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Developing a Growth Strategy Backed with Supportive Data and Analytics

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Can you predict what the lifetime value of your client will be?  If not, you might be investing in the wrong relationships. This is one example of how using predictive analytics within professional services firms can have a significant impact on your bottom line.  If you’re a law firm deciding whether you should hold a particular event, predictive analytics can help you estimate the attendance and therefore the financial value of holding the event in the first place.

Perhaps you are trying to maximize the effectiveness of your sales team. With fifty opportunities in your pipeline how do you determine which one to invest time and resources on?  Using data about the industry, employee count, revenue, Twitter sentiment, time of year, etc. you can develop a model that will help predict the likelihood of that particular opportunity to close and invest your time and effort appropriately.

How can you streamline this machine learning and predictive analytics as a professional services firm? We have six steps to help transition these ideas into a repeatable process for your organization.

Our Six-Step Predictive Analytics Method

Identify the Business Problem

The value in a predictive analytics solution is in being able to be hyper-focused on a specific business challenge. Maybe you’re a Third-Party Administrator (TPA) and are trying to determine whether a particular transaction is a qualified expense.  Rather than sampling these transactions, machine learning can review all of them.  This would reduce your overall risk and improve efficiencies. It is critical to define not only the broad business challenge, but the specific problem that can be addressed through predictive analytics.

Assess the Data

When looking at a business problem, it’s important to first determine whether data is available to address the specific problem. For example, if the details of the transaction (specific product purchased, where purchased, etc.) are not available, then determining whether the transaction is valid would be difficult. When working with an organization we assess the impact of a business problem, but also the ability for that organization to solve for it with relevant data.

Transform and Refine

With a clearly defined business problem and relevant data available, you can then work to transform and refine the data so that it is more useable and consumable by predictive analytics tools. This often involves data profiling to ensure we have a clear understanding of the data in question, as well as data enrichment to supplement with additional, related data that may not have been part of the original data set.

Build the Model

This is where the heavy lifting of data science comes into play. Whether working on classification, regression, clustering or recommendation models, data scientists can use tools such as Azure Machine Learning Studio, R Studio and Cortana Analytics tools to create a model that exemplifies the business problem in a predictive fashion.

Validate and Deploy

Once you have a working model, it is important to validate the results in a limited test to ensure that the model is working as intended. This typically takes place through a parallel process where we continue using the current method of planning or forecasting, but run our model alongside.  Through this process, you can identify any potential opportunities for improvement in the model. Once any improvements are made we deploy the model through a web application programming interface (API).

Incorporate and Evaluate

Once you have an effective model in place with a web accessible API, we can incorporate the model into our business process. This can be accomplished very simply by connecting the API to something like Excel for simple data entry or by incorporating it into a line of business applications for real-time decision making.

Patterns are all around us, but many of them are too complex for most humans to discover. The predictive analytics capabilities available today make uncovering these patterns possible. With recent innovations and technology, we can now take a human forecasting problem and turn it into a machine learning solution.

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