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Improving sales forecasting using AI

Summary

The importance of accurate sales forecasting

Artificial Intelligence is transforming employee scheduling and improving outcomes by over 40% through the use of historical sales, economic patterns, and external variables to generate intricate demand predictions. Being able to predict how busy or slow a business location will be is crucial for creating effective schedules. By providing managers with accurate sales forecasts down to 15-minute increments, you can eliminate half of all scheduling errors. This leads to happier employees, fewer frustrated customers, increased sales, and a stronger bottom line.

Integrating forecasting into the system

While some of our customers have forecasting teams, they often focus on budgeting needs rather than the granular level required for effective scheduling. To address this, we have developed an integrated forecasting module that can prove its effectiveness before rollout.  Unlike generative AI models, which are still in their infancy, our predictive AI models for sales forecasting are mature and can be ready for real life use within weeks without any of the dangers or predictive AI tools.

Getting started with sales forecasting AI

  1. 1

    Define your business goal:

    Determine the level of accuracy needed in your forecast to create better schedules. Generally, this is around 50% of what one employee can produce in an hour. I.e. If you need one extra employee every time you sell another £100 worth of stuff, then £50 might be a good number to base this off.

  2. 2

    Calculate the tolerable level of error:

    Mathematically determine the level of error your model can tolerate without impacting scheduling decisions.

  3. 3

    Try a basic model:

    Test a simple model, such as averaging the last three weeks’ sales, in a spreadsheet to establish a baseline. You could do this in a complex spreadsheet if you wanted to.

  4. 4

    Work it out:

    How often was the simple model within the tolerance, above it, or below?

  5. 5

    Assess the cost of rudimentary forecasting:

    Estimate the extra wages paid during overstaffed periods and the lost profit or customer dissatisfaction during understaffed periods. This will give you an idea of the potential gains from improved forecasting.

  6. 6

    Benchmark your options:

    Workforce.com also allows you to bring in your own model. You can also have internal data science resources or contractors build models.

Regardless of which model works best for your business, Workforce.com enables you to deliver the model to your managers and schedulers in a way they can effectively use it. By leveraging AI  sales forecasting, you’ll optimise your scheduling, boost employee satisfaction, enhance customer experience, and ultimately drive your bottom line.