This Fireside Chat and post was originally produced by Impetus on May 25, 2021, hosted by Natalie Yeadon.
Philip Poulidis (CEO & Co-founder) and Orchid Jahanshahi (VP, Life Sciences) from ODAIA sat down with me for a deep dive into the use of AI and big data for customer journey mapping and dynamic segmentation, data integration, and omnichannel customer engagement! They also shared their predictions for the future and much more.
Q: We hear a lot about “big data” and we just want to illuminate what this means in Pharma/Biotech. What are these data silos? What are we collecting currently and where is this information coming from? Maybe you can give us a bit of a historical view: prior to the ODAIA concept or solution, what was everybody doing with these data?
Orchid: It hasn’t been changed for a long time; the 30 years that I spent in pharma is sort of the same way. The thing that has changed though, of course, there are CRMs and there is technology, and marketing automation is relatively new as well. But, in terms of the process of a marketing team or a brand team and how they interact with sales and marketing. That process has been top-down, meaning, from the very beginning, there were tons of data inputs that go in there. A lot of its survey-based data, which is on a limited number of physicians, I think in Canada, we would look at 150, let’s say neurologists, to try to spend millions of dollars on those neurologists later on.
A lot of money is spent afterward, but a lot of the assumptions come from surveys and some historical data and progressions that we did on simple spreadsheets. Then, we would simply build the segments, sometimes on our own, sometimes with the use of consulting companies, and we funnel that down to the sales team. It was always understood, and it is, I think, to this day, that marketing starts the process and then your salespeople or your automation should be your hands and arms and they roll out the strategy, they are tactical.
What we’re talking about here really excites me, because I did spend half of my life in marketing and the last half in sales, is to see the incredible brainpower and professionals. The sales team used to not be able to shape the business fast enough. The feedback would have to go back into the cycle a year later, a little bit like Groundhog day; take the feedback, and then push it down again. This was literally a yearly process and we would maybe redo this once a quarter, but rarely more than that.
We would be stuck seeing something like a pandemic, something maybe less exciting as what happened. Those dynamic changes in the market would not be acted upon by the company as fast as they could because the representatives were not in a position to bring back that data into the strategic realm. I’ll go back to Philip to explain why Maptual sort of turns this upside down. It’s a little bit of a revolution and thinking, I believe.
Philip: With that foundation, over the last number of years, the one thing that I think is undisputed is that the volume of data being collected by pharma companies has grown exponentially. This includes data on the healthcare professional engagements, whether it’s captured in CRM or in the marketing automation platforms, patient support program data, data from claims, prescription transaction data, and even information on physician key opinion leaders and in terms of their influence within their field, in the publications that they’ve authored for example, or studies that they’ve published.
All of these data are currently residing in siloed databases where the average rep doesn’t have access to every single one of these data sources in order to be able to collate them together, look for insights, and then make decisions in terms of their territory planning based on those valuable insights. The human mind frankly can’t possibly comprehend any of the correlations that exist between datasets. If you look at really big vast datasets of HCP engagement or prescription transactions or data from patient support programs, the patient journeys for example, all of that data in combination, to try to analyze that, to look for correlations, to look for insights that are extracted so that you can inform your sales approach, your marketing approach, all of that can’t really be done by humans unless you spend countless hours with countless people working on data analytics. This is where AI comes in.
Before we get to the AI piece, one of the things that we’re trying to solve here is to make the commercial or customer-facing elements of pharma a lot more efficient than what they were in the past. When you look at the statistics in terms of how much money is being spent by pharma companies on the sales and marketing of their brands in the US alone, I think the latest number I saw was around 30 billion dollars collectively on sales and marketing. 20 billion of that 30 is directed at selling and marketing to HCPs. That’s a huge number. Now, with a tool like Maptual, if we can bring all of the disparate data sources together, if we can aggregate them in one place, give the teams a single pane of glass within which they can get all of the insights, start layering predictions on top of that predictive analytics so that we can predict what Orchid mentioned earlier, in terms of the static approach to territory planning which is typically done once a year, with maybe reviews done every quarter or maybe semi-annually. We can do this in real-time.
As soon as there are changing market conditions like a pandemic or any other changing market condition, or a competitive product that comes into the market and is competing against your product now, we can adapt to that in real-time using our AI analytics. This is really what we’re talking about is saving time, saving the energy that goes into planning, and, hopefully, reducing that 20 billion dollars spend by 5, 10, 15, 20 percent so that that can be applied back into drug discovery, patient care, where there’s going to be a really big impact…
For more of our discussion, you can watch the whole Fireside Chat with Philip Poulidis and Orchid Jahanshahi, or listen to the podcast version, [here].
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