How to Incorporate AI in Marketing?

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AI holds enormous promise for transforming the marketing function. AI applications, particularly machine learning, require vast amounts of high-quality data, which marketers should constantly look for. Sources of data such as internal transactions, outside suppliers, and potential acquisitions can improve AI models’ accuracy.

AI can be used to fully automate certain types of decisions in the marketing process, taking humans out of the loop entirely. For example, where digital ads are served up almost instantaneously to users, programmatic ad buying is an area where AI can be used significantly. In other domains, AI can present recommendations to a person with a choice, such as suggesting a movie to a consumer or a strategy to a marketing executive.

While implementing AI in the marketing process can present challenges, firms should move to more automated decisions wherever possible. The most significant returns from marketing AI will be found in this area. Despite its lower technical sophistication, stand-alone task-automation AI can still be hard to configure for specific workflows and requires companies to acquire practical AI skills. Careful integration of human and machine tasks is necessary to ensure that the AI augments people’s skills and does not create problems.

Incorporating AI into third-party platforms can also be tricky, and companies must consider customers’ interests. The more innovative and more integrated AI applications are, customers may have worries about privacy, security, and data ownership. To guarantee privacy and maintain customers’ trust, marketing agencies should establish ethics and privacy review boards, with both marketing and legal experts, to vet AI projects, particularly those that involve customer data or algorithms that may be prone to bias.

Despite the hype, AI can only accomplish narrow tasks and cannot run an entire marketing function or process. However, it offers substantial benefits to marketers and its rapidly growing capabilities. Marketers should start developing a strategy today to take advantage of AI’s current functionality and its likely future. AI will ultimately transform marketing, but it is a journey that will take decades. Marketing functions and organizations must focus on building AI capabilities and addressing potential risks.

Steps to Incorporate AI in Marketing

To incorporate AI in their marketing strategies, marketing agencies need to follow the steps below:

  1. Look for new data sources: AI applications require high-quality data, particularly machine learning. Marketers should look for new data sources such as internal transactions, external suppliers, and potential acquisitions.
  2. Move to more automated decisions: Automated decisions allow repetitive, high-speed tasks, taking humans out of the loop entirely. This approach is essential for programmatic ad buying and other high-speed decision-making processes.
  3. Tackle the challenges and risks: Implementing even the most straightforward AI applications can present difficulties. Marketers should know the challenges and risks of deploying AI and take steps to mitigate them.
  4. Consider the customer’s interests: As AI applications become more sophisticated, customers may have concerns about privacy, security, and data ownership. Marketers should ensure that privacy and security controls are transparent, customers have some say over how their data is collected and used, and they get fair value in exchange.
  5. Develop AI skills: Companies must acquire the necessary AI skills to configure AI applications. Marketers should consider developing AI skills within the organization to ensure the smooth deployment of AI.
  6. Establish ethics and privacy review boards: CMOs should establish ethics and privacy review boards with marketing and legal experts to vet AI projects. This is particularly important for projects that involve customer data or algorithms that may be prone to bias.
  7. Develop a long-term AI strategy: AI will take decades to transform marketing, and the marketing function will need to pay long-term attention to building AI capabilities. Marketers should start developing a strategy today to take advantage of AI’s current functionality and its likely future.

Incorporating AI into marketing strategies has the potential to transform the industry. Companies should tap into new data sources and constantly look for ways to increase their data quality and quantity. AI can automate routine decisions, provide recommendations, and improve customer experiences. However, implementing AI has challenges like integration difficulties, privacy and security concerns, and bias issues.  The next section will explore some existing AI platforms marketers can use.

AI Platforms & Tools

AI-powered platforms and tools provide marketers with new and innovative ways to reach and engage their target audience, resulting in more personalized and effective marketing campaigns. From chatbots to predictive analytics to recommendation engines, AI is helping marketers automate and optimize their efforts, making it easier to understand their target audience, generate impactful messages, and make informed decisions about their marketing strategies. In this context, it’s essential to understand the different AI tools and platforms available to marketing agencies.


Customer relationship management (CRM) tools

CRM tools use AI algorithms to analyze customer data and provide insights into customer behavior and preferences. This information can be used to automate targeted marketing campaigns and personalize communication with customers.

Following are some of the popular CRM tools available:

  1. Salesforce: Salesforce is a cloud-based CRM platform that offers a wide range of tools for managing sales, marketing, and customer service. It provides lead management, opportunity tracking, account management, and contact management.
  2. Hubspot is an all-in-one marketing, sales, and customer service platform. It includes lead generation, lead management, email marketing, and customer relationship management tools.
  3. Zoho CRM: Zoho CRM is a cloud-based platform that offers lead and opportunity management, sales forecasting, and customer segmentation features. It also includes a suite of productivity tools and integrations with other Zoho applications.
  4. Freshsales: Freshsales is a CRM platform designed for small and medium-sized businesses. It offers lead and opportunity management, contact management, email marketing, and lead scoring.
  5. Pipedrive: Pipedrive is a sales-focused CRM platform offering deal and pipeline management, lead and contact management, and email tracking. It is designed to help sales teams stay organized and focused on closing deals.

