Research Blog - Customer Intelligence

OK - here's a hypothesis, no, more of an analogy: Options (calls and puts) are second order transactions. They're transactions about transactions, and they involve a shift in the time dimension and a capping or limiting in the value dimension. Similarly, we can have decisions about decisions; we can decide today that "I will make a decision 6 months hence" or "No matter what, Phil won't be deciding the issue". These are second order, or metadecisions. We can also make contingent decisions: "I will review your salary in 6 months. If revenue hasn't increased, you will not be getting an increase." No doubt a large chunk of what we mean by "manage" could be described as decisions about decisions about ... ad infinitum.

From an information-theoretic point of view, what is going on here? Well, to some extent we're creating options, and to another extent we're eliminating options. For the salary-review example, the manager has decided to remove the option of "increase" (implicitly leaving only "stay the same" or "decrease") contingent on some variable. Perhaps an approach is to enumerate all possible decision outcomes, and assign a probability of it being selected (from the point of view of the manager). Eg. "increase", "constant" and "decrease" are all equally likely. Hence, we can look at the entropy of the decision space, D:

E[-log D]

Obviously, the selection "increase" hinges on a random variable, R, that relates to revenue and the decision rule. By comparing entropy before and after certain events, we are measuring the change in decision selection entropy NOT as a measure of information - but intelligence. The events that lead to a change in entropy (or propensity to decide a certain way) would fall into three types:

1) Change in option structure (eg. merging, eliminating, creating) "I've been told I can't give you a decrease, regardless or revenue".
2) Change in decision rules (eg. contigency) "If revenue hasn't increased by 10%, you won't be getting an increase".
3) Change in parameters (eg. variable uncertainty) "Revenue will remain constant with 95% certainty".

Generally speaking, people like having options and will pay money to keep their options open. However, markets like people to relinquish options, so that it can operate more efficiently through planning and risk-sharing. For example, renting (should be) dearer than taking out a mortgage. Or if you promise to give Target all your business, you should get a modest discount. Basically, you help out the market, and the market kicks some back your way.

If options are valuable (and freely traded in secondary markets), why then, would managers eliminate them? (Partion their decision space.) Why would they knowingly in advance reduce the courses of action available to them? First guess: they rarely do it. Most managers I've dealt with are extremely reluctant to do this, and don't want to see targets or similar on their product reports. No one wants their business case coming back to bite them on the bum.

Second guess: it's a communication thing. Specifically, it's a negotiation thing. The motivation for telling your staff about the salary review, and the fact that it's tied to revenue, is an incentivation technique. The manager thinks that her staff will work better (ie increase revenues) knowing this: they will act differently ie make different decisions. The existence of this decision rule in the manager's head is a decision variable in the head of the staff. Thus, it falls into the domain of "threats and promises".

Third guess: it's a communication/negotiation thing between the manager and her boss/company. "See, I'm managing my staff for performance - please give me my bonus".

Where does this leave us? Perhaps a measurable and testable (ie normative/postivist) theory of decison-making could provide us with a basis for arguing what the effects of decision rules and parameters are. By linking these effects to money via Utility Theory, we could subsume the question of "what resources should I expend on changing my decision selection propensities?" into general Utility Theory (microeconomics and game theory). This then, might be of help to people when managing their information and improving the quality of decisions, and hence increase social welfare.

Wow - this time a month's delay. That's a new record!

Papers: I'm reading a set of papers by John Mingers, a leading thinker in the fundamental questions underpinning systems theories, including information systems. Particularly, I'm reading about information and meaning, and how autopoiesis can help explain this. This is definitely not for the faint-hearted, and in fact, reading this and related material makes me think that to really participate in this dialogue you'd need to have spent some time in universities - preferably Californian - during the late 60s, if you know what I mean. The most understandable (to me) idea of picked up so far is that "information is the propositional content of a sign". This is related to my concept of information ("information is the change in uncertainty of a proposition"), but in a way that's not entirely clear to me.

I'm also reading selected papers from ECIS 2000, particularly those dealing with economic analyses of information, such as operation of markets, and those dealing with customer operations, such as data mining.

