Use data science to anticipate customer requirements
Pharma is using cloud-based customer relationship management systems to gather vast amounts of data from diverse sources. The next step is analysis of customers' attitudes and behaviours to provide recommendations to the reps tailored to each particular engagement, says Jan van den Burg.
In the past, life sciences sales reps communicated with their customers face to face, on the telephone or via paper. Notes were scribbled down and letters filed away for future reference. It was difficult to collate this information in a single, easily accessible place, which made it challenging to share it across the company. In that pre-digital age, reps recommended and sold products based on past experience and the personal knowledge that they had of their customers. However, this approach often failed to take into account factors such as new customer preferences, market events or patient needs.
Today, in our hyper-connected digital world, data is everywhere. Every department – from sales to medical affairs to marketing – routinely collects data. Not only is the volume of data growing – so is the number of customer stakeholders and channels used to engage them. This data is crucial, yet the industry struggles to distil and integrate it for proper sales and marketing use. The challenge is that there is too much information for even the most experienced operator to absorb, comprehend and act upon quickly.
A common problem for many companies is that interactions with customers are disjointed because of siloed organisational structures and data systems. As a result, customers may be 'touched' by the same companies multiple times per week – or even per day – for a single brand. If not coordinated, this can do more harm than good, as it will create customer fatigue. For sales and marketing to be truly effective in this data-intensive world, it is necessary to determine what message to send, when, and through what channel, to each individual healthcare provider (HCP).
Break down silos
Modern, cloud-based customer relationship management (CRM) systems make it easier to break down the silos and bring together HCP interaction data into a single, real-time view of the customer. However, the next step is to use that data to drive insight on the customer's attitudes and behaviour and provide recommendations for each particular engagement. A growing number of life sciences companies are using data science to mine the wealth of information collected and combine it with business strategy, in order to create specific sales and marketing calls to action.
Data science makes it possible to rapidly analyse large sets of data and anticipate customers' needs. By correlating customer engagement data with customer behaviour, sales and marketing teams can begin to predict what they want and serve it up in advance. For example, if you know that a specific segment of HCPs responds to a unique sequence of product messaging, you can proactively provide the same sequence of information to similar customers.
Marketers at retail companies have long been using data science in this way to not only understand consumer behaviour, but also to recognise cause-and-effect relationships. This is like Amazon's recommendation engine, which offers relevant choices based on prior experience each time a customer searches for, or orders, a product. The science gets smarter with more data, and the same is true for the life sciences industry.
Strategic recommendations
Data science can also provide strategic recommendations for ways to interact with a customer – through which channels and with which messages. Life sciences companies can provide tailored information that HCPs need on demand, how and when they want it. This is critical, as the rise of precision medicine and increasingly complex treatments require an ongoing, bidirectional flow of information.
This level of insight can make a major difference in the field, empowering the sales force by including 'coaching' recommendations within each rep's regular workflow. For example, if a doctor visits a website to learn about a new drug, the rep will know this ahead of the next interaction. A suggestion might prompt the rep to email specific clinical information before visiting the doctor, which would make the meeting more relevant and better prepare the rep to address questions.
Smarter data science engine
Maximising the power of data science in this way is like having an experienced sales coach available on demand. It also helps new sales reps to develop faster into more seasoned reps who better leverage channels such as email, which can still present a significant learning curve for some. Reps can – and should – still draw upon their own knowledge of the customer to decide if automated suggestions are appropriate. However, they can make decisions from a more informed vantage point, distilled from the vast amount of data gathered from each customer interaction, regardless of the channel. Reps can finally 'close the loop' by either rejecting the suggestions with commentary or providing feedback on actions taken. All of this data can, in turn, be fed back into the system, creating a smarter data science engine based on customer patterns and results.
Ultimately, the industry's goal is to improve sales productivity by making the customer's needs central to the engagement process. Data science-driven CRM systems help sales and marketing teams drive the right information to the right HCPs so they can make the most informed treatment decisions. This helps to make the life sciences industry a true partner in the delivery of better healthcare.
About the author:
Jan van den Burg is vice president, Commercial Strategy, at Veeva Systems. He is responsible for strategy and product marketing, focusing on the European market. He has over 20 years' experience in the software and services industry, mostly in the pharmaceuticals sector.
Prior to this, Jan led the Life Sciences Sales & Marketing group in IBM Global Business Services, engaging at strategic level with top 20 pharma companies on CRM, closed loop marketing, multichannel and digital marketing, as well as digital asset management. Before that, he set up and ran the European business for Proscape Technologies, the then leader in closed loop marketing.
With a BSc in Engineering and an MSc in Business Administration from the University of Twente, in the Netherlands, Jan began his career at Cap Gemini, followed by a move to the UK where he worked with PWC Consulting.
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