A Case Study on Collaboration with UX and Data Engineering
Last year I wrote about building AI-enabled solutions, and I promised to share some case studies which led me to develop framework I shared. If you haven’t read that initial post you can find it here – Building AI-enabled Solutions – Why a Human-Centered Lens is Key. This case study is mainly about Context and Users’ Mental Models – not much about Algorithm Design, which will be the primary focus of my next post.
Introduction
I’ve spent a significant part of my career enabling sales reps – as an operator, a UX researcher and then leader in large-scale software implementations and in software development, and eventually leading my own sales and delivery team. At SAP my teams and I worked with large enterprise, mid-market / territory-based, and tele-sales reps. At ZS we worked with all kinds of pharmaceutical reps, from primary care to specialty (like oncology) and medical device reps.
I’ve spent more than 20 years observing, researching, enabling, and building software for commercial organizations. It is my cumulative learnings about this user population that inform this post.
Enabling the B2B Sales Rep
When I was working at SAP, the average age of the large enterprise account executive (LE AE) was 47. Today, the average age of the pharma sales rep is also 47. These seasoned professionals have decades of experience, and in some cases they have been working in the same territory or account(s) for multiple decades. Any sales-enabling solution needs to be designed with that in mind; these reps want to do their jobs well, but given their tenure in role they will not take kindly to being told what to do by a piece of software.
Historically, ZS’ business was in sales enablement, and the firm worked in close collaboration with clients to inform how the pharma industry goes to market today. Each time a salesforce is being re/deployed for a new product, a similar process is followed – (1) equitable territories are defined (2) reps are aligned to those territories under the direction of a sales manager or regional director (3) call plans are developed for each sales rep based on historical data about the most impactful reach and frequency with each healthcare provider. And finally compensation plans are created and tracked based on the goals established. ZS has software and professional services to assist clients in all of these areas.
Reach is the number of unique individuals, and frequency is the average number of times an individual hears the message. Reach and frequency need to be balanced; exposing many more physicians to a message is important for new product launch, but high frequency is more likely to keep an in-market drug at top of mind for a prescribing physician.
It’s important to note that these efforts – usually conducted via statistical analysis – are balanced with the knowledge reps have of their territory and physicians they call on. There is a wealth of data available about physicians, their prescribing behavior, and how they respond to different live and digital touchpoints. But that data is notoriously hard to keep clean – doctors move, they die, they have offices in multiple territories. All that must be taken into account before the rep can plan their day, week, or month.
In software sales, a Customer Relationship Management (CRM) system enables reps to communicate their pipeline and deal progress to management. In pharma sales, reps are not selling directly but rather communicating to drive awareness and demands for the products “in their bag”. In this case, the CRM tracks reach and frequency but also regulatory compliance – reps have to document what they spoke about, and what materials they used (if any). The arrival of the iPad and mobile business intelligence solutions put new insights in the hands of the rep, enabling them to more easily recall prior conversations, view prescribing history, and plan their next interaction – and paving the way for new kinds of sales enablement.
Context of Use
Preparedness Best Practices Are Unrealistic
In the pharmaceutical industry, a typical primary care sales rep is expected to speak with 8-12 physicians per day, which requires 40-60 physician interactions per week. They also need time to prepare for those interactions.
As my team and I learned more about pharma reps and how they were working, it became clear that – even if their schedule was perfectly orchestrated each week – expectations of rep preparedness for those interactions was probably unrealistic. Reviewing data and preparing meaningful conversations with 40 physicians would require an additional full day of work each week.
Expectations for specialty reps (e.g. oncology) are somewhat different; reps might aim for the same number of calls, but have a shorter list of “high potential” doctors to focus on. The price of oncology products is more expensive, ensuring that live sales rep interactions are still a good investment. Those physician interactions are more complex, require depth of knowledge, and are typically handled by the most seasoned reps.
Pharma is a data rich industry, and analysts love having such a wealth of information at their disposal. But they don’t consider what it’s like for a rep in the car on wifi (or tethered to their phone) with 5 minutes to prep for her next call.
