Beyond the Silos: The Power of Customer 360 in Life Sciences

DD LS Podcast Season 2 Episode 1
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Welcome back to the Data-Driven podcast where we explore how modern data strategies, fuel transformation across industries.
My name is Kevin Keenan, and I'm senior director of Communications at Reltio.
Today we're diving into the life sciences sector, a world where precision is everything, but data is often messy. From fragmented HCP and HCO records to siloed data, trapped in disconnected systems, life sciences companies face a unique set of challenges.
My guest is Sriraj Rajaram senior product manager at Reltio. He's been deep in the trenches helping life sciences companies build a real-time customer 360 view that unifies messy multi-source data and transforms it into trusted, usable insights. We'll talk about what makes life sciences data so complex, the role of customer 360 and commercial effectiveness.
And how improving data quality can [00:01:00] directly impact patient outcomes. Compliance and the bottom line. Let's get into it, but before we get started, Sriraj, why don't you tell me a little bit about your role at Reltio?
Sure. Thanks Kevin. Thanks for having me on the podcast. Yes. As you mentioned, I'm part of the product management organization here at Reltio with a specialized focus on our customer 360 solution and platform. I've been in the master data space for a very long time, almost two decades, and this is just a logical extension of that role into the next area of interest.
That's a long time to be in the industry. So you bring a lot of knowledge to the table. That's great. So let's start from the beginning. Could you explain to us what is a customer 360 solution and why is it so critical in life sciences industry today? Sure. A lot of organizations have solved the master data challenge, which is understanding who their customers are, who people within the customer organizations are basically resolving the identity of your [00:02:00] customers, your suppliers, and so on.
As you've understood their identity, it's also useful to understand. The additional data sets besides their identity, what their preferences are, how they review your products, what they think about your solutions and services, how they interact with your various teams within the organization, whether it be sales, service, or marketing, and how all of these customers that you interact with are interrelated to one another.
When you can create that comprehensive view, it helps increase. The efficiency of your processes, as well as the ability for you to message in a more specialized manner to your customers. Imagine if you are a, if you are a pharmaceutical sales rep and you are talking to a doctor, obviously it's important to know who the doctor is, what their credentials are, but if you also know more about the doctor in terms of, which conferences they've attended or which websites they've browsed and how they're related to other doctors that you may be talking to, [00:03:00] that increases. The ability for you to influence that doctor and create that sale opportunity much more significantly. And that's what Customer 360 is in a nutshell.
Yes. Interesting. So as someone who's been in the industry for so long, how do you explain the evolution of what we have now today, uh, as customer 360 versus what we had say 10 or 20 years ago even.
Sure. I remember when I started in the data management space the be all and end all of the space was the data warehouse, right? Everybody thought about creating a data warehouse from where they would, look at analytics, they would look at operational systems and so on and so forth.
But everything if you spoke about data, it was all about the warehouse. And then we started chipping away at the warehouse because this warehouse was this big monolith. That was presumed to do everything that you wanted around data, but that wasn't always the case because when you have such a large monolithic system in the organization, its capabilities aren't specialized.
It's a [00:04:00] generalized system. So then came about master data management, which is, hey, you wanna understand who your customer is, you need a separate system that helps you do that, which is called Master data Management, which. Applies very specific principles of data management so that you can understand the identity of that customer.
Then came about separation of analytics. Okay, this is how you do analytics, or you do operational analytics, and so on and so forth. And now the evolution is when you link all of these things together. And you have your core identity of your customers, your suppliers, and you're able to link them with transactional behavior, what they've purchased, what service calls they've made, what marketing events have they responded to.
Now you are getting this evolution from identity to the full, comprehensive view of this customer that allows a better engagement with the end-to-end customer.
We've certainly come a long way. That's really interesting. So how do you build something like this, um, [00:05:00] in today's enterprise that serves so many different users that have so many different needs?
Sure. Let's just take pharmaceutical organizations as an example. There are various teams within the pharmaceutical company. There are commercial teams, there are r and d teams, there are marketing teams, and there are medical affairs teams, and so on and so forth. So if you were to take all of this.
Let's look at what each of these teams require, right? Commercial teams are interested. The primary folks on the commercial team are the sales representatives. They're looking to understand the physician the doctor or the hospital that they're gonna be engaging with. They want a comprehensive view of that profile.
They want to know who this doctor is, who this hospital is, what kind of interactions the organizations had with this pharmaceutical company in the past, what conversations they may have. Avoiding a redundant outreach in some form of manner, right? So that's one form of it. The second part of it, if you look at it from the other aspect of commercial, which is the marketing, they want to be able to segment the audience.
