allows businesses to respect privacy rights, handle sensitive information securely and mitigate potential risks associated with AI.

Future of AI in business

AI and automation solutions provide businesses with targeted insights and capabilities to navigate the complexities of today’s digital landscape.

By embracing AI with a focus on strategic and ethical implementation, businesses are preparing for the future and actively shaping it. They are creating a landscape where technology and human ingenuity converge to drive unprecedented growth and customer satisfaction.

By leveraging Sprout Social’s AI-driven tools, businesses can anticipate customer needs, speed up personalized content, craft messages that resonate, and develop data-driven and customer-centric strategies.

Schedule a demo to discover how Sprout can transform your marketing and care strategies.

The post The role of artificial intelligence in business in 2024 appeared first on Sprout Social.

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.

For instance, Sprout’s Optimal Send Times feature, powered by our ViralPost® technology, identifies the best send times for posting your content on a specific day. The capability calculates a list of suggested times based on engagement factors to optimize the reach of your content so you can ensure your efforts are result-driven.

Business process management

BPM helps in automatically analyzing what methods are optimal at different stages in the approval cycle of a project so they can be replicated to reach the desired result. This requires the software to identify an effective strategy, create a process management model, and then analyze and measure the effectiveness of that model.

A common application of BPM is in procurement. BPM-powered AI automation automates the entire purchase process, which involves stages such as creating a purchase order, its approval, processing, delivery and payment.

Customer experience

AI automation tools can predict customer needs by analyzing demographic and behavioral trends in historical data, enabling you to anticipate market gaps and act accordingly. You’re also able to streamline customer interactions across different channels like your website, social, app and brick and mortar stores to provide a seamless experience.

When all these elements come together, they serve to enhance your overall customer experience and brand satisfaction.

Customer service

Intelligent automation enables customer service AI so teams can identify common issues and frequently asked questions to program rules-based chatbots and virtual assistants. These tools are critical during peak times with high service requests as they can handle a multitude of inquiries efficiently and quickly. In turn, this AI-powered approach reduces wait times and allows you to offer customer support 24/7 while ensuring your customer service teams are not burnt out.

If incorporating a virtual assistant on your social channel or website seems daunting, consider beginning with a rules-based one to enrich your existing customer care approach. Sprout’s rules-based Bot Builder helps you map out a conversion tree for every question and response between you and the customer. Plus, you can train your chatbot to move the conversation to a live customer care team when the request becomes too complex.

Conversational AI

AI automation tools enable businesses to understand customer data contextually through conversational AI. These tools remember the previous interactions with a user by analyzing the tone in their responses and identifying them as positive or negative. They then help you apply those insights to an ongoing conversation so you can adapt your responses based on the perceived emotions of the customer. This personalization enriches the quality of the interaction and makes you more memorable to the user.

AI automation is also pivotal in understanding and developing voice-based systems, enabling speech recognition, real-time transcription and voice generation to mimic human-like spoken responses for easier interactions. Siri and Alexa are common examples of conversational AI.

The future of intelligent automation

AI automation is helping companies across industries innovate and tap into newer market segments. It’s helping them increase market share and geographic reach, explore new product innovations and optimize costs. So whether you’re looking to master competitive benchmarking, improve your content strategy or build your brand reputation more strategically, embrace the power of intelligent automation to pave the best path forward.

See how Sprout’s AI and automation can help you improve your business.

The post How to supercharge your marketing strategy with AI automation appeared first on Sprout Social.

According to The 2023 State of Social Media Report, 96% of business leaders agree that AI and machine learning (ML) can help companies significantly improve decision-making. They’re eagerly adopting AI-enabled data for AI marketing to thrive and evolve in a data-driven and dynamic business environment.

In this article, we take a deep dive into AI analytics and how it differs from traditional data analysis methods for brand intelligence. We’ll also see how it can provide companies, like you, with sustainable market growth and an edge over your competitors.

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What is AI analytics?

AI analytics is the process of extracting useful information from big data using advanced machine learning (ML) and AI tasks like natural language processing (NLP). It is used to identify patterns and trends in data for informed decision-making.

When applied in marketing, AI analytics extracts meaningful insights from various data sources such as social media listening, review websites, customer forums and survey data. These insights help cross-functional teams, such as marketing and sales, product research, human resources and PR, perform their jobs more effectively and efficiently.

And because data analysis is automated, it removes dependencies on manual tasks, increasing the accuracy of results and timeliness of business intelligence reports. It also frees up time so employees can focus on more strategic and complex parts of their jobs.

What is the difference between AI analytics and traditional analytics?

AI analytics uses machine learning algorithms to analyze big data, which enables it to process large volumes of complex, unstructured raw data quickly, accurately and easily. Traditional analytics, in contrast, requires more manual data analysis.

