Retail’s Race to 2026: Win with PredictHQ

Forecast Accuracy has downstream impacts in WFM and Inventory Management

The fastest path to ROI is improving demand forecast accuracy. Even a 0.5% improvement can deliver hundreds of millions of dollars in savings or ROI for large retailers.

Forecast improvements immediately unlock value downstream in:

• Workforce Management – Smarter scheduling ensures the right labor mix during peak and off-peak hours.

• Inventory Management – Supply chain decisions align with real demand, reducing stockouts and overstock tied to events, holidays, and seasonal surges.

Forecast Accuracy

Retail businesses are highly impacted by fluctuating demand driven by real‑world signals like concerts, local happenings, public and school holidays, as well as observances. That’s why forecasting teams use PredictHQ Features or Forecasts to achieve elevated forecast accuracies that inform workforce planning.

PredictHQ offers two distinct solutions for integrating event data signals into demand forecasting models:

• Features API

• Forecasts API

[1] Features API: Pre-Built Event Signals for Retail Forecasting

While PredictHQ's raw event data from Events API is rich, it isn’t immediately structured for direct modeling. Teams often find building their own features from this data to be both time‑consuming and complex. Our Features product addresses this by providing pre‑built, demand‑relevant, time‑series signals, ready for immediate use in demand forecasting models upon request.

It’s high‑volume, event‑specific, and filled with real‑world edge cases that are non‑trivial to handle, such as:

• Aggregating attendance for multi‑day or overlapping events

• Accounting for leading and lagging demand effects (e.g. shoulder nights around a concert, incremental demand patterns days ahead of Mother’s Day, Valentine’s Day)

[2] Forecasts API:Pre-Computed Demand Forecasts with Event Impact

For organizations with established forecasting pipelines or those exploring model comparisons, PredictHQ's Forecasts API offers an alternative. This API delivers demand forecasts that already incorporate the impact of events, seasonality, and local dynamics. It’s designed for teams looking to:

• Utilize it as a baseline, racehorse, or ensemble component in their existing models.

• Evaluate the distinct contribution of event‑aware demand to their forecasts.

• Accelerate iteration and avoid premature engineering work by leveraging pre‑computed forecasts.

The Forecasts API also presents a low‑risk method for demonstrating the value of event‑aware forecasting before committing to more extensive architectural changes.

How Retailers Should Define Success Metrics with PredictHQ

If you are not measuring, you are just practicing. Seeing value or ROI with PredictHQ starts with defining your success metrics.

Two core metrics

If you’re not measuring, you’re just practicing. The most successful retail teams define success metrics upfront:

• Accuracy – MAPE is most common, though WAPE is often better for seasonal or low-demand businesses.

• ROI – Tie improvements to labor cost savings, inventory efficiency, and revenue lift.

Example:

A 0.5% MAPE improvement can turn into ~$40k in labor cost savings across just one retail store.

How to Implement PredictHQ in Retail Forecasting Models

Questions to ask yourself:

• What is my current tech stack around demand forecasting?

• What models are we currently using?

• Are those models in production?

• Are there any external features already in use?

• What is the current performance of the forecasts?

• Do I have a dedicated AI, ML or data scientist dedicated to this project?

With Features API

1. Use Suggested Radius to define impact zones for each store.

2. Apply Beam to identify relevant features per location.

3. Incorporate features into your baseline forecasts.

With Forecasts API

1. Generate demand forecasts directly.

2. Feed them into workforce and supply chain planning systems.

Start with one business unit (e.g., workforce management). Success there creates momentum for broader rollout.

Best Practices for Workforce Management with PredictHQ

In summary, the teams that define success, collaborate, and prepare for scale are the most successful at implementing solutions that deliver value across business units.

• Define Clear Success Criteria Early – Start with a measurable improvement target (e.g., forecast accuracy by 0.5%). Clear goals keep projects focused and make success tangible.

• Establish a Strong Baseline – Have your forecasting model in production and baseline performance measured before adding new external data. This ensures you know the true impact PredictHQ delivers.

• Take a Collaborative, Iterative Approach – Treat PredictHQ as a partner in experimentation. Small adjustments — like how holidays or event categories are modeled — can deliver meaningful accuracy gains.

• Test Across the Full Year – Event data is seasonal. Don’t rely on a short sample period (like 12 weeks). Testing across multiple time windows avoids bias and shows the full value of event‑driven forecasting.

• Combine Automation with Domain Knowledge – Use local context and human logic alongside predictive models. Checking specific days, locations, and event categories ensures forecasts reflect real‑world patterns.

• Engage at All Levels – Business leaders and technical teams should align on both the big‑picture value and the detailed success metrics. This joint ownership keeps initiatives on track.

• Prepare for Scale – Event data at enterprise scale requires thoughtful integration, not just plug‑and‑play. Iteration and tuning are part of unlocking maximum ROI.

Why Retailers That Adopt PredictHQ by 2026 Will Win

Retailers that win the race to 2026 will be those that embrace AI-powered demand forecasting today. PredictHQ removes the complexity of event-driven modeling, accelerates ROI, and delivers measurable results across labor, supply chain, and operations.

Now is the time to modernize. The results speak for themselves.