Navigate uncharted

Embrace change

Three triggers have converged to engulf the current life science marketing landscape in a perfect storm.

Content marketing is ubiquitous. Routinely non-linear B2B customer journeys involve, on average, between six to ten stakeholders plus eight touchpoints. And as competition and complexities within niche areas, especially manufacturing of novel therapeutic modalities, have skyrocketed, related consumables, software and capital equipment are more sophisticated.

Deposited in the aftermath, is a staggering amount of marketing data. But herein lies one challenge. How can life science marketers translate this data into a competitive advantage?

Redefine boundaries

Marketing data is often decentralized in multiple file formats where silos limit accessibility. Data accuracy, completeness or timeliness oscillates, crippling usability and encouraging paralysis by analysis.

Such data also winds up in dashboards. When drawing comparisons, for example, dashboards are beneficial due to their visual appeal and ease of creation. However, dashboards are descriptive and backwards-looking. They paint a picture of what has happened and why.

But to fully harness the value of marketing-derived data, life science marketing teams need to go one step further by exploring what is likely to happen and what action should be taken in order to achieve a desired objective.

Transform ambition into reality

Discussions about maximizing the mileage of one’s data inevitably gravitate towards AI. Since large language models (LLMs) are now equipped to write code, for example via the Code Interpreter plugin for ChatGPT-4 from OpenAI or GitHub’s Copilot, the democratization of data analysis is a distinct possibility. At a minimum, historically manual and/or repetitive tasks could be on the verge of becoming less cumbersome, creating time for more value-added activities.

Although AI has started to augment and reshape how certain marketing functions are performed, caution remains mandatory. AI tools are not fully autonomous nor do they possess domain knowledge. Meanwhile, applications employing AI resemble a black box. When outputs appear and inner workings are masked, skepticism is natural.

In addition, as ChatGPT becomes mainstream but relies on user inputs to continuously improve predictive capabilities, uploading proprietary marketing data ignites privacy and confidentiality concerns. Other cloud-based marketing AI tools offer alternatives, but costs escalate quickly due to license fees, desired functionality and training of personnel.

How can Confluminetix help?

Simply put, by combining the best of both worlds. Familiarity with data mining and machine learning helps extract insights, together with life science marketing expertise to contextualize data and calibrate results, especially those generated via AI.