Even though traditional data analysis has been around for a long time, it has certain drawbacks that have led to the rising demand for AI analytics. For one, traditional analytics is not very effective in analyzing complex or large volumes of data.

More importantly, it can only analyze a limited scope of historical data to provide descriptive, diagnostic, predictive and prescriptive insights.

Here’s how AI analytics has transformed traditional analytics:

• Descriptive analytics: Interprets performance based on existing data and focuses on reporting a snapshot summary of the insights. AI analytics gives a more comprehensive report on continuous performance using trend analysis so brands have a holistic view of the various factors and changes over time.
• Diagnostic analytics: It is used to understand a business outcome by analyzing historical data for causation analysis. It identifies relationships in data to understand the root cause of an outcome. AI can analyze large volumes of historical and real-time data from disparate sources to find patterns, trends and anomalies quickly and more efficiently.
• Predictive analytics: Used to predict future trends and outcomes based on historical data using statistical models and forecasting techniques. AI uses machine learning and deep learning to predict future trends, providing faster, more accurate forecasts.
• Prescriptive analytics: Provides actionable insights from predictive analysis insights to improve business performance going forward. It is a combination of historical data and predictive variables, which can make the suggestions obsolete by the time you put them to use for a campaign. These AI insights enable you to analyze data in real-time by computing the change in variables as they happen so businesses can monitor and pivot campaign strategies promptly.

What are the four pillars of AI Analytics?

AI analytics depends on four critical AI sub-tasks to provide insights—machine learning, natural language processing, neural networks (NNs) and deep learning (DL). These technologies work together to extract meaningful information from raw and disparate data.

Machine learning

Machine learning helps AI analysis tools automatically identify data patterns so they can be synthesized for actionable insights. It assists in processing big data efficiently to identify relevant data points for predictive analysis.

This enables companies to, for instance, automate customer data analysis by processing customer experience feedback, product reviews and social listening data, in near real-time, to get timely insights.

Natural language processing

Natural language processing enables an AI analytics tool to understand and interpret human language so it can process and analyze it for meaningful information. NLP is pivotal to accurately analyzing social media listening data, customer reviews, blogs, news articles and other text-based data, including numbers and emojis.

Neural networks

Neural networks are algorithms built to understand relationships between data points identified by machine learning. They are inspired by how the human brain functions and comprise interconnected layers of nodes or neurons that remember relationships in data and learn from them. As a result, the AI tool progressively becomes smarter and more accurate with time, thus also making it cost-effective in the long run.

Deep learning

Deep learning algorithms are advanced NNs that process complex raw data using multiple layers of interconnected nodes. This enables it to adapt to a variety of data types and datasets and extract deeper insights for far more sophisticated analysis than traditional data analytics.

Why should we use AI for data analysis?

AI analytics empowers companies to automate repetitive tasks and process complex data more efficiently. Thus making scaling easy and frictionless. These tools can process thousands of API calls in minutes, giving you critical information about your brand, customer care and competitive analysis in a matter of minutes. This helps your team focus on more strategic aspects like providing stellar customer service and brand experience.

Sample this—per The 2023 Sprout Social Index ™, 76% of consumers notice and appreciate when companies prioritize customer support. Customers expect personalized service that’s more than just resolving an issue quickly. They want a personalized interaction that’s authentic and engaged.

Achieving this level of personalization can only be done by having timely and accurate data at hand that will tell you how best to please consumers and nurture your loyal customer base.

For example, Sprout’s Listening capability will inform you of what topics are trending in social conversations about your brand, what related terms customers are using and the sentiment around those topics. You get a complete picture of what’s top-of-mind in social conversations and uncover common themes and topics regarding your brand, products and service.

What are the benefits of applying AI analytics?

AI is transforming how businesses are operating by helping them automate tasks, so they can divert talent to areas that are more complex and strategic. Thus, enabling companies to grow and scale faster than before. Here are some key ways in which AI analytics is benefitting businesses today.

Automation

AI analysis helps businesses automate statistical calculations to monitor key performance indicator (KPI) metrics across platforms. This drives efficiency as time-consuming tasks are cut down and data analysis is expedited to derive relevant brand and customer insights. This ML-based automation gives back precious time to focus on planning and strategy while reducing workflow friction and workload.

For example, with an AI tool, companies no longer need to assign personnel to check every customer email or ticket to understand the request or complaint. These tools can seamlessly analyze all incoming messages and assign them sentiment so team members can quickly triage responses. In Sprout, also view spikes in brand mentions to keep a tab on brand sentiment and monitor potential crises.

Scalability

AI analytics helps alleviate a company’s growth pains by efficiently handling large datasets from disparate sources to provide real-time insights—something not possible with traditional data analysis methods.

