What is hyperlocal forecasting?

Hyperlocal forecasting takes the context of each demand driver within each location into account to make accurate demand forecasts on the most granular level.

If ever there was a time to work smarter - and use all the possible tools available to optimize your business - then it’s right now. There’s a global labor shortage and across the board costs are spiralling all while consumer expectations are going through the roof.

For service-intensive industries like retail and hospitality, the need to have the right people, in the right place, doing the right thing, at the right time is of paramount importance. Get this right and all of a sudden everything else becomes much easier.

But with so many factors to consider - especially balancing both your staffing levels and customer demand - it can feel like trying to solve a Rubik’s cube while locked in a dark room, wearing a blindfold.

So how do you do it? How do you staff to ensure you get the customer service levels you want while avoiding over or understaffing? How do you save costs, avoid lost sales and grow the reputation of your brand by delivering exceptional CX?

The answer (or at least the best place to start) is hyperlocal forecasting.

What does hyperlocal forecasting mean?

Wouldn’t time travel or the ability to see into the future be great? If we knew what was coming before it happened we could plan and prepare for it to make sure we’re ready. But with Delorean time machines and flux capacitors in short supply, we have to find another solution to see into the future.

Hyperlocal forecasting is an AI-solution that takes the context of each demand driver within each individual location into account to make accurate demand forecasts on the most granular level.

It uses algorithms to predict future customer demand and the best AI tools will help capture different demand drivers like transactions, footfall and revenue per location and department, making forecasts more accurate than ever before.

The best examples of hyperlocal forecasts combine historical data with events, public holidays or promotions - basically anything that could affect your business demand. You can even identify peak and dull periods on a 15 minute, daily and weekly level.

Hyperlocal forecasting in action

If you’re a retailer with dozens of stores across the globe, you know as well as we do that every store will be different.

Say you sell sporting goods and one of your stores is around the corner from a sports stadium.  There’s a big game taking place at the weekend, beautiful weather is forecast and it’s a public holiday - which means you’re going to be super busy and you’ll need more staff to cover the increased demand. 

Compare this to one of your other stores that’s in the mountains and thrives in the winter selling winter sports equipment. The labor demand forecasts for both locations on this specific public holiday are going to be very different. 

Using the same forecasting method across both stores (and the entire business) simply doesn’t make sense. Historical averaging and other one-size-fits-all approaches are often not accurate enough to be useful and require managers to overlay their personal experience to get a reasonable estimate of demand over time. And when we start to venture into what is essentially guess work, the chances of the forecasts being accurate rapidly decrease. 

However, with hyperlocal demand forecasting, you can use AI when it’s plugged into your WFM system to calculate the required headcount for you - based on your forecast. You'll be served with the optimal labor hours you need to make your business perform at its peak. On top of that, you'll make sure that your staff have the right skills for each shift, and that you as an employee don't break any labor standards. Everybody wins.

Using hyperlocal forecasting algorithms equipped with machine learning methods to crunch all existing historical data will significantly lead to higher accuracy results when compared to results from averaging sales of the last three months. Events are less likely to be overlooked and the impact of each event on every demand driver is better quantifiable.

Why use hyperlocal forecasting?

Avoid over & understaffing: more accurate forecasts virtually eliminate the risk of over or understaffing. This both saves you money because you won’t be paying staff you don’t need or forking out to pay huge amounts of overtime and increases sales because you have the right staffing levels to meet demand. Understaffing is a huge problem. According to recent research, 50% of frontline workers said they’d experienced understaffing. Out of these, 83% said it makes work more stressful, and 40% said customer service suffers because of it. Better forecasting is the perfect solution for understaffing problems.

Provide exceptional CX: whether you run a thriving restaurant, a busy bar or a bustling store, your customer service performance will decrease if you don’t have the right number of staff working at any given time. That’s a problem, because your customers want and expect an exceptional experience. Failing to meet these expectations results in a poor experience, and this means your customers will leave. In fact 86% of consumers say they’ll leave a brand after only two (!) poor customer experiences. In the long run, your business will therefore get hit too. Like the example above, customers will leave if service is bad or wait times are too long. This smacks you straight in the pocket as you lose revenue. But it goes even further than this; a poor customer experience will also give your brand’s reputation a hammering. With hyperlocal forecasting it doesn’t have to be this way.

Make better, data-driven decisions: By being able to track a huge number of variables for better forecast accuracy and create location-specific forecasts you can make better business decisions. Data will always talk if you're prepared to listen. This is exactly what AI-driven demand forecasting does. Listen and you’ll be able to make better, more informed decisions - whether that’s in relation to headcount, leave requests, holiday planning and more.

Scale with automation

Trying to match your scheduled staff to your forecast demand, without the help of tech, is a near impossible task. You can schedule on a hunch, but scheduling with the help of hyperlocal, AI-powered demand forecasting is better because you can get forecasts based on historical and real-time data for different locations and departments. 

Hyperlocal forecasting is fast, it’s accurate, and with the right tools, it quickly scales across stores. It’s the foundation for cost-effective and efficient workforce management. No matter how complex scheduling gets you’ll never underperform or overspend; your employees will always be right where they need to be.

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