The Virtual Vendor Index (VVI™) is designed to gather information on pricing base on a combination of SKU (Stock Keeping Unit) numbers, Part Numbers, Product Description, Product Names or similar designations, and associated published list prices. The VVI™ employs Robotic Process Automation (RPA) to gather the information and is supplemented by manual input as necessary. Each request requires a new run of the RPA’s as there is no history preserved. The position of the item within it’s natural ‘product life’ is also factored in, for example End of Support announcement pending. A corollary of this is the actual maturity of the technology itself on the natural tech maturity S curve.
Another input source is the available financial information of the manufacturer, publisher and where applicable reseller and distributor etc. Of particular importance are measures that are imputed through the various margins and ratios. Other obvious data points are fiscal quarters and the system does not rely on just the current financial statements but uses multiple quarters worth of data.
While the data for public organizations are readily available most privately-held technology companies do provide sufficient guidance to serve as input sources to the VVI. In addition, there are often proxy measures – for example if the company is held by a venture capital fund – then the funds run rate, conversion rate and history etc. are available.
For organizations that transact their B2B through channel partners, value added resellers, integrators and correspondingly through distributors as well – these entities financials also become another set of data. Within this area there is what used to be a murky world of deal registrations, opportunity identification etc. This is now much more codified and as such can be built into the VVI model.
One other key data feed is the sales team, at each point of the sales chain (i.e., publisher, distributor, channel managers, reseller sales and professional services/integrator). Their respective sales performance, commissions, total compensation, stage in the fiscal or half fiscal year etc. are factored into the model.
Geographic information with respect to state taxes, equivalent pricing portfolios, legal registrations that may limit competitive access and funding sources are also factored in and appropriately weighted.