This is the second of two blogs reviewing artificial intelligence use cases to support sales pipeline accuracy.
The first blog focused on artificial intelligence for guided selling. Guided selling is key to maintaining a realistic sales pipeline by ensuring that sales team rigor, discipline and process are consistent across all opportunities.
This blog focuses on sales pipeline management and sales rep coaching. It includes tools to help:
- Understand which deals have the highest likelihood of closing
- Planning next steps for each opportunity
- Creating insights from detail buyer/seller interactions to help tell the current sales story
- Tools to support the weekly 1:1s with the sales reps
Guided selling, sales pipeline management and sales rep coaching are required to support an accurate sales forecast. The objective is too understand sales pipeline reality - is the pipeline “real”.
Current State of Sales Pipeline Management and Sales Rep Coaching
Effective sales opportunity management results in a clean sales pipeline, however, too often, sales opportunity management is:
- A manual process of interrogating the sales rep on opportunity status and insuring sales compliance. Too much time is spent on the labor-intensive mechanics of keeping the pipeline up-to-date rather than a process of strategizing on tasks to move the opportunity forward.
- A periodic process requiring weekly or monthly “cut” of the sales pipeline from the CRM system into Excel for pipeline analysis. This “cut” is then “massaged” with manual updates to have a quasi-current view of the pipeline. This process falls short of providing a comprehensive view of each opportunity and does not provide the necessary pipeline visibility.
- Reactive vs proactive because opportunity information is limited and doesn’t provide a comprehensive view of the opportunities for strategizing next steps.
Sales rep coaching is often overlooked and viewed as a compliance task rather than a strategic activity. However, from the perspective of impacting change, coaching is the catalyst to the people component that brings process and technology together.
Sales Pipeline Visibility
Prior into digging into the AI based use cases, one of the key steps toward efficient sales pipeline management doesn’t require AI.
It requires making the sales pipeline visible, always up to date and providing the ability to link to all detail sales interactions/notes/emails. This can’t be done easily and scalable in Excel. It requires a graphic interface that includes all the sales opportunities. It can be filtered by opportunity attributes, and by sales rep hierarchy. It needs the flexibility to be organized different ways depending on the question being asked. It includes the ability to reorder and filter the sales pipeline by:
- Sales stage
- Rep commit
- Sales rep
- Close date
It needs to provide visual clues as to ideal customer profile fit, key next steps, drill down, and alerts. In summary it needs to be one source to provide all the details and all the support for any questions being asked about opportunities in the sales pipeline.
If you are interested on seeing an approach to visualizing the sales pipeline check out this link.
Three AI Use Cases for Sales Pipeline Management and Coaching
The ultimate challenge of sales pipeline management is to understand what is “real” and what should be removed from the pipeline. It is easy to add opportunities to the sales pipeline, but it is a much harder decision to remove them. Many sales pipelines contain opportunities that have been forgotten, delayed, or became multi-push zombies.
The table below maps key challenges in sales pipeline management and coaching to specific AI use cases. The first two challenges focus on “what is real”. The third use case focuses on how to “coach” the sales rep.
The following three use cases are designed to help the sales teams optimize sales execution.
The first use case for sales pipeline management helps identify the top opportunities via an AI generated opportunity health score. An opportunity health score is a scoring system that ranks individual sales opportunities overall health as it relates to closing the sale. The score ranges from 0 to 100. 100 means the deal is closed. The higher the score the higher likelihood the opportunity is on the right track. The score is not just an accumulation of key activities, it is also time based. Events and interactions that happened more recently are more significant. If those interactions are a month or two old they may not be relevant or they may be a detractor to the opportunity. The graph below shows how a health score may change over time on an opportunity.
Around November/December the health score was 50, then something happened, and it moved down to 20 before moving through the sales cycle and closing.
The health score is based on both predictive and prescriptive calculations.
- Predictive calculations – are AI driven calculations based on analyzing previous close-won and close-lost sales patterns based on opportunity attributes and sales signals and triggers
- Prescriptive calculations – are rule based calculations managed by the company for signals and triggers that may take too long for the AI driven calculations to “learn”, or they may be happen too infrequently
Like all AI driven measures, it is important to avoid any “black box” health scores. While a health score is useful, it is more useful as a “glass box”. The “glass box” decomposes the health score to show you the key activities and characteristics that drive the score. The individual calculations that drive the health score are also used to create alerts and can be used as a basis for prescribing next steps for this opportunity (as described in the previous blog). The screenshot below provides an example of the components of a health score.
