Contact us

Demand Sensing

Request Consultation Request Consultation

Raise your demand sensing accuracy based on internal and external data with advanced algorithms. Optimize the short-term forecast by leveraging fresh sales data and probabilistic forecasting techniques. Capture the full value of demand sensing by adjusting inventory targets in a complementary manner.

How C5i’s Demand Sensing Solutions Help

Drive last-mile adoption & business impact through ease of consumption and role-based insights

Data
Analytics
Insights
Actions

Data

  • Sell out
  • Business inputs
  • Market insights

Analytics

  • Baseline statistical forecasting at the customer level
  • Store clustering

Insights

  • Understand demand patterns
  • OoS signals
  • Trade Promotion/NPI Compliance

Actions

  • Field Sales Manager (FSM): Adjust store visit plan by priority
  • Key Account Manager (KAM): Adjust with the retailer’s next promotions implementation plan
  • Demand Planner: Adjust Forecasting & Replenishment plan

Data

  • External: Campaign intensity, Competitor positions, Regional strategy, Weighted distribution
  • Product: Target Customer, Seasonality, Brand, Product Category
  • Attributes: Product, Launch strategy

Analytics

  • Forecast by determining best-fit launch curve
  • Define model and estimate totals

Insights

  • Determine the initial launch pattern
  • Track deviation: Actual sales vs. forecast
  • Extrapolate adjustment from sample to total forecast
  • Estimate sales impact by attribute

Actions

  • Adjust forecasts
  • Adjust production & supply plan
  • Enrich NPI forecast
  • Apply launch patterns & time split and download the forecast to the SKU level

Data

  • Causal variables: Base price, promotional price, innovation
  • Economic trend
  • Consumer demand (POS)

Analytics

  • Econometric models: Time Series, Multivariate Linear Regression, ARIMA with external predictor variables (ARIMAX)
  • Determination of Best Fit forecasting method (with MAPE, MAD, RMSE)

Insights

  • Sales (PoS) & causal variable insights
  • Calculate the long-term forecast by target scenario

Actions

  • Budget decision-making based on proposed scenarios
  • Evaluation of production capacity planning for the next few years

Outcome
Drive 15%–25% improvement in forecast accuracy with
2%–10% improvement in service levels.

Connected Intelligence with AI-powered Augmented Analytics Platform
Discovery

Powered by Generative AI

Deliver relevant, actionable, and human-friendly insights across multiple consumption mediums and personas to create an insights-first culture that rewards data-driven decision-making

Automated insights generation from connected enterprise and external data

Descriptive, Diagnostic, Predictive, and Prescriptive Analytics driving actionable insights

Persona-based approach to provide contextual insights on near real-time basis

High adoption with curated natural language insights available on chat, voice, enterprise BI platforms, executive presentations, emails, Teams, Slack, etc.

Tracking of impact of decision-making on key performance indicators (KPIs)

Important Metrics

Speed-to-actionable insights reduced from days to seconds
45% increase in analytics adoption by use of generative and conversational AI
30% time savings with a single source of truth and Natural Language querying
~20% revenue impact with timely, data-driven decision-making

Recognition for Discovery

Gartner

Gartner Market Guide for Augmented Analytics, 2022

Gartner

Gartner Market Guide for Augmented Analytics Tools, 2021

Forrester

Forrester’s The People-Oriented Text Analytics Platforms Landscape, Q1 2022

Forrester

Forrester’s New Tech: AI-Enabled Consumer Intelligence Solutions, Q2 2020

Gartner

Gartner Hype Cycle for Customer Experience Analytics, 2022

C5i’s Approach to Demand Sensing [Patent filed]

A Method and System of Generating a Predictive Model for Predicting Consumer Purchase Behavior

The method comprises: generating, by a processor, online data associated with topic-related searches performed by online users; ingesting, by the processor, the online data with pre-stored research data, wherein the pre-stored research data indicates history data about the topic; processing, by the processor, the online data with the pre-stored research data to determine search patterns of online users and user-behavior information of online users; and generating, by the processor, the predictive model by analyzing the search pattern of online users and user-behavior information of online users.

Resourses

WHITEPAPER

Connected Intelligence – Supply Chain: A Force Multiplier

CASE STUDY

Improved Regional Warehouse Capacity, Service Levels and Inventory Fill Rate using Course5 Discovery

CASE STUDY

Finished Goods Waste Reduction using AI powered Insights

Industries

Solutions

Our IPs

About Us

Contact Us

Follow us

© 2024 C5i, All Rights Reserved.

Terms of Use  |  Privacy Notice  |  Do Not Sell My Information