Define Hypothesis and Scope Effort

In the Discovery phase, the Define Hypothesis and Scope Effort process step enables you to capture the overall goals of the analytical solution. With these goals comes an alignment to an overarching business objective. Our Monetization Strategy drives revenue or costs savings with measurable outcomes that can be tied back to a larger corporate initiative.

The hypothesis and problem statement are important to capture because they will provide you direction on the type of Monetization Strategy to deploy. It is our recommendation that you leverage your business levers in the development of your business objective and hypotheses. The business levers serve as a great anchor point to align to your corporate, individual department, and/or project objectives. In addition, they are very useful in the creation of your hypothesis and finally your actions. They should be one of the threads that ties your solution together.

You may find that after going through several working sessions, you have more than one hypothesis to solve your business problem, which is perfectly okay. If you start to have more than three or four, you need to prioritize them.

Let's use an example from the Business Levers section. We are in a high-growth sector and are racing with competitors to sign up as many customers as possible for our software product. We deploy a Customer Acquisition strategy through our sales force, leveraging Commissions and Rebates and Sales Reps as the two Business Levers.

• Business Objective: Grow revenue and market share through aggressive customer acquisition campaigns.
• Hypothesis: We can achieve 25 percent growth in revenue through optimizing our sales rep workforce size and territory along with changes in sales commission structure.

These two statements are enough to get us started. We will need to work to narrow our hypothesis through our Decision Analysis phase, but for now it is a good start.

The three main items to capture out of this process step for your Monetization Strategy are:

1. Alignment to or the creation of your business objectives
2. Development of your hypothesis
3. Utilizing the business lever to tie the solutions together

Question Analysis

The Question Analysis process step captures the questions you will want to understand to diagnose the opportunity or issue. These questions help yield the type of data that we want to source from various systems. In addition, by understanding the manager's line of questioning, we get a view into their thought process of how they diagnose an opportunity or issue.

More often than not, we develop more questions than we can answer, so we need to prioritize the list of questions to the most important ones that fit our strategy. Once we have the list of questions, our job turns to understanding the various data sources associated with the questions to ensure we can build an analytical solution.

As an example, let's take our business objective and hypothesis above and deconstruct them in a working session with several managers, analysts, and subject matter experts. In this session we uncover the Questions, Decisions, Actions, and Metrics associated with the sales rep workforce size and territory. Let's start with the Question Analysis by performing a root cause:

1. Q1: When you look at the sales team size and territory, what is the first question you ask yourself?

   Answer: What is the productivity of each of my sales reps?

2. Q2: What is the second question you ask yourself?

   Answer: What is the potential sales for each of their territories?

3. Q3: What is the next question you ask yourself?

   Answer: Based on the productivity of my sales rep, I ask, Can they handle the size of their market/territory? Is it too big or small?

4. Q4: What is the next question you ask yourself?

   Answer: Based on the answer above, I might decide to redistribute territory between sales reps or add new sales reps.

By the time we got to the third question we found an area or root cause that might need further diagnostics. In the fourth question, we got to a potential decision that the team may make, which is usually the stopping point for that line of questioning. Each of these questions leads us to data sources that we need.

The two main items to capture out of this process step for your Monetization Strategy are:

1. Question Analysis, prioritized
2. Data subject areas

Key Decisions

Along with the Question Analysis process step, the Key Decisions are a major input into the creation of the Monetization Strategy. From the Key Decisions process step, we gather the decisions to make as a result of the Question Analysis. These decisions typically occur in the Diagnose phase of the analytical cycle.

During the Key Decisions process step, usually resulting from the same working session as the Question Analysis, we typically generate too many decisions and metrics. With each decision that is captured, we also record Success Metrics that enable the decision. We cover this in the section ahead on Success Metrics.

As an example, let's use our working session above to identify the decisions that the team will make. There are two decisions wrapped in question 4 above, which are:

1. Should I redistribute territory?
2. Should I hire new sales reps?

The main point to capture out of this process step for your Monetization Strategy is:

• Key Decisions, prioritized

Action Levers

The Action Levers process step is one of the most important steps for your Monetization Strategy. The objective is to find the associated actions (or tactics) with each decision. These are the actions you will execute to capture the desired benefit. Recording the actions in this process step helps us determine what we can enable in our Monetization Strategy.

A decision may have multiple actions; it will be our job to prioritize them and determine the key actions to enable. Prioritization is important as well as anchoring back to the business objectives, hypothesis, and business levers. Each action should tie back to our business levers and these should tie back to both our hypothesis and business objective. If they do not, we may need to change our actions.

As a reminder, one of the key components of a Monetization Strategy is the fact that it is an actionable strategy. If our actions are too high level, vague, or not feasible to execute within your organization, you need to rethink alternative actions.

To continue on with our example, there may be several actions associated with each decision. We concluded with two decisions; let's look at possible actions:

• Should I redistribute territory?
  • Action 1: Redistribute territory to the most productive sales reps.
  • Action 2: Give sales reps with big territories a smaller territory to make room for additional sales reps.
• Should I hire new sales reps?
  • Action 3: Hire sales reps for newly created territory.

