Platt Perspective on Business and Technology

Quantifying business intelligence valuation in terms of systems-indeterminacy 6: deterministic and stochastic valuation models and methodologies and their alternatives 2

Posted in macroeconomics by Timothy Platt on January 7, 2014

This is my sixth installment to a series in which I discuss and analyze the valuation of information in a business context and from a due diligence and risk remediation perspective, and in terms of what in a physical systems context would be called quantum indeterminacy (see Macroeconomics and Business, postings 137 and following for Parts 1-5.)

I began discussing deterministic and stochastic models in Part 5, and as part of that also at least began a discussion of semi-deterministic valuation models and wildcard variables too. And I focused in Part 5 on the valuation of business intelligence as considered from the information holding and potentially selling business’ perspective. I continue that discussion here, this time turning to consider this same set of valuation issues but from the buyer’s and marketplace’s perspectives. And I will at least begin the process of connecting these approaches together into a more inclusive single model, as ultimately the true market value of any product or service that goes to market is set by whatever valuation can be mutually agreed to by buyer and seller and in the face of competitive marketplace pressures – and as those pressures arise on both the buyer and seller sides.

• Here that means buyer-facing competitive pressures from other buyers whose purchasing activity might shift fair market value arrived at,
• As well as seller-facing pressures arising from the simultaneous presence of directly competing product or service offerings that a potential buyer might select instead.
• But I begin here in the buyer’s side of this transaction relationship.

A prospective buyer of a rivalrous product such as a desktop computer or a chair would be expected to know up-front and before making any purchasing decision precisely what they were getting and how much this would be worth to them. A comparable situation arises for the types of standardized information product offerings discussed in Part 3 of this series (e.g. like-for-like interchangeable sales leads to prospective customers who offer similar likely-sales value, where it does not necessarily matter precisely who those specific potential customers are as individuals.)

A comparable situation arises when a buyer is purchasing rights to a patent. They can know precisely what was entered into that patent filing as that information is a matter of public record. They would certainly know if they are purchasing exclusive rights to that patent and taking ownership of it, or simply purchasing license rights to use it and if so for what period of time and under what terms. They would know the direct costs involved for this. And with information from that patent filing in hand and information on their own business and its processes and needs, they should at least have a fairly clear idea as to the level of value they would obtain from acquiring this information. And they should have a fairly clear idea of the expenses that implementing this information purchase would involve at least as a baseline cost, barring possible cost overruns. There are still going to be sources of uncertainty here and even the possibility of wildcard variables that could skew all calculations attempted. What would happen here, as a perhaps extreme example if a patent purchased or licensed were to suddenly become challenged as to its true ownership by a third party claiming prior discovery or ownership? But barring wildcards and the unpredictable, this is a second scenario where valuation determination from the buyer side would likely follow a relatively clearly deterministic form.

As with a seller-side valuation (see Part 5), buyer-side valuation determination becomes overtly stochastic with possible and probably values rather than single set values when trade secret information is entering the marketplace. As already noted in this series, the buyer of necessity is going to have to make a due diligence decision here as to what a cost-effective price would be for them, without knowing in full enough detail what is contained in that trade secret to be able to make a fully informed due diligence analysis.

And this brings me to the full analysis where buyer and seller determinations come together or not, with any finalized agreement to sell and buy constituting a realized true market value.

• When buyers and sellers can both arrive at their own determinations of fair value and for determining price ranges that they would bargain within, according to more deterministic means,
• And when there is specific precedent in the marketplace for establishing agreed-to price points for the type of information package in question, price arrived at is most likely going to fall within a relatively narrow range, and certainly when buyers can obtain like-for-like equivalent information from an alternative seller if the one initially considered for buying from insists on a price point higher than the perceived market standard,
• Unless they can convincingly argue that a lower price alternative offered by their seller competition is not really like-for-like for value accrued to the buyer. That is in effect the marketing mantra of automotive industry leads sellers where they all seek to present their leads as being more selective for quality than those of their competitors, and for offering higher value to the dealership buyer.
• But following up on this example, dealerships seeking to buy cost-effective, productive leads do track the performance of the leads that they purchase from different providers. As such they know when and by how much, any given leads provider’s products are likely to differ from a marketplace average for value per lead.
• It is easy to track how many completed sales on average develop out of 100 leads purchased, to pick a number more or less at random that is likely to be large enough for use as a sample size for at least basic statistical analysis. And it is easy to calculate the overall leads costs that accrue to those completed sales where they have to cover for bad leads purchased too – the rest of the leads purchased from that provider, that did not work out. (If you purchase 100 prequalified leads at $10 per lead and 10 of them convert into sales that you would not in all probability have otherwise made, then you are paying on average, $100 for each of those leads that you were able to convert when covering the expenses of also having purchased the other 90. If you make a profit of $1000 per car sold on average from those successful leads, before accounting for this expense but net of all other expenses faced, this $100 expense element may seem high but it is still going to be very cost-effective. If, on the other hand you make one sale on average from this hundred, then at least on this face of this analysis actual profits would become impossible. So unless this leads provider were to either lower their costs or improve the quality of their leads – or probably both, it would be bad business practice to purchase any leads at all from them.)

Up to here I have considered more deterministically calculable scenarios. I am going to continue this discussion in a next series installment where I will coordinately consider buyer and seller side valuation assumptions and calculations for less fully knowable business intelligence. I will consider trade secrets there and will also add in predictive analyses and the data that supports them as marketable business intelligence too. Meanwhile, you can find this and related postings at Macroeconomics and Business.

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