Aggregate level forecasts
The primary forecast of this sub-team is the aggregate-level sales forecast. With this project, we forecast the sales for the upcoming X weeks, both on the weekly and daily levels. To give a bit of context around aggregation, one possible level of aggregation could be the sales of the company as a whole. Such a forecast can help with making company-level decisions and working on setting goals and expectations. Another possible level would be sales that come through the warehouses of bol, which is crucial for operations and workforce allocation.
An important common characteristic of most aggregate-level forecasts in our team is that they also depend on the sales forecast (making them downstream forecasts), as sales are often the primary driver of many other metrics that we are forecasting.
This leads us to another crucial forecast, which is the customer support interaction forecast. With this project, we provide an estimate of how many interactions our customer support agents can expect within the next weeks. This forecast is crucial for the business, as we do not want to over-forecast, which would lead to overstaffing of customer support. On the other hand, we also do not want to under-forecast, as that would lead to extended waiting times for our customers.
To make sure that our services (webshop, app) scale well during the peak period (November and December), we also provide a request forecast, that is, how many requests the services can expect during the busy periods.
Lastly, we provide a range of logistics-related forecasts. Bol has multiple warehouses in which we store both our own items, and the items of our partners who would like to use bol’s logistical capabilities to make their business operate smoothly. As such, we provide a few different forecasts related to logistics.
The first one is logistics outbound forecasts, that is, a forecast indicating how many items will leave our warehouses in the coming weeks. Similarly, we provide an inbound forecast, which focuses on items arriving in our warehouses. Additionally, we also provide a more specialized inbound forecast that further divides the incoming items by the type of package they arrive in (for example, a pallet vs a box). That is important as those different kinds of packages are processed by different stations within the warehouses and we need to make sure they are staffed appropriately.
Item level forecasts
The second sub-team focuses on item-level forecasts. Bol offers around 36 million unique items on the platform, and for most of those, we do need to provide demand forecasts. Those predictions are used for stocking purposes. This way, we try to anticipate the needs of our customers and order any items they might require well in advance so that we can deliver it to them as soon as possible.
Additionally, the team provides a dedicated forecast that can handle newly released items and pre-orders. With this forecast, the stakeholders can anticipate how many items will sell one day before the release and within the next month after the release. This way, we can make sure that we have enough copies of FIFA or Stephen King’s latest novel.
Finally, our team also developed a promotional uplift forecast, which helps to evaluate the uplift in sales of a given item based on the price discount and the duration of the promotion. This forecast is used by our specialists to make better, data-driven decisions when it comes to designing promotions.