When integrated with a company’s customer relationship management (CRM) and business intelligence software, such tools exponentially increase the output of targeted insights by cross-referencing data points and identifying patterns in customer profiles and purchase patterns. Thus, giving a more holistic view of your current customers and prospects.

For example, Sprout’s Salesforce integration enables you to deliver exceptional customer experiences at every touchpoint by leveraging social data throughout the CRM. Create personalized touch points throughout the customer journey and increase the impact of your initiatives while implementing efficient workflows across the teams.

Accuracy

Results from AI data analysis are more accurate than manual data analysis methods. This is because there’s less scope for human error, biases or limitations such as excluding data subjectively, or while calculating complex metrics, especially from multiple sources.

Business applications of AI analytics

Companies use AI analytics to derive a stronger return on investment (ROI) from their social and digital data, while championing productivity and growth through intelligent automation. Let’s dig deeper.

Sentiment analysis

Sentiment analysis algorithms scan feedback data to prioritize key themes and topics, giving brands an in-depth view of common complaints or recurring product or service issues. In this way, brands can develop proactive strategies to ensure better customer experience to boost brand loyalty.

Sprout’s sentiment analysis tool gives you a snapshot of your brand sentiment by highlighting positive, negative and neutral sentiment in messages and social listening data. You can see sentiment trends over periods and analyze themes and conversations based on topics effortlessly. Use this data to improvise your product messaging, enhance customer care interactions and refine marketing strategies.

Demand forecasting

AI analytics enables companies to better understand customer interactions and purchase motivations. It helps them analyze large datasets quickly and efficiently to understand individual preferences and behavior. This helps a brand predict future demand based on specific customer demographics and market indicators. It also helps them enhance their offerings by customizing them specifically for different customer segments.

Sample this. A hotel, anticipating a surge in guests during the summer months, wants to provide a higher degree of personalized guest experience to achieve greater customer satisfaction. The company can get valuable insights in this regard by using an AI analytics tool to analyze customer messages, customer care emails, reviews and guest profiles. Through this, they may discover that most guests would appreciate childcare facilities so they can enjoy their stay even more.

By adding this facility during the summer months, the hotel is not only prepared for the increased demand but also leverages the opportunity to improve customer satisfaction, and thus stronger loyalty.

Business monitoring

AI analytics can analyze data in news articles, forums like Reddit and social conversations simultaneously, on both your brand and competitors. You can use this data to discover gaps in brand perception and take remedial steps for reputation management or to improve products or services.

In Sprout, you can also track competitor performance on different channels. For example, you can analyze your competitors on Facebook by getting in-depth metrics on KPIs such as public engagement average, fans average, audience growth and other metrics.

Top 5 tools that use AI for data analytics

AI analytics tools offer varying degrees of details and insights. That’s why you must choose one based on your specific needs and characteristics, such as your industry, company size and target audience. You’ll also need to consider if the AI tool will seamlessly integrate into your existing IT infrastructure and workflows, and whether it will fulfill your scaling needs.

Here’s a list of industry-leading AI analytics tools that can provide you with the kind of insights you need to grow your business.

Sprout Social

Sprout Social is an all-in-one social media management solution that uses AI analytics for social media management, marketing, customer care and competitive intelligence. It also integrates with a variety of digital data sources including review sites like Reddit, CRMs like Salesforce and business intelligence tools like Tableau.

Our powerful social listening tool analyzes more than 600 million messages a day, identifying key topics to give you targeted insights. Analyze social conversations and customer messages to find topics that resonate the most with your audience and use those insights to inform your content and customer care strategy.

Conduct B2C and B2B market research effortlessly and strengthen your competitive strategy by using Sprout’s competitive analysis feature. Find impactful insights from key metrics like topic summaries, audience demographics and total engagements to inform your decisions and conduct a market-gap analysis to find new avenues for growth.

Create impactful posts using our Suggestions by AI Assist feature, enabled by AI prompts. Plus, use Sprout’s patented ViralPost technology to ensure your posts reach your intended audience at the most optimal times. The technology calculates your best times to publish content and enables you to automate or manually post them for maximum engagement and impact.

Sprout’s advanced AI-enabled Smart Inbox helps you quickly identify sentiment in incoming messages and review feeds. The posts are automatically assigned as positive, neutral or negative so customer care teams can prioritize messages accordingly and act swiftly.

Adopt these and other intuitive Sprout features to bring your data to life and transform your social and digital marketing strategies. Tie them all together using our intuitive report templates and share insights securely across teams within the organization for faster, smoother collaboration.

Sprout seamlessly fits into your existing tech stack without a manual setup so you’re quickly up and running. And since it’s powered by advanced machine learning, it continuously learns from your data, becoming smarter with time. Thus, alleviating the need for regular investment in upgrading your software.