The key input into our Monetization Strategy from the Action Levers process step is:

• Action Levers, prioritized

Success Metrics

With each decision that is captured, we also record Success Metrics that enable the decision. These Success Metrics will be used in our Monetization Strategy through a Decision Matrix. Since the metric is driving the decision, displaying the metric in the Decision Matrix along with a monetary value provides greater relevancy to the metric and drives up the quality of the decision.

Another key component of our Monetization Strategy is the ability to measure the outcome to determine performance. The Success Metrics process step also defines how we are going to measure our actions. We want the ability not only to take action on our strategy but also to measure and learn from it. A Success Metric may serve as both one that drives a decision and one that can be measured, but not always.

Let's continue on with our example. We understand our two decisions and need to know what success metrics drive each decision. Below are the two decisions along with the potential associated metrics:

1. Should I redistribute territory?
   • Market Potential by territory
   • Market Share by territory
   • Actual Sales per sales rep
   • Quota by sales rep
2. Should I hire new sales reps?
   • Current number of sales reps

From these metrics, we should be able to derive the number of new sales reps needed if we find that the existing territories can support additional bandwidth.

The key input into our Monetization Strategy from the Success Metrics process step is:

• Success Metrics that enable decisions
• Success Metrics that enable measurement of actions

Data Development

From the Question Analysis process step, we determine the data sources needed for our strategy. In the Data Development step, we build out data structures to support the various analytical needs, including our Monetization Strategy.

The process for building out the Analytical Structures to support the Monetization Strategy is iterative in nature. As we continue to refine the strategy, metrics, thresholds, and actions we want to deploy, we need to work with the data development team to support the needed changes.

The solution may range from a large de-normalized table to a star schema with dimensions and facts or something in between. Work with your data team to find the right Analytical Structure for your solution that is performant and easy to maintain.

In our example, what are the data sources we would need to source from? From our Question Analysis and Key Decisions, we see that they include the following subject areas:

• Sales
• Sales rep
• Market share
• Territory
• Commissions

The key input into our Monetization Strategy from the Data Development process step is:

• Data sources
• Additional metrics

Data Science/Decision Theory

To bring your Monetization Strategy to life, the deployment of techniques associated with Data Science and Decision Theory are needed. Data Science helps us determine the insights associated with the metrics. Decision Theory helps to determine the best way to structure the decision for the end user. You will want to leverage techniques such as segmentation, profiling, propensity, velocity, opportunity, choice architecture, and decision matrix to generate insights and structure the decision to drive your strategy.

Many of our guiding principles, which we will review in the next chapter, are rooted in data science and decision theory. These include:

1. Grounded in data science
2. Monetary value
3. Confidence factor or probability
4. Decision Matrix
5. Measurable

Spending significant time working through which techniques you will utilize requires working with a data scientist to vet the applicability of the technique and the supporting dataset for the strategy. Like the Data Development process step, this step is iterative and requires many cycles to finalize. Another output of leveraging data science is that the method is based in science, which engenders trust from the users of the solution. They are more likely to utilize the analytical solution if they trust the outcome of the Monetization Strategy for making a decision.

Let's finish our example by applying some new metrics leveraging data science and utilizing decision theory to compose a decision matrix. To assist in answering the question of how many sales reps we should hire, we are going to add a new metric, Market Velocity. This metric takes into account how many sales reps we have in a market and the time it will take them to mature the market based on their quota. We also add a Number of New Sales Reps Needed metric, which takes the velocity metric along with number of sales reps and market share to determine the number of new sales reps needed to mature the market.

Since we are in a race to capture market share, we set an aggressive goal to get to 50 percent market share in the next two years. How many reps do we need to accomplish this task?

Let's put all of our information into our requirements template and then create our decision matrix. Note that we refined our hypothesis at this point to reflect our updated goals.

From our requirements, we put together a decision matrix and completed our Monetization Strategy with specific tactics to deploy (see Table 5.1). Based on our decision matrix, we can see that the average quota per rep is $10 M and our current rep total is 28. We need to decide how many sales reps we need to hire to meet our goal of 50 percent market share assuming each sales rep achieves 100 percent of quota every year.

With the Market Velocity metric, we can see how many years it takes to capture 50 percent of the market share for that territory with our current number of reps. To accelerate this and capture 50 percent market share in two years, we look at the Number of Rep Hires Needed metric to help us make our decision. Overall, the Northeast territory has one of the biggest potential opportunities to accelerate with 15 hires needed.

In total, the sales manager needs to hire 38 sales reps, more than doubling the size of the sales team, to reach the goal. The new sales reps, at full quota, should have a sales contribution of $769,500,000 in market share to the firm. This along with their existing market share and the existing sales reps' production over the next two years accomplishes the goal of 50 percent market share.

While doubling your workforce and meeting full quota for all reps is a tall order, the Monetization Strategy may point to specific tactics the sales manager needs to deploy for underserved territories like the Northeast and Southwest.

Depending on your solution, you may have many inputs into your Monetization Strategy from the Data Science and Decision Theory process steps, some of which may include:

• Decision Matrix
• Choice architecture
• Correlations
• Thresholds
• Trends and forecasting
• Segmentation
• Cluster analysis
• Predictive and explanatory models
• Probability factor
• Velocity