sean zinsmeister

  • The modern marketer’s guide to machine learning algorithms

    Most marketing (and sales) teams have seemingly simple goals: identify your best customers, target prospects who look like them, facilitate a positive buying experience, and bring these prospects into your customer community. The challenge with this is that companies are faced with an onslaught of data, making it impossible to economically throw humans at each of the aforementioned objectives.

    Marketing Land- 23 readers -
  • What does intent data mean for the data-driven marketer?

    As big data increasingly becomes more accessible, marketers are looking for ways to make it more scalable and actionable in order to better target prospects in various stages of the buyer journey. Intent data is synonymous with this topic, but it understandably causes a great deal of perplexity for many marketers.

    Marketing Land- 15 readers -
  • Achieving Hyper-Segmentation To Reach Personalization At Scale

    With the rising adoption of machine learning and automation, a human touch becomes more important than ever. Companies that have figured out how to utilize data to create deeper personalization are not only improving the overall customer experience by talking in a language customers appreciate; they’re also winning more deals and generating more revenue dollars.

    Marketing Land- 17 readers -
  • Infer Net New Leads: Identify and Send the Best Leads in Salesforce

    Businesses are struggling to interpret mountains of data about their customers and what motivates them. It’s nearly impossible to see the forest from the trees when people are focused on their system of record vs. extracting useful insights from all the signals in disparate systems like Salesforce, Marketo and Google Analytics, as well as unstructured sources from the web.

    Marketing Technology Blog- 20 readers -