We had one client who designed a dashboard that gave reps access to the national sales data on their iPads. That may seem like wonderful transparency, but loading an entire national database when while sitting in the physicians’ parking lot is an unmitigated disaster from a user experience perspective. The reps had to drill down from the national level down to their territory and into a physician view before they could review the data, seek insights, and figure out how to have a meaningful conversation with a physician about their prescribing habits or their patient population. The rep spent all the time that they had before their appointment just trying to get to the data to load, leaving them very little time for analysis and preparation.
In our observational research we saw all kinds of workarounds to these challenges – reps working through their planned calls at home and taking notes they could carry with them, or printing spreadsheets and carrying notebook with critical (and sensitive!) information, or putting screenshots into iPhoto to reference throughout the day.
All these findings gave us a new understanding of a previously invisible bottleneck in sales – prepping for a physician interaction was a challenge and opportunity to be addressed. Our goal was to develop a new, complementary sales-enabling solution that was more grounded in the reps’ daily reality.
Initially dubbed Personalized Analytics, our goal was to strip time and complexity away, and to provide the rep exactly the insights (not raw data, not analytics) that they needed in the rushed moments before a physician interaction.
Consumer Experiences Drive Enterprise Innovation
In 2016 I presented at ZS’s Impact Summit and I shared some of the big technical shifts that inspired our thinking about the art of the possible for new sales-enabling solutions.
In our technical teams, there was a lot of excitement about using chatbots and natural language processing (NLP) to enable the reps. In fact, we initially thought the new solution might be a conversational platform to deliver insights from existing repositories. Spoiler; we abandoned voice interaction for MVP after conducting user research.
When we started to shadow reps or speak to them about their needs, we were trying to figure out how to optimize their day as part of this solution, how to find efficiencies, get to targets sooner, have more effective conversations with the physicians in their territory. Sales reps spend a lot of idle time in the car and in waiting rooms, even when they have an appointment. To us, it seemed as though the time in the car could be used more efficiently.
However, as it turns out, most of reps are driving company cars, those cars that are insured by the companies they work for, and the insurance policies prohibit the use of the phone in the car. So what we thought would be a time-saving idea ended up being impossible. And using the idle time waiting in the doctor’s office proved impractical – they couldn’t use voice-to-text while surrounded by patients and office staff.
Even though natural language processing was an exciting feature from a technology perspective, it was deprioritized for MVP. Though today it is an integral part of the go-to-market story for the solution:
Users’ Mental Models
During our years of observational research with reps, we noted that they struggled to extract meaningful insights from all the data and analytics they received from headquarters.
Together with our consulting colleagues, we asked ourselves whether we could use proprietary algorithm to surface insights – with focus on consumption and immediate action – rather than analysis.
We had a wealth of data, KPIs, and analytics collected over decades of consulting work. Our consulting experts pulled that information together and data engineering created a proprietary repository. Together, they created logical groupings of metrics.
Those “logical” groupings were an essential part of how the recommendations or next best action would appear in the interface. As we refined our prototype through usability testing, we found that the data model itself needed to change to reflect how the users thought about their work. I think one of our most important achievements in this product was proposing new groupings of insights in a way that resonated for our target audience.
I think it was unexpected for the engineering team to realize that the UX research could impact data engineering work, and our work together reinforced why such tight collaboration is necessary, especially in the early phases of building and iterating a product.
Algorithm Refinement
For this product, our insights about sales reps time constraints and context of use deeply informed the product concept. We weren’t explicitly involved in algorithm design, something we sought to rectify in our next project.
In Closing
As the solution came to life in code, we began to seek pilot customers, and I took point on presenting the solution to clients. It turned out that the tasks that we developed for usability testing were a strong foundation for demo scenarios:
We did not have a dedicated pre-sales function, and my deep understanding of the rationale for the design(s) enabled me to address client inquiries in real time.
This project was one of the most exciting projects of my career, because it built on a wealth of historical knowledge about reps, while still finding surprising new formative insights to shape product direction. It also led to some unexpected and rewarding collaborations with data engineering and product marketing.
Feedback from many rounds of usability testing confirmed we had imagined a product that was easy to use and met a real need. It is now in use by more than 100K sales reps worldwide.
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