The interactive quote is the new quote: what behavioural research says about simulators, co-creation and price transparency

A quote used to be the document a salesperson sent after the meeting, and in an AI-native company it is becoming the screen the buyer manipulates during the meeting, or alone, at eleven at night, with the price recomputing under each adjustment. The generation step is now cheap enough to run on every click, which is a technical fact, but the decisive questions it raises are behavioural: how a person filters and compares options, what building the configuration does to the value they attach to it, what they conclude from a price that appears instantly, and where the cursor should start. Twenty years of research, much of it from Harvard Business School, answers those questions with measurements, and this article puts the measurements side by side, corrections included.

• Interactive decision aids, tested in a controlled experiment in 2000, cut the number of alternatives a shopper seriously considered from 3.77 to 2.19 while raising the quality of what remained.
• Assembling an object yourself raised what people would pay for it by about 63% in the original IKEA-effect study, on one strict condition: the assembly has to finish.
• A cost breakdown shown next to the price raised purchase probability by 21.1% in a preregistered field experiment, through trust rather than through any change in the price itself.
• Starting the configurator fully loaded produces a higher total than starting it bare, and the asymmetry behind that result has a measured coefficient, about 2.25, that a professional buyer can also recognise as pressure.

1. Why the quote stops being a document in an AI-native company

The commercial tooling shipped in mid-2026 moved quote generation from a scarce, human step to a continuous one, which relocates the quote’s value from its production to the moment the buyer decides in front of it.

• Salesforce’s Agentforce Commerce release of 24 June 2026 made a business-to-business Buyer Agent generally available, with “Round-Trip Quoting” described as “seamless cart-to-quote-to-cart negotiation” for enterprises with several purchases in flight at once [1].
• OneBill’s CPQ360.ai, announced on 6 August 2026, “governs every quote, rep-built or AI agent-built, under the same pricing rules, margin guardrails, and approval logic”, which states the new division of labour plainly: the rules stay with the seller, the drafting can come from an agent [2].
• Once drafting is free, the differentiating part of a quote is what the buyer sees, adjusts, understands and accepts, and that part was studied by behavioural researchers for two decades before the tooling caught up.
• The quote therefore joins the list of work interfaces that a chat window cannot replace: a conversation can request a quote, but a decision over volumes, options and periods needs a surface that shows all of them at once.

This article opens a series on the AI-native company of 2027, one commercial or organisational artefact at a time, and starts with the quote because it is the place where the seller’s economic model and the buyer’s cognitive model meet on the same screen.

2. A buyer decides in two stages, and a simulator serves each one differently

Gerald Häubl and Valerie Trifts modelled the online purchase as a screening stage followed by an in-depth comparison, then measured, in a controlled experiment, what two tools did to each stage: a recommendation agent that ranks alternatives from the shopper’s stated preferences, and a comparison matrix that lays selected alternatives side by side [3].

• With the recommendation agent, the share of subjects who bought a non-dominated alternative, meaning one that no other option beat on every attribute, rose from about 65% to about 93%, and the share who switched to another product when offered the chance afterwards fell from about 60% to slightly above 20%.
• With the comparison matrix, the average number of alternatives seriously considered for purchase fell from 3.77 to 2.19, while the share of non-dominated options in that smaller set rose from about 0.57 to about 0.68, and post-purchase switching fell from 44% to 38%.
• Confidence in the choice rose with the recommendation agent (6.71 against 6.41 on a nine-point scale) and did not move with the matrix, so the two tools act on different stages and are not interchangeable.
• The authors’ summary is the design brief for any quote simulator: the aids allowed consumers “to make much better decisions while expending substantially less effort”.

The reason those tools work is the cost of the effort they remove, and the literature names that cost precisely enough to guide the design of a configurator.