TopOPPS automatically analyzes past close-won and closed-lost sales patterns to understand signals and triggers that impact the overall scoring of opportunity health. It organizes the score along three categories:
- “Stages + Stage Milestones”
- How well the opportunity fits in the “ideal customer profile”
In the above example the opportunity loses 15 points because there is no current activity.
The second and third use cases provide Sales Management with observations and insights related to the sales rep rigor, discipline and processes applied to sales opportunities. These show up in two use cases:
- At the opportunity level where sales managers can help coach and strategize with the sales rep on the next steps and messaging for each opportunity. Coaching and guidance at this level has a dramatic impact on the overall health of the opportunity and ensures the rigor, discipline and processes are consistent. It improves pipeline hygiene and accuracy.
- At the sales rep level by focusing on key sales rep metrics over time. The challenge with key metrics is not every sales manager can correctly interpret and apply metrics to coach the sales rep.
AI Driven Insights at the Opportunity Level
Insights at the opportunity level flag sales interactions and sales patterns that are significant and need attention. They are designed to surface insights about deals at risk, help deciding what to do next and reduce manual sales rep processes. Insights also provide a quick update for sales management, so they spend most of their time during 1:1’s strategizing next steps rather than interrogating the sales rep on each deal.
These insights use specific time-based algorithms designed to understand sales interactions that cause an opportunity to accelerate or decelerate in the sales cycle. These algorithms build objective benchmarks of sales execution in order to identify the top drivers of selling effectiveness and buyer engagement. The benchmarks are continually revised with each close-won/close-lost opportunity.
AI Driven Insights at the Sales Rep Level
AI driven insights at the sales rep level are for optimizing sales rep performance. These insights are applied via sales rep coaching and become the catalyst for guiding sales rep behavior.
Unfortunately, sales managers are not trained to coach sales reps. Typically, they are promoted because they were very successful at selling. AI driven insights provide an additional data-driven perspective to help sales managers better manage their team. This is accomplished by:
- Aggregating key metrics by sales rep, and sales period, across multiple sales periods and compare it across other sales reps and sales teams over time to understand the trend.
- Applying AI to interpret the metrics and deliver insights about the results so there is a consistent analysis being performed across the entire company.
The screen below provides an example of a sales rep coaching screen that focuses on both key metrics and insights about the key metrics.
The screen above highlights key data driven insights to support sales rep coaching. The green horizontal arrow highlights trend tracking across key metrics. This screen shows a short list metrics, but it includes 30 plus metrics including bookings, commits, pushes and win/loss groups of metrics. The green circled area highlights AI driven insights. When an insight is highlighted in blue, it also highlights the metrics that support that insight.
Gartner calls out the benefits in these use cases in their research “Optimize Sales Execution With Artificial Intelligence for Guided Selling, 2019” by stating:
“These two new functions – statistically derived insights and predictive next-best actions – make this technology development very relevant to application leaders. Collectively these functions, in the form of AI-based guided selling, are important for optimizing sales execution – an objective shared by all sellers.”
An accurate sales pipeline is critical for generating an accurate sales forecast. There are many aspects of sales pipeline management including guiding the sales process; highlighting opportunities most likely too close; and providing coaching insights both at the opportunity level and the sales rep level.
Sixth in a blog series on “AI for Sales”
This is the sixth blog in the series - AI for Sales Forecasting.
The first blog: Artificial Intelligence [AI] For Sales Forecasting provides an overview of AI for Sales and outlines of what will be covered in this blog series.
The second blog: AI for Sales – AI/Machine Learning Primer for Sales provides a baseline understanding of AI and examples of how it is applied.
The third blog: AI for Sales - In the Marketing Funnel
The fourth blog: AI for Sales - Enhanced Data Collection
The fifth blog: AI for Sales – AI for Sales – Guided Selling
The next blog will be on: applying AI to numerically calculate the sale forecast.