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What Is Product Performance Forecasting?

Sales forecasting is the system by which future sales volumes are estimated—at what price, during which time, and in which market. Product performance forecasting enables businesses to make informed decisions in both the short term and the long term. Machine learning software is continually improved, providing businesses with better analysis all the time—faster and more reliably than manual methods.


Why Is It Important to Have a Good Product Performance Forecasting Model?

A good machine learning (ML) model provides a more accurate forecast, accelerates data processing speed, automates forecast updates based on the recent data, analyzes more data, identifies hidden patterns in data, creates a robust system, and increases adaptability to changes.

Machine learning is geared to improve the following forecasting methods:

  1. Supplier Relationship Management—supplier purchases and orders projected into the future
  2. Customer Relationship Management—indicates which product categories should be purchased in the next period from a specific store
  3. Order Fulfillment and Logistics—supply chain optimization
  4. Marketing Campaigns—ads and marketing campaigns adjusted to better reach customers
  5. Manufacturing Flow Management—supervision of the whole manufacturing process

What Internal Data Should I Have for a Good Product Performance Forecasting Model?

Product performance forecasting relies on good quality data, so a wide range of dependable data is curcial. E-commerce sales data, sales transactions, purchase orders, inventory, your own POS information, loyalty cards, customer service, websites, reviews, marketing campaigns, apps, in-store devices, texts, and some CRM data should be used to build a good prediction model.

What External Data is Essential for a Good Product Performance Forecasting Model?

External data that is essential for a good model includes: weather, customer shipping receipts, macroeconomic indicators, government census, and click streams.

What External Data May Prove Useful for a Good Product Performance Forecasting Model?

The following external data may prove useful: third party syndicated data, customer POS information, household panel data, geolocation devices, and social media.

What Are the Main Challenges of the Product Performance Forecasting Use Case?

Almost all forecast tools rely on historical data to predict future outcomes, so estimating forecasts for new products can be challenging. This problem is particularly evident in industries like consumer electronics, fashion, and books, where new product introductions account for the bulk of sales. To tackle such problems, companies usually resort to historical data of similar products to build a forecast.

Tackling variance in sales volume reveals another challenge: sales revenue is dynamic, influenced by economic, cultural, and legal factors. It is very difficult to determine the exact influence these labile outside influences have on historical sales patterns, and whether they will continue to influence trends.

Interesting Case Studies and Blogs to Look Into

Just Analytics: Sales Forecasting in the Age of Machine Learning
Optymyze: 7 Ways Machine Learning Boosts Sales Performance and Drives Revenue Growth

Tangible Examples of Impact

[Additionally,] at a multinational food company, more than 30% of items are sold on promotion—accounting for nearly 70% of forecast error. The global foods company wanted to predict promotional lift to baseline demand to get timely production and balanced inventory deployment for channel and store supplies. Using machine learning, the company lowered forecast error 20% and lost sales by 30%. It increased service level to 98.6%, and realized a 30% reduction in product obsolescence. It also cut demand planner workload in half, allowing planners to focus on more value-added activities.

Five Ways Machine Learning Can Improve Demand Forecasting

Relevant datasets

Quadrant – Point of Interest (POI) Data

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Quadrant – Point of Interest Data measures physical store and website visits so companies can evaluate their performance. Quadrant provides nineteen different PoI categories; they also update their data regularly to provide the most efficient analysis.

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Edge by Ascential – Retail Insight

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Included as part of a software service, Edge by Ascential’s Retail Insight tracks retail data on thousands of retailers in nearly 200 markets to help you analyze your competition, measure your customers’ engagement, track market trends, and predict product performance. 

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Fiind Account-Based Marketing


Account-Based Marketing by Fiind provide businesses with AI powered target accounts based on their fit and buying intent along with services for measuring and optimizing targeted campaigns.

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Fiind Data Stewardship


Fiind’s Data Stewardship services provide data management for businesses to enhance the success of their marketing and sales results.

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Fluent Mobile User Acquisition

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Mobile User Acquisition by Fluent allows businesses to have more successful campaigns and more engagement through mobile app users.

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