What data does Nextail use for forecasting demand?

Giona Capurso, Product Manager, provided insights to answer this frequently asked question.

In the face of uncertainty, robust demand forecasts are more important than ever

The lingering pandemic, mass supply chain disruptions, and ephemeral customer preferences mean that static, top-down demand forecasting based solely on historical data just won’t cut it. This approach renders retailers too inflexible when things don’t play out as expected.To ensure that they can dynamically align supply and demand under such uncertainty is to take a bottom-up approach to inventory management, especially when it comes to demand forecasting.

Nextail generates a robust demand forecast that helps retailers understand the probability of demand for each and every item, in every size, in every retail location, online and off, for a given planning horizon, or weeks of desired coverage. After that, the Nextail optimization engine will maximize sales even further by ensuring that stock is allocated to stores/channels with the highest probability of selling these products to make sure that each movement is “worth it” from a financial and efficiency standpoint.

But of course to do all that, Nextail needs a lot of data. Applying AI and machine learning, Nextail brings together diverse data types from a number of sources.

What are the different types of data Nextail uses to calculate long and short-term demand forecasts?

Nextail uses a variety of data types to forecast demand

Nextail primarily draws data from 3 different sources to calculate demand forecasts and subsequently optimize them: retailer data, external data, and Nextail-generated data.

Retailer data: Structured and unstructured

Firstly, Nextail “ingests”, or collects different types of internal data that a retailer has been consciously or unconsciously collecting, including data from ERPs and even random files on a retailer’s desktop.

When a retailer begins partnering with Nextail, a team of experts carries out a technical integration in order to review what retailer data is available, check its “integrity” (accuracy, completeness, reliability), standardize it, and make sure that this data can automatically and correctly be fed into Nextail for use.

From that point on, retailer data is used for:

  • Master data: This information will be used across Nextail to best understand a retailer’s logistics, including information on stores, products, warehouses, categories, and more.
  • Daily data: This more variable information informs Nextail of the daily state of the retailer’s stock, merchandising configurations, sales, shipments, etc.

This data is generally either “structured” and “unstructured”.

Structured retailer data

By “structured” data, we mean data that has already been collected, organized and stored in some way, like data coming from a company’s ERP. For example, this data includes sales and stock data at the most granular level (by SKU) on a daily basis.

Unstructured retailer data

Nextail also uses a retailer’s “unstructured” data, which is data that hasn’t yet been organized or is perhaps a mix of a variety of data types from across different systems of a retailer’s business. Data of this type includes things like uncategorized product images, visual merchandising requirements, store assortment plans or promotional calendars, among others.

Nextail-generated data

Nextail also manages data pertaining to each retailer, even if the retailer hasn’t been recording it. This data is automatically generated based on the previously collected structured and unstructured retailer data.

This data informs decision-making and helps retailers measure the potential impact of carrying out Nextail-delivered decisions, and includes data points such as:

  • Product attributes – When there is a lack of data for a product or when opening a new store, Nextail can identify comparable products being sold currently or in the past, through product master data or through visual recognition, a type of artificial intelligence, using product imagery.

Size curves and other store demographic data – Nextail can calculate demographic information per SKU and store such as size curves which are the weight each size of each product has in terms of sales in a given store.

  • Top products and stores – Nextail can inform retailers of their best performing products and stores across their networks. They can view the performance and availability of each product in each store, as well as other information such as how each product compares to another or when and where the sale of each took place.
  • Stockouts – The unavailability of stock across a retailer’s network helps retailers understand the impact of products that are missing but shouldn’t be and to ensure sufficient replenishments in the future. Nextail measures two types of stockouts.
  • Real – When a SKU isn’t available in store but IS available in the warehouse.
  • Absolute – When a SKU isn’t available in the store regardless of whether or not it is available in the warehouse.
  • Lost sales – Nextail calculates “lost sales” to measure the impact of stockouts on a retailer’s bottom line by combining the stockout metric with an internal forecast. It is able to pinpoint cases in which better assortment and distribution would be worth the investment of stock adjustments.
  • Zero sales – Nextail also lists stores in which best-selling products have had zero or minimal sales in the last seven days which can help clue retailers into possible causes such as display issues, missing stock, etc.
  • Ghost stock – When Nextail detects formerly strong-selling, in-stock items that have stopped selling over the last seven days, this is considered “ghost stock”, alerting retailers to possible tracking discrepancies, theft, or lost sales.

External data

Finally, the fourth type of data that Nextail demand algorithms consider is external data. This data does not come from a specific retailer itself, but is data that represents elements from outside of the organization or from the industry at large that might affect the elasticity of demand. These elements generally include, but are not limited to, popularly-accepted major sales events, major holidays, weather, social trends, and of late, even pandemics.

But one of the most important external data factors that Nextail considers is that of seasonality, or the points in the year with observable demand shifts that tend to be repeatable. For example, certain periods of the year may have periods of regularly increased demand (e.g. Black Friday, winter holiday season, back to school season) or decreased demand (e.g. the periods immediately preceding or following major sales events). Nextail calculates seasonality at both store and product-group level, meaning that different categories of products have different seasonalities.

Since seasonality directly affects demand forecasts, Nextail uses this information to inform a more accurate short-term forecast. In combination with retail and other data sources, Nextail is able to forecast future demand per SKU, store, and day.

Better data leads to better forecasts and immediate benefits

Nextail collects and processes all of the data above in order to generate the most robust, granular, and hyper-local demand forecasts possible. With them, retailers understand the likelihood of every single product selling at every single location over a set period of time.

Not only does this mean that they are more likely to sell 5-10% more items at full price throughout the season, they’re also less prone to stockouts and lost sales opportunities, all while reducing coverage. In the meantime, they can hold on to more warehouse stock for longer as it becomes necessary in different channels as time goes by.

Most importantly, Nextail demand forecasts are based on the freshest data available at all times, so retailers are always ready to adapt their strategies both during times of mass uncertainty and times of relatively standard operating conditions (if such a thing still exists!).

In the “Ask Nextail” FAQ series, we’ve compiled the top most frequently asked questions that we receive. Team members from various teams across Nextail help to answer these questions, bringing expertise from their work helping retailers understand, implement, and be successful managing the radical change that comes with digital transformation in retail.

If you’d like to learn more about Nextail AI and data-driven demand forecasting, contact us.

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