The optimism of #Purpose & science of #design in forging #digital customer experiences! Opinions my own!

“Next Best Action (NBA) reflects the efforts of marketers to present the offer that a particular customer is most likely to accept, or any content, moreover, that is most appreciated by the customer.” — that was a 2010-2012 definition, mostly written by “classic” db/ data driven software providers. Circa 2018, the narrative has been hijacked by digital natives, DMPs, DSPs and content-rich providers playing the ‘experiential’ tune. Two sides of the coin, both worlds converging to solve the marketer’s dilemma. They say the experience is the product – so here we are.

Also it follows “when the right offer is delivered to the right customers, via the right communication channel the needs of marketers and customers are best served – bcos when customers received irrelevant offers and messages, it damages both their experience of the brand and the brand’s ability to sell products”.
We all know that when done well, NBA dramatically increases customer acquisition rates, cross-sell revenues, as well as long-term customer loyalty and value. It also provides a superlative CX for customers, underlying the point that what is best for the customer is also best for the business. But I’d like to venture that CX is the primary benefit resulting from NBA, and everything else is a lagging indicator. It’s a more authentic statement, and defo more purpose-driven; in the first place why we need NBA.
To break the CX barrier with NBA, we need to catalyse combustion of *customer* insights using decision engines – these include:
(1) Data fabric
(2) Analytics.
(3) Business rules
(4) Decisioning, arbitration & self learning engine.
I’d like to spend some time talking about each – giving it a spin circa 2012 and present day 2018 requirements and expectations.

(1) Data fabric. Defo a must, as customers traverse multi-verse journeys, using multiple devices + the omni-channel misnomer;  it’s all about non-stop/contiguous CX and the ever elusive customer ‘footprint’ . Most organisations today have been working on this for years, usually hinged on a data management strategy – but the focus is now on the fidelity and spectrum of data made available. Moreover, streaming data in motion (transient vector states) is the new norm, and somewhat a prerequisite these days

(2)Analytics. Predictive is now passe. A lot of thinking for adaptive contact strategies lie with prescriptive and even cognitive analytics. The quick step for practitioners is in the use of highly-tailored (business problems) prescriptive use cases; it seems while before it was good enough to know “when”, today we need specifics “what”. The narrative here has always been multi-faceted.. but first principles apply: what are we solving for?

You’d be hearing a lot more multi-armed bandit solvers these days (essentially in the prescriptive range – but a lot more tightly coupled with the problem).
(3) Business rules. Staple for most businesses, and possibly a “jeet kune do”, most elegant/ simple solution for some lines of business e.g. the prepaid segment in the telecommunications sector.
Decisioning engines. The breadth in which these engines process stimuli and spit out decisions is no longer a constraint today; the ability to self-learn and adapt is obviously a premium + also the capability to ingest ‘softer’ darker data from social, stringing together anonymous identities etc. (think Gigya).

Most of these reinvigorated/ galvanised marketing platforms today serve  dual purposes

1. Consistency, delivering optimal customer experiences and inflection points (interactions) throughout the customer lifecycle – for both consumer and enterprise businesses.

2. Revenue boost, cost take-out/ efficiency gains with “analytics everywhere” – it’s the best way to force feed/ ingest -> “insights into action”; marketing sciences as applied to the customer lifecycle management disciplines of the 90s.