8. A sizeable proportion of consumers are
happy for companies to use their
personal data, providing they benefit
through more targeted marketing
Photo credit: http://www.ey.com/Media/vwLUExtFile/BigData/$FILE/ey-bigdata_v3.png
9. “There are a lot of
small data problems
that occur in big data.
They don’t disappear
because you’ve got
lots of the stuff.
They get worse.”
David Spiegelhalter
Winton Professor of the Public Understanding of
Risk at Cambridge University
10. 'don’t care – big data is a
pointless marketing term’
Online Measurement and Strategy Report 2013 by Econsultancy, July 2013
8%Marketers say…
11. Time available to analyse data in Google Analytics is too
little, so adding more data to the 'pile' to analyse will only lead
to less insight, not more.
Little to none. We know we need to gather and analyse the
available date to run our marketing and our business better,
but 'big data' is not the driver of this.
We have tonnes of data and sometime it's difficult to analyse,
but this has always been a problem and always will be as
data acquisition will keep growing.
Not sure what "big data" means.
Online Measurement and Strategy Report 2013 by Econsultancy, July 2013
15. Opportunities for ^ improvement
1. Observational (Passive) inputs
– Non-solicited, non-structured, non-validated
– Basis of a hypothesis – indicative insights,
trends
1. Direct engagement (Active) inputs
– (somewhat) structured, solicited etc.
– Tied (hopefully) to business questions
– Still has a ‘human’ component
In both cases we are exploiting real-time data
marketing
16.
17. 70M+ websites
Full Twitter ‘Firehose’ feed
All major social media
• >100,000 verified
healthcare professional
(HCP) sources covering
websites & social media
Typically 2-5% of all public social media
conversation for a health topic coming from HCPs
18. 89,824,885
processed social profiles
377,744
algorithm selected profiles
88,569
human validated profiles
24,519
HCP authored blogs & sites
208million tweets
152thousand tweets per day
Source: Creation Pinpoint, data correct at Jan 2014
20. The digital world changes the
model of influence
Traditional KOL model: Emerging DOL model:
KOL relationships are different to digital opinion leader (DOL) relationships
Hierarchy typically based on seniority,
experience, publications etc.
Collaborative ‘flattened’ relationships,
not ordinarily common in real-world
21. HCP community network
Cardiologist
Academic Physician
Academic Surgeon
Anesthetist
Dental surgeon
Hospital Director
Media Physician
Medical Biologist
Medical student
Neurologist
Neurosurgeon
Nurse
Oncologist
Orthopedic registrar
Pediatrician
Pharmacist
Physician
Psychiatrist
Neurolaw
Rheumatologist
Sports Therapist
Trauma Anesthetist
Trauma Physician
Various
Nodes: 13,781(4.35%)
Edges: 35,886(9.05%)
Note:
This diagram represents ~10% of the HCPs connected to those
talking about the study topics (shown as colored circles)
23. Creation Pinpoint sample study, inflammation among conversations of UK healthcare professionals 01 Dec 2012-30 Nov 2013
24. Photo credit: National Library of Ireland on The Commons / Foter / No known copyright restrictions
25.
26. Proof-of-concept real-time NLP
A: Initial data insights B: Future strategic approach
Analysis of an anonymized sample dataset
to determine the visual outputs and
information insights that are possible.
An exploratory exercise to find ways that
medical information can potentially service
commercial strategy development.
Key components include:
Based on learning from the sample data
set, and the evaluation of various tools and
processes for developing these insights,
recommendations to be made for how
PharmaCo might use this type of data in an
on-going implementation.
Key components include:
• Assessing data opportunities • Pricing and feature comparison
• Analysis and experimental approaches • Handling of languages other than English
• Types of outputs possible • Metrics and potential success indicators
• Presentation of findings • Potential real-time integration
27.
