Target Rebuilds Store Workflows Around An Employee App

Target has rebuilt the way store work is handed out. The retailer collected input from employees across 50 stores and used it to upgrade myDay, its store operations app, so that the tool can better assess real-time inventory needs, team availability and sales forecasting. The refreshed app groups similar tasks and orders employee to-do lists by urgency and value, while a new myDay Leader View gives team leaders a personalised home screen they can pin metrics to. Target also said its Scan to Divert function helped decide whether scanned products belong on the sales floor or in the backroom.

Key Conclusions

Task prioritisation turns store labour into a measurable queue

The most consequential change is not the interface but the ordering logic. Once tasks are ranked by urgency and value rather than by shift sequence, store labour stops being an undifferentiated block of hours and becomes a queue with measurable throughput. That queue can be inspected hour by hour, compared across stores and tied back to the inventory and fulfilment outcomes it was supposed to produce.

Backroom decisions are where inventory accuracy is actually won

Scan to Divert addresses a problem most retailers under-report: products that are physically received but never reach a sellable position. The feature helps employees decide whether a scanned product goes to the sales floor or stays in the backroom based on demand and space availability (Retail Dive). Routing errors show up later as phantom stock, cancelled pickups and refunded orders, which is why this single workflow carries disproportionate weight in any inventory accuracy programme.

Target framed the outcome in financial terms, saying the function contributed $25 million in value that can be reinvested in guest experience and store operations. That framing matters because it converts a store-level workflow into a P&L line, which is the only language that reliably unlocks further investment in store technology.

The direction of travel is consistent across the sector. Walmart introduced one-hour express store pickup and an ASL service ahead of the holidays (Retail Dive), compressing the promise window for digital orders and pushing more pressure onto the same store teams. When the promise tightens, the accuracy of every routing decision becomes commercially visible within the same day.

What The myDay Update Actually Changes

Three layers, three different owners

The first layer is data capture: what the store knows about on-hand units, labour availability and incoming demand. The second layer is decision routing: how the app converts that knowledge into a ranked task list. The third layer is feedback: whether the outcome of each task is written back so the next ranking is better. Target has shipped improvements to the first two layers and signalled the third by promising AI-enabled insights in upcoming updates.

Ownership differs by layer, and this is where most deployments stall. Data capture belongs to inventory and operations teams, decision routing belongs to product and engineering, and feedback loops belong to whoever owns store performance targets. When a single app spans all three without clear ownership, improvements in one layer quietly degrade another.

There is also a labour-relations dimension. The retailer collected input from employees across 50 stores to identify areas to improve workflows (Retail Dive), which is a materially different process from handing down a tool. Stores that were not consulted still have to adopt the same workflow, so the rollout plan matters as much as the feature set.

Best Practices

Instrument the queue before optimising it

Before changing how tasks are ranked, capture a baseline of queue depth, task completion time and handoff failures for at least four weeks. Without a baseline, any later improvement claim is unfalsifiable, and store managers will reasonably treat the new tool as another reporting obligation rather than a work aid.

Second, define a small number of queue health metrics and keep them stable. Suggested set: percentage of tasks completed within the assigned window, average time between task creation and first action, and the share of tasks reassigned across shifts. Three stable metrics beat a dashboard of twenty that nobody reconciles.

Third, close the loop from task outcome back to the ranking model. If a task is routinely marked complete without changing the underlying shelf or inventory state, the ranking logic is optimising for closure rather than for the outcome. Retailers with agentic store tooling describe the same requirement from the other direction, noting that store systems must monitor real-time conditions from order fulfilment and labour availability to shelf compliance and safety (NVIDIA).

Common Mistakes

The first mistake is treating the app as a communications channel. Push notifications and task lists look similar, but a task carries an expected state change while a notification does not. If the tool cannot distinguish between the two, completion rates will look healthy while shelves remain unchanged.

The second mistake is measuring adoption instead of execution. Login counts and daily active users tell you the app was opened, not that inventory accuracy improved. Pair any adoption metric with at least one operational outcome such as phantom stock rate or pickup cancellation rate.

The third mistake is ignoring the seasonal amplitude. Task volume in a peak trading week can be several times the baseline, and a ranking model tuned on quiet weeks will mis-order priorities exactly when ordering matters most. Validate the ranking logic against peak-week data before peak week arrives.

Task Priority As An Operating Metric

Priority is usually treated as a display setting. In practice it is an operating metric with two components: what the system believes is urgent, and what the store believes is urgent. The gap between those two beliefs is the single best diagnostic for whether a store tool is trusted.

Make the gap visible. When a store overrides a ranking, log the override and the stated reason. Overrides clustered on one task type indicate a modelling error; overrides clustered on one shift indicate a staffing or training issue; overrides spread evenly indicate that the ranking inputs are simply not trusted.

The same logic applies to cross-functional work. A store that has to reconcile shelf compliance, fulfilment promises and labour scheduling on one screen will optimise whichever metric is reported to it most frequently. Aligning reporting cadence across those three areas is often more effective than adding another feature.

Summary

Target's myDay upgrade is best read as a workflow redesign rather than an app refresh. Ranking tasks by urgency and value, and routing scanned inventory between floor and backroom, both convert store labour into measurable throughput. The $25 million value figure shows the retailer is already translating that throughput into financial language.

For other retailers the transferable lesson is sequencing rather than features: baseline the queue for several weeks, define a small and stable metric set, then close the feedback loop from task outcome back to ranking. Skipping the baseline step is the most common reason store technology investments fail to produce a defensible return, and it is also the most common reason store managers quietly stop trusting the tool.

Data Sources

FAQ

What did Target change in its myDay app?

A：It grouped similar tasks, ordered employee to-do lists by urgency and value, added a customisable Leader View home screen, and improved the Scan to Divert workflow for routing scanned inventory between the sales floor and the backroom.

How much value did Scan to Divert contribute?

A：Target said the function contributed $25 million in value that can be reinvested in guest experience and store operations.

Why does task prioritisation matter commercially?

A：Once tasks are ranked by urgency and value rather than shift sequence, store labour becomes a measurable queue that can be inspected hourly and linked to inventory and fulfilment outcomes.

What metrics should a store tool baseline first?

A：Queue depth, task completion time within the assigned window, and the share of tasks reassigned across shifts. Four weeks of baseline data is a practical minimum.

Why log ranking overrides?

A：Overrides clustered on one task type point to a modelling error, clusters on one shift point to staffing or training gaps, and evenly spread overrides suggest the ranking inputs are not trusted.