Strategic Optimization in FMCG with Terno AI

Prescriptive Analytics on Product Positioning & Sales

Introduction

The FMCG (Fast-Moving Consumer Goods) sector is one of the most competitive landscapes, where shelf placement, pricing, and promotional strategies directly impact consumer choices. In such a dynamic environment, companies must go beyond descriptive analytics (“what happened”) and predictive analytics (“what might happen”) to prescriptive analytics — “what should we do?”

This is where Terno AI proves invaluable. By interacting with a dataset conversationally, Terno AI not only identifies trends but also recommends optimization strategies. In this blog, I’ll demonstrate how I used Terno AI on the dataset Impact of Product Positioning on Sales to derive actionable strategies for pricing, positioning, promotions, and portfolio optimization.

Dataset & Prompts explored via Terno AI: Conversation Link

The Dataset: Impact of Product Positioning on Sales

Prompt: “Describe the dataset, listing all columns with definitions and data types. Identify the purpose of each column. Provide a short recap of the dataset structure and its most important columns for prediction and optimization.”

By narrowing focus to high-value columns (e.g., price, promotion, positioning score, demographics), this step ensures modeling efforts are aligned with real drivers of sales. In practice, it helps analysts avoid data noise and ensures businesses prioritize levers that genuinely move revenue. It bridges analytics with real-world decision-making efficiency.

Terno Insight:

The dataset includes:

• Product Category & Product ID – identifiers for product classification.
• Price & Competitor Price – pricing benchmarks.
• Sales Volume – key performance measures.
• Product Position – shelf placement.
• Promotion Flag – whether a product was promoted.
• Seasonality & Foot Traffic – contextual demand drivers.
• Demographics – target customer segments.

For predictive and optimization tasks, the most critical columns are Price, Competitor Price, Sales Volume, Positioning, Promotion, and Category.

Identifying Predictive Signals

Prompt: “Identify the columns most relevant for predictive modeling.”

This helps rank features by importance for forecasting sales or optimizing promotions. Businesses can invest in collecting better data for top features (e.g., more accurate competitor pricing data). In real terms, it ensures predictive models are not only statistically strong but also business-relevant—supporting reliable demand planning and campaign optimization.

Terno Insight:

Terno AI identified Price, Competitor Price, Product Positioning, Product Category, Foot Traffic, Promotion Status, Demographics and Seasonality as strong predictors of sales performance. These variables serve as levers for optimization strategies.

Detecting Underperformers

Prompt: “Detect categories with low price but low sales volume and suggest possible strategies to boost performance.”

This pinpoints products that should be selling more given their affordability but aren’t. It suggests issues like poor visibility, weak branding, or misaligned consumer fit. Businesses can then adjust marketing messaging, reposition the SKU, or explore alternative distribution channels. This prevents wasted shelf space and helps convert weak SKUs into profitable ones.

Terno Insight:

The Food category was identified as low-priced yet underperforming.

Suggested strategies:

• Promotion Bundles: Offer “mix‐and‐match” packs (e.g. snack combos) to raise average order value.
• Premium Placement: Move low-performers to end-caps or front-of-store “grab-and-go” sections to increase visibility.
• Cross-Merchandising: Display with complementary products (drinks, condiments) to spur impulse buys.

Premium Positioning Opportunities

Prompt: “Identify the top 2 categories that could be targeted for premium positioning based on high sales efficiency and high positioning scores”

Premium positioning can yield higher margins if chosen wisely. This prompt finds categories already showing efficiency and strong perception, making them candidates for premiumization. For decision-making, it supports price-tier expansion strategies and allows targeting consumers willing to trade up. It’s a roadmap for margin growth rather than chasing volume.

Terno Insight:

• Electronics and Clothing emerged as best candidates.
• Both categories combine strong positioning scores with above-average sales efficiency, making them suitable for premium shelf space and value-added promotions.

