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How to shift from absolute to differential value

communicating differential value

Why do your customers buy from you?

Although it would seem straightforward that the laws protecting workers should also apply to workers in what is described as the ‘gig economy’ or ‘platform-based work’, there is much debate – and confusion – on this issue. This lack of clarity stems in part from the novelty of platform-based work. There has also been an effort to conceal the nature of platform-based work through buzzwords such as ‘favours’, ‘rides’, and ‘tasks’ as well as the practice common to many platforms of classifying their workers as independent contractors. Platform-based work includes ‘crowdwork’ and ‘work-on-demand via apps’. In crowdwork, workers complete small jobs or tasks through online platforms, such as Amazon Mechanical Turk, Crowdflower, and Clickworker.  In ‘work-on-demand via apps,’ workers perform duties such as providing transport, cleaning, home repairs, or running errands, but the workers learn about these jobs through mobile apps, from companies such as Uber, Taskrabbit, and Handy. The jobs are performed locally.

Platforms mediate extensively the transactions they have with their workers, and also between the customers and the workers.  Platforms often fix the price of the service as well as define the terms and conditions of the service, or they allow the clients to define the terms (but not the worker). The platform may define the schedule or the details of the work, including instructing workers to wear uniforms, to use specific tools, or to treat customers in a particular way. Many platforms have performance review systems that allow customers to rate the workers and they use these ratings to limit the ability of lower-rated workers to access jobs, including by excluding workers from their system. The amount of direction and discipline that clients and platforms impose on workers, in many instances amounts to the degree of control that is normally reserved to employers and is normally accompanied by labour protections such as the minimum wage, limits on working time, and contributions to social security. This recent ILO study provides more detailed analysis on these features of platform-based work.

Wilson says cashiers–74% of whom are women–are likely to the first overtaken by the automation wave. Also likely to be affected are retail salespeople, who may not be needed as shoppers increasingly consult their phones for information about sizes, colors, and availability. “Smartphones have all kinds of information about the products you want to buy, so the need for salespeople is considerably less,” he says.For example, Bloomingdale’s has tested smart fitting rooms with wall-mounted tablets allowing customers to scan items and view other colors and sizes and receive recommendations to “complete the look.” Home Depot says four self-checkout systems occupy the space of three normal aisles and obviate the need for two human cashiers. Amazon’s Go concept stores have no cashiers at all, enabling shoppers to pay for everything through their phones.

More than a few thoughts emerged from the exercise, but the one that stands out is that the customer, the ultimate arbiter of success, must be held in awe.

Most of the strategies and practices advocated by Amazon’s founder have broad applicability, but a central mystery remains: Bezos himself, his combination of early life experience, intellect, emotional abilities and communication skills. Being Bezos isn’t teachable.

The decisions made by algorithmic credit scoring applications are not only said to be more accurate in predicting risk than traditional scoring methods; its champions argue they are also fairer because the algorithm is unswayed by the racial, gender, and socioeconomic biases that have skewed access to credit in the past.

Algorithmic credit scores might seem futuristic, but these practices do have roots in credit scoring practices of yore. Early credit agencies, for example, hired human reporters to dig into their customers’ credit histories. The reports were largely compiled from local gossip and colored by the speculations of the predominantly white, male middle class reporters. Remarks about race and class, asides about housekeeping, and speculations about sexual orientation all abounded.

By 1935, whole neighborhoods in the U.S. were classified according to their credit characteristics. A map from that year of Greater Atlanta comes color-coded in shades of blue (desirable), yellow (definitely declining) and red (hazardous). The legend recalls a time when an individual’s chances of receiving a mortgage were shaped by their geographic status.