Power BI

Power BI is a business analytics tool built for data analysis and insight visualization. It provides comprehensive brand insights by mapping millions of data points from different sources of varying complexities. Its especially useful if you’re proficient in Microsoft Excel as you can easily create and modify reports based on your requirements and share them with teams. You do need to have a coding background as you’ll need it to understand data analysis expressions (DAX) during data modeling and working with M code for data transformations.

MonkeyLearn

MonkeyLearn is a text analysis tool that uses AI for data analytics. It can be used for automating customer feedback and survey data analysis to identify topics relevant to your target audience and extract useful insights. Although aimed to help with customer experience management, the tool is not efficient in handling a high volume of customer care tickets, which makes it difficult to scale or integrate with customer support needs.

Tableau

Tableau is an analytics and data visualization platform you can use to understand your data for meaningful brand insights and to create reports in real-time. Like Sprout, Tableau offers robust connectivity across various data sources and offers a unified view of your data and insights. Its comprehensive capabilities require you to be trained on it so you can use the tool to its fullest potential.

Sisense

Sisense is an AI data analytics tool that can access multiple online data sources to provide insights in flexible reports. It can be used by teams who don’t have advanced technical expertise to conduct preliminary data analysis for business intelligence. The tool has a suite of advanced features as well but you’ll need to be trained on them before you can start using them for insights.

The future of AI in data analytics

AI analytics has mostly been used in critical industries and large enterprises such as healthcare and manufacturing to expedite processes, identify data fluctuations and get actionable insights. However, recent advancements in machine learning are poised to make it a standard practice across industries.

For one, AI tools have become more affordable and user-friendly. They are enabling brands to embrace innovation and face the competition more confidently. Companies can gather business intelligence from varied data sources easily for a deeper understanding of their market. Which in turn can be used to personalize customer interactions, enhance brand satisfaction and expand into new markets with foresight and precision.

Take advantage of AI-enabled automation to optimize and see the ROI of your social and digital data. Explore these cutting-edge AI marketing tools to propel your business forward by leveraging social data to win 2024 and beyond.

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Luckily, innovative technologies like artificial intelligence (AI) offer long-term solutions to meet these challenges. They are revolutionizing marketing, with an increasing number of marketers reporting that AI capabilities have helped them boost creativity and workflow efficiency. More and more teams are using AI marketing to channel their energy toward developing compelling content while efficiently managing arduous tasks like data analysis, reporting and message management.

In this article, we explore AI use cases in marketing that are top priorities for teams as they tighten their seatbelts for 2024. Keep reading for actionable insights that will help you meet your revenue goals and marketing objectives, head-on.

Table of contents:

• The marketing disciplines where AI had a positive impact
• The top 7 AI + social marketing use cases for 2024
• How to adopt AI in a mindful way
• Stay tuned in as AI use cases in marketing evolve

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The marketing disciplines where AI had a positive impact

According to The 2023 Sprout Social Index™, 81% of the 900 marketers surveyed say AI has already had a positive impact on their work. Seventy-eight percent feel AI has benefited them in creative areas like content ideation, and another 73% report a positive impact on productivity.

AI is also helping social teams understand and respond to audiences better. It’s providing them with critical brand insights from social listening to inspire engaging content, personalize customer care and dig deeper into performance metrics.

Further, AI-driven marketing automation is assisting marketers in increasing speed and efficiency. It’s enabling them to offload time-consuming tasks such as managing digital ads, email campaigns and social post scheduling so they have more time for strategy-building.

Not surprisingly, according to a Q2 2023 Sprout Pulse Survey of 255 social marketers, 71% are already integrating AI and automation tools into their workflow, and 82% of them report positive results.

The top 7 AI + social marketing use cases for 2024

Given these encouraging results, more companies plan to leverage AI in 2024 to strengthen their marketing teams and prime their overall business strategy. Here are the most prominent AI use cases in marketing for social media managers as they prepare for the coming year.

1. Analyzing social media data

Social media data is a treasure trove of brand and customer insights that AI tools effortlessly dig into to surface critical information. The State of Social Media Report found 95% of leaders look at social data to inform business decisions such as lead generation, product development and competitor analysis. Thus, social media data analysis is empowering not only marketing teams but also cross-functional ones.

Competitor monitoring is another AI use case in marketing, important for 92% of business leaders in 2024 per the report, to improve brand positioning.

AI tools extract competitor insights by using powerful semantic search and other AI algorithms from social listening data. For example, Sprout analyzes social data using named entity recognition (NER) to identify and analyze competing brands and their content to provide you with actionable insights to improve your brand performance.

The capability digs into competitor content engagements, post frequency, hashtag usage and other key performance areas by using keywords and @mentions you determine. Thus, cutting through the noise of thousands of social conversations in seconds to give you data that matters to your brand.

