covid19 sales predictions

By Tal Eden, Chief Data Scientist at Curve.tech

Predictions

Machine Learning is the ability of computers or machines to learn without being explicitly programmed. Arthur Samuel

Supervised learning is a specific field of machine learning in which a machine learns to label data based on a set of prelabeled examples. Sales prediction is a specific example of supervised learning. We have a labeled data set of past sales, and we generate a prediction for future sales.

In the example of retailers – we want to infer the future sales volume of each product in each point of sale based on historical data such as past sales, item price, date (whether it is a holiday or not), etc. In the past several years, we have seen a spike in the number of businesses using such predictive components in their planning processes as technology gradually became more accessible.

One of the greatest adversaries of sales prediction (and of any prediction engine in general) are black swan events. The machine cannot reliably predict any longer because the historical (labeled) data is no longer relevant. For example, historical sales data after a financial crisis like the one that occurred after the collapse of Lehmann Brothers in 2008 can no longer serve as a basis for prediction because people have less money and therefore tend to buy less. The environment in which the predictive engine was created has changed so drastically that it has rendered the engine unusable. Another prominent example is the Coronavirus outbreak that we are currently experiencing.

Our machine cannot predict accurately because history is irrelevant; here, history cannot and does not repeat itself.

Sales have been altered completely – whether online or offline.

Fashion retailers have suffered a significant decrease in sales volume. A prominent example in this industry is Adidas; the German sportswear company reported that it expects first-quarter sales to drop by $1.14 billion in greater China. Like most fashion retail stores, they have had to close physical stores, and people are buying less sportswear and leisurewear.

While sales in the fashion industry have plummeted, sales in the online food industry are skyrocketing. According to Nielsen, last year, just 4% of grocery sales in the US came online. However, with people avoiding crowds or being quarantined, the alternative is to buy online. Downloads of Instacart, Walmart’s grocery app, and Shipt increased 218%, 160%, and 124%, respectively compared with the prior year.

Can we still predict?

Do not despair! Although times are changing, businesses who can adapt rapidly have the opportunity to make the best out of the crisis and leverage it for future growth. Below are a few suggestions on how to deal with this situation in terms of demand forecasts – and all its implications – sales, financial planning, orders, and deliveries?

  1. Understand the New World Order
    Create a comprehensive, real-time BI dashboard. You can see which products are seeing an increase or a decrease in sales. Walmart has reported an increase in sales for tops, whereas a decline in sales for bottoms (thank you video conferencing).
  2. Integrate external data sources – You need to understand how different events influence this new “order” – the number of infected people, whether schools have been closed or not, is there a curfew in place and more.
  3. Working without Historical Data
    When there is little historical data to rely on, data should be sampled at a higher frequency. If you were generating predictions on a monthly basis, you should switch to daily predictions, and if you were doing daily predictions, we recommend real-time analysis. 

    This will benefit the company twofold:
  • Information will be gained quicker
  • Sensitivity to changes will be higher even without long term predictions

Accordingly, we will need a different set of ACTIONS than those for long term predictions:

  1. Inventory: Your inventory strategy in times of crisis should be continuously changing based on data-driven insights. A few examples are mobilizing products intended for offline stores to online stores or making sure you have enough stock in locations in which the stock is likely to be consumed.
  2. Marketing strategy: Your marketing strategy should reflect the changing environment. Identify strong sellers, feature them, and use your marketing budget for them rather than “weaker” products or try to push products that display a strong negative trend before the trend sets a new baseline.
  3. Be ready to draw rapid insights and translate them to quick actions – When the environment is changing rapidly, businesses who cannot adapt in a timely fashion will be rendered irrelevant. You should be ready to make business-impacting decisions based on your new insights and predictions.

While we cannot predict the future reliably in times of uncertainty, we can draw meaningful conclusions regarding the rapidly changing environment.  This helps us harness the technology to help us identify immediate trends, plan our next steps, and how to weather the storm the best way possible.

Do you want to hear more about how you can use machine learning in your business? reach out to us for a consultation at info@curve.tech

2 replies
  1. Rony Pikarski
    Rony Pikarski says:

    Thanks for an interesting article. I would like to add that instead of waiting for the historical data, expert knowledge can be used.
    Bayesian Bayesian and Dynamic models allow forecasting and risk estimation even with little data and situations with a lot of uncertainty assisted by expert knowledge.

    Reply

Leave a Reply

Want to join the discussion?
Feel free to contribute!

Leave a Reply

Your email address will not be published. Required fields are marked *