By Brijesh Malkan, VP of Product
As advisory firms seek to address three interdependent challenges – identifying and addressing growth opportunities, improving operational efficiency and managing talent in a hybrid working environment, Sales Enablement has emerged as a key initiative in 2022 for forward looking leaders.
In a recent Singletrack customer research survey, support for growth strategies and sales analytics were among the top priorities expressed by Heads of Sales and Research.
Sales enablement – a definition
For the unfamiliar, sales enablement simply means providing your sales professionals with the resources required to successfully close more revenue opportunities. These resources can include:
- Data and analytics to help identify and convert growth opportunities (as well as mitigate downside risks)
- Engagement tools to interact with existing and potential customers
- Content and insights to demonstrate the value of your products and services
- Workflow automation tools to implement and institutionalise sales playbooks and best practice, enabling sales professionals to focus on high value activities
- Integrate playbooks and human insight with ML/AI driven guided next actions based on a deep understanding of the customer, revenue potential and services
The risk of the generic
To address these needs, it is often tempting to implement a “standard” sales enablement platform with a view to aligning this to capital markets needs. However, our experience shows that this approach often leads to suboptimal outcomes at best, and at worst degradation of revenue generating capacity as data insights and workflows guide counter-productive actions.
To guard against these risks, leaders should ensure that their sales enablement workflows can address the following needs:
- Distinguish between different characteristics and behaviours of a diverse corporate and buy side customer base (e.g. hedge fund demand for fast money, quick to digest trading insights vs. institutional demand for more thematic market insights) when identifying growth opportunities and at risk customers
- Integrate cost to serve and revenue analytics to enable smart servicing
- Take into consideration a broad range of inputs including interactions, research engagement, event attendance, profitability, trading activity and holdings data when recommending services
- Be able to pull from across the advisory service delivery function when automating tasks (e.g. sending a relevant research report to a portfolio manager who recently attended a meeting with a corporate, or inviting a trader to an analyst marketing event after they read a set of reports by the same analyst)
- Track activities and measure outcomes to quickly course-correct playbooks and align junior resource actions with the most effective sales and research professionals
- Measure the level of activity and impact on revenue to support talent management and effort/revenue attribution
In recent years, we have become increasingly aware of the behavioural biases that can affect complex decision making in uncertain situations. Sales and research professionals are often confronted with the conditions that are primed for the use of heuristics – making decisions in a volatile and fast moving environment with incomplete information.
Examples of how such biases influence capital markets advisory decisions include:
- Overconfidence that certain deals and opportunities will close in a short period of time
- The implicit assumption that higher touch services are always superior to lower touch ones, irrespective of the client’s consumption habits or needs
- Overly focusing on a small cluster of (the same) clients regularly in the belief this maximises revenue potential
The use of data and analytics can help protect against counterproductive biases, however, here too, one must guard against compounding the underlying issues. For example:
- Attempting to manage talent and operational effort through an exercise in mean reversion (bringing up performance to the average), rather than aligning to revenue impact
- Clustering clients together based on an arbitrary set of attributes, rather than behavioural and commercial traits
- Over reliance and fixation on specific scores and analytics in isolation, over the meaning, context and opportunities they reveal
To limit the risks of behavioural biases in data analytics, it is important to take a holistic view, ensuring analytics are aligned to business outcomes (e.g. revenue growth) and deliver both data, insights and guided next actions to support human expertise.
Overall, sales enablement can be an incredibly powerful tool for capital markets firms as they continue their journey towards data-driven advisory. However, taking the beaten path with a popular non industry-specific platform can result in a missed opportunity.