Another key area is influencer marketing. AI models monitor posts, interactions and audience demographics of potential influencers so marketers can map their suitability for brand partnerships. Sprout’s recent acquisition of Tagger further cements how social data analysis and AI capabilities are converging to manage intelligence, reporting and workflows for influencer marketing.

2. Content creation

The 2023 Index found that content creation still remains one of the most time-consuming tasks for marketers. It’s not just the creative ideation that takes up time and mind space but also the stress of ensuring the content is timely, relevant, engaging and differentiated enough to stop audiences from scrolling past.

Thankfully, social media managers and teams can rely on dedicated AI-enabled social management tools for content creation and ideation without the pitfalls of generic AI tools.

For example, Sprout’s Suggestions by AI Assist feature helps you create engaging, brand-tailored posts in seconds by giving you three outgoing copy options. Sprout understands nuances in social chatter by using natural language processing (NLP) and provides relevant content recommendations based on the data, leading to compelling content that translates into better leads and improved conversions.

This way, marketing teams increase the impact of their social strategy and get back time to concentrate on developing winning campaigns.

3. Social media advertising and campaign targeting

Social media advertising and campaign reporting are key AI use cases in marketing. Marketers are using AI to optimize social media advertising and make ads more attractive to audiences by analyzing engagement behavior and audience preferences. For example, this Coca-Cola campaign combines a compelling storytelling technique with generative AI to create an intriguing ad video.

Machine learning (ML) algorithms now automatically conduct A/B tests on different ad variations to continuously optimize ad campaigns by learning which elements perform best for different customer segments. This leads to more targeted, personalized ads. AI-powered insights, coupled with predictive analytics, automatically suggest relevant products and services based on past user interactions to increase campaign targeting and performance.

All these advantages enable marketers to maximize paid advertising and improve targeting results in a fraction of the time it would take to analyze and customize them manually.

Similarly, AI tools can create dynamic ads that automatically update product information and prices based on user behavior and choice. This maximizes your return on investment (ROI) while saving your marketing team the cumbersome task of monitoring and adjusting ad copy manually.

Check out Sprout’s social ad reporting capabilities to boost ROI from the paid content you’re scheduling.

4. Social media scheduling and posting

Social media teams juggle multiple priorities, from responding to timely customer issues and queries to making sure they don’t miss scheduling content and post deadlines. That’s why social marketers seek to manage posts and schedules much in advance so they can prioritize campaigns and manage team workflows better.

AI marketing tools automate these functions seamlessly, and with precision, saving teams time and effort. For example, Sprout’s AI capabilities automate social media scheduling and posting by determining the best times to post for maximum audience engagement and impressions. Machine learning algorithms analyze engagement metrics over periods of time to provide several scheduling options in the form of Optimal Send Times and hashtag recommendations to ensure optimal post engagement.

This enables marketers to plan, organize and schedule social posts across networks including Facebook, Instagram and LinkedIn with data-driven accuracy.

5. Building chatbots

Chatbots are a compelling AI use case in marketing. And 54% of marketers plan to use them at scale in 2024 for social customer care, along with other resources like FAQs and customer forums, per the 2023 Index.

Chatbots enable brands to deliver real-time, personalized interactions with customers for round-the-clock responses to inquiries. This goes a long way to enhance customer satisfaction and build strong brand relationships, given that 16% of customers expect brands to respond immediately, and 23% within two hours.

Chatbots enabled by ML and neural networks become smarter as they process more information and gather valuable data on user behavior. These virtual agents, however, need to be trained, adapted to your tech stack and monitored. Makeup brand Sephora uses AI chatbot Kik to connect with its customers and for live influencer interaction to drive engagement.

Rules-based chatbots, on the other hand, are simpler. They can easily be set up in a matter of minutes and enable brands to offer 24/7 availability to their customers.

Whether you use an AI-enabled chatbot or a rules-based one like Sprout’s, you can boost efficiency multifold by answering product queries, providing recommendations and guiding users through the sales funnel even when your marketing team is unavailable. What can be better for customer support than that?

6. Social media measurement

Social performance analytics have become a critical part of a brand’s overall strategy. A whopping 60% of marketers plan to measure and quantify the value of social engagements in terms of revenue impact to meet their 2024 business goals.

And given the rise in leadership teams’ involvement in a brand’s social engine, 32% of marketers say they now share social metrics with their executive leadership on a weekly basis.

ML models like Sprout automatically and accurately measure quantitative and qualitative social metrics within minutes, sparing you hours of manual engagement and performance analysis. This bodes well for marketers who want to enhance their social media ROI while navigating social media’s ever-changing landscape.