Okay? [00:06:00] Which, they wanna be able to segment them by regions, by certain target groups. For example, cardiologists versus oncologists, and then run campaigns against them. They also want to be able to, if you are a direct to consumer, for example, if you are a medical device company and you have devices that directly touch patients, you want to be able to segment your patients here's a diabetic patient versus a different kind of patient.
So you can segment them and then ship samples or, say a targeted messaging towards them in a specific manner. Oh, that's really cool. Yeah. Interesting. All right, so let's zoom in on the pharmaceutical sales rep. What does their day look like before and after using Customer 360?
Similarly, maybe take us through a use case for marketing as well. Sure. Let's just take an example of a sales rep John Smith. He's joined the pharmaceutical company and he's been allocated a new territory in this territory. There are, I. 30 physicians that he needs to contact and have conversations [00:07:00] with by looking up by using a customer 360 platform such as Reltios, John can start understanding which customers have had higher engagement.
Like for example, of those 30 doctors are in his territory. Five of them may be actively visiting the website of this pharmaceutical company, looking for documentation, making calls to the support groups, asking for help on contraindications. Does this drug interact with another drug that they're prescribing for a patient, side effects, so on and so forth.
When he, when John logs into the system, he's able to better identify which doctors he should be prioritizing for contact so that he can have a maximum influence on them and also help achieve his goals and metrics for the area. Similarly, on the marketing side when a marketer is. Trying to do an outreach campaign.
Let's say there is a new launch of a new therapy or a new drug, the marketer is able, if they have the information that's provided through a customer 360 platform, [00:08:00] they're able to better segment those audiences as opposed to making a generic email blast. And we all get those every day, right? And we just delete them.
The more targeted and more specific that messaging is, and the audience is also more specific, then there is more effectiveness to that campaign. So that increases the efficiency of the marketing campaign as well. Yeah, absolutely. That sounds really cool. From a product perspective as the expert on the product here what are the essential features that make a customer 360 work in life sciences?
In other words what's under the hood, so to speak? Sure. I think the core features for a Successful Customer 360 platform, the core elements are first of all, having a single unified profile for the audience that you wanna reach out to, whether it's doctors, hospitals, patients, clinical investigators, so on and so forth.
You wanna make sure that profile is cleansed, it's de-duplicated, it has the right set of demographics. It's [00:09:00] enriched, so that you have more insights into, for example, the doctor's license credentials and so on and so forth. Second part of it is you want the ability for the platform or the system to support comprehensive interaction history in a chronological manner.
So you want to know every touch point and interaction that the stakeholders had with the organization. In a timeline in, with a timeline associated with it. So for example, calls, emails, meetings, sample drop-offs. You wanna have all of these available so that you can then get the view of the identity associated with all their interactions.
Third, having a relationship mapping or some sort of a graph that shows the relationships between physicians products. Other physicians or hospitals so you can then create this many to many relationship mapping that helps you understand the influence of your network even better. Fourth, especially from a marketing standpoint, is consent and compliance [00:10:00] management.
You don't wanna be blasting emails and messages to audience members who have explicitly stated that they don't want to. Receive them. That's in this day and age of compliance and privacy, it's a major it's a major criteria that the platform must support. Yeah. Third, and sorry.
Fifth is the ability to provide at least basic analytics and insights or the ability to integrate with more robust analytics and insights. I'm not saying that the Customer 360 platform itself has to be the analytics platform, but it should have some basic analytic capabilities, like understanding what the engagement frequency is, doing some derived calculations such as lifetime value, net promoter score, or at least the ability to send it downstream.
To a more robust analytics platform that can do more, slicing and dicing of the data that's required. Yeah. Last but not least, the ability to integrate with third party systems. There are a lot of third party data providers regardless of the industry you are in, whether it's pharmaceutical, financial [00:11:00] services, whatnot, that are able to enrich the data set that you have and complement it with additional data elements and attributes that can help you create a better 360 degree view of the audience.
These are the key elements of a good 360 degree platform. Okay. Yeah, that's interesting. It sounds it, it could be, it touches a lot of different departments across an organization. How does a company or an enterprise determine how it's, whether it's delivering value and what of value it's delivering in?
KPIs or any metrics that might be tracked specifically in life sciences. How do they know they're getting value outta this the customer 360. Sure. Let's break this down so the C3 60 platform is a platform that can be used by many groups within pharma company, whether it's commercial, clinical marketing, r and d, so on and so forth.
So let's just take a few sample metrics from each of these audiences. Yeah. If you are in the commercial audience, then some of the [00:12:00] metrics that you could use to measure the effectiveness. Of a C3 60 platform is, for example sales rep productivity with the help of, with the help of such data that I mentioned earlier.
