How Transformer Table turned intent into revenue

Transformer Table used Consio across calls and messages to turn high-intent shoppers into revenue.

Helping shoppers make a high-consideration purchase

Transformer Table designs expandable, space-saving furniture built to adapt to different rooms, occasions, and household needs.

Products such as expandable dining tables and modular furniture represent a meaningful purchase for customers. Before ordering, shoppers may want reassurance around dimensions, finishes, configurations, delivery, product fit, or which setup works best for their space.

Those questions become even more important when the average attributed order is worth $3,000.

From November 1, 2025 through August 20, 2026, Transformer Table used Consio to engage customers across phone calls and messages, generating $2,947,878.16 in attributed revenue.

Across the period, 32,856 unique customers were contacted, 8,703 were reached, and 1,000 attributed sales were generated.

The challenge

Furniture is rarely an impulse purchase, especially at a higher price point.

Customers can spend significant time comparing products, evaluating their space, considering different configurations, and deciding whether a purchase is right for their home.

That creates valuable intent throughout the buying journey:

• A shopper creating an account but not yet purchasing

• A customer adding furniture to cart and leaving

• A shopper reaching checkout without completing the order

• A customer replying to a message with a question

• A buyer calling directly for help before making a decision

For Transformer Table, the opportunity was to turn those signals into timely conversations at scale, while understanding which interactions ultimately resulted in revenue.

The strategy

Transformer Table built its Consio operation around three high-intent outbound campaigns, inbound conversations, and large-scale messaging.

Recovering abandoned checkouts

The strongest outbound campaign focused on shoppers who had reached checkout within the previous 15 days but had not completed their purchase.

Across the period, the Abandoned Checkouts – Last 15 Days campaign completed 19,679 calls.

It generated:

• $1,193,913.06 in attributed revenue

• 10.5% connection rate

• 13.45% conversion rate on connected calls

This single campaign generated more than $1.19M, showing the value of reaching shoppers at one of the highest-intent moments in the customer journey.

Rather than allowing a high-value checkout to disappear, Transformer Table could create a direct conversation around whatever was preventing the customer from completing the purchase.

Re-engaging abandoned carts

Transformer Table also contacted customers who added products to their cart but did not proceed through checkout.

The Abandoned Carts – Last 15 Days campaign completed 17,218 calls and generated $240,520.37 in attributed revenue.

Connected calls converted at 3.39%, creating another revenue stream from shoppers who had demonstrated product interest but were earlier in the purchasing journey.

Following up with new signups

A third campaign targeted new signups within seven days.

These customers had already established a relationship with Transformer Table but had not necessarily completed an order.

Across 18,773 completed calls, the campaign generated $147,692.04 in attributed revenue.

Together, the three campaigns allowed Transformer Table to prioritize outreach according to the strength and recency of each customer's buying intent.

Connecting calls and messaging

Phone calls were only one part of the operation.

During the period, Transformer Table exchanged 54,562 SMS messages, including 41,148 sent messages and 13,394 customer replies.

Messages accounted for $1,226,000.05 in attributed revenue, making messaging the largest individual attribution channel during the period.

Consio allowed Transformer Table to combine calls and SMS rather than treating them as disconnected customer touchpoints.

A conversation could continue across channels while remaining tied to the same customer journey and resulting purchase.

Supporting inbound demand

Transformer Table also handled incoming customer conversations through Consio.

Across the period, the team received 1,809 inbound calls, answering 1,196 of them.

Inbound conversations generated $456,860.22 in attributed revenue.

With an average inbound call lasting 4 minutes and 36 seconds, these conversations gave customers the opportunity to discuss more detailed questions before purchasing.

Extending coverage with AI

Transformer Table also deployed John – AI inbound fallback to help handle incoming demand when a human agent was not available.

John handled 435 inbound calls, resulting in 36 attributed sales and $109,501.27 in attributed revenue.

The AI agent achieved an 8.43% conversion rate, extending coverage while continuing to contribute directly to revenue.

The results

From November 1, 2025 through August 20, 2026, Transformer Table generated:

• $2,947,878.16 in total attributed revenue

• 1,000 attributed sales

• 56,933 completed calls

• 54,562 SMS exchanged

• 32,856 unique customers contacted

The abandoned-checkout campaign alone generated $1,193,913.06 in attributed revenue, with 13.45% of connected calls converting into purchases.

At the same time, messaging generated another $1.23M, showing that the opportunity extended beyond any single channel.

Turning high-value intent into conversations

Transformer Table shows what phone and messaging can look like when they become part of the ecommerce revenue engine.

A shopper abandoning a checkout, someone creating an account, a customer replying by SMS, and a buyer calling with a question all represent different signals of intent.

Consio gave Transformer Table a way to act on those signals through outbound calls, inbound conversations, SMS, voicemail, AI coverage, and revenue attribution.

Across ten months, those interactions generated almost $3M in attributed revenue, turning conversations into a measurable part of Transformer Table's high-consideration buying journey.