28. 28Drill-down by area of interest i.e pharmacistDrill-down by area of interest i.e pharmacist
Four+ clear ‘problem’ products for pharmacistsFour+ clear ‘problem’ products for pharmacists
29. What happens if we focus on a word like ‘fridge’What happens if we focus on a word like ‘fridge’
30. 30
Clear issue already
detectable week one,
escalation within business
to avoid week two peak
Clear issue already
detectable week one,
escalation within business
to avoid week two peak
Normal
Issue
32. Know
Know
Don’t Know
Don’t Know
What we know we know What we don’t know we know
What we don’t know we don’t knowWhat we know we don’t know
Customer
Information
Source:Adapted from http://www.doceo.co.uk/tools/knowing.htm
33. Thoughts (and some tools)
1. Getting started: need education for marketing departments to
develop understanding of the power of indicative insights…
– what data do we already have, or could we have
– how to ‘munge’ it to answer behavioral or segmentation
questions – beyond the obvious, in real-time
1. Create content (dynamic?) for specific segments/needs
2. Allow customers to set their own preferences (then learn!)
3. Once you have the basics, start to explore machine learning
algorithms and predictive analytics
Can Pharma be as ‘clever’ as Amazon or Netflix? Of course!
1. Online HCP insights research: Creation Pinpoint
2. Social Network Analysis: Gephi/Anaconda
3. Integrations and data scraping: Import.io
4. Location visualization: CartoDB
5. Natural language processing: Brandwatch, Lexalytics, Semantria, Clarabridge
6. Structured and unstructured data: Omniscope
Editor's Notes
Show of hands – who thinks this is funny (at all). Keep your hands up if you also like Star Wars. This was for you!
Now, I took an intuitive guess that a bunch of data geeks might be interested in Star Wars and Lego and would find this humorous.
The best marketing, is about presenting an idea that resonates with an audience in a way that generates interest. If you know what the audience likes, really likes, you can resonate – and in some cases, change behavior.
And now we have the possibility of big data… to think in terms of country, city, street, or community… A scale once only possible with the budget of NASA is now possible for most organizations for a reasonable price.
Tapping in to big data for marketing really means going beyond what you say or think about yourself – it’s the evidence from what you actually do. Behavioral trends and indicators. Yes, evidence!
Perhaps surprisingly, people willingly share personal information about themselves which is a goldmine for healthcare marketers who are willing to embrace the challenge.
We know how some companies have developed their business model around behavioral data.
Consider the man who stormed into a Target near Minneapolis and complained to the manager that the company was sending coupons for baby clothes and maternity wear to his teenage daughter. The manager apologized profusely and later called to apologize again – only to be told that the teenager was indeed pregnant. Her father hadn’t realized. Target, after analyzing her purchases of unscented wipes and magnesium supplements, had. http://www.ft.com/cms/s/2/21a6e7d8-b479-11e3-a09a-00144feabdc0.html#axzz2yQ2QQfQX
Consumers knowingly permit personal data to be stored and processed by companies, if that means TARGETED marketing and less ‘noise’.
Online survey of just over 2,000 consumers and 748 senior business decision makers.
In recent times (May 2014) there has been an increasing number of warnings from experts in academia and big business about the ‘hype’ of big data.
http://www.ft.com/cms/s/2/21a6e7d8-b479-11e3-a09a-00144feabdc0.html#axzz2yQ2QQfQX
Marketers are not always understanding how ‘big data’ might be relevant to them. Is it science, is it statistics, is it market research, or is it analytics?
There were 896 respondents to the research request, which took the form of an online survey in Q2, 2013.
Key facts about the sample include:
Two-thirds of the respondents (67%) stated that they were based in the UK, with a further 18% coming from the rest of Europe. Asia Pacific, North America and the MENA region were also represented.
Almost one in five (19%) respondents said their principal role was as a data analyst.
Almost one third (31%) of UK respondents came from a company with a turnover over £150 million. 40% of non-UK respondents came from companies with a turnover in excess of US $150 million.
The most well represented business sectors for in-house marketers are retail (16%), financial services (12%), travel (8%) and publishing (7%).
For supply-side respondents, 58% stated they worked for a digital agency, with a further 17% as self-employed consultants.
Mostly retail, finance, travel, publishing, - Freeform responses to the question: “what effect has big data had on the web analysts in your organization?”
We know that the pharma industry is constrained by regulation; we know that FMCG and other consumer sectors are able to innovate and pilot technologies and techniques on the cutting edge. The ‘lag’ is not negative. It gives us the opportunity to learn from other sectors, before implementing (hopefully) best practice within the constraints of our industry.
The reality is that Pharma is still thinking in discrete channels, as a portfolio of channels, and trying to work out ‘Multi-channel Marketing (MCM)’ – i.e. how to make it work holistically and cohesively.