In the example, are sales rep able to prioritize and make more calls during a day increase in the number of quality of interactions per rep, increase in the adoption rate of the platform per, for example. So that's a particular measure you could use. Also, what is the engagement in coverage? For example, percentage of target physicians engaged per quarter.
Frequency of touch points per physician should improve as you if the reps are using the platform in the current manner. And also, ultimately it translates into better commercial outcomes like, prescription lift or sales growth in areas where. Are areas where the sales can be tracked from a marketing perspective.
You can track improvements in campaign metrics, hire email opens, for example. If you're segmenting it correctly and you're actually reaching out more refined audiences and the click through rates or targeting should actually improve. [00:13:00] There should be higher event or attendance by key customers, and the conversion from qualified leads to sales opportunities might also improve.
On the clinical side, you might see improvements in, for example, trial execution efficiency, such as number of average number of protocol deviations or queries per site might decrease if communication is improved between investigators and patients and such. Also you'll have operational cost savings by streamlining trial management and reducing the delays.
There are definitely cost implications and that can be very beneficial for an organization. So these are some examples of how you could track the output and the impact of a customer 360 platform across multiple groups. Okay. That's great. You talked about an MV MVP approach focused on HP profiles.
How do companies get started with that without boiling the ocean, so to speak? Sure. That's a great question. And I always recommend starting with an MVP [00:14:00] because you can work out a lot of the kinks, a lot of the organizational roadblocks in an MVP manner without having to sacrifice too much time, effort, and so on.
So ideally, if you were a pharmaceutical company and you were looking to experiment with, let's say, a physician 360, where you have. The identity of the physician linked with all their interactions and behaviors, and you are now delivering that. What I would recommend is, first of all, storing their demographic information, storing their location and contact details.
All of this in a cleansed, enriched, and deduplicate manner, associating physician specialty with the demographic information as well as institutional affiliations and peer affiliations. So all of this would comprise of the physician's profile. Then you also would store or bring in interaction information, for example, promotional activities such as, how many visits have been made between a pharma sales rep and the physician.
How many product samples has the physician been [00:15:00] provided, and what kind of events has the physician attended? You could also bring in educational interactions such as, which websites have been they've been visiting, what speaker programs have they attended, and what advisory boards are they part of.
Lastly, you could also bring in some additional interactions such as consulting agreements where they've. They've agreed for some paid consulting on market strategy or product design, where they've agreed to be part of a market research group or formulary discussions. So these would constitute some of the interaction data that you would bring in as part of the MVP.
Okay. Third part is, oh yeah, go ahead. The third part is derived attributes. For example, what kind of at derived attributes do you think you want to calculate on this profile and interaction data? For example, engagement score to indicate the level of positive interaction that it hits that a physician's having with the pharma company.
Recency score. What's the most recent? Interactions that they've had with you, right? So these are examples of derived [00:16:00] attributes. And then you could bring in third party data sets, such as what is the volume of their prescription? Like you can buy third party data sets that indicate this doctor has been prescribing more of this drug versus your competitor's drug, so on and so forth.
So you could bring in that information. So all of this. Would then enable a use case where like the, if you go back to that hypothetical use case that we talked about, where there is a rep called John Smith and he's trying to plan his meeting with doctors. He has, he could prioritize based on this, he could prioritize which doctors he should be visiting or interacting with based on their prescription feeds as well as their interactions in recently.
He could also look at, derived scores in terms of engagement to figure out which ones are already well engaged and which ones aren't. So he could prioritize them as well. And this helps him to not only prepare for the meeting, but also plan a script for the meeting that he could then engage, use for engagement with the, with that doctor when during the meeting.
Yeah. So it's highly customized. Yes. How [00:17:00] long would something like this take? Or how long would you say on average it takes get approach up and running? In general, that varies a lot, but what we've observed is a customer, we've seen customers go from zero to one with such an MVP approach in under, three to four months.
It's pretty, pretty fast given the volume of data that we're talking about. But it's, yeah, we've seen a lot of customers achieve material success in three to four month MVPs. Wow, that's fast. And how does that compare versus 20 years ago when you started in the industry? Yeah.
Yeah. I think the fact that 20 years ago we didn't have SaaS and, everything had to be installed. You had to provision hardware and all those infrastructural requirements. EXI had to be catered for. You know that the infrastructural requirements alone would take you three months.
