It is an essential activity for every business to identify and analyze brands, products, services or attributes that have the greatest influence on the final target achievement. It also enables organization leaders to get an idea of the possible impact on business outcomes if they work on different factors that drive performance for a business or organization. Key driver analysis helps in solving this problem by recognizing the importance of utilizing data to make evidence-based decisions. Key driver analysis addresses the questions like “which combination of possible explanatory attributes best explain the outcomes we see for some question of interest?” and “what is the unique contribution of each predictor of outcome?”. The analysis can be based on statistical measures of relationship between each attribute and an overall measure of performance of an account manager, sales region, product, or service. Many statistical and analytical methods have become popular to solve this problem.
Now, why is key driver analysis important to Sales? In addition to examining typical measures of sales performance, understanding those attributes which are actually driving good performance is the way to make the sales data and analysis actionable. Let’s take the case of a sales manager. It is possible that his pipeline looks on track to achieve his goals, but, if metrics such as pipeline conversion are either ignored or not estimated accurately, might lead to him missing his goals. Key driver analysis can help a sales manager have clear visibility into performance across different key drivers of booking attainment. In the above example, if the sales manager is able to see that pipeline conversion is a key metric for bookings attainment and that he is not doing good there, he can either build more funnel or start following up on existing leads more attentively to mitigate deal cancellations. This analysis can also provide insights for improving performance and retaining customers. There can always be hidden attributes that may go unnoticed but are equally important indicators of performance. Combining the experience of a sales manager with an automated model, trained on historical data using statistics and machine learning techniques, used to provide this kind of analysis, can lead to astounding results. The use of automated modeling enables a salesperson to keep track of multiple metrics more effectively, although, it does not remove the need for his judgment. When coupled with good judgment, these insights can be a powerful tool in the hands of a sales manager.
Let’s understand with a real-time example: BRIDGEi2i recently partnered with a Fortune 500 technology giant to find the key drivers of sales team performance. The client wanted to understand why certain teams were meeting their targets while others weren’t able to. They wanted to pro-actively identify teams that might not achieve their target so that they can initiate an intervention at the right time and improve performance. BRIDGEi2i came up with a list of key indicators of sales performance by using regression techniques. These key indicators were categorized under 5 dimensions:
- Plan: Size of plan allocation and plan growth from last quarter
- Opportunity: Size of opportunities by $ value, composition of funnel by number of opportunities and $ value by various stages of opportunity
- Bookings: Booking trajectory as % of plan through the period, % discounts given, $ value of returns and mix of bookings from products v/s services or architecture
- Sales team: Booking yield per sales staff and experience of the sales staff working with the client
- Customers and Partners: Profile of customers – verticals, size (employee, revenue) and number of partners and quality (tier & certifications) of partners
BRIDGEi2i also enabled better visibility for Sales Leaders by creating visualizations of how teams are performing against each of the key indicators of sales performance. This was followed by benchmarking the team’s performance across different key indicators at the beginning of a quarter and after end of 1st and 2nd month of a quarter. This analysis helped the client predict the poor performing teams with 77% accuracy, which further enabled them to work on improvement areas and improve target adherence.
We have brought together our experience working with Sales organizations and our expertise in Machine Learning and visualization to build Sales Decision Engine (SDE), to enable sales managers take informed decisions and drive actionable outcomes. SDE is a custom sales analytics solution, that brings together pre-built algorithms, visualizations and API data connectors based on unique client needs. SDE is a one stop shop for sales managers for managing their sales team’s performance by giving actionable insights around funnel, bookings, goal achievement, funnel coverage, win rates and average sales cycle. Apart from these, SDE includes key features such as:
- Use of predictive analytics to forecast sales pipeline and bookings to get an idea of how much bookings a sales person will do if he follows the same trend as followed in past, and how much funnel he would have to achieve his target.
- Use of opportunity scoring algorithm to find out what leads to pursue in order to achieve better conversion rate.
- Use of key driver analysis to find out what are the Key Performance Indicators (KPIs) of bookings attainment and to see how a sales manager is performing across these key indicators.
Coming back to key driver analysis, let’s understand how we have addressed the requirement of a sales manager to know what are the KPIs of his bookings attainment. A sales manager typically is provided a lot of metrics and the importance of these metrics are derived instincts, mostly the simpler ones like total funnel, total bookings, and funnel coverage, But the importance of most of the derived metrics and some other hidden metrics goes unrealized. With SDE, the whole scenario changes; SDE’s key driver analysis is a more data-centric, quantitative approach to interpreting data, going beyond one’s gut-feeling. SDE analyzes sales manager’s final bookings achievement against his performance across a large number of attributes, having both positive and negative impact on sales performance, for the same kind of market segment or region. Out of these large number of attributes we take out top 16 key performance indicators and then flag each sales manager’s performance across these KPIs as either poor, average or excellent. There is a note against each KPI comparing a sales manager’s performance with the team’s, mentioning where he/she stands as compared to others in the team, and what is the bucket for achieving good performance for that performance indicator. This type of key driver analysis gives managers deeper insight into sales effectiveness and make necessary course corrections.
This is the first blog in the series of blogs to be published by BRIDGEi2i to understand the need of Machine Learning in Sales Analytics. In the upcoming blogs, we will talk in detail about how the use of statistics and machine learning techniques coupled with great visualization tool can help sales managers improve visibility into sales performance, about what worked well and what are the improvement areas, and what opportunities they should pursue, for better conversion results. We will also talk about other key features of SDE mentioned above.
BRIDGEi2i provides Business Analytics Solutions to enterprises globally, enabling them to achieve accelerated business impact harnessing the power of data. With a combination of domain expertise, advanced analytics capabilities, and technology accelerators, To know more, head over to www.bridgei2i.com