Taking the guesswork out of sales, predictive analytics could lead way to improved pharma rep decisions

Automated suggestions can help pharma reps who are searching for the next step in the sales process. But more than just automatic tips, emerging intelligent data-backed suggestions can transform inexperienced or underperforming reps using the mold of a pharma's top performers.

That's the pitch from cloud-based software vendor Veeva, which recently announced it's adding data-informed suggestions to its CRM system in November. Called CRM Suggestions, the feature is free to system users and goes hand-in-hand with the announcement of Veeva's Data Science Partner program naming ZS Associates and Aktana as the first partners. Those two data science engines will be pre-integrated into CRM Suggestions to make it easier to get started.

Veeva's Paul Shawah

"The problem we're trying to solve in pharma is that they generally have larger sales forces--from a couple of hundred to a couple of thousand people. By definition, there are variations in the sales force," said Paul Shawah, VP of product marketing at Veeva. "Some high performing reps already understand what to do and can predict customer needs based on their experience. But other reps need more support. ... (The system) can learn from both of them. It can learn from things that work and things that don't."

Then as sales staffers use the system--either by taking the given suggestion or turning it down with commentary--that additional data is fed back into the system, along with any resulting customer actions. Over time, that creates in a smarter data engine that can show aggregate customer patterns and results.

Predictive analytics has long been used by consumer marketers to decipher consumer behaviors and deduce cause-and-effect relationships. Amazon's ($AMZN) recommendation engine and Microsoft's ($MSFT) Cortana digital assistant, for example, are both smart software programs that learn from users in making recommendations. And like Veeva's suggestions, the more the system is used, the better it gets at making those projections.

The software addresses another problem in pharma, which like many other industries, captures enormous volumes of data daily. Today, most pharma reps can tap into literal mountains of data about their physician clients, from buying and browsing habits to feedback on products and marketing messages. But as Shawah pointed out, reps tend not to do that because the raw data is voluminous, overwhelming and not easy to use.

Oracle and Salesforce are other popular customer relationship management systems used by pharma

-see the press release