predictive inventory case study

Using Machine Learning to Predict
Out of Stocks

How Seek’s predictive machine learning
capabilities recaptured $50M...

in annual ROI for a $10B+ CPG brand, proactively identifying & solving out of stocks with their largest US retail partners.

The Problem:

Out of stocks cost retailers billions of dollars per year in uncaptured sales opportunities resulting in the burden of lost sales, fines, and unsatisfied consumers for CPG brands. So, why aren’t they able to solve this problem effectively today?

There are [3] main challenges facing every CPG brand when it comes to solving out of stocks: too much data (at the store/item/day level), outdated technology with limited scale, and a lack of analytics & machine learning expertise in-house. For this $10B+ CPG brand, these [3] challenges resulted in millions of dollars in lost opportunity and 
fines per year.

The Solution:

Seek’s Insight Cloud platform is built specifically to solve these [3] challenges... and to do so for the business user. Seek’s Predictive Inventory Insight was able to identify and solve $50M+ worth of out of stocks annually by:

[1] Being Cloud-Native: built on a cloud-native infrastructure, Seek’s Insight Cloud has near-infinite scale – enabling customers to leverage billions of rows of data without worrying about performance.

[2] Leverage ML: leveraging advanced predictive and machine learning capabilities Seek’s Insight Cloud empowers customers with the most advanced forecasting models in the market...incorporating historical sales patterns, inventory trends, and even external features such as weather and truck traffic to generate store/item level forecasts with 
pinpoint accuracy.

[3] Being Prescriptive: Seek’s Insight Cloud platform doesn’t solely identify the problem – it prescribes the recommended action that your business teams can take – in this case recommending store/item level order quantities to be ordered by day for the next 14 days in order to solve upcoming out of stocks (and rolled up to distribution center, warehouse levels as needed).

Results:

With near-infinite scale, actionable POS data sets at the deepest granularity, and Seek’s proprietary predictive & ML solutions, we empowered this $10B+ CPG brand to save $50M+annually in out of stocks across multiple 
retail partners.

First, Seek Insight Cloud includes the capability to create enriched, location-based store profiles, pinpointing shopper personas, demographics, and other external factors that are helpful in identifying and targeting your core consumers.

For this brand, they were able to leverage these enriched store profiles to identify where they could go deeper with their core Hispanic consumer, while also expanding into new, reachable consumer segments such as ‘Gen Z Gamers’ (identified through leveraging Seek’s ML capabilities on top of social graph-networks)... Seek’s Expanded Distribution solution then enabled their team to proactively identify each location where these consumer profiles are concentrated and predict future sales rates for additional distribution in these locations. This created a clear, robust, and consumer-driven selling story built on top of multiple data sources and Seek ML capabilities, enabling their sales team to drive additional distribution across retailers.

More case studies:

Predictive Inventory Case Study

How Seek’s predictive machine learning capabilities recaptured $50M in annual ROI for a $10B+ CPG brand, proactively identifying & solving out of stocks with their largest US retail partners

Case Study: Distribution Expander

How Seek enabled a $500M+ emerging soft drink brand to tell a more compelling, data driven sales story and drive $10M+ in distribution opportunities in a matter of clicks by leveraging point-of-sale, consumer segmentation, and demographic data.