These are just a few examples of SaaS CRM tools. A marketing agency’s specific CRM tool will depend on its specific needs and goals.

Chatbots

Chatbots can be integrated into websites or mobile apps to provide 24/7 customer support and help answer frequently asked questions. They can also be used to automate lead generation and qualification processes.

  1. ManyChat: ManyChat is a chatbot platform that allows businesses to build chatbots for Facebook Messenger and other messaging apps. It includes automated messaging, lead capture, and integration with other tools and platforms.
  2. MobileMonkey: MobileMonkey is a chatbot platform explicitly designed for Facebook Messenger and SMS messaging. It offers automated messaging, lead capture, and other tools and platform integration.
  3. Tars: Tars is a chatbot platform that allows businesses to build chatbots for websites and mobile apps. It includes automated messaging, lead capture, and integration with other tools and platforms.
  4. Chatfuel: Chatfuel is a chatbot platform that allows businesses to build chatbots for Facebook Messenger and other messaging apps. It offers automated messaging, lead capture, and other tools and platform integration.
  5. BotStar: BotStar is a chatbot platform that offers natural language processing (NLP) and machine learning capabilities. It allows businesses to build chatbots for websites and messaging apps, including features such as automated messaging, lead capture, and integration with other tools and platforms.

These are just a few examples of chatbot tools. The specific chatbot tool a marketing agency chooses will depend on its specific needs and goals and the messaging platform it wants to target.

Predictive analytics tools

Predictive analytics tools use AI algorithms to analyze data and predict future events or trends. This information can be used to automate targeted marketing campaigns, optimize pricing strategies, and improve customer experiences.

Here are a few examples of SaaS (Software as a Service) predictive analytics tools:

  1. Salesforce Einstein: Salesforce Einstein is a predictive analytics platform integrated into the Salesforce CRM. It offers predictive lead scoring, opportunity forecasting, and personalized product recommendations.
  2. C3.ai: C3.ai is a cloud-based predictive analytics platform that provides machine learning and artificial intelligence capabilities. It is designed for enterprise customers and includes predictive maintenance and fraud detection features.
  3. H2O.ai: H2O.ai is a cloud-based predictive analytics platform that provides machine learning and artificial intelligence capabilities. It includes predictive modeling, data visualization, and collaboration tools.
  4. Google Cloud AI Platform: Google Cloud AI Platform is a cloud-based predictive analytics platform that provides machine learning and artificial intelligence capabilities. It includes predictive modeling, data visualization, and collaboration tools.
  5. Alteryx Analytics: Alteryx Analytics is a cloud-based predictive analytics platform that provides machine learning and artificial intelligence capabilities. It includes predictive modeling, data visualization, and collaboration tools.

These are just a few examples of SaaS predictive analytics tools. The specific tool a marketing agency chooses will depend on its specific needs and goals and the data sources and platforms it wants to integrate with.

Recommendation engines

Recommendation engines use AI algorithms to analyze customer data and provide personalized product or content recommendations. This information can be used to automate targeted marketing campaigns and improve the customer experience.

Here are a few examples of SaaS (Software as a Service) recommendation engine tools:

  1. Amazon Personalize: Amazon Personalize is a machine learning-powered recommendation engine part of the Amazon Web Services (AWS) suite of tools. It offers personalized product recommendations, real-time personalization, and recommendations based on individual preferences and behaviors.
  2. Persado: Persado is a recommendation engine that uses artificial intelligence and machine learning to generate personalized and emotionally impactful messages for marketing campaigns.
  3. Recombee: Recombee is a recommendation engine that provides real-time personalized recommendations for e-commerce businesses. It includes product recommendations, user recommendations, and behavioral targeting.
  4. Stamped.io: Stamped.io is a recommendation engine for e-commerce businesses that provides personalized product recommendations, social proof recommendations, and email recommendations.
  5. Endorse: Endorse is a recommendation engine for e-commerce businesses that provides personalized product recommendations based on a user’s browsing and purchase history.

Conclusion

Artificial Intelligence (AI) in marketing has become increasingly important in today’s digital landscape. AI-powered tools such as chatbots, predictive analytics, and recommendation engines allow marketing agencies to automate and personalize their marketing efforts, leading to more effective and efficient marketing campaigns. By leveraging AI, marketing agencies can better understand their target audience, generate more personalized and emotionally impactful messages, and make more informed decisions about their marketing strategies. As AI technology continues to evolve, it will likely play an even more central role in the future of marketing, providing businesses with valuable insights and the ability to reach and engage their target audience in new and innovative ways.