Marketing : the game has changed .

The nature of marketing has changed considerably in the last decade. Personalisation, the holy grail of marketing,  which aims to engage with customers in their own context and to align to wants and needs and behaviours of individuals has become now an imperative. There is a shift in the balance of power between the consumers and companies  driven by the ability by consumers to access and share information easily via online media; the consumers can compare, seek advice, share their experiences online. Brand perception has become more fluid, more fragile.

The flip side of this is that companies now also have access to vast amounts of data about the consumers outside their own walls beyond what they have historically stored in their CRM systems (own store data, affiliate network data through loyalty programs etc); they can also reach customers where they really are: social media.

The good news is that the recent (re-)emergence of AI, specifically statistical learning techniques ( so-called Machine Learning ), has made it possible for companies to base their marketing decisions on analytics-based insights. It's now become now possible to use sophisticated analytics tools, which were only available to larger companies, at reasonable cost and at scale: the interesting development is that such tools are now available , practically at no cost such as R and Python or SaaS offering with subscription pricing ( use as needed); the only investment required being the AI expertise for the judicious use of tools with appropriate domain-specific know-how.. It's now possible , this using large datasets, to elicit features that drive brand perception, customer choice and loyalty, assign a dollar value to products/features based on perceived value, optimize product portfolio and profit, recommend products for cross sale and up-sale, align messaging to so-called segments of one.

We have been witnessing a great deal of buzz around digital transformation initiatives. Digital transformation initiatives do not need to be turned into the monster inside-out projects of the past for so-called enterprise transformation. It's possible to reap great rewards by adopting a pragmatic, actionable approach based on identifying and prioritizing areas where machine learning and broader AI can be deployed .