But they don’t see that big data analytics and predictive preference-based content is the way to go for the future (and for now, where possible). Or, if they do, it is a bridge too far in terms of legal and medical approvals.
Yet the customer is ultimately a professional person, with behavioral traits like any other consumer. Sure, a rational and qualified professional – but at the cure, a human not a number.
Now let’s look at some ‘low-hanging’ fruit in two cases which show how the concepts of big data can be used to improve pharmaceutical marketing.
The first is a study of healthcare professionals (customers) and how to distil the signal from the noise. The population of people talking about health is as large as the population of people using the Internet.
Healthcare professional conversations may typically contribute between 2-5 % of the content in a given therapy area. Of course, the use case is very different.
You may look at the 100,000 sources in Creation Pinpoint and think that this is not ‘big data’. True.
Obtaining these individuals and analyzing their behavioral signals – that big data!
It is also their geo-location data – when and where they are contributing intelligence to the healthcare conversation.
Importantly, we can also understand the evolving networks of content distribution and influence.
Relationship marketing or influencer marketing is not new to Pharmaceutical companies. However, the rise of the DOL (digital opinion leader) is a paradigm shift. This requires big data solutions.
You will have all by now seen a social network analysis i.e. nodes and edges. Graphing and visualization of data still creates good opportunities for human analysts to spot outliers and areas of interest. Once the scale increases sufficiently, we end up with a hairball – something which is not quite as useful. Even here, just 10% of a data set looks colorful, but hardly insightful
We can also build a profiles database of individuals – sure, there are concerns around privacy. However, much of the social data is publicly available and according to the terms and conditions accepted by each user of the social platform, permissible for use.
It’s is real-time, ongoing ‘buzz’ monitoring, but the more powerful and regularly commissioned work is about historical analysis to inform future plans. Here we see a demonstration dashboard from Brandwatch, modified by Creation Pinpoint technology and Creation Healthcare analysts. We can detect emerging treatment types that no one could have pre-empted or expected. It was not part of our ontology, but will be from now on. With such tools we can discover the unknown unknowns.
The accuracy, relevance, and timeliness of medical information is critical to the success of treatments, and of course pharmaceutical companies in general. Managing risks, concerns, and reputation can determine the value of the stock price for a company. It is fundamental and important!
Doctors are having a hard time keeping up with the vast reams of medical information which is now available. Watson may not be a doctor, but is so far proving to be a good diagnostician!
What are the applications if such machine learning systems are turned onto our own datasets?
Still, we don’t have to be that advanced to start improving pharmaceutical marketing. In one project, we tool a look at contact records from a medical information database. So, calls, emails, and other contacts from mainly healthcare professionals seeking answers to medical and technical product questions. The ultimate dataset includes around 1 million records per annum in ~27 languages.
Starting with the structure data, and extrapolation of data into geocoded records, it is possible to create a summary dashboard that instantly gives more insights than the typical service oriented KPIs like turn-around-time. Here, our dashboard is interactive.
So, if I click on the category ‘pharmacist’ I can quickly identify that there are four products which have repeated queries. If not already, I can have my marketing team produce a campaign directed at resolving these queries proactively, potentially alleviating unnecessary concerns and helping to build the knowledge needed at the point of dispensing.
Or in another dashboard, I can investigate unstructured data by focusing in on human identified anomalies. What happens when I click ‘fridge’ – well I see which products are having repeated questions relating to this topic and I can see through context sensitive charts the other tokens and specific verbatim concerns. This may lead to a new SRD (standard response document) or in a more data-driven model, my HCP portal may dynamically update to ensure that this topic is served as a higher priority in search.
Mapping verbatim tokens over time, I can determine anomalies relative to ‘normal’. Automated triggers and ‘intelligence events’ can notify my cross functional team to prevent unnecessary resource wastage or reputational damage. In this case, a simple packaging change resulted in numerous queries because it was no longer clear whether the product could be stored out of the fridge.
Identifying anomalies can lead to better customer service through medical representative and the field force. Here, the data prompts an investigation of Newport and Lincoln – why is there a disproportionate leaning on medical information? Is this good? Does it expose an unmet need or information gap?
A final model for helping our marketing teams to think about the possibilities of big data.