Introduction: Understanding the Digital Shopper

Every e-commerce business thrives on one core journey: users view a product, add it to their cart, and—ideally—complete the purchase. But somewhere along that path, a significant portion of shoppers fall off.

My goal was simple:
Identify where shoppers drop off and why.

Instead of writing SQL or stitching together dashboards, I used TernoAI, a no-code analytical assistant that understands natural-language commands. Throughout this analysis, I asked TernoAI a series of prompts, and it returned complete, accurate insights within seconds.

Step 1: Reconstructing the Funnel

To begin, I asked:

Prompt:

“Find the number of unique users who viewed products, added to cart, and completed purchases in marketing_ecom.”

TernoAI scanned the datasource and returned the key funnel stages:

Immediately, two insights became clear:

1. Interest is strong.

Every user who viewed a product also added it to their cart—an extremely positive sign for product discovery and catalog quality.

2. Checkout is the problem.

Despite perfect engagement up to the cart, only one-third completed their purchase.

This is the main break point in the funnel.

Step 2: Measuring Conversion at Each Stage

To quantify exactly how effectively users move from one stage to another, I prompted:

Prompt:

“Calculate the conversion rate for (1) view–add to cart, and (2) add to cart–purchase.”

The results were:

This clearly shows that while product pages are performing exceptionally well, the checkout process is losing 66% of users.

From a business perspective, this is where optimization efforts will have the highest revenue impact.

Step 3: Which Age Groups Convert the Best?

To understand user behavior more deeply, I asked:

Prompt:

“Which age groups have the lowest and highest purchase conversions?”

TernoAI segmented users by age and analyzed their purchase behavior:

While the difference may appear small, it reflects meaningful behavioral patterns:

• Younger shoppers (18–24) explore more but hesitate during checkout—typically due to price sensitivity or lower trust.

• Older shoppers (45–54) convert more consistently—suggesting clearer intent and stronger buying confidence.
  This insight guides more personalized marketing strategies.

Step 4: Category-Level Performance — Where Do Users Drop Off Most?

Next, I wanted to identify whether certain product categories drive more drop-off than others.

Prompt:

“Analyze funnel conversion for each product category and show which categories have the highest abandonment.”

Since add-to-cart events didn’t include product IDs, TernoAI analyzed view-to-purchase conversions instead.

Categories with the Highest Abandonment (~76%)

• Blazers & Jackets

• Clothing Sets
  These categories tend to involve sizing uncertainty, higher pricing, or style risk, all common drivers of hesitation.

Categories with the Lowest Abandonment (~72%)

• Underwear

• Suits & Sport Coats
  These items generally offer more predictable sizing, making users more confident in purchasing.

This insight is crucial for category managers, UX teams, and merchandising strategy.

Step 5: Visualizing the Funnel

To visualize the user flow, I requested:

Prompt:

“Create a funnel chart showing number of users who viewed, added to cart, and purchased.”

TernoAI generated a clean funnel chart that made the sharp drop at the purchase stage visually unmistakable—perfect for presentations or stakeholder reports.

Step 6: Turning Insights Into Action

With the funnel mapped and drop-off points identified, I asked TernoAI:

Prompt:

“Give marketing recommendations to reduce funnel drop-off and improve conversions.”

Here are the key strategies supported by the data:

1. Optimize Checkout (Top Priority)

This is the largest source of revenue loss.

• Enable guest checkout

• Offer fast payment methods (UPI, Paytm, Apple/Google Pay)

• Display shipping fees earlier

• Add trust badges (“Secure Checkout”, “Free Returns”)

• Reduce checkout steps

• Send abandoned-cart reminders
  Even incremental improvements here can lift conversions meaningfully.

2. Strengthen Confidence in High-Abandonment Categories

Especially Blazers, Jackets, and Clothing Sets.

• Enhanced size and fit guidance

• More photos of models with height/fit details

• Real customer reviews with images

• Clear, flexible return policies

• Fit-prediction tools
  Reducing uncertainty leads to higher conversions.

3. Tailor Messaging by Age Segment

For 18–24 (low conversion):

• Student discounts

• Buy Now Pay Later (BNPL)

• Influencer-led content

• Prominent return guarantees
  For 45–54 (high-intent):

• Premium bundles

• Loyalty program rewards

• Personalized recommendations
  Segmentation ensures marketing feels relevant rather than generic.

4. A/B Test Key Touchpoints

A/B testing helps identify which changes truly improve conversions.

Test:

• Different checkout button text (e.g., “Complete Order” vs. “Secure Checkout”)
• Placement of trust badges, return policies, or shipping info
• One-page vs. multi-step checkout layouts
• Visibility of discounts, promo codes, or free-shipping banners
• Tone and timing of abandoned-cart reminders

Even small winning variations can create measurable lifts in conversion rates.

Conclusion: The Funnel Is a Conversation, Not Just a Metric

This analysis demonstrated how much you can uncover with the right questions.

Using TernoAI, I was able to:

• Map the customer journey end-to-end

• Identify the exact point of drop-off.

• Compare behavior across age groups.

• Understand category-level friction

• Generate actionable, revenue-focused strategies.
  All without writing a single line of code.

Beneath every number is a shopper making a decision.

And when we understand those decisions

We can build better, more confident buying experiences.

https://nishtha.app.terno.ai/chat/9273249c-e3b2-4032-a75e-d4fd91869177