Guest Post

Tips for Effective Demand Forecasting

February 27, 2020

Guest post written by Brandyn Morelli. Brandyn is CEO of Tilt Metrics, a B2B growth agency based out of Providence, RI. He is also the co-founder of HelloCecil, a SaaS platform helping small businesses make smarter hires through video interviewing.

With demand forecasting, you can ensure you’re ready for your shoppers throughout the season. Forecasting is the key to optimizing your supply chain and making sure you’re ready when demand hits.

With demand forecasting, you can:

  • Set an accurate budget that improves inventory management
  • Maximize sales
  • Optimize your supply chain
  • And minimize supply and restocking issues

Fortunately, forecasting isn’t as difficult as it might seem. It can seem daunting to implement a forecasting model, but if you start small, you can create an effective system with key data that’s easy to build on.

Start with a simple forecasting model and build out from there:

There are several types of demand forecasting methods, but for the sake of the example, we’ll use something simple you can start with and build on to meet your needs.

Historical reports are the first and best place to start when it comes to demand forecasting. Look back at your reports from last year, including your sales growth week-by-week.

Also, review the first three-quarters of the previous year compared to this year and see how demand has changed and then estimate your growth rate.

Once you have that information, you have a simple growth forecast you can expand on with more complex data.

However, that’s just the starting point. Once you have a historical reference point, dive into consumer behavior metrics (as many as you can dig up) to determine how their behavior will change this season.

If you have forecasting data from last year, look back to see how accurate you were and what factors you used to determine that forecast. Also, pull data from your industry or vertical as opposed to just your own sales metrics to get a more accurate view.

Any kind of predictive analytics you have can be helpful here, so consider all possible data points you can take advantage of.

Use consumer-centric metrics:

Good demand forecasting is consumer-centric at its heart.

Sure, there might be all kinds of other factors outside the consumer that could affect demand, but virtually every one of those is only important when in the context of the consumer’s response to them.

That means the better you can get into the mind of your end consumer, the more accurate your forecasting is going to be.

To do that, you want to know:

  • What do shoppers want?
  • When do they plan on getting it?
  • How much do they want to pay?
  • How is the economy going to affect consumer spending and behavior as a whole (related to each of the above three categories)?

Beyond just historical data and economic and consumer forecasts, consider using new data channels such as social media to gauge demand.

This could be something simple, such as asking your own customers how and when they plan to do their shopping or running searches to find out how often and what people are saying about your product and shopping in general.

Monitor, so you can quickly adapt to changes:

It’s not enough just to forecast demand for a particular timeframe. You need to continue to monitor on a daily basis so you can quickly adapt to any changes that occur.

Monitor to see whether your actual results are matching your predictions. If not, find out why and make the necessary adjustments.

Maintain real-time visibility of your supply chains and remain flexible when those changes are necessary so you can adapt quickly, minimizing supply issues and maximizing revenue.

Ultimately, there’s no way to forecast with complete accuracy, so comparing your predictions with your results in real-time, monitoring changes, and being flexible when you need to adapt will ensure you can make the most of your demand forecasting.

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