How can AI improve medical device sales and customer engagement?

AI is a hot topic in the medical device industry these days. But what does AI mean for the commercial and sales teams? How can it help a medical device sales professional drive customer engagement and revenue generation? This blog post reviews how to use machine learning to better target prospects, create personalized pricing, identify buying signals, prioritize leads, and more. Read on to learn more about this exciting new technology that could be changing the way we do business forever!

COVID-19 has affected multiple aspects of medical technology—on the positive side, it has created the demand for rapid and accurate tests and an adequate supply of personal protective equipment and medical consumables. The shift of some health care to virtual is an exciting opportunity for a lot of medical device companies. However, delayed nonurgent care, and the broader economic, trade and supply chain issues accompanying the pandemic has caused the industry to accelerate the digitalisation of its commercial and supply chain processes. As per our survey of 200 medical device companies, 75 of these organisations are running some form of digital transformation programme or another.

Moreover, even before the pandemic, pressure has been building over the last decade within the industry about the need to reform its selling and commercial models. As of 2019, according to BCG’s milkman study, on average, MedTech companies were still spending two to three times more on selling, general, and administrative (SG&A) expenses (as a percent of the costs of goods sold) than the typical technology or industrial company. 

A closer inspection of the industry’s selling model reveals the following archetypes based on the customer type that buys the product: (1) selling primarily to clinicians – this is driven by heavy engagement with clinicians to drive product uptake and commercialisation and often requires a high level of customisation as every clinician wants to inject its own preferences and choices into the decision-mix; (2)  selling to administrative decision-makers and procurers – in this model, the product is sold predominantly on the basis of price-value dynamics, with price being one of the most important drivers for deal-making; or (3) a hybrid approach targeting both groups – this includes a mix of clinician and administrative stakeholders exerting their influence over the buying process, and often requires higher degrees of stakeholder engagement. However, for all the three selling archetypes, the overall sales productivity has remained consistently low since the last decade as the industry still relies on the old selling approaches with process inefficiencies.

A conventional sales team in a fast-growing medical device company is constantly shifting and growing. It is not uncommon to face challenges related to inefficient processes and bottlenecks that impact the customer and client experience. Moreover, evolving product portfolios, omnichannel expectations, different customer segments, fluctuating price points, and the need for custom configuration further complicate the sales process. 

By optimizing the quote to cash processes with the help of Artificial Intelligence, medical device sales teams can eliminate sales process inefficiencies and improve overall customer engagement.

Below we highlight 5 areas of direct improvement based on our work within the sector: 

1. Sales reps are finding it difficult to develop pricing for tenders and direct opportunities: Across the companies that we surveyed, almost 90% of them highlighted the need to streamline tender discovery, price development, discounting, and bid generation process. Within the industry, it takes from a few days to several weeks to develop a bid for the tenders, depending on the complexity of the product (and service combinations) and multiple price-list variations. Additionally, most sales leaders and regional managers acknowledged that there is very limited visibility in the tender channels and the current legacy solutions lack the level of sophistication, integrated data on buyers and competitors, and intelligence needed to bid for tenders efficiently. By using AI-based tender data extracted from different buyer systems and integrating it with a CPQ system can allow the sales team to drive tender performance across the portfolio. Indeed, at Vamstar, our customers see an uplift of 3x to 5x revenues using our AI-based tender and contract marketplace intelligence. 
2. Sales quotes and tender bids are being created with the help of Word or Excel: Most sales teams rely on spreadsheets to maintain data, leading to inconsistencies in quoting and price configuration and creating standardization challenges from sales to supply chain. Today, we live at a time when we have access to Machine Learning and advanced data mapping technologies to create multi-dimensional systems to resolve configuration, pricing, and quoting challenges – a task which excel/word alone is unable to handle. Indeed, cloud-based, automated pricing and quoting tools can manage databases of extensive product SKUs with advanced pricing rules. According to Gartner, companies that are using cloud-based automation with tools like CPQ can generate 2x more proposals, quotes, bids, and RFP responses per month compared to traditional sales organisations. 
3. Sales reps are spending a lot of time doing manual data entry and quote generation: Medical device industry representatives spend an average of 43% of their time doing manual data entry. Additionally, quote generation and other administrative tasks further take time away from customer engagement and revenue-generating activities. With the help of AI, embedded within CPQ, sales teams can automate much of these repetitive and cumbersome tasks, enabling reps to focus on delivering the best possible solutions and experiences to the customers. Moreover, according to Aberdeen Research, 91% of sales teams and 87% of sales reps using CPQ software achieve their sales quotas. Compare this to their non-CPQ counterparts that are only able to meet 56% and 20% of their quotas. 
4. Sales quotes are inaccurate and inconsistent: Medical device companies are challenged by up-to-date records on discounts, product or SKU additions and compliance changes. This can lead to inaccurate quoting for customers which in turn negatively impacts the customer experience. As sales grow it becomes important than ever to maintain a compliant environment that is consistent throughout all departments within your company – from manufacturing down through marketing teams! 
5. Sales teams are manually doing market intelligence or worse not using any insights in quoting decisions: According to our survey, most companies complained about the lack of consistent market intelligence to drive product, pricing, and discounting decisions. Indeed, even when this intelligence is being generated by a different department or through a third-party resource, it is not being utilised at the point of quote generation. Moreover, as the majority of customers want to discuss pricing at the first meeting, it leaves very little room for sales reps to have data-driven insights to power product and pricing recommendations. An AI-powered CPQ system can enable intelligent feedback and allow real-time collaboration among all mandatory stakeholders. This can further improve efficiency and the quality of customer engagement.

The medical device industry is undergoing transformation. With a high degree of uncertainty coupled with long lead times, it’s difficult to manage sales processes effectively and efficiently. What if there was an AI-based CPQ system that could optimise your entire sales process and improve customer engagement scores? That would mean faster quoting and bidding cycles, more accurate forecasts for new tendering opportunities, improved forecasting models across product lines – all backed by real-time market intelligence to drive strategic decision making. Sign-up with us to find out more!