Unlocking Market Insights in FMCG Using Terno AI
Introduction
Fast-Moving Consumer Goods (FMCG) is one of the most dynamic industries, driven by constantly evolving consumer behavior, pricing strategies, and product positioning. For FMCG brands, understanding where and how a product is positioned in the market is often the difference between becoming a bestseller or fading out in crowded shelves.
Traditionally, market research for FMCG has required manual data analysis, surveys, and statistical modeling. But with the rise of AI-driven tools like Terno AI, businesses can now uncover insights much faster by simply asking questions in natural language.
In this blog, I’ll demonstrate how I used Terno AI on a dataset titled “Impact of Product Positioning on Sales” to extract valuable insights about the FMCG landscape. The analysis highlights how product placement, promotions, and branding strategies influence sales performance — all with the help of conversational AI.
👉 Dataset & Prompts explored via Terno AI: Conversation Link
The Dataset: Impact of Product Positioning on Sales
The dataset I used provides structured information on FMCG products, including:
- Product Category (e.g., Beverages, Snacks, Household items)
- Price Range (Low, Medium, Premium)
- Placement Strategy (Eye-level shelf, Bottom shelf, End cap, etc.)
- Promotion Strategy (Discounts, Bundling, Advertising)
- Sales Volume & Revenue Impact
This dataset serves as an excellent base to test how AI can:
- Identify trends in FMCG product positioning.
- Understand which strategies correlate with higher sales.
- Provide recommendations for brands to maximize impact.
Methodology: Using Terno AI for Market Insights
Unlike traditional data analysis where you write SQL queries or Python scripts, Terno AI allows you to interact with the dataset conversationally. By typing prompts, I was able to ask targeted business questions and immediately get insights with charts, tables, and summaries.
Here’s how I approached it:
- Upload the dataset into Terno AI.
- Frame prompts as business questions (e.g., “Which product category performs best at eye-level shelves?”).
- Refine prompts iteratively to drill deeper into trends.
- Save insights & visualizations for reporting.
Below, I’ll share a few key prompts I used and the insights Terno AI generated.
Insights from Terno AI
After uploading the dataset into Terno AI, I interacted with it using a series of prompts to explore patterns, test hypotheses, and visualize trends. Below are a few key prompts and the insights generated.
1. Dataset Structure and Purpose
Prompt:
“Describe the attached dataset, listing all columns with definitions and data types. Identify the purpose of each column.”
This prompt establishes a shared understanding of the dataset. By clearly defining each column (e.g., sales volume, product category, price, promotion flag, demographics), decision-makers avoid misinterpretations and analysts know which fields are numerical vs categorical. In real market decisions, this ensures that the business uses accurate variables for pricing strategies, promotion effectiveness, and consumer targeting. It’s the foundation for building trust in the data before deeper analysis.
Insight:
2. Unique Value Exploration
Prompt:
“List unique values for each categorical column in the dataset.”
Understanding the unique categories for product type, season, demographic segments, and promotion flags helps map the market landscape. It reveals the variety of product positioning options, consumer groups, and promotional methods. For businesses, this step is vital to check data completeness (no missing or inconsistent categories) and identify underrepresented market segments. In practice, it helps managers decide whether to expand offerings, refine customer segmentation, or consolidate redundant categories.
Insight:
The dataset covers categories like Food, Clothing, Electronics, demographic groups such as Families, College Students, Seniors, Young Adults, and product positions like Aisle, End-cap, Front-of-store. This confirmed the diversity of strategies available for positioning and targeting.
3. Product Position Distribution
Prompt:
“Show the distribution of product positions — Plot the distribution of product positions as a bar chart, showing counts for each position type.”
Placement—front-of-store, aisle, end-cap—can significantly impact sales. Visualizing this distribution uncovers how many products benefit from premium shelf space versus those relegated to less visible spots. In real market terms, retailers and FMCG companies use this insight to renegotiate placement with store partners, rebalance exposure for underperforming SKUs, or justify trade spend for premium displays. It provides tangible leverage in retailer negotiations.
Insight:
The chart revealed End-cap and Aisle placements as the most common strategies, with fewer items placed at Front-of-store. This hinted at potential competitive advantage for brands able to secure premium front-facing spots.
4. Seasonality Patterns
Prompt:
“Show sales volume by season — Plot sales volume by season over time as a seasonal line chart.”
Seasonality affects demand for FMCG categories (e.g., beverages in summer, chocolates in winter holidays). This chart highlights sales spikes and troughs across the year. Real-world applications include aligning production schedules, inventory stocking, and promotion timing with peak seasons. Marketers can plan targeted seasonal campaigns, while supply chain teams can avoid stockouts or overproduction.
Insight:
Terno AI showed a clear spike during seasonal peaks, where seasonal products achieved more sales volume compared to non-seasonal products.
5. Top-Selling Products
Prompt:
“Show the top 10 products with the highest total sales volume as a horizontal bar chart.”
