How to Apply Predictive Analytics

Predictive Analytics allows people to make better decisions about how to spend their limited time, energy and money. The potential impact of predictive analytics on business will be similar to the personal computer, relational database and Internet. The power of predictive analytics is that it is a scientific business process improvement method that can be used to model complicated and hard to measure actives, such as why people buy something or which employees are likely to leave. Many business executives understand this potential and are excited about applying predictive analytics to their businesses.
CAN has 4 years of experience helping 200+ companies realize the benefits of predictive analytics. We have developed a 6 stage process for applying predictive analytics to our clients’ businesses that maximizes our clients return on investment, increases their chances for success, and makes sure that the results of our research are applied.
Six Steps to Applying Predictive Analytics
The first stage is to define the company’s mission, vision and values. We want to know why the company was started, and why it exists. We want to know what they want to accomplish in the future. Most importantly we want to know how they do business; what values they have that are unique and perminant even when the strategy changes. This understanding set the priorities and filters that guide future discussions. The second stage is to define the company’s goals. Unlike mission, vision and values, goals have clear beginnings and ends and typically can be accomplish in less than a year. Companies typically have one to three goals. Goals should be in alignment with the company’s vision for the future, and should be accomplished in a way that adheres to the company’s values. The third stage is to define the business question to be answered. The business question is about business process improvement, and should not involve technology or research questions. When answered a business questions should have a noticeable impact on at least one of the three parts of a business; sales, operations and administrative support.
The Business Question
The fourth stage is to determine what resources are available. Resources include political approval, availability of necessary data, and determining research methodology. It is important to note that we only determine research methodology once we have defined the business question. If we don’t have the necessary resources to answer the business question, we go back to stage 3 and try to refine the business question to fit the available resources. It is also during this stage that we determine which of CAN’s resources are best for the client. There are two basic options; custom solution or CAN’s products. If possible we try to answer the business question using one of CAN’s 5 products. This allows us to minimize cost while increasing chances for success. Our 5 products are designed to answer 5 key business questions that the majority of business owners have: 1. Tracker: Who is most likely to purchase my product next? 2. Capture: Where and when should I spend my marketing budget? 3. Pulse: How do I attract and retain my best customers? 4. Beacon: Which employees are most likely to leave and why? 5. Terrain: What sales are likely to be next quarter? If a business question can’t be answered using one of CAN’s products, we offer custom solutions. Many of CAN’s clients leverage our custom solutions to develop a competitive advantage in predictive analytics. When developing custom solutions it is essential that we become apart of our clients team and fully understand their business, goals and resources. Before committing to any custom projects, CAN requires that we build a proof of concept. The purpose of the proof of concept is to make sure that we fully understand what we need to build and that we have all the necessary resources. The fifth stage is to determine how the models will be implemented. While our research is complex, we make sure that our work is easy to understand and use, because that is how it gets implemented. We use 4 methods to implement our research, and often combine multiple methods depending on what the client’s goals are.
Reporting Predictive Analytics
1. Formal Reports help our clients understand the nuances and details of our research. Formal reports are most useful when the results of our research will influence a company’s strategy, will be used by a small and specialized audience, and frequent updates are not required. 2. Marketing Summaries provide our clients with colorful and easy to understand summaries of our research. Many of our clients use these summaries as marketing pieces to communicate quickly with large and unspecialized audiences about research that impact future strategy. Marketing pieces go beyond executive summaries because they can be used be used to inform executives, employees, customers and the community. 3. Dashboards help our clients quickly get the up to date information that need to run their operations. While formal reports and marketing summaries often include data visualizations, dashboards are unique because they can be quickly updated and display key information on a single screen that can be monitored at a glance. Dashboards are typically used by a small and specialized audience that is trained to understand and use the information on the dashboard. Dashboards can also be very useful for sensitive information, because administrators can control access by user on a need to know basis. 4. Workflow Integration provides our clients with the ability to use our research to impact the activities and operations of large number of people through the systems they are currently using. Workflow integration is useful because users don’t have to learn or get in the habit of using a new system. The predictive models and coefficients that CAN develops act as a filter to the current database and either users are presented with familiar data fields or if need new fields. The method that we choose depends on who the audience will be and how they will use the results. The fewer the people that need access to our research the more important security and control becomes. If the use of our research is for strategy development then we typically publish a formal report. However if the use of our research is to optimize operations then we publish it as a dashboard, marketing piece, or integrate it into the software you are currently using. The sixth step is to evaluate the model. As part of developing models we run tests to make sure that they are statistical robust. However, it is important to further evaluate a model before and after we implement it. The first evaluation criteria is does the model answer the intended business question. The second criteria is does the model produce results that reflect reality. While the model might be statistical robust, it is useless if it produces misleading results that the experts in your business know aren’t true. The third evaluation criteria is once implemented does the model produce the expected results. Predictive analytics is a very new field. While the technology is exciting, it is predictive analytics ability to answer hard to answer or previously impossible to answer business questions that is most exciting. What separates CAN from our competition is our focus on making sure that we answer our clients’ business questions, instead of being enamored by the technology. Our hope is that we can help our clients apply predictive analytics to their businesses, and that our 6 stage process helps them maximize their return on investment, increase changes for success, and makes sure that the results of our research are applied.

