Reaching for the Holy Grail of Advertising

There is a concept of perfect information and data reduction that is presently unreachable.

The Holy Grail of data reduction, if you will, (no offense to anyone with the metaphor I hope) is the ideal to close the loop between ad procurement and revenue booking which flows through the CRM (Customer Relations Management Tools) and several other conduits, but lets keep it simple at this point.

The data needs to flow all the way to accounting and back out to ad procurement. This is necessary to meet business goals. Because in the chart of accounts we are able to pull our most essential statements, P&L, Balance Sheet, Cash Flow. In other words, every new employee, come or gone, every increment of change of COGS (Cost of Goods Sold) affects our algorithm for purchasing advertising.

This is unfortunate . . . however, we are on that path. When one spends an ad dollar it would be nice to know exactly what the return of that dollar was. The return is a function of many things, most importantly the business goals of the organization spending that dollar. The goals usually contain items such as net revenue, new customers, and an understanding of the CLV (Customer Lifetime Value) and the (CRV) Customer Referral Value.

Ironically, in a beatifically written recent article in the HBR (Harvard Business Review), it was shocking to understand the revelation of how these two variables interact. In summary, the customers with high CRV may be wroth a lot more if the data is extrapolated than the customers with high CLV. And even more ironically, they are often not the same people, although in any matrix of data there will be overlap with complicated analysis such as these.

This Global vision as stated in the title will be addressed in a series of Blogs as the topic is much too long for one post. It could be a book lengths piece, and perhaps it will.

There presently is a massive disconnect, even with modern analytics, that most companies do not have the internal resources, cannot afford to outsource, or the outsource partners just do not understand these dynamics. Warp is a reach for the whole grail of prefect data closure and reduction. It is on a healthy way toward that goal.

This brief introduction will expound as the march forward to discuss the tribulations of the disconnect, what we can do now, and how we will continue to approach that omega point of the Grail. I suspect within the next decade we will be very very close. Unlike Arthur, or his Python counterparts it will not elude us this time.