You're considering using AI in your marketing analytics. But how do you know it's the right choice?

In my experience, no company's data is as clean and model-ready as they think at project launch. Work with the project lead or vendor to get clear guidance for the data needed to train the model and maintain it ongoing.

Clearly define business goals and data needs. Identify gaps where AI can automate tasks or provide better insights over current tools. Assess AI vendors for fit, accuracy, explainability, and ease of integration. Run controlled pilots to test AI efficacy vs analyst output. Evaluate impact on operations not just accuracy, ensuring easy adoption. Rigorously test solutions against use cases to determine if AI delivers true efficiency and insight gains over existing analytics. Choose AI strategically, not just for the sake of using it.

Like most things, you get what you put in. The quality of outputs are highly dependent on the quality of the input, including human prompting and execution. But you most also take into account other factors when auditing your data: - Centralization: does your data live centrally? Is it easily digestible? - Maintenance. Will you need to retroactively “clean” your data? How will future data be maintained? - Resources. What is the overall cost? What is the impact? Ultimately, what is your data being used FOR? Determine true purpose and business need, evaluate your current data and identify the improvement it will need for a true sustainable model.

Clearly define business goals and data needs. Identify gaps where AI can automate tasks or provide better insights over current tools. Assess AI vendors for fit, accuracy, explainability, and ease of integration. Run controlled pilots to test AI efficacy vs analyst output. Evaluate impact on operations not just accuracy, ensuring easy adoption. Rigorously test solutions against use cases to determine if AI delivers true efficiency and insight gains over existing analytics. Choose AI strategically, not just for the sake of using it.

Incorporating AI into marketing analytics is a strategic decision. It involves aligning AI with marketing goals, assessing data complexity, and evaluating resource availability. Businesses should consider the competitive advantage, scalability, and conduct a cost-benefit analysis. Staying updated on industry trends is crucial. By weighing these factors, businesses can determine if AI integration aligns with their objectives effectively.

The first question you have to ask is what it replaces. In terms of effort and time before investing. AI is the right choice for your marketing analytics, but could you consider working on these before you make your case? - Pinpoint exact marketing challenges where AI can provide solutions, such as customer segmentation, predictive analytics, or personalization. - Assess the quantity and quality of data you have, to train models effectively. - Evaluate the potential return on investment, and the expected increase in marketing efficiency and effectiveness. (ROI) - if your team has the necessary technical skills or if basic training could add that.

Define use cases where you want to implement AI understand current scenario and what would you want AI to achieve. For example advertising if there is a number days it takes to produce a campaign AI could reduce the lead time and produce a variety of campaigns. Make sure the right data architecture and data model setup so that AI can navigate through the model and find insights. Use AI where you have the above already setup.

Remember that any custom AI solution you build in-house will have to be regularly tuned or retrained, requiring staff with that expertise and bandwidth.

Remember AI is supposed to help you complete things more efficiently, so you can REALLOCATE that time in bettering the results. Consider time and cost savings, and how that will benefit the team as a whole. Determine what insights will provide the most impact, and work backwards to decide if an AI solution will be valuable during the process.