Monday, May 21, 2018

Observations on the New Era of Retail Brand Management and Humanoid Robots

 Driven by the introduction of humanoid style social robots we are rapidly entering a new era of consumer engagement and with it a new opportunity for marketers and retailers to secure competitive advantage. The initial introductory phases of robots in consumer engagement models have been mostly about fast food restaurants where they work as receptionists/order takers, in consumer banking as product concierge spokespersons, and in airports as information guides. While that will continue, more businesses engaged in a wider variety of consumer ‘retailing’ are finding the use of robots simply too compelling to ignore.

The driving factor is not that robots are less expensive than humans or are being used to replace workers. The real driving factors are that businesses can learn from the robot’s interactions with consumers at a speed and at a depth not possible via human staff and that consumers can benefit from the knowledge conveyed by the robot. Paper-based consumer survey information or store based data collection/extraction exercises can take months to filter and categorize. Today’s technologies enable that time to be reduced to minutes. A properly designed robot used in a consumer retail ‘brand management’ engagement model will add significant value to the consumer relationship and a competitive advantage to the business.

The lure of humanoid robots in a retail settings rests on the robot’s attraction and engagement power. Consumers are drawn to the aura of an interaction with a humanoid robot and the robot’s mobility to ‘meet and greet’ and even lead them on the route through the store to the product of their choice. More deeply is the anthropomorphic effect whereby people trust robots in many instances more than humans.

The digital toolset of consumer engagement platforms that already includes smartphones, chatbots, computer tablets, laptops, interactive digital kiosks has now expanded to add humanoid retail / concierge robots.

A few examples set the stage.
A robot ‘sommelier’ in a wine store that can recommend a wine respective of your taste and price range having recognized you as a returning previous purchaser and knowing your prior experiences would certainly enhance the loyalty and satisfaction of the customer.
A robot in a cosmetics or clothing store that can communicate the ‘hottest’ colors based on the current preferences of ‘celebrities’ or even identify the specific fashion being worn by a celebrity and motivate a purchase suggestion in that store would certainly add to the desirability of shopping there.

With a robot interaction, however, we can now instantly discover quantitative and qualitative data that can guide and refine our consumer engagement strategy.  Properly designed the qualitative element of a robot-based engagement model would enable us to learn/assess/ estimate: age; gender and language for example. Quantitatively, we could learn ‘hot topic’/ issue preferences or the appearance of new or increased demand for certain items or styles etc. and even the emotional posture of the consumer. Decisions about inventory and sales promotions become ever more knowledge-based and data-driven.

What this means is that the engineering of successful future robot-based consumer engagement application design will need to embrace new concepts for consumer engagement, an extensive embrace of gamification strategies, access to third-party information resources, real-time data logging, artificial intelligence and machine learning capabilities.

Furthermore, for larger retailers the need to manage a ‘fleet’ of deployed robots from a central location becomes critical. The essential rules for all adopters of robots in retail will be to be sure to frequently and constantly refresh the robot’s general interaction script with new versions, items and news as no one will be stimulated to revisit a boring repetitive robot; and ever strive to raise the level of personal engagement and gamification. The more custom the engagement interaction to the individual consumer the more powerful the model e.g. show your loyalty card to the robot to prompt it to recall the specific ink cartridge that you need for that printer.

Michael Radice is Chairman of the Technology Advisory Board for ChartaCloud and and a provider of the leading software for Pepper.