Dashboard Design: Teaching Strategic and Analytical Thinking

At CAN, we exist to provide our clients with leading edge methodologies that are both effective and easy to use.  This requires that we constantly learn about new tools and techniques, and hone a fine edge on the ones we keep to provide to our clients.

Previously on our blog, we have discussed the application of dashboards and aspects of dashboard design that facilitate rapid perception by the human brain.  How about using dashboards as a way to teach users a way of thinking?  In this blog, we will discuss using dashboards to promote strategic thinking through guided analysis.
One of our clients approached CAN with the following predicament.  Their enterprise operates nationwide with several districts responsible for operations within their unique geographic region.  Every year, the strategic planning division would produce a thick binder reviewing each districts market forecasts, opportunities, and past performance.  The intent was to assist the non-technical managers and business development of each district to think about trends in the market and industry to get more sales.  Although very well produced and full of useful information, these binders acted mostly as a reference and did little to encourage analysis by the end-user.
Our solution was to use the same information used to build the binders and create views using Tableau.  At first, these views replicated the familiar visualizations found in binders with an added level of interaction.  Then, we started to add new data sources into the existing information.  We connected industry forecasts, census data, economic indicators, past performance and connected all this functionality to a dashboard where the end user is able to bring in these factors at their command.  Populating the dashboard with the raw materials required for analysis, is the first stage.
The second stage is defining the business questions that the users need to answer to run their business.  We interviewed the executives on the strategic planning team and in several of the district offices to define what the most important business questions they needed to answer to run their business.  Instead of providing managers of each district with binders that pushed facts and figures at them, we created a work book of questions that needed to be answered and how the answers could be applied to running their district.
The third stage is doing most, not all, of the users’ work for them.  What I mean by this is producing dashboards that are 90% completed for the types of questions the user will want to answer.  Our goal is to support the user in asking questions and getting answers, not simply handing them the answers or making them build their own dashboards.  So, we build pre-made views for them.  For example, one aspect of our client’s business functions was closely related to population growth.  We produced a dashboard that integrated population growth figures for the past several years with our client’s historical sales figures and billable hours.  The district manager, interested in staffing requirements, can population changes across the region with his current staffing and identify where adjustments and hiring are likely to take place.
In designing guided analysis, the bottom line is producing dashboards that solve the business question that users need to answer.  This requires that the designers understand the purpose of each dashboard, how it will be used, and what the user intends to get out of it.  If your goal is to achieve data-driven decisions from non-technical managers, you must design so that the user is on the right track with the controls, but ultimately require their interaction and thinking to reach the outcome.
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