• Herbert Simon introduced bounded rationality in 1955: a decision-maker with limited computation stops at a satisfactory option rather than the optimal one [4].
• John Payne, James Bettman and Eric Johnson showed in 1993 that people trade accuracy for effort adaptively, choosing simpler strategies as the task grows [5], and Steven Shugan had already priced that effort in 1980 as “the cost of thinking”, rising with the number of attributes to compare [6].
• Debora Thompson, Rebecca Hamilton and Roland Rust documented “feature fatigue” in 2005: extra capabilities attract before purchase and burden after it, so a quote that lists every option at once sells the configuration it then makes hard to live with [7].
• A 2025 study in a simulated online contracting scenario (206 participants) found that a computer-based decision aid improved decision quality mainly for participants with a low need for cognition, those least inclined to process information systematically [8]; it is a single recent study, cited as such, and it points at the buyer who benefits most from a guided quote.
• The practical structure follows: a first step that qualifies the use case and filters, then a live matrix in which the buyer adjusts parameters and sees the cross-effects of volume tiers, discounts and incompatibility rules without computing them mentally. Revealing variables progressively as choices narrow is a widespread interface practice with no peer-reviewed measurement tied to quote completion, so this article recommends it as a design convention and not as an established finding.

3. A buyer who builds the quote values it more, if the build completes

Michael Norton, Daniel Mochon and Dan Ariely named the IKEA effect in 2012 after a series of experiments in which participants assembled IKEA storage boxes, folded origami and built Lego sets, and the first of those experiments gives the size of the effect that a configurator can hope to trigger [9].

• Builders bid an average of $0.78 for the box they had assembled, against $0.48 for non-builders offered an identical pre-assembled box, an increase of about 63% for labour that added nothing to the object.
• The effect appeared for utilitarian and hedonic products alike, and for participants who professed no interest in do-it-yourself projects, so it does not depend on a hobbyist profile.
• Jon Pierce, Tatiana Kostova and Kurt Dirks had already described the underlying state, psychological ownership, and its three routes: control over the object, intimate knowledge of it, and investment of the self in it [10] [11]; a configurator opens all three for a buyer who sets the parameters, reads their consequences and spends effort on the arrangement.
• In mass customisation, Nikolaus Franke, Martin Schreier and Ulrike Kaiser measured an “I designed it myself” effect: self-designed products earned a higher willingness to pay beyond what preference fit and design effort explained, mediated by feelings of accomplishment [12]. A companion study by Franke and Schreier the same year located a further part of the premium in the effort and enjoyment of the design process itself [13].

The boundary condition matters more than the headline figure, because it is the one a badly built configurator violates first.

• Norton and his co-authors found that the effect requires successful completion: when participants built and then destroyed their creations, or failed to complete them, the IKEA effect dissipated [9].
• A configurator that dead-ends on an incompatibility, loses the buyer’s entries on an error, or demands more effort than the purchase warrants produces frustration where it should produce ownership, and the perceived value of the offer falls below that of a plain fixed-price quote.
• The design consequences are immediate feedback on every adjustment, no blocking error, an always-available path to a valid configuration, and a visual recap of the project the buyer has just co-designed, which is the artefact ownership attaches to.

Behavioural dimension	Static fixed-price quote	Quote co-built on a simulator
Buyer’s role	Passive recipient of a top-down proposal	Active co-author calibrating the solution to a specific need
Ownership before purchase	None at the moment the quote arrives	Psychological ownership forming before any signature
Price sensitivity	Attention concentrated on the total amount	Resistance diluted by the value attached to the effort invested
Behaviour at closing	Frequent unilateral renegotiation of the number	Stronger adherence to a result that came from the buyer’s own trade-offs

4. Showing the computation is worth more than answering instantly

The engineering instinct behind a quote engine is to answer in milliseconds, and Ryan Buell and Michael Norton showed in 2011 why that instinct undersells the work the engine actually does [14].

• Across experiments with online travel and online dating sites, participants valued a site that displayed the operations in progress over a short delay (querying each airline, assembling itineraries, checking availability) above an instant site returning identical results.
• The mechanism they identified is perceived effort: a visible display of work on the user’s behalf triggers a norm of reciprocity, and the result is judged more valuable because the provider is seen to have worked for it.
• The same delay without the display did not help, since a wait that explains nothing reads as a technical defect rather than as effