In this episode of Talk Life Science Podcast, Laura Browne interviews Eric Teboul, Vice President of Sales and Marketing at Horiba Scientific. Together, they explore the art of marketing to scientists by employing data-driven marketing approaches.
The conversation commences with an exploration of Eric's background and his 23 year career in international business. Drawing upon his experiences as an engineer, Eric emphasizes the invaluable hands-on communication he has had with their customers. As Vice President, he oversees customer-focused functions like sales, applications, digital marketing performance, and service.
Horiba Scientific stands out due to its involvement in various cutting-edge industries, including environmental, alternative energy, biopharma, healthcare, and electronics. Their wide-ranging expertise demonstrates their commitment to pushing the boundaries of scientific possibility.
The Importance of Tailored Strategies
Laura raises a crucial question when she asks if Horiba Scientific's marketing approach differs across different markets. Eric confirms that it does, shedding light on the importance of tailoring marketing metrics to suit the unique characteristics of each product, audience, and sub-industry. To achieve this, Eric emphasizes the significance of using data-driven information to guide messaging, along with recognizing the power of personalization and relevancy in forming an effective marketing plan and gaining marketing qualified leads.
Delving deeper, Laura and Eric delve into the topic of data metrics employed by Horiba. Eric shares valuable insights on the extensive process Horiba underwent to develop an infrastructure for gathering and storing data. He also emphasizes their search for the right digital technology to align with goals in order to gain competitive advantage and reach potential customers.
Data Silos and Non-Disclosure Agreements
Laura and Eric discuss the crucial issue of breaking down data silos. Eric talks about the challenges of strict non-disclosure agreements in the age of big data and finding a balance between privacy and effective data analysis for different team members. His firm conclusion is that breaking down silos and fostering information sharing usually leads to better overall results for marketing teams.
Fluid Skillsets Boost Productivity
To illustrate the importance of finding common ground between marketers and scientists, Laura delves into the details of Eric's digital marketing strategy. This includes examining how local regulations impact the deployment of global messages and customer service, the monitoring of key performance indicators (KPIs), and the essential task of building strong relationships within the sales team. By fostering collaboration and understanding between these two groups, both sides are able to maximize their contributions and deliver high quality results.
To further emphasize the significance of this collaboration, Eric shares the story of a data scientist within their team. This individual was initially involved in sales but demonstrated exceptional aptitude for a specific data tool.
Recognizing their talent, Eric suggested that they focus on working with data full time. With additional training and attending seminars, this person swiftly transitioned into the role of a data scientist. The success did not stop there - when they partnered with a marketer who shared a similar interest and understanding of data, their productivity and effectiveness soared.
This was not a one-time occurence either. Eric shares another anecdote, this time about a journalist who joined Horiba and skillfully distilled the company's complex materials into accessible language for their publication. Impressed with their ability to clearly explain intricate concepts, Horiba hired this journalist, recognizing the immense value they brought to the team.
Storytelling and Emotion in Scientific Marketing
Things get personal when Laura raises a thought-provoking point about the overlooked aspect of storytelling and emotion in the scientific marketing profession. The emphasis on science and data often leads to a neglect of the human element, particularly when the information is predominantly exchanged among scientists themselves.
Eric candidly shares his own struggle with this dilemma, acknowledging the challenging journey of reconciling his engineering background and scientific mindset with the need to infuse emotion into his work. Balancing the scientific with the emotional, and the objective with the personal, presents an ongoing difficulty for him. Moreover, this struggle extends to his employees and team members, who also grapple with incorporating this essential layer of human connection into their work.
How can we bridge the gap between the technical and the emotional in scientific marketing? How can we harness the power of storytelling and human connection to effectively engage and communicate with consumers? By delving into this conundrum, Laura and Eric invite us to reconsider our approach and challenge established norms in the field.
Success and Failure
When talking about success, Eric contemplates that his greatest success is yet to come. Still, he recognizes that his major failing was to follow conventional methods for too long, resulting in wasted time, money, and missed opportunities. He acknowledges that embracing innovation and developing superior models and methods earlier on could have propelled Horiba even further if he'd been brave enough.
Will AI Close the Gap Between Descriptive and Prescriptive Analytics?
As the podcast comes to an end, Eric answers the question posed by our previous guest Jeff Zonderman of Bruker. Last time, Jeff asked how modern companies can overcome the dilution of attention caused by market oversaturation.
Eric's enlightening response highlights the importance of good management who can make tough choices and focus the team.
Then it's Eric's turn to ask a new question and it's a question that has been on everyone's lips, and was even featured for a 3 parts series in freakonomics: How will the rise of AI disrupt data driven marketing, and will it close the gap between descriptive and prescriptive analytics?
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