Segmentation & Targeting: We used their prescriber-level data from IQVIA to create segments of prescribers by market potential and share.

Sales Force Sizing: We used our proprietary analytic methodology to calculate the optimal call level by segment to be applied to the prescriber universe. This allowed us to estimate the sales force size if full U.S. coverage had been possible.

Sales Territory Design: Using our Sales Territory Map software system, we loaded the optimal call levels by ZIP code into the alignment along with the existing territory alignment and representative home addresses. We were able to design a 90 territory alignment that covered over 85+% of market potential, minimized representative disruption, kept as many sales representatives as feasible, and eliminated all of the vacant territories.

Segmentation & Targeting: We purchased a database of hospitals on their behalf and created a segmentation based on bed size, purchasing group, and prior sales to the hospital.

Sales Force Sizing: Using our segmentation, we worked with the sales force and marketing teams to estimate the optimal time allocation to each hospital segment. This analysis suggested a much higher sales force size, therefore we also worked with their finance teams to create a new sales forecast and ROI analysis for senior management.

Sales Territory Design: Using our Sales Territory Map software system, we identified the optimal headquarter cities and territory alignments for approximately 35 sales territories in the U.S.

Call Planning: For each of the newly-reconfigured territories, we created a call list of target hospitals along with the optimal call frequency for each territory.

Quota Setting: Working with sales management, we used the new territory configurations to allocate the quarterly sales quotas. This process was extremely helpful for morale, as it allowed territory quotas to decrease for each of the existing territories at such time as their territories shrank due to the increased sales force size. Representatives were comfortable with the reduced travel and lower quota requirements of the revised territories, while still allowing the company to meet and eventually exceed their higher total revenue objectives.

Representative Feedback: We collected spreadsheets from the sales force periodically within the first year of the new target list to update the original list of hospitals and their purchasing group affiliations. Once the data was clean, we were able to deliver to them an accurate database which laid the foundation of their CRM system.

Segmentation & Targeting: Using a spreadsheet of their current customers and sales, we were able to identify the top NAICS and SIC codes where they do the most business. We then analyzed the relationship between NAICS code, company size, and prior sales information to create a highly predictive model for future sales.

Sales Force Sizing: We were able to use our segmentation model to identify the top three areas of the country where an additional sales representative would be most beneficial.

Sales Territory Design: Using our Sales Territory Map software system, we mapped all of the existing territories and worked with the Region managers to create new sales territories for expansion.

Call Planning: Using a list provider, we purchased a list of additional companies in each territory that our model predicted would have the highest potential based on their NAICS code and company size. This new prospect list was so successful that the process was repeated every six months, each time adding new industry groups. The company found that having a specific industry brochure / marketing material / plan for two to three months per industry was a great way to get the sales force focused on expansion to new clients.

Quota Setting: Working with sales management, our model became the basis for determining the quotas for each territory. These new quotas tended to reinforce what sales management had already believed to be the territories with the highest / lowest opportunities, but having a quantifiable metric gave them much more confidence to set quotas.

Segmentation & Targeting: Using census bureau information in the U.S. and in Canada, we were able to create a metric for market opportunity based on the number of middle and high income households with owner-occupied homes.

Sales Territory Design: Using our Sales Territory Map software system, we designed over 1,900 franchise territories in the U.S. and over 180 franchise territories in Canada. This allowed each prospective franchisee to be informed of the franchise size, location, measure of opportunity, and territory map for their prospective territory on their discovery day.

Segmentation & Targeting: We've acquired 5-digit ZIP code level counts of companies meeting this franchisor's criteria for high-potential accounts from a list provider. This allows us to know the potential number of customers within a territory. We have validated this data against the sales in existing franchisees which allows potential franchisees to have a great deal of confidence in the amount of opportunity in each territory.

Sales Territory Design: Using our Sales Territory Map software system, we webconference with the franchisor and potential franchisee on each discovery day to work with the potential franchisee to design their optimal territory configuration subject to the constraints of the franchisor. This gets tremendous buy-in from the potential franchisee as it allows them input into their protected territory. At the end of the discovery day, the prospective franchisee has a map in hand that can be used as part of their franchise agreement.

Reporting & Dashboards: We worked with the pharmaceutical company's HR and legal teams to create a set of metrics for each State that would determine whether or not sales representatives (from potentially out-of-State) would be permitted to make sales calls within different parts of their territories (which could cross State and county lines). Our analysts updated a spreadsheet weekly to identify what types of businesses were allowed to be open for each State (and potentially county or city) for the following week. In this project, we used hundreds of data sources (governor's websites, twitter feeds, news articles, etc.) to distill the decision of whether or not the representatives could do their jobs to a simple green light vs. red light for each representative for each State in their territory.