Seminars: Lasty Tuesday I attented an industry seminar on creating value from Clickstream Analytics. It was a bit disappointing: in a nutshell SAS has put out a web log analyser, and the National Museum of Australia has started to analyse its web logs. Welcome to 1997.

This afternoon I attented a seminar by Prof Lofti Zadeh, a particularly famous researcher from the electrical engineer discipline who crossed over into computer science, but now appears to be heading fully into cognitive science (he developed fuzzy logic and possibility theory amongst things). His seminar was on precisiated natural language. The idea is that traditional analytical tools like calculus, predicate logic and probability theory are too limited in their ability to express propositions ("Robert is very honest", "it is hot today"). So he is promoting an approach to allow one to do formal computation on propositions by imposing constraints on them: it's a way of formally reasoning as you would with logic ("all men are mortal"), except you can incorporate "loose" terms such as "usually" and "about". In essence, it's a formalisation of the semantics in natural language: somewhat of a Holy Grail. I'm pretty sure he turned off a lot of linguists with his use of mathematics - good, I say.

People: I've meet a few times with Graeme. We're currently looking at two issues: 1) The ongoing intellectual property issue; 2) the 1-pager for Bill Nankervis (industry sponsor).

1) The University wants me to sign over all my IP rights to it. This causes me some concern, as it is the University's policy for post-grads to own their IP, except if they're in industry partnerships. My goal is to "open source" my research so that I (and anyone else) can criticise, use, extend and teach it to others. In academia, this done through publishing papers and theses. As sole and exclusive owners of the IP in perpetuity, the University can do what it likes: sell it, bury it, whatever. This makes me uncomfortable, as Australian universities - sadly - are under enormous funding pressure as the government weans them off public money. I'm in ongoing negotiations about how to best ensure that this state of affairs doesn't impact on my agenda.

2) I'm having to narrow and refine my resarch question further. I've tried coming at it from the top down, so now I'm trying from the bottom up:

The Satisfaction Wager: I Bet You Want Fries With That. A Game-Theoretic Approach to Anticipating Customer Requirements.

It seems that even when you get away from thinking about business and information technology, and start thinking about customers and information, I still read a lot of authors who talk in business-centric terms, about organisation functions: billing, sales, marketing, product development and so on. I'm trying to think within a paradigm of information about customers and hence ask "what sort of information does an organisation need about its customers to satisfy them?". I map it out from the point of view of what an organisation does with customer requirements.

Fulfilling Customer Requirements. Eg. Customer contact history. Service request/order state. Operations
Anticipating Customer Requirements. Eg. Customer demand. Changes in circumstances. Channel preferences. Planning/Sales/Marketing
Creating Customer Requirements. Eg. Customer opinions. Market expectations. Development/PR

I'm not entirely sure what a customer requirement is: there's a lot of literature around requirements engineering/analysis, but I think this is from a point of view of developing systems for use by an organisation that operates on customers. I'm talking here about something that looks more like a value proposition.

Anyway, grouping it this way might be a way into understanding the value of different types of information. For example, the "fulfilling" side of things is concerned with the "database world", records, tables, lists of details. The stakes for correctness are high: If you install ADSL in the unit next door, you won't get 80% of the money. The "anticipating" side is the "statistical world", where you deal with guesses: if someone gets onto a marketer's campaign target list and turns out to not be interested, it's not the end of the world. Finally, the "creating" side is where we deal with extremely fuzzy concepts of information to do with perceptions and opinions, such as "unfavourable" news articles, endorsements, sponsorship and the whole "branding" and "reputation" thing.

This latter category is definitely out of scope for me: I think I'll focus my efforts on the interaction between the database world and the statistical world. Hence the facetious title above: if you stood in a Maccas and observed the "up-sell" process, how would you assign value to the information involved? Ie what resources (risks) should you expend (accept) to (potentially) acquire what information? Is current order information enough? Does it help if you have historical information? How much difference does having an individual customer's history make, compared to a history of similar customers (segment history)? Does information about the customer's appearance matter? (Eg. compare in-shop with drive-through.) What about information pertaining to future behaviour (compare take-away with eat-in)? Lastly, what about the interaction of information about the McWorker? The time of day? The location? Etc.