Social Listening for Scientists - BLA Case Study

1. Social listening for scientists (A B2B case study)

2. This case study represents works undertaken by BLA’s Social team for GE Healthcare Life-Sciences. All project insights have been masked and pixelated to preserve client confidentiality. None of this material is to be reproduced or published without the express permission of Bottom Line Analytics Ltd and GE Healthcare Lifesciences Ltd. Disclaimer

3. Drug Discovery Drug Development Preclinical trials Clinical trials New Drug Application Large-scale manufacturing GE’s Diverse customers Synthesis Purification Metabolism Screening Toxicity Efficacy Efficacy Adverse events Dosage Productivity Efficiency GE customers cover the entire life-science spectrum from initial drug discovery to clinical trials and large manufacturers.

4. The Scientific Purchase Journey Recognition Exploration Research Evaluation Experience Use Purchase Scientific Purchase Journey Customer Due Diligence Begins Customer’s First Serious Engagement with Sales Purchase 57% Complete Customers are choosing to delay commercial conversa ons with suppliers Source: McKinsey

5. Adding value early on Recognition Exploration Research Evaluation Purchase Use Trusted, relevant content Direct Marketing Need recognised Scientist confirms need Scientist assesses options Scientist evaluates selected solutions Scientist chooses the most suitable solution Scientist commentates on experience / loyalty loop eCommerce Customer care The focus for GE Lifesciences digital team has been on creating collateral and brochures serving the Evaluation, Purchase and Use stage. The changing dynamics of the customer journey meant a shifting of emphasis to the earlier stages. Therefore, creating trusted content that resonates with prospective customers through these early stages became crucial.

6. Initial brief to Area of Interest: Protein Purification Protein purification is a series of processes intended to isolate one or a few proteins from a complex mixture, usually cells, tissues or whole organisms. Protein purification is a vital process used in drug discovery, medicinal treatments and product development within the life sciences sector. Listening Objectives  To listen into discourse around Protein Purification from across social and digital platforms.  To size, scale and trend key themes and topics as they relate to Protein Purification.  To generate actionable insights GE Life sciences editorial, scientific writers and bloggers could use to create digital content that resonates with the audience (researchers and doctoral students operating within the laboratory).

7. From unstructured corpus to content applications 1: Learn the language 2: Process 4: Application 3: Insight

8. Understanding the Language An initial workshop and card sorting exercise was undertaken to collect insights from internal specialists and stakeholders on the language being used to discuss ‘Protein Purification’. Sales Customer Care Marketing R&D

9. Assigning Structure: Themes & Topics Applying scientific knowledge from stakeholders we placed structure to the language in terms of Themes and Topics within.

10. 1: Learn the language 2: Process 4: Application 3: Insight Capture volume, trends and channels

11. Topics triggered • Protein Purification • Chromatography • Sepharose • Recombinant Protein AND Purification in close proximity Some Language rules used • Text/Word Proximity • Lexical Co-occurrences Channel logged Date logged Pattern recognition Using Linguistics* * Rooted in Stance Shift AnalysisTM

12. Science Groups 1 3,314 records Blogs & Forums 28,399 records Science Groups 2 6,237 records Twitter 10,098 records Pre-spam 48,048 records Post-spam 37,707 recordsData is cleaned for erroneous text, non words and words with a multiplicity of meanings such as ‘Protein’ Cleaning out spam & non words Year Blogs and Forums Science Groups 1 Science Groups 2 Twitter 2010 31 37 2011 540 90 2012 1050 1045 1341 2013 12369 944 2343 482 2014 7423 754 2424 6832 Total 20842 3314 6235 7314

13. 1: Learn the language 2: Process 4: Application 3: Insight Insight and segmentation

14. Conversations close to the Laboratory 9,551 Conversations overlapping Process and Outcome/application 550 Broader conversations further away from the Laboratory 27,606 Identifying target channels Science Groups 1 3,314 records Science Groups 2 6,237 records Blogs & Forums 28,399 records Twitter 10,098 records

15. Sizing & Scaling Discussion Themes by cluster

16. I will suggest if you want to check protein protein interaction using chromatography, to run anion exchange chromatography. If your complex coelutes with the 36kDa protein, it will be another evidence of course could mean they have the same charge, if you know the sequences you coudl calculate the isoelectric point of the complex and of teh 36 kDa protein. Thsi chromatography based on charge separation might help to discriminate, Coelution is one of the criterium for protein protein interaction. Problem using high length column is that you might dilute your sample and as dilution favors dissocitaion you might be in trouble. Drilldown into Topics within Themes

18. 1: Learn the language 2: Process 4: Application 3: Insight Applications to content and digital assets

19. Ongoing Value Creation Guidance on Website Taxonomy Scientific content creation SEO Keyword inventory management Direct Indirect

20. With an aim to understanding scientists’ interests and the language they used, BLA allowed us to mine platforms, even niche networks, and pull large volumes of data, which were quickly sorted and validated with BLA’s proprietary quality-checks. What really impressed me was the approach the team took to understanding our business and customers, so as to best interpret the data. Each stage of their process yielded value to us, from working with their enthusiastic social team to set up the tool, filtering out the slang and noise, to ultimately identifying trends and language. For a complex and niche market like ours, having the data and confidence to make connections and validate our assumptions was great. The insights and things we learned shaped our content and website approach. Our vernacular, communications and content are more relevant and insightful for working with them. Dimithri Wignarajah, Head of Social & Content GE Healthcare Life Sciences. Client feedback

21. Masood Akhtar Managing Partner, (EMEA) Bottom Line Analytics E: ma@bottomlineanalytics.com M: +44 7970 789 663 www.bottomlineanalytics.com Further Contact

Social Listening for Scientists - BLA Case Study

Social Listening for Scientists - BLA Case Study

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