7. Sentiment analysis

Marketers are turning to sentiment analysis to assess the tone and sentiment expressed in comments, posts and conversations around their brand to determine whether they are positive, negative or neutral. This is a critical AI capability considering 44% of marketers, per The State of Social Media Report, use sentiment mining to understand customer feedback and improve how they respond to issues.

Analyzing sentiment in social chatter also helps brands spot early indications of negative sentiment and take proactive measures before a situation escalates.

For example, in Sprout, you can detect unusual spikes in brand mentions and monitor whether they are negative or positive. This enables you to actively monitor your reputation to ensure brand health. Similarly, sentiment analysis algorithms also tag incoming messages as positive and negative so your social customer care teams can prioritize them based on how critical they are.

How to adopt AI in a mindful way

Creating connections and building community requires a lot of time and effort, both of which are limited for already strapped social marketing teams. AI can address this challenge by automating functions, simplifying workflows and increasing team transparency. However, there is apprehension from both social marketers as well as customers. While social teams worry about job displacement, 42% of consumers, per the 2023 Social Index, are apprehensive about brands using AI in social media interactions fearing reduced human interaction.

Leadership teams can help manage these concerns by paving the way for implementing org-wide AI use cases in marketing in a thoughtful manner. This includes working closely with the IT and legal team to choose the right AI tools for use by different departments. Plus, initiating and developing an effective AI use policy for employees so there is a concrete framework they can work around to benefit from AI software.

This is important because intentionally incorporating AI tools into your tech stack can offload manual and cumbersome tasks such as posting, scheduling and performance analytics, so marketing teams can focus on work that truly depends on their expertise. This includes AI analytics, audience research, creating compelling content, driving audience engagement and more importantly, personalized customer care.

Stay tuned in as AI use cases in marketing evolve

Navigating social media’s meandering terrain is no easy feat. Marketing leaders today need to keep a close ear to the ground to capitalize on changing market trends to outsmart competitors and win customer loyalty. These social insights will empower your marketing teams and provide them with the direction they need to build on every opportunity to be agile and elevate your brand.

Stay tuned on the latest social trends to forge real business impact into your marketing strategy. Download the Sprout Social Index™ today.

The post Top AI use cases in marketing to elevate your 2024 strategy appeared first on Sprout Social.

In this session, we will cover:

• How brands can best utilize social media for quick, impactful, omni-present care 
• How to create competitive care strategies using AI to scale efficiency and data analysis 
• How personalization is fueling the future of exceptional care experiences

This session will be led by Sprout Social’s President, Ryan Barretto, and Vice President, Solutions Engineering, Colleen Geiselhart. They will also discuss how Sprout Social’s global partnership and integrations with Salesforce provide a unique perspective on how the world’s leading brands are harnessing the power of social data and insights to build successful Customer 360 strategies.

The post Social Media, AI, and the Future of Omni-Channel Care appeared first on Sprout Social.

Audiences are no longer just passively consuming branded content, they’re looking for it. 68% of consumers say they follow a brand on social to stay informed about new products or services. And over half of consumers say the most memorable thing a brand can do is respond to customers, underscoring the increased expectation for highly personalized engagements. 

As buyer experiences continue to go digital, consumer attitudes have shifted and social media is now the new mall. There’s an opportunity for your brand to capitalize on this shift, but to do so effectively, you must hone in on how your social team can make the most business impact and ultimately, drive revenue.

Dive into our newly released Index report to guide your brand on how to refine its social media playbook and give you the data to demonstrate social’s org-wide impact.

We’ll discuss:

• Four key investments guiding the socially sophisticated marketer 
• What consumers are looking for from your brand today
• How to effectively communicate social’s impact on revenue to business leaders

Want to get the most out of this event and connect with other like-minded professionals? Join our community, The Arboretum, to keep the conversation going, access additional resources and get exclusive swag.

Your speakers:

The post New Index Data: Refine Your Playbook for Social Sophistication appeared first on Sprout Social.

As social media teams across the world figure out strategies to not only incorporate Threads  but also excel at it, this webinar will examine what we’ve learnt so far and explore thoughts on what we think is in store. 

Hear insights from social media consultant and industry analyst, Matt Navarra, and Senior Social Media Manager from financial favourite and industry disrupter, Monzo.

You will learn:

• The current state of Threads and how early adopter brands leverage its capabilities
• How businesses can use Threads for community building, customer care and more
• Key distinctions and differentiations between Threads strategy vs. other social channels
• Predictions for 2024 and beyond

Your speakers:

The post The Rise and Fall Of New Platforms: What Threads Has Taught Us About Emerging Social Media appeared first on Sprout Social.

