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Big data might be everywhere, but its commercial use across pharma is still wanting. Sales and advanced analytics shouldn’t be mutually exclusive

By Ross Davies on Jan 1, 2019

Data has become one of the world’s most sought-after commodities. So much so it is often compared to oil. There is a key difference, however. Once crude oil is extracted from the ground, it is transported and refined into petroleum products that have value. Tapping into the value of data is far more arcane; hence the reason tech companies are so cagey.

The pharma industry is acutely aware of this. Despite having vast troves of data, there remains a conspicuous lag in the use of data to support the operations of sales and marketing teams.

The obvious answer to the quandary is that pharma’s digitalisation shift is still a work in progress.

“Right now, pretty much every pharma company says it is going through a digital transformation,” explains Bharti Rai, Vice President of Commercial Effectiveness at Novartis.

“They want to use data for data-driven decision-making. But there are questions marks over whether business leaders in pharma – who have traditionally grown up in sales and marketing – actually understand what it takes to do that.”

Rai is as best placed as anybody to offer an insight on the fractured marriage between data and commercial departments, having experience of working both beats. Prior to assuming to her new role at Novartis in December, she served for two years as Chief Commercial Data Officer at Bayer Pharmaceuticals.

For this imbalance to be redressed, these “business leaders” Rai alludes to will need to divest themselves of certain outmoded approaches. For instance, data analytics professionals working within the industry today are yet to enjoy the same levels of appreciation afforded to traditional sales and marketing – in spite of the latter’s increasing need on the former to engage with customers.

“Data analytics want to be equal partners to marketing and sales,” says Rai. “If you’re a pharma company saying you want to power everything by digital data then those departments that run data really need to have a seat at the table. That also means bringing in the right people who understand this and are strategic enough to make it a reality.”

This will necessitate a step-change in the commercial recruitment process. The remit of sales operations might once have been solely around the art of selling, but today’s sales teams need to be as equally tech-smart.

“Personally, I don’t want to hire someone who has done nothing but traditional sales ops for the past 20 years,” admits Rai. “I’d rather get different people from analytics or perhaps Silicon Valley. Start-ups are also a great place to go if you’re looking to attract new talent. We need to learn from other industries rather than looking only at pharma talent.”

As Vice President and Global Head of Commercial Data Solutions at Sanofi, Anish Shindore shares a similar role to Rai. However, his experiences of working with sales teams suggest there might be not so much of a pushback when it comes to utilising big data.

“Actually, I’d say there’s a great willingness to get sales teams onboard to use data to better know their customers and what’s happening beyond their own scope,” he says. “That’s one of reasons why these huge CRM programmes are booming right now within pharma – they provide sales with that information.  

“Sales reps are beginning to understand that their relationships with customers are not just fed by interactions, but by external sources too. They are becoming more used to seeing and using more customer-centric data.”

Shindore, who is now just over a year into his role at Sanofi, describes his responsibilities as “technical” rather than “technological”. It’s as much to do with bringing commercial areas of the company up to speed with the ideaof data-driven decision-making as it the actual implementation of technological frameworks.

“You can only do so much on the technology level, but there has to be change in management on the commercial side within marketing and sales departments,” he says. “This means working closely with the commercial guys and demonstrating to them the benefits of a good data strategy.”

That said, there are more advanced areas of big data analytics which remain beyond the grasp of most reps. For instance, “next best action” – which uses the likes of life-event patterns and social media interactions to increase customer interaction – is something “people haven’t really nailed yet”.

Rai agrees that arming her sales and marketing teams at Novartis with “additional high-quality analytics” represents the next frontier, whereby partnering with the best vendors will be essential.

“We’re looking to partner with a strategic vendor on the analytics side,” she says. “I don’t want to pay for FCs and resources – I want pay for outcomes. In the next two to three years, I also think we will have invested in a solid foundation, but that requires a technology investment. Furthermore, we are looking into an operating model which focusses on operating these kinds of processes and systems at a scale that is tied closely to the business.”   

More broadly speaking, pharma will need to pull its finger out to truly reconcile commercial departments with the big data revolution that’s taking place outside their office walls.

One suspects pharma to be guilty, at times, of paying lip service when it comes to using digital data to better customer interaction.

Recruiting from the latest hip start-up in the Valley is a recruitment avenue worth exploring, but retention rates of such hires is likely to be low if they enter workplaces that truly don’t value their skillsets. Instead, it’s more likely – and beneficial – that pharma start modernising attitudes from within first. But as Rai rightly suggests: “This might take some time.”