DiscoverLife, Science and MarketingJaleel Shujath: Applying scientific rigour to marketing through continuous experimentation, data analysis, and learning
Jaleel Shujath: Applying scientific rigour to marketing through continuous experimentation, data analysis, and learning

Jaleel Shujath: Applying scientific rigour to marketing through continuous experimentation, data analysis, and learning

Update: 2024-01-09
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Life, Science, and Marketing Podcast: Episode 10

Guest: Jaleel Shujath, ex-scientist turned marketing expert

Jaleel's Background:

  • Ex-scientist with a biology degree and lab experience
  • Over 20 years working in life science marketing at GE Healthcare, ATCC, Absorption Systems, and more
  • Passionate about science and discovery from an early age

Key Discussion Points:

  • Applying scientific principles like hypothesis testing and data analysis to optimise marketing efforts
  • Embracing both successes and failures as learning opportunities to improve campaigns
  • Tracking marketing data rigorously to showcase value and defend budgets
  • Tailoring marketing approaches to each unique company’s needs and market landscape
  • Using curiosity to fuel impactful messaging and innovative tactics
  • Networking consistently to build relationships and learn from others

Key Quotes:

  • "As scientists, we always promote the successes right and the successful experiment, the new discovery...people rarely show the failures."
  • "Make sure the data is solid because in science, scientists will always critique each other’s experiments right? And they're looking at it with a hard eye."
  • "I think if we were to go to anybody, any marketer...what percentage of your endeavours or initiatives are you know, knockout out of the park type of initiative? If they were being honest...maybe 50 percent, maybe 40 percent."
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Jaleel Shujath: Applying scientific rigour to marketing through continuous experimentation, data analysis, and learning

Jaleel Shujath: Applying scientific rigour to marketing through continuous experimentation, data analysis, and learning