How we helped a global B2B manufacturing leader increase their market share through opportunity scoring modelling

14%

Increase in EMEA win rate

A powerful new process

To build upon for sales efficiency

50%

Reduction of inaccurate data in CRM system

THE SITUATION

For a multinational company with worldwide sales and operations, maintaining a solid market share through sales can be a challenge – one that our client (a global chemical company turning over 6 billion a year) was facing.

With so many moving parts in their global operations, the sales teams did not have enough time or information to properly evaluate or prioritize every sales opportunity according to a standard process. They lost deals and projects they were hoping to win resulting in lower sales win rates and inaccurate overall sales forecasts for future quarters. Here’s what we did to help them.

Opportunity Scoring 1
Opportunity Scoring 2

THE SOLUTION

To help sales teams qualify their deals more easily, we first understood their existing qualification process. Then we developed a machine learning model that learned what successful deals looked like, based on thousands of historical deal profiles of which the outcome was already known. The model is now used to score open opportunities based on their likelihood of being won. This enhancement is fully embedded into the sales team’s existing process, ‘simply’ offering a small improvement in the form of a decision support tool.

Through a dashboard embedded directly in the CRM tool, sales teams are able to attain a ‘second opinion’ on their deals’ likely success.  Additionally, the sales leader can look at all sales teams’ deals together, and get a ‘second opinion’ on their overall forecast. For example, in the below graph, we can see insufficient opportunities in the higher-win category to achieve the order intake target, which indicates a risk.

Further filtering can be done by forecast category, business segment, sales stage, team, and expected order date to create a top list of deals requiring attention, which can then be discussed with the relevant teams and targeted support given where needed.

Example executive summary of all markets

THE SUCCESS

By first understanding and then adapting to the sales teams’ existing process, we were able to bring a significant enhancement to the qualification process, without requiring the sales teams to make a major change in their way of working.

By bringing the benefits of machine learning in an easy way to the team, we helped our client achieve greater success in a variety of sales areas, including a 14% improvement in win rate in several EMEA countries after only six months. A 50% reduction of late opportunity entries to the CRM was a result of sales-people realizing that, when they input more accurate data into the system, they are able to get an accurate scoring, or a second opinion, on their deals. The new system helped them to target the right deals, and use their scarce time and supporting resources more efficiently.

The opportunity scoring model also set the stage for future increases in data-driven decision making when using the improved CRM. The team is now piloting a similar approach to score leads coming into the funnel before they are converted into opportunities. In time, this will further improve the quality of leads that enter the funnel, as well as providing direction to marketing on leads that still require some nurturing before they can be converted into an opportunity.

How we helped a global B2B manufacturing leader increase their market share through opportunity scoring modelling

14%

Increase in EMEA win rate

A powerful new process

To build upon for sales efficiency

50%

Reduction of inaccurate data in CRM system

THE SITUATION

For a multinational company with worldwide sales and operations, maintaining a solid market share through sales can be a challenge – one that our client (a global chemical company turning over 6 billion a year) was facing. .

With so many moving parts in their global operations, the sales teams did not have enough time or information to properly evaluate or prioritize every sales opportunity according to a standard process. They lost deals and projects they were hoping to win resulting in lower sales win rates and inaccurate overall sales forecasts for future quarters . Here’s what we did to help them.

Opportunity Scoring 1
Opportunity Scoring 2

THE SOLUTION

To help sales teams qualify their deals more easily, we first understood their existing qualification process. Then we developed a machine learning model that learned what successful deals looked like, based on thousands of historical deal profiles of which the outcome was already known. The model is now used to score open opportunities based on their likelihood of being won. This enhancement is fully embedded into the sales team’s existing process, ‘simply’ offering a small improvement in the form of a decision support tool.

Through a dashboard embedded directly in the CRM tool, sales teams are able to attain a ‘second opinion’ on their deals’ likely success.  Additionally, the sales leader can look at all sales teams’ deals together, and get a ‘second opinion’ on their overall forecast. For example, in the below graph, we can see insufficient opportunities in the higher-win category to achieve the order intake target, which indicates a risk.

