For a sales team, measuring their performance and sales completion has traditionally revolved around outcome metrics. Sales volume, growth rates, meetings, market share and other outcome metrics have long been the primary indicators of success. If the numbers look strong, performance is assumed to be strong. But there’s a challenge many pharma leaders don’t account for.
For decades, companies have relied on outcome metrics because they are straightforward and measurable. But outcome metrics often lag, and sales data can take weeks, if not months, to fully reflect representatives’ performance and whether their efforts have paid off.
To address this gap, many pharma organizations have moved towards generative Artificial intelligence solutions capabilities. Instead of waiting for sales results, organizations can access the quality of sales activities objectively. So, instead of counting calls, emails, meetings or demos, they can now analyze the impact of each interaction, especially when the interaction (such as effective communication and relationship building) is tied to sales.
At the same time, these tools empower sales teams by giving them better guidance, insights and stronger support.
AI innovation for sales
Access to the right data at the right time is integral for sales success. Where some teams struggle to access updated sales, territory alignments, or identify market changes, others are overburdened with data scattered across systems and repositories, making it increasingly difficult for reps to plan sales strategies.
Artificial intelligence solutions can help bridge the information gap. It can also help teams:
- Streamline pre-call planning with instant access to up-to-date information.
- Consult with an AI expert who can help sales teams summarize data or provide deeper insights, such as sales trends, past interactions, as well as, recommend strategies, while on the move.
- Get instant answers to frequently asked questions, allowing teams to address incentive compensation queries and logistical concerns while reducing reliance on leaders, IT teams and business analysts.
- Democratize data while maintaining compliance.
- Strengthen customer relations and improve sales outcomes by enabling deeper, data-driven conversations.
- Predict customer needs and recommend next best actions.
The right tools can also be seamlessly integrated with existing technologies to empower teams with the right knowledge to build stronger customer relationships.
Managing AI models for business impact
Deploying and managing machine learning (ML) models while driving tangible results requires ongoing management and optimization. AI platforms serve as a centralized control center for ML models. These platforms also enable data scientists and operations teams to scale, automate and connect AI with agents to derive value. Often embedded with broader sales effectiveness consulting strategies, these platforms empower sales and marketing teams to:
- Manage model training and deployment as operations expand globally.
- Automate routines, allowing data scientists to focus on innovation and not on manual tasks. As well as, managing and overcoming changes and updates in AI, ensuring reliable decision-making and sustainable growth.
- Scale and integrate AI across team’s workflows to improve customer engagement and sales productivity.
AI is redefining how sales performance is measured. Pharma organizations that plan to evolve beyond tradition and stay competitive, adapting their workflows and teams to the AI infrastructure as soon as possible, are the only way forward.
