Brick-and-mortar stores are scrambling to understand their customers. Facing intense competition from online retailers who can gather extensive data about user behavior, traditional retailers have also started tracking customers’ in-store activities. From special equipment that picks up cell phone signals in the area to software that identifies people in video footage, brick-and-mortars are learning more about their customers — often without them knowing.

As these tools have become more popular, they have begun to draw the attention of privacy regulators and advocates. The Federal Trade Commission, for example, held a meeting on mobile device tracking to discuss the risks to consumer privacy, and the Future of Privacy Forum established a program to allow consumers to opt out of mobile device tracking. Academic researchers and journalists continue to investigate the implications of in-store tracking for consumer privacy.

But in addition to the concerns retail tracking raises for consumers, these technologies have serious implications for workers. This effect of data collection is often overlooked. Debates about consumer privacy have largely missed the fact that firms’ ability to develop a better understanding of consumers also impacts workers’ day-to-day experiences, their job security, and their financial well-being.

Recognizing that data collection about consumers also affects workers complicates how we typically think about surveillance and privacy. Generally, we think of surveillance as being a relationship between two parties — the observer and the observed — and privacy as a means by which the observed can limit the observer’s power in that relationship.

But our research suggests that data collection frequently also impacts people other than the those being surveilled. We call this dynamic refractive surveillance. In other words, collecting information about one group can facilitate control over an entirely different group. In our ongoing study, we investigate this dynamic in the context of retail tracking, to understand how data collection about customers can impact how retail workers are managed.

Consider, for instance, the use of clienteling software. This concept has a long history in retail. In some stores, salespeople commonly maintain a personal “book of business”— a list of clients with whom she has repeated contact, a personal, long-term relationship, and a specialized knowledge of product preferences, purchase history, and the like. Customers may specifically seek out that salesperson when they make purchases in the store. The practices in which staff engage to maintain these relationships are known in the industry as clienteling.

In addition to boosting sales for retailers, clienteling can provide value for salespeople. Beyond the commissions that salespeople may earn, developing special relationships with customers makes workers more valuable to their employer — because they have extensive, internal knowledge about particular customers that may be hard to glean from other sources.

But new retail technologies may diminish the value of workers’ customer knowledge, in two ways. First, retailers know more about customers from other data sources (e.g., out-of-store activities, browsing history, etc.), and they are increasingly able to use these data to personalize marketing campaigns and promotions to them, potentially reducing the value of clienteling practices. Second, the nature of clienteling itself is changing: new clienteling technologies make consumer data available to staff through tablets which they carry on the floor. These serve to externalize workers’ knowledge about customers, as many retailers are requiring workers to input information about customer preferences into these systems, rather than keeping it in their own heads or in personal paper records.

The aim of these systems is, ostensibly, to give shoppers a seamless customer service experience, in which all salespeople have access to the same information about their preferences. But the result of this is that workers become more readily substitutable for one another, which reduces their value to the firm. Having a well-honed book of business is less of a bargaining chip for a worker — say, in negotiating a raise — when any salesperson can access the information you’ve gleaned about a customer. The refractive dynamic here is clear: detailed information about customers can undermine workers’ negotiating position and job security.

Customer data can also impact when and how often staff work. Many stores use scheduling software to make predictions about how many workers they’ll need in the store at any given time. This helps managers allocate staff as efficiently as possible to reduce excess labor costs and provide optimal customer service. While scheduling algorithms take workers’ availability and performance into account, they may also consider all manner of customer information, including purchase histories and in-store behavior.

Data-driven scheduling software can be highly destabilizing for workers. Achieving efficiency has often meant bringing in workers for very short shifts, or breaking day-long shifts into smaller work periods spread across the day. Because stores’ demand forecasts can change in real time, in response to new data, workers are more often required to remain “on call” and available to work in case more hands are needed. The software exacerbates scheduling practices that make it difficult for workers to take on other jobs, to take classes, or to make plans for childcare.

In-store tracking technologies directed initially at consumers have begun to play an important role in worker monitoring and evaluation as well. Because companies needed a way to exclude staff from the signals captured by these devices, vendors have, ironically, developed ways to identify and keep much more effective track of workers (for instance, by issuing workers special Bluetooth beacons that distinguish them from customers). As a result, firms that specialized in customer tracking have expanded their offerings to include services to monitor workers’ behavior, evaluate their performance, and direct their future actions.

These vendors now promise to help retailers track how staff circulate over the course of their shift, when salespeople encounter customers, and whether those interactions translate into sales. These data give retailers more ways to evaluate workers’ relative contributions to the business. So sales staff who might have been assessed as a group according to aggregate daily sales, or as individuals according to casual and intermittent observations, may now be subject to fine-grained, ongoing scrutiny on the job. With more comprehensive and granular consumer data comes more comprehensive and granular worker evaluation.

These data also figure into the way managers assign and direct staff. Vendors say managers can reduce idle time on the floor, assign staff to tasks for which the data suggest they are best prepared, and orchestrate encounters between staff and customers that are most likely to result in sales. Such practices are increasingly common in call centers, and promise to remake the experience of work on the retail floor along similar lines.

Collecting data about customers, then, can have non-intuitive effects on workers — by potentially reducing their bargaining power, contributing to schedule instability, and subjecting them to new types of evaluation. Our notion of refractive surveillance highlights a very practical need to build data-driven systems that acknowledge and balance the many interests at stake.

These are still early days for in-store tracking. Managers have an opportunity to explore how to collect customer data in ways that both respect consumers’ privacy and advance the legitimate interests of workers. Already, vendors have begun to integrate workers’ concerns about stability into their procedures for optimizing workers’ shifts, showing that retailers can pursue a range of goals with the same techniques. Making relevant information available to staff as they interact with customers, accurately forecasting customer traffic well in advance, and making worker evaluations and task-assignment decisions more data-driven and transparent can, ultimately, serve workers’ interests — but doing so first requires understanding how wide-ranging the consequences of data collection can be.