5 Steps for CMOs to Incorporate AI Into Their Marketing Strategy 

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Enterprise marketing departments are fully embracing artificial intelligence to better engage customers and drive more backend operating efficiencies. In fact, global revenue of AI in marketing is anticipated to grow to $107.4 billion in 2028 from $27.4 billion in 2023, according to Statista.  

AI marketing tools, software designed to optimize different aspects of the marketing strategy, are already streamlining marketing campaigns, enhancing customer targeting and personalization, and automating repetitive tasks so CMOs and their teams can focus on high-value undertakings.  

AI marketing tools are already accomplishing the following tasks:  

  • Email personalization, upping open rates and engagement 
  • A/B testing and segmenting audiences to deliver personalized content and offers 
  • Ad targeting to identify specific audiences and optimize ad placement  
  • Social media optimization, harnessing engagement metrics to determine the best time for posting 
  • Customer service chatbots, answering customer queries 

Yet while many marketing departments have already added AI functionality to their marketing toolkit, there is still plenty of room to further leverage AI integration. 

CMOs and AI adoption shouldn’t be a complicated affair. IBM outlines five steps to incorporate AI into enterprise marketing campaigns:  

  1. Establish goals – Set out goals and expectations, looking at what worked and what didn’t in past campaigns – and how AI can help improve outcomes this time around. Have marketing stakeholders align on these expectations, then choose the right AI solution and set key performance metrics (KPIs) around it to gauge success. 
  2. Acquire the right talent – Marketing teams usually don’t include data scientists with an AI background, yet their expertise can be an ingredient for success in your marketing initiatives. These skills can be hired to be an in-house resource, or you can connect with a third party for help in AI-powered marketing automation. The choice mostly depends on how much CMOs or directors are willing to invest.  
  3. Adhere to data privacy laws – Compliance shouldn’t get in the way of seamless AI integration in marketing strategy optimization. Always use customer data for AI marketing training and implementation with discernment and a high regard for privacy laws. Throughout the entire process, make sure that your customers’ data are secure, private, and used with consent.  
  4. Test the quality of data – Prioritize data quality to achieve your desired results. Assess the accuracy and relevance of the data that the AI marketing tool is being trained on. AI tools should be trained on data that accurately reflects customer patterns and intentions, or else you will fail to get insights into customer behaviors or strategic data from such information.  
  5. Choose the solution that’s right for you – AI solutions come in many varying capabilities and platforms. Following the first four steps – from goal setting to ensuring data accuracy and quality – will make it easy to choose the right AI marketing tool.  

Piloting AI in marketing involves thoughtful risk mitigation, an openness to learning, alignment with objectives, data gathering, stakeholder buy-in and engagement, and scalability assessment, to name a few. It also involves refining and optimizing, managing resources, and ensuring ethical and regulatory compliance.  

It’s critically important to understand the value of automating certain activities and tracking factors such as time and cost savings to reach business goals efficiently and ultimately boost the bottom line.  

Related Readings:

Gen AI: A Game Changer for CMOs

AI-Powered Personalized Marketing Fast Becoming a ‘Must Have’

5 Use Cases of AI in Marketing in 2024

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