C-Tek: Sales Force Optimization Across 12 Cities
Quantitative reallocation of C-Tek's sales reps across 12 US metros. Models effort response, scenario-tests best, expected, and worst-case profitability, and recommends an action plan.
Goal of the model.
The purpose of the model is to optimize the allocation of the salesforce for C-Tek. The main objective of the case is to find the most ideal number of employees hired for each branch.
Inputs
- Base scenario. Effort and outcome values for the base scenario. Used for calibration first, then as a benchmark.
- Effort data. Used to estimate the different levels of effort and the sales response to changes in effort.
- Impact data. For each segment, this allows estimating the outcomes for different levels of impact.
- Segment constraints. Specific constraints which cannot be violated.
Outputs
- Optimized sales rep allocation. The model recommends increasing reps from 52 to 97.22.
- Looking at the output, sales reps should be redistributed from low-performing regions to high-performance regions.
- Comparison of scenarios: high performing, low performing, and base.
- Projected impact of new breakthrough products.
Limitations
- The model is driven by assumptions.
- The projected 20 to 25% growth depends on successful execution and is a prediction.
- Adding more sales reps does not always lead to an increased number of sales.
- The model does not take into account real-world scenarios. Real scenarios will have many more variables.
Unconstrained scenario.
In an unconstrained scenario, C-Tek is able to allocate resources without restrictions on the amount of reps. Using this, the model can maximize revenue. As seen on Appendix 2:
- Removing all sales reps reduces sales by an average of 48.4%.
- Removing one sales rep reduces sales to an average of 77%.
- Increasing one sales rep increases sales to an average of 120.5%.
- A large increase in sales reps increases sales to an average of 158.9%.
Optimization results
- Total optimized sales effort increases by 87%.
- Largest increases were in Atlanta (146.5%), Dallas (138.4%), and Seattle (119.7%).
- Lowest are Chicago, Twin Cities, and Cincinnati.
- Net margins increase by 34.4%.
- Largest increases of net margins were in Atlanta (75%), Dallas (55.3%), and Cleveland (51.9%).
| City | Base effort | Optimized effort | % change |
|---|---|---|---|
| Los Angeles | 5.00 | 10.91 | 118.2% |
| San Francisco | 4.00 | 6.25 | 56.2% |
| Seattle | 3.00 | 6.59 | 119.7% |
| Boston | 4.00 | 7.42 | 85.6% |
| Philadelphia | 5.00 | 8.12 | 62.4% |
| Cleveland | 4.00 | 7.73 | 93.2% |
| Atlanta | 3.00 | 7.39 | 146.5% |
| Nashville | 3.00 | 5.94 | 98.0% |
| High Point | 4.00 | 6.07 | 51.8% |
| Dallas | 3.00 | 7.15 | 138.4% |
| Chicago | 5.00 | 7.35 | 46.9% |
| Cincinnati | 3.00 | 6.18 | 106.1% |
| St. Louis | 3.00 | 5.09 | 69.7% |
| Twin Cities | 3.00 | 5.02 | 67.2% |
| Total | 52.00 | 97.22 | 87.0% |
The report states that the unconstrained solution does not violate any specific constraints, which means adding constraints would not change the solution. Sales reps should be relocated from low performing cities to higher return markets. Sales reps in Chicago, Twin Cities, and Cincinnati should go to Atlanta, Dallas, and Seattle.
| Scenario | Profit | Change in (%) |
|---|---|---|
| Best guess | 35% | Profitable, but may depend on optimization |
| Very optimistic | 45% | Much higher change in %, allows for more flexibility to increase number of sales rep |
| Worst case | 20% | Low profit, this should result in only high efficiency markets receiving more resources |
Breakthrough product.
If product development is able to create a new breakthrough product, the size of the market could increase by 20 to 25% with average profit margins approaching 40%. Having an expanding market means more revenue can be generated through increasing sales reps.
Optimal strategy
- Relocate resources from Chicago, Twin Cities, and Cincinnati to high growth regions such as Atlanta, Dallas, and Seattle. This results in being able to leverage the full 40% profit margin.
- Expand and hire more reps, since the expected profitability allows for new hires.
- Provide more advanced training to sales reps, making sure that everybody understands the new product.
Expected result
- If the market expands as predicted, revenue and profitability could increase a significant amount.
- An increase in sales reps, training, and technology would allow for returns to be maximized.
Recommendations.
- Reallocate sales reps. Shift resources from low performing regions to high performing regions. This allows for an optimal distribution between differing branches, based on the data received in the analysis.
- Prepare for expansion. If there is a new product breakthrough, a salesforce increase allows for increased demand to be supported. Prioritize and train sales reps for the new product breakthrough.
- Increase sales efficiency. Using the table created in question 1. If worst case scenario, avoid expansion and do not increase the salesforce. If very optimistic scenario, expand and attempt to hire more sales reps.