Although it has sparked excitement and enthusiasm, there are concerns surrounding the ethics of AI. Like many innovations, the tech industry’s vision for the metaverse had eerie similarities to media like Black Mirror and Snow Crash. And with works like Parable of the Sower, the Machine and I, Robot within the cultural zeitgeist, it’s understandable why sci-fi fans, researchers and technologists alike warn of the dangers of ignoring AI ethics.

In this article we’ll define what AI ethics are, why brands should be concerned and the top ethical issues facing marketers, including job security, misinformation and algorithmic bias. We’ll also share five steps to help you maintain ethical AI practices within teams and across the organization.

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What are AI ethics?

AI ethics is a system of moral principles and professional practices used to responsibly inform the development and outcomes of artificial intelligence technology. It also refers to the study of how to optimize impact and reduce the risks and/or consequences of AI.

Leading tech companies, government entities like the United Nations and research and data science communities have worked to shape and publish guidelines to address ethical issues. For example, the United Nations Educational, Scientific and Cultural Organization (UNESCO) published the first global standard on AI ethics in November 2021: The Recommendation on the Ethics of Artificial Intelligence.

There are some AI regulations in place at the country and local levels, but as AI and other emerging technologies grow, businesses should expect more government regulation. As AI integrates further into our lives, AI ethics becomes a critical part of digital literacy.

Why AI ethics matter

Companies are already investing in AI, but the difficulty is ensuring responsible use.

According to The 2023 State of Social Media: AI and Data Take Center Stage report, business leaders expect increased investments in AI over the next few years. Our report also found 98% of business leaders agree that companies need to better understand the potential of AI and machine learning (ML) technology for long-term success.

While AI can improve performance, boost efficiency and generate positive business outcomes, brands are also experiencing unforeseen consequences of its application. This can stem from lack of research or biased datasets, among other reasons. AI misuse or neglecting ethical concerns can result in a damaged brand reputation, product failures, litigation and regulatory issues.

The first step to upholding ethical standards across teams within your organization begins with understanding the issues marketers face, so you can shape a plan to mitigate these business risks and safeguard your brand.

What AI ethics issues are top of mind for marketers

There are a variety of AI ethical concerns in the tech industry including, but not limited to, the following:

• False content generation
• Explainability
• Societal impact
• Technology misuse
• Bias
• Data responsibility and privacy
• Fairness
• Robustness
• Transparency
• Environmental sustainability
• Diversity and inclusion
• Moral agency and value alignment
• Trust and accountability

Some believe AI can help build more inclusive technologies, systems and services that can cater to diverse populations. The key is to establish ethical frameworks, regulations and mechanisms to ensure responsible use.

A member of the Arboretum, Sprout’s community forum, noted AI has the potential to promote inclusivity and reduce biases/discrimination by ensuring fair, unbiased decision making processes. By addressing issues such as algorithmic bias in AI development, it could be possible to shape a future where AI is a positive force of change.

Along with the potential for positive change, there are also opportunities for misuse or unethical use of AI as it becomes more powerful. Our community discussed several risks including privacy infringement, manipulation of public opinion and autonomous weapons.

Comments like these just scratch the surface of ethical concerns across industries, but the top issues for marketers include: job security, privacy, bias and discrimination, misinformation and disinformation, and intellectual property/copyright issues, which we’ll dive into detail in the next section.

Impact on jobs and job displacement

Robots securing world domination is the least of our worries—at least for now.

And that’s because researchers and experts are not threatened by technology singularity, or the idea that AI will surpass human intelligence and replicate traits like social skills. They are aware of AI’s limitations and the potential ramifications of job replacement.

The goal of researching and investing in AI isn’t to replace humans—it’s to help us save time and effort to do more impactful things. Flock Freight’s Director of Social Media and Partnerships, Bob Wolfley shared a great analogy for AI: “AI is like the dishwasher or washing machine in our homes. Think of all the time you save not washing dishes or clothes by hand.”

In our series Unread, members of Sprout’s marketing and creative team discussed how they currently use AI, from indulging in personalized shopping to using features like ViralPost® to help schedule social posts. Watch the video below to hear their hot takes on the benefits and fears of AI, including job replacement:

Privacy concerns

Concerns surrounding data privacy, protection and security are top of mind for brands. Security investments are an increasing priority for businesses as they seek to avoid any opportunities for surveillance, hacking and cyberattacks. As personalization becomes more popular, brands are implementing best practices for collecting, storing and analyzing data to protect customers and organizations.

Algorithmic bias and discrimination

Since it learns from data, a poorly constructed or trained AI can demonstrate bias against underrepresented subsets of data. There have been several large cases of bias with AI-generated artwork, chatbots, facial recognition software, algorithms and AI tools for hiring practices.

For example, several TikTok and Twitter [rebranded to X as of July 2023] users called out a thread featuring “#SouthSudan Barbie” adorned with guns, a negative stereotype associated with a region grappling with socio-political issues such as genocide and refugee crises.

