2021-10-30 at

Marketing Organisations : Taxonomy

*Marketing* - broadly refers to all the activities regarding supply and demand, in a value-chain. Customers are on one hand, and the supply-chain is on the other. In some stakeholder management formations, I might label this as the *quality* team, which protects the *customer's* interests.

Marketing ➡️ *Product* teams - tend to mean the strategy, R&D, and ops, for the stuff that customers pay for officially on receipts. This includes all the ... *things* they take home (breakfasts), *spaces* they can experience (park tickets), and *services* they receive (tuition, massages, etc.).

*Marketing ➡️ Services* teams - tend to mean the strategy, R&D, and ops, done to *enhance or enable* the first team, but customers don't get billed for this in receipts. The biggest example is *marketing communications* / marcom. Under marcom, structurally *brand management* comes first ... which is designing the *personality* , and *promise* of the business ... most other activities serve the brand! Example : in a brick'n'mortar business, the *e-commerce* storefront operations is a service, and it is not exactly marcom, but beside marcom. Yet in an e-commerce business, this might be reclassified from "service" to "product", as here the online storefront would be front and centre.

With juvenile companies,  the nominal "marketing department" only does marcom, and some other marketing services. And the nominal "ops team" is actually the marketed product team.

This is important to note because a junior marcom staff may think they do "marketing" but actually they do only services, and sometimes only the marcom part.

When someone says they need coaching or hires in "marketing", we can drill down to ask, "product or services (as defined above)?", and "if under services, is it just marcom?", and "if under marcom, which channel, or segment, or horizontal?"

2021-10-20 at

Telepathy

Every now and then someone wants to quibble about how seeing a sunset is different from seeing a digital representation of that sunset. From time to time I bother to spell it out. If you've ever had the joy of listening to music, or of looking at a photo ... perhaps you may have considered how everything you see and hear without exception can be reduced to marks on paper, or a stupid little piece of plastic, and then reassembled and put back into someone ELSE's head.

Set aside your feelings for just a second ... and contemplate this fact, and that these problems were solved DECADES ago. Reload your feelings, without their visual and aural accompaniments (including those in your mind's eye) ... you see, the feelings also have a shape, and that is an introduction to how you can eventually send those over the wire too.

Data Science Fallacies

Common fallacies I have encountered while hiring a data team :

1. Your human experience of the world is fundamentally different from a computer's experience of the world. It's not. The algorithms of data science are the same sort of process that enabled you to learn how to walk, talk, and discover your hobbies.

2. The biggest opportunity in businesses today is data management. It's not - it's knowledge management. Most businesses suck at knowledge management because they don't understand 1.

3. Becoming good at data science means learning data science tools. It does not. This is by far the most frustrating issue I encounter in young executives. Becoming good at data science requires you to figure out 1. 

Peace.