Meet Nanit
Nanit is a leader in smart baby-monitoring technology, using real, personalized data to help families better understand their child’s sleep, growth, and development. Their system combines innovative hardware, computer vision, and data-backed insights to support parents with a holistic view of family wellness.
Inside the company, Nanit’s data and analytics team supports decision-making across sales, marketing, product, and more. As they looked to improve the accuracy of their sales forecasting, the team sought a predictive analytics platform that could deliver deeper visibility into demand patterns, speed up model development, and strengthen confidence in their forecasts.
The Challenge
Before adopting Pecan AI, Nanit relied on internal, homegrown models built in Python or Excel. These approaches provided limited predictive power, making it difficult to accurately forecast sales or clearly understand the factors influencing demand.
The analytics team wanted to:
- Improve sales forecasting accuracy
- Identify the variables most responsible for driving sales
- Gain clearer visibility into demand patterns
- Build forecasts leadership could trust
But building sophisticated predictive models required more time, technical expertise, and data-science resources than the team had available.
“With Pecan AI, we developed accurate, transparent sales forecasts in just three weeks – work that would have taken more than double the time without the tool. The accuracy, visibility, and partnership we received made all the difference.”
Amir Baruch, Director of Data Analytics at Nanit
The Solution
Why Pecan?
Nanit turned to Pecan AI as a cutting-edge tool that could modernize its sales forecasting. Pecan offered direct warehouse connections or manual uploads, enabling the team to start modeling immediately.
“We wanted a cutting-edge tool, came across Pecan AI, and are very happy we did because it’s been extremely useful,” says Amir Baruch.
Throughout onboarding, Pecan’s team provided hands-on guidance via Slack, email, and video sessions, ensuring momentum from day one and helping Nanit get the most out of the platform quickly.
Fast, guided onboarding
Within three days, Nanit generated its first predictive model. Weekly sessions with Pecan’s customer success, product, and technical experts helped refine data structures, annotate fields correctly, and resolve blockers quickly.
“While you can technically use the model without support, the level of guidance they provide makes the initial learning curve much smoother,” notes Amir.
Deeper visibility and trust in predictions
Pecan provided full transparency into how predictions were made. By exploring decision trees, thresholds, and drivers, the analytics team gained clarity on the variables influencing sales performance.
This is where one of the most meaningful insights emerged:
from over 20 internal assumptions to just four true sales drivers.
Pecan’s explainability features helped validate Nanit’s hypotheses and increased leadership’s trust in the forecasts.
Rapid iteration and continuous improvement
Pecan enabled Nanit’s team to explore different forecasting approaches, testing data inputs, refining models, and validating results without limitations, based on the questions they wanted to answer.
Instead of being constrained to a single model, the team could experiment freely and improve their forecasts, ultimately leading to more accurate and reliable results.
The Impact
Faster, more accurate sales forecasting
Using Pecan’s predictive models, Nanit was able to generate reliable sales forecasts across SKUs and distribution channels, reaching up to 80% accuracy. What was previously a manual, time-intensive process became faster, more consistent, and data-driven.
Focus on what actually drives demand
Pecan’s model narrowed 20+ possible sales drivers down to the four most impactful variables, sharpening focus and eliminating noise from the company’s analysis.
Greater confidence in forecasts
By leveraging an external AI tool, Nanit was able to validate its internal analytics with an objective, outside-in perspective. The results gave leadership greater confidence in the forecasts and the decisions based on them.
Partnership and ease of use
“Having worked in data analytics for ten years, I can say Pecan is far better than any other tool I’ve used – both in ease of use and transparency into how the model works,” says Amir.
“They truly act as partners. They spend time with you, work alongside you, and genuinely treat your success as their success.”
Key Takeaway
With Pecan AI, Nanit transformed sales forecasting from a manual, assumption-driven process into an automated, highly transparent capability, enabling more accurate forecasts, clearer visibility into demand, and more confident decision-making across the business.