With bias entering even lower stakes AI uses cases like this, the question becomes how do we work against bias and discrimination when the training datasets can lend themselves to bias?

Misinformation and disinformation

Like humans, AI isn’t perfect. AI responses to prompts can be inaccurate and there are fears of people spreading misinformation with malicious intent. Along with threats of disinformation, there’s potential for brand crisis and reputational damage, especially without the appropriate safeguards and protocols in place.

Intellectual property and copyright issues

You’ve probably seen the Harry Potter cast as characters in a Wes Anderson film or the citizens of Bikini Bottom singing renditions of popular songs. These are examples of how many are using AI to use people’s image and likeness or intellectual property.

AI is an excellent sparring partner for creative tasks like brainstorming and creating outlines, but depending on how the outputs are used, it could lead to copyright infringement, plagiarism and intellectual property violations. For example, a group of artists filed a lawsuit against Midjourney and Stability AI in January 2023 claiming the tools infringed on the rights of millions of artists. Generative AI opens a legal can of worms and there’s still a lot of ground to cover, but creating proactive rules and frameworks will help mitigate ethical risks.

5 steps to maintain AI ethics within teams

Here are five steps to help guide your plan for mitigating AI ethical risks:

1. Set internal ground rules and responsibilities for AI use

Consider establishing an AI ethics team of ethicists, legal experts, technologists and leaders to help establish ground rules for your organization. For example, only using generative AI for drafts and brainstorming, but not for externally published content is an excellent ground rule.

Along with these ground rules, define the role and responsibilities of each team member involved in AI, including the ethics team. Set your goals and values for AI as well. This will help set the foundation for your AI usage policy and best practices.

2. Define and audit the role of AI

AI can’t replace content creators, social media strategists or really any role in marketing. Identify the AI tasks that require human oversight or intervention and pinpoint the goals of your AI ethics policy to help craft processes for developing, managing and communicating about AI.

Once you identify the goals of your ethics policy, identify gaps and opportunities for AI at your organization. Consider the following questions:

• How does the organization currently use AI and how do we want to use it in the future?
• What software and analysis can help us mitigate business risks?
• What gaps do technology and analysis create? How do we fill them?
• What tests or experiments do we need to conduct?
• What existing solutions can we use with current best practices for our product teams?
• How will you use data and insights?
• How will we establish our brand positioning and messaging for AI technologies and ethics?

3. Develop an airtight vendor evaluation process

Partner with your IT and legal teams to properly vet any tools with AI capabilities and establish an ethical risk process. Their expertise will help you evaluate new considerations such as the dataset a tool is trained on and the controls vendors have in place to mitigate AI bias. A due diligence process for every tool before launching externally or internally will help you mitigate future risks.

4. Maintain transparency with disclosures

Collaborate with your legal and/or privacy teams to develop external messaging and/or disclaimers to indicate where and when your brand relies on AI. These messages can be used for content, customer care, etc. For example, TikTok updated their community guidelines to require creators to label AI-generated content. Communicating your ethical standards and frameworks for championing AI ethics will help gain the trust of peers, prospects and customers.

5. Continue education across leadership and teams

AI isn’t something business leadership can rush into. Like any new wave of emerging innovation, there will be a learning curve, on top of new technological milestones. Help level the playing field by hosting internal trainings and workshops to educate all team members, leaders and stakeholders on AI ethics and how to build it responsibly.

Do the right thing with AI ethics

Considering ethics isn’t just the right thing to do—it’s a critical component of leveraging AI technology in business.

Learn more perspectives from leaders and marketers on how AI will impact the future of social in our webinar, along with other findings from The 2023 State of Social Media Report and tips for creating impactful social content.

The post AI ethics: How marketers should embrace innovation responsibly appeared first on Sprout Social.

AI releases have been dominating headlines, capturing everyone’s attention–consumers and C-suite executives alike. Despite current macroeconomic trends, social media budgets are expected to rise–but leaders are eyeing emerging technologies to ensure they get the most bang for their buck. 86% of business leaders agree implementation of AI and machine learning (ML) technology are critical for long-term business success.

Join us for a discussion with Sprout Social’s Mike Blight, Senior Market Research and Insights Manager, and Sarah Corley, Demand Generation Strategist, as they share how leaders and marketers think AI will fundamentally change how they operate along with other recent findings from The 2023 State of Social Media Report. 

In this discussion, you’ll:

• Learn business leaders’ perspective on the future of social
• See examples of brands using AI in their marketing
• Get tips for creating impactful social copy with AI

Your Speakers:

The post The State of Social Media & AI: Accelerate Your Impact in 2023 appeared first on Sprout Social.