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Data modeler that reads financial statements - the next frontier for Predictive Analytics

I was recently invited to join a conference related to predictive ananlytics. I oddly find that I am one of the few people that will speak about predictive modeling in the B2B market. If ever there is any development needed in our field - predictive modeling's application in the wholesale/ Business to business market is the key. Think about the following: The data we have is boardly categorized into: client's data and your firm's or other firms' transaction /web browsing data.

To me, reading transaction data for the B2B and B2C are similar, as it's purely transactional and behavioral. This is not the main problem of the B2B application.

And we are pretty advanced in terms of analyzing retail client's data, meaning that there is no shortage of young energetic people that can interpret consumers's demographic data: what are they, what they do etc. BUT when it comes to business to business segment, there is a real shortage of people that can build or of projects that need predictive models.

In terms of corporate/business entity, financial statements are key to understand their behaviors. The current supply of financial statements reader will go to: Finance, Accounting, Credit Risk, Relationship Manager/Banker, Stock/Bond analyst.  Where are the people that read Financial statements and yet do Predictive Modeling for marketing purpose? If you can get 20% more of the customers for the bigger ticket businesses segments: it's more than 80% improvement of the firms' baseline given the heavier weight of the B2B than B2C. Why we can do predictive modeling on firms if we have numerous business directory, external financial data providers?

What are the barriers?

1. Understanding of the Economic Data: if reading census or geographical information system is key to know the demographic of a retail population; reading macro/micro economic data is then an per-requisite for B2B modeller;

2. Knowing Accounting/Financial Statements: just like reading the salary and occupation of a person to access his/her purchasing power, we also need to know the accounting standard and financial statements of the company.

3. Each-company-is-different mentality: The first common reason for corporate modeling is - every company is so different, there is no way to use standardized model for prediction. True if the data cannot be captured. But it can also be collected in the model building process to supplement the purely financial variables.

From a practitioner point-of-view, the bottle-neck is truly not about which software runs faster, which database is better, it's more about the supply of these talents.

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