The company is engaged in technology, engineering, construction, manufacturing and financial services and operates in more than 30 countries worldwide and generates a revenue of US$17 billion. Its electrical business unit offers a wide range of switchgear, electrical systems, automation solutions, energy management systems and metering solutions.
THE CHALLENGE
The company’s electrical business unit distributes its products through a wide network of channel partners across all international markets. With an aim to further streamline the partner on-boarding process and enhance their channel partners’ performances, the company sought a robust on-boarding and performance management system. The objective was to derive an accurate and in-depth understanding of the key performance attributes of partners through seeking answers to relevant questions, such as:
- Who are the most valuable channel partners and why?
- Why do some channel partners not perform well?
- How can the company help all channel partners to achieve their targets?
- What are the factors that drive better sales growth?
- How can the company incentivize and motivate the channel partners?
THE SOLUTION
Prescience developed an advanced analytics solution that gains advantage through machine learning and artificial intelligence to understand the key factors that influence the performance of channel partners. The solution also proactively identifies and ranks channel partners based on their performances and potential, allowing the client to intervene and take remedial actions quickly, through access to real-time data.
Data Preparation: Workshops with key business executives, salespersons and channel partners helped us to understand the business structure, processes and paradigms. Based on these discussions and our business and market acumen, we prepared the data derived from multiple data sources to be used for analysis.
Data Modelling: We deployed multiple statistical models to generate multiple forecasts and selected the best fit model output. Channel partners were clustered using the existing criteria of measuring performance. Machine learning algorithms helped us determine important factors that impact performance and enhance the performance measurement metrics. We also used new performance measurement metrics to evaluate and index channel partners in an order, using an intuitive scoring method.
Visualization: We created intuitive, integrated and interactive dashboards to derive an accurate and in-depth understanding of the key performance attributes of partners, without compromising on access to data-points and details for each of the channel partners.
THE IMPACT
By deploying an advanced analytics solution, Prescience has empowered the client to evaluate the performance of their channel partners based on critical factors such as location, potential and aligning to organization strategy instead of just the revenue growth and sales targets. While earlier, the on-boarding process was manual, mechanical and ad-hoc, the new solution made the process more refined, ensuring that only the eligible candidates are on-boarded to the partner program.
The decisions to choose the right channel partner are now data-driven and backed by actionable insights instead of gut feeling and guesswork. This change in the on-boarding approach has helped the client to enhance their revenue and manage their partners better by identifying their top and bottom performing partners, accurately.
The solution also helps in identifying uncaptured demand in a certain location, thus enabling the client to on-board more partners specific to that location, to increase their presence. While higher revenues are usually the key performance indicator for a solution of this kind, our focus on bringing in an evolved and holistic user experience ensured that the client was in a position to incentivize partners, which, in turn, motivated the partners to perform better and contribute to delivering better revenues.