Further filtering can be done by forecast category, business segment, sales stage, team, and expected order date to create a top list of deals requiring attention, which can then be discussed with the relevant teams and targeted support given where needed.

Example executive summary of all markets

THE SUCCESS

By first understanding and then adapting to the sales teams’ existing process, we were able to bring a significant enhancement to the qualification process, without requiring the sales teams to make a major change in their way of working.

By bringing the benefits of machine learning in an easy way to the team, we helped our client achieve greater success in a variety of sales areas, including a 14% improvement in win rate in several EMEA countries after only six months. A 50% reduction of late opportunity entries to the CRM was a result of sales-people realizing that, when they input more accurate data into the system, they are able to get an accurate scoring, or second opinion, on their deals. The new system helped them to target the right deals, and use their scarce time and supporting resources more efficiently.

The opportunity scoring model also set the stage for future increases in data-driven decision making when using the improved CRM. The team is now piloting a similar approach to score leads coming into the funnel, before they are converted into opportunities. In time, this will further improve the quality of leads that enter the funnel, as well as providing direction to marketing on leads that still require some nurturing before they can be converted into an opportunity.

How we helped a global B2B manufacturing leader increase their market share through opportunity scoring modelling

14%

Increase in EMEA win rate increase

A powerful new process

To build upon for future sales efficiency

50%

Reduction of inaccurate data in CRM system

THE SITUATION

For a multinational company with worldwide sales and operations, maintaining a solid market share through sales can be a challenge – one that our client (a global chemical company turning over 6 billion a year) was facing. .

With so many moving parts in their global operations, the sales teams did not have enough time or information to properly evaluate or prioritize every sales opportunity according to a standard process. They lost deals and projects they were hoping to win resulting in lower sales win rates and inaccurate overall sales forecasts for future quarters . Here’s what we did to help them.

Opportunity Scoring 1
Opportunity Scoring 2

THE SOLUTION

To help sales teams qualify their deals more easily, we first understood their existing qualification process. Then we developed a machine learning model that learned what successful deals looked like, based on thousands of historical deal profiles of which the outcome was already known. The model is now used to score open opportunities based on their likelihood of being won. This enhancement is fully embedded into the sales team’s existing process, ‘simply’ offering a small improvement in the form of a decision support tool.

Through a dashboard embedded directly in the CRM tool, sales teams are able to attain a ‘second opinion’ on their deals’ likely success.  Additionally, the sales leader can look at all sales teams’ deals together, and get a ‘second opinion’ on their overall forecast. For example, in the below graph, we can see insufficient opportunities in the higher-win category to achieve the order intake target, which indicates a risk.

Further filtering can be done by forecast category, business segment, sales stage, team, and expected order date to create a top list of deals requiring attention, which can then be discussed with the relevant teams and targeted support given where needed.

Example executive summary of all markets

THE SUCCESS

By first understanding and then adapting to the sales teams’ existing process, we were able to bring a significant enhancement to the qualification process, without requiring the sales teams to make a major change in their way of working.

By bringing the benefits of machine learning in an easy way to the team, we helped our client achieve greater success in a variety of sales areas, including a 14% improvement in win rate in several EMEA countries after only six months. A 50% reduction of late opportunity entries to the CRM was a result of sales-people realizing that, when they input more accurate data into the system, they are able to get an accurate scoring, or second opinion, on their deals. The new system helped them to target the right deals, and use their scarce time and supporting resources more efficiently.

The opportunity scoring model also set the stage for future increases in data-driven decision making when using the improved CRM. The team is now piloting a similar approach to score leads coming into the funnel, before they are converted into opportunities. In time, this will further improve the quality of leads that enter the funnel, as well as providing direction to marketing on leads that still require some nurturing before they can be converted into an opportunity.