Blue Canyon is often asked to provide rapid answers to complex problems. When this occurs, I am reminded of one of my first managers who said, “When you get asked a question, there are 1-hour answers, 24-hour answers, and 1-week answers.” What he was really telling me is to take the time to find a “good enough” answer for the situation.

In business, the pace of action is increasing, and our clients are seeking rapid improvements in sales and profitability. When executives ask us how they can increase their market share rapidly, a quick, flippant 60-second answer might be to increase sales. The longer answer, however, might involve determining how to increase sales profitably.

When faced with these requests, we often build a model to provide an answer. Yet the demand for speed often means that the framework needs to be simple, but remain realistic to the known facts. Following are some suggestions on how to avoid common pitfalls when you are asked for a quick response.

1. Get the Foundation Right and Build on Additions Later

Recently we were asked to help a client define the ideal got-to-market options for his business. To approach this request we began by looking at the client’s current sales and market share data to determine the status quo by type of channel in specific territories for a group of products. From this foundation, we then augmented our analysis with other modules that dealt with specific territory market share performance and growth targets.

2. Speed Demands Simplicity and Organization in an Analytic Model

Take the time to develop a simple block diagram to define metrics and measurements accurately. In fact, a good model embodies more than the result.  It expresses the factors that drive results.  For example, one metric to consider is market share. The question to ask is whether you are measuring the right variable and defining it correctly.

3. Strike a Careful Balance Between Gathering Facts and Making Assumptions

The time and cost of seeking better data needs to be weighed against the improvement in the result likely to be obtained. This is particularly important when time is of the essence. We have found that clearly defining assumptions and making them easily changeable minimizes some heartburn. Sensitivity analysis can eliminate concerns by allowing testing of a range of values.  When changes in an assumption make small differences in a result then why bother too much about improving the data? On the other hand when there are data values that dramatically swing final results, sensitivity analysis highlights these values/assumptions and require further discussion

4. Test Early Results

In all projects but particularly a time compressed effort testing early results for reasonableness by comparison to external information or convention wisdom increases confidence in the results. For example, a sales deployment model driven by construction activity might look at ranges of sales per employee or compare sales goals with projected sales and known projects from Dodge, IIR data or government housing starts.

Although we frequently are asked to provide an answer for today’s environment, a good framework can also support improved decision making the next time the organization is faced with similar questions. In practice there may be eternal principles in construction of a go-to-market model, but very few eternal models and even fewer eternal answers. In most frameworks, tomorrow’s answers will be different than today’s. A good framework provides a touchstone for answering future questions, a good place to begin but not necessarily to end. Markets are dynamic. There will be new competitors, different channel partners, and new gizmos, widgets or doohickeys. Entirely new product categories may be added. With successful implementation, goals will be raised and new questions asked.

Although there is temptation to take the answers and run, take the opportunity to actively learn. The industry knowledge reflected within the model—though the best available at its creation—will not be perfect or eternal. Document what assumptions need to be changed.

• Have new roles and responsibilities made revenue growth targets?
• Does that answer change resource deployment?
• Are competitors establishing new channels of distribution?
• Does that mitigate, disrupt or change market share targets?
• Has the product or project mix changed so that the model needs to reflect the change?

Through active monitoring there is the opportunity to improve later model forecasts and results by changing the assumptions or adding a new module to better capture a market. Potential learning is vast, but only if the organization is prepared to treat the framework as a dynamic and not static exercise.