Nowadays, with the advent of SaaS and Reltio being a leading SaaS platform in this area provisioning is the matter of hours, and then all you have to do is get into the aspects [00:18:00] of loading the data and managing the data assets within the platform. Oh yeah, definitely. Interesting. Speaking of rt, we've talked a lot about AI at rt and with our customers in the last few weeks.
We had some interesting announcements coming out about MCP the MCP server. So there's some, there's a lot of ai, coming into the product, but also we're also enabling organizations to unlock their AI capabilities. How do you see AI enhancing the value of Customer 360?
Especially around next, next best actions insights or trial optimization. Yeah, for sure. If you go back to that same example that we've been talking about, which is. Rep John Smith. He's been assigned a new territory. There's 30 physicians in there. And he's trying to prepare his initial set of meetings.
Now without the help of ai, he would have to manually review the interactions and the history with those [00:19:00] 30 doctors in his territory and decide how to prioritize. With the help of ai, conversational ai, he can actually ask AI to help understand, hey, which doctors have you know, their interactions. Give a summary of those interactions and help AI prioritize for him, the doctors he should be meeting, the first five doctors he should be meeting.
Furthermore, based on the interactions that the doctors had, for example, the doctors attended a recent webinar or a seminar, he could have AI craft an email. I. That says, Hey, thank you so much for attending a recent webinar. I would appreciate an opportunity to talk to you more about the new therapy or drug that we have, and AI could help craft that, send it to the doctor, and also send a meeting scheduling.
So right here, you have compressed all this manual effort. Because you have all those insights within the C3 60 platform, and AI has access to that. With the help of these large language models and such, now AI can summarize that, draft an email, create a meeting schedule, all [00:20:00] within context, right? It's not just a generic email that's being blasted, but in the context of how the doctor has been interacting with the company.
AI can leverage all of that. And craft all of these things, saving the sales reps so much time and efficiency. That's a big benefit that I can see as a very low hanging fruit that's easily achievable along with the MVP. Great. So along with that, I guess one of the things that we also talk a lot about TIO is the, when it comes to customer data, what should an organization or a leader, a data leader, IT leader, think about when it comes to modernizing customer data?
Absolutely. I think life sciences has been on the forefront of managing customer data because a, it's a highly regulated industry, and all the touch points between customers and the organization are monitored by regulatory agencies. B, they've been at the forefront of building this technology.
I remember the entire concept of master data was actually built on the backbone of pharmaceutical companies. They were the early [00:21:00] adopters of this. And now I think. The advent of C3 60 and ai, these are important elements that they should be thinking about infusing into existing platforms. Why is it important is because the C3 60 has been this elusive elixir that we've been chasing for so long.
But with platforms such as Reltio and our inherent capabilities in unifying data, which is the foundational asset, we can now truly build the C3 60. And there's this elements of zero copy that we've in, that we've incorporated within Reltio. So we can. Readily access all this interaction data regardless of where it's stored in your data lake or data warehouse.
Reltio can access it, leverage it, consume it, process it, and provide that complete vision of the C3 60. And additionally, reltio's ability to now support AI integration with our MCP servers and such, the ability to process that easily [00:22:00] through large language models. Helps a lot of modern leaders in pharmaceutical companies bring the power of their data assets to actual business value to actually create business impact.
All organizations have massive data assets. A lot of these pharma companies have been around for 20 years, 30 years more, and so they have data assets that have been piling up. The problem has always been. How do we refine it? How do we consume it, and how do we actually translate that into business impact?
Anybody can produce a report of tables and metrics and stats, but how do you translate that into actionable insights? That's the power of Reltio 360 combined with ai. And I think that's where customers should be thinking about is how can we bring all those data assets that we have over these years into actionable insights.
Wow. That's great. Yeah. Very good. Thank you so much. That was a great discussion. I really appreciate you joining us today and look forward to speaking with you again. [00:23:00] Okay, awesome.
That's a wrap on today's episode of the Data-Driven Podcast. Big thank you to Sriraj Rajaram for sharing his insights on the critical role of customer 360 and life sciences. If you're in life sciences and struggling with data silos, stale records, or complex compliance requirements, the message is clear.
It's time to rethink your data foundation. A modern, unified customer 360 isn't just a tech upgrade. It's a business imperative in today's environment.
And if you're hungry for more insights like these, don't miss DataDriven in Orlando, Florida. Next February 23rd to 25th. It's our premier thought leadership conference for data leaders and practitioners alike, and it's where the best minds in data and AI come together to share what's coming next.
Visit reltio.com/Datadriven and register today.
Don't forget to subscribe. [00:24:00] Leave us a review and share this episode of Data-Driven. Thanks for listening, and until next time, stay curious. Stay data driven.