Price Sensitivity Simulation

Prompt: “Simulate the impact of a 10% price increase on sales efficiency for each category, assuming current relationships hold. Show the results in a table and visualization.”

This scenario analysis tests pricing elasticity without risking real revenue. Managers can understand which categories tolerate higher prices and which are highly price-sensitive. It enables evidence-based pricing adjustments, protecting margins while minimizing customer churn.

Terno Insight:

The simulation showed that a uniform 10 % price increase reduces average sales efficiency by about 9.1 % across all categories (Clothing, Electronics, and Food).

Sales Reallocation Strategy

Prompt: “List the percentage of uplift of sales volume and the product categories which contribute in the uplift of overall sales volume if we simulate the effect of reallocating 15% of sales efforts (budget, marketing, etc.) from the lowest-performing quartile of categories to the highest-performing quartile.”

Resource allocation is often uneven. This analysis quantifies the uplift if budgets or promotions shift from low-performing to high-performing categories. In the real market, it’s a direct guide for budget optimization—ensuring money flows where it generates the most growth. It helps marketing teams justify resource reallocation with hard numbers.

Terno Insight:

• Reallocation resulted in an estimated 3.97% uplift in total sales volume.
• Most of the uplift came from Electronics and Clothing, which benefited most from the shifted investment.

Market Position: Red vs Blue Ocean

Prompt: “Identify categories in competitive ‘red ocean’ zones (many similar products, high price competition) versus ‘blue ocean’ zones (few competitors, unique positioning). Provide strategic recommendations.”

Not all categories are equal—some are saturated with competition (“red ocean”), while others offer white space opportunities (“blue ocean”). This prompt helps businesses avoid margin-eroding price wars and instead invest in differentiation. In practice, companies decide whether to compete aggressively on price, innovate into new niches, or exit overly crowded categories.

Terno Insight:

• Red Ocean: Clothing items — highly competitive, price-sensitive.
• Transitional: Electronics and Food segments.

ROI Ranking

Prompt: “Rank product categories by potential return on investment (ROI) if additional resources were allocated, using current dataset metrics as input.”

Terno Insight:

Portfolio Optimization

Prompt: “Determine the minimum product portfolio that would retain at least 80% of total sales volume. List top 10 products.”

Often, 20% of SKUs deliver 80% of revenue. This prompt identifies the “vital few” so businesses can simplify portfolios without losing scale. For supply chain and retail ops, it means reduced complexity, better inventory turns, and easier shelf management. Strategically, it sharpens focus on what really matters.

Terno Insight:

Terno AI identified a core portfolio of 10 products (largely Electronics and Clothing items) that together contributed over 80% of sales volume.

Actionable Strategies

Prompt: “Summarise actionable strategies for improving market position based on the above analyses.”

This prompt distills all findings into clear, prioritized business actions. Whether it’s shifting promotions, repricing SKUs, reallocating budgets, or rationalizing portfolios, this ensures insights are not left theoretical. For decision-makers, it serves as a roadmap—tying analytics directly to execution, competitive strategy, and measurable ROI.

Terno Insight:

Prescriptive Recommendations:

• Pricing: Pilot 5% increases in Elastic categories (Electronics).
• Placement: Prioritize end-cap and front-of-store for Electronics and Clothing.
• Promotions: Focus promotions on Families and College Students, the most responsive demographics.
• Portfolio: Concentrate resources on the top 10 products that drive 80% of sales.
• Reallocation: Shift budgets from underperforming Food for maximum lift.

Conclusion

Through prescriptive analysis on the Impact of Product Positioning on Sales dataset, Terno AI demonstrated its ability to move beyond traditional analytics into decision support and strategic optimization.

By simulating pricing scenarios, reallocating resources, and highlighting blue-ocean opportunities, Terno AI helped design a roadmap for FMCG growth that is data-driven, adaptive, and actionable.

 Explore the interactive session here: Terno AI Link