The Future of Brand Management

Posted on April 17, 2020

Updated on October 21, 2020

2 min read time

The Future of Brand Management

Autonomous Brand Execution. That’s the future of brand management.

But how do we get there, and why is it a good thing?

Businesses need Efficiency in their structures, to thrive. Less resource, better focused and more productive generally defines efficiency. So, having fewer people and lower costs in the achievement of optimal brand management is at least an implicit goal for every CEO managing an enterprise.

Marketing needs Effectiveness. Ensuring every amount spent works hard for a brand, building competitive advantage and ensuring profitable returns generally defines effectiveness. So, having people truly enabled to achieve maximum returns from a premium asset like a brand is at least an implicit goal for every CMO.

How does one institute both Efficiency and Effectiveness into Brand Management?

It's not easy. Take this analysis: there are roughly 13 levers a brand manager can pull in a year (for example, Pricing, Packaging, Communication, Distribution, Merchandising, and so on). And on any of those, there are a number of ‘Lever-Actions’ they can take (for example, raise price, maintain price, lower price, and  so on).

It’s not a calculation often made, but this results in approximately 25 million different choices of actions to take with a brand – that is what a brand manager, perhaps inadvertently, is faced with during the course of a year. We manage this with a sense of what is right, guided by experience and best practice all around us. It is, nevertheless, an Art.

The future of brand management involves transforming that Art into a Science.

Data Science to be more explicit, and Artificial Intelligence to be precise. This involves teaching machines which of those 25 million lever actions are best to take, in any given scenario. It’s a massive ‘intellectual mapping’ challenge. It involves triangulating these lever-actions with consumer sentiment data (both conscious and sub-conscious to emulate how the mind truly works in brand relationships), with commercial data (like media investments, rates of sale, distribution), and even external events data (like political movements, sporting events, or emergencies like Covid-19).

By doing so, not only are correlations evident, it also becomes possible to define causative actions. In short, AI lets us know exactly whether doing x causes y. Or, more accessibly, that this content, in that channel, for the defined target audience causes them to buy more.

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Bringing AI into Brand Management enables much more.

It allows fewer resources to extract premium returns from brand assets. It sweats each dollar better. And because it’s a data-driven approach, it’s possible to integrate it into corporate systems and dashboards. No longer are brands simply intangible assets listed as goodwill on balance-sheets. They become a tangible asset that a business can manage.

In the transition from Art to Science, some important things happen. We move from Brand Monitoring, to Brand Guidance, to Brand Management. First we observe and comment, then we say what we believe should be done, and finally we manage what a brand does.

This involves moving from Predictive Brand Guidance (where advice is given that x will result in y), to Automated Brand Management (where the AI has proven itself to be right in its decisions, but others need to execute them), and finally to Autonomous Brand Execution (where, with integration into the brand owner’s systems, AI-driven brand management can create content and place it in relevant channels).

This final phase – Autonomous Brand Execution -  begins with simple tasks, such as producing ‘now 10% bigger’ online banners, knowing how best to execute such communication, and in time, takes on the Chess Grand Master equivalent, by coming up with creative ideas, such as ‘Harpic is Cocaine for Toilets’, or some other such ‘unpredictable’ break-through insight.

At ProQuo AI, that is our mission.

To assist brand owners to manage their brands for optimal return, by knowing which actions to take to achieve expressed goals. To succeed, we use better measures, measured better, a host of AI-tools which understand the trajectory of brands in particular categories and how they develop through specific actions, and business model which allows the gathering of essential data at massive scale, but delivered to our customers and clients at low cost.

We’re excited to be pioneering such deep innovation that we believe will be transformative of the world of marketing, making businesses more efficient, and brand managers both more effective and more fulfilled.

 

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