Identifying the “hero products” is crucial—these often drive disproportionate revenue and visibility. By ranking them, companies can focus on ensuring their consistent availability, premium positioning, and promotional support. In practice, retailers may allocate extra shelf space or highlight these products in flyers. Manufacturers can also prioritize innovation around adjacent SKUs to ride the success of star performers.
Insight:
A small set of Clothing and Electronics products dominated the leaderboard..
6. Category Comparisons
Prompt:
“Compare total sales volume by product category using a grouped bar chart.”
This prompt uncovers category-level strengths and weaknesses. Seeing which categories dominate vs. lag provides input for resource allocation decisions (budget, marketing, R&D). Retailers can optimize assortment by trimming weak categories, while FMCG companies may identify opportunities for market entry or expansion. It supports strategic decisions around category management and promotional depth.
Insight:
Clothing consistently led in sales volume, followed by Food and Electronics.
7. Promotion Effectiveness
Prompt:
“Compare average sales volume for products on promotion vs. not on promotion using a side-by-side bar chart.”
Promotions can drive sales, but sometimes they only shift purchase timing without growing the category. By comparing averages, businesses learn whether promotions truly lift incremental sales. For market decisions, this informs pricing strategy, avoids overuse of discounts, and highlights which products benefit most from promotional pushes. It ensures marketing spend translates into revenue rather than margin erosion.
Insight:
Products on promotion achieved significantly lifted sales.
8. Foot Traffic Dynamics
Prompt:
“Compare average foot traffic for promotional vs. non-promotional products using a bar chart.”
Beyond sales, promotions often aim to attract store visits or online traffic. This prompt links promotional activity to consumer footfall. For decision-making, it shows whether promotions serve as effective customer acquisition tools. Retailers may prioritize traffic-driving SKUs for front displays, while manufacturers may co-invest in promotions with proven traffic benefits.
Insight:
Products on promotion achieved higher foot-traffic scores (~2.06 vs. 1.98), highlighting promotions as a driver of in-store engagement, not just sales volume.
9. Promotions by Positioning
Prompt:
“Show which promotions work best in which positions using a heatmap.”
Promotions and product placement interact—an end-cap display with a discount may outperform the same discount in an aisle. The heatmap reveals these synergies. Retailers and manufacturers use this insight to optimize planograms and promotional strategies together. It reduces wasted marketing spend by tailoring promotion mechanics (e.g., coupons, bundles) to the right shelf positions.
Insight:
Promotions were most effective when combined with end-cap and aisle placements, delivering outsized sales lift compared to front store placements.
10. Demographic Influence
Prompt:
“Compare sales volume across consumer demographics using a stacked bar chart.”
This prompt highlights which demographic groups—age, income, family size—drive sales for each product type. Businesses can align marketing campaigns with the most lucrative segments or identify underserved groups for expansion. For example, if younger consumers dominate snack purchases, brands may invest in digital campaigns; if families dominate, multipacks may be prioritized.
Insight:
Prompt:
“Show which demographic segments respond best to promotions using a grouped bar chart.”
Promotional sensitivity differs—some groups wait for discounts, while others buy regardless. This prompt surfaces those patterns. Real-world use: avoid overspending promotions on inelastic groups and instead target responsive demographics. This ensures higher ROI and builds precision marketing strategies (e.g., student discounts, senior loyalty programs).
Insight:
- College Students and Young Adults drove the highest overall sales.
- Families, however, showed the greatest promotional lift. This suggests tailoring campaigns to family shoppers could unlock large gains.
11. Headline Market Insights
Prompt:
“Summarize key market insights and competitive advantages found in the dataset.”
This is the synthesis step where raw numbers become actionable business insights. The summary connects data trends to competitive positioning: which products outperform peers, which demographics are loyal, which promotions deliver ROI. For executives, this delivers a story of where the company wins today and where competitive opportunities exist tomorrow.
Insight:
Why Terno AI Makes a Difference in FMCG Analysis
Here’s what stood out while using Terno AI for this dataset:
- No-code exploration → Business teams can directly query datasets without technical help.
- Iterative insights → Each prompt builds on previous outputs, just like a conversation.
- Visualization-ready outputs → AI generates charts and tables instantly, saving time in reporting.
- Actionable recommendations → Beyond just describing patterns, the tool highlights what actions can be taken.
For FMCG brands, this means faster go-to-market strategies, sharper category management, and better ROI on promotions.
Conclusion
The FMCG industry thrives on speed, precision, and consumer understanding. With tools like Terno AI, companies can quickly test hypotheses, analyze positioning strategies, and uncover revenue opportunities — without needing a team of analysts or complex software.
The Impact of Product Positioning on Sales dataset showed us how placement and promotion strategies can significantly shape consumer buying decisions. By leveraging AI, brands can continuously refine their market strategy, stay ahead of competitors, and deliver value where it matters most.
👉 Curious to explore the interactive prompts and visualizations I used?
Check them out here: Terno AI Session Link.
