We couldn’t agree more with your perspective. Common roadblocks we see are data quality or data accessibility. The right data, strong hygiene and governance practices, and a continuous feedback loop to refine the model are key to a project's success when working with our clients to implement AI solutions. It doesn’t matter if it’s leveraging in-platform AI capabilities like Salesforce or Marketo or a bespoke application, a solid data foundation is key to long term success and delivering truly personalized experiences.
Digital Marketing Strategy | CRM | eMail Strategy | Personalization | Lead Generation | CX | eCommerce
This Marctech article really resonated with me. I hear clients tell me all the time that they want to use more AI, especially when it comes to personalizing their marketing experiences across email and web. I love hearing this because it has the potential to create a highly personalized experience that connects with customer needs and increases the potential to drive business results. The reality is that the data fundamentals are not always in place and efforts often fall short of their potential. AI isn’t a magic bullet. It needs the tried and true data practices to feed the engine. Capturing customer needs, creating content to address those needs and stitching it together so it’s available when customers want it is still the foundation a marketer needs. What has been your experience with this? Are you also running into data challenges when it comes to AI-driven marketing automation? https://lnkd.in/eSFWaxkF