Excerpts in a custom social listening engagement with GE Life-sciences around Protein Purification. This uses our proprietary Language based approach to bring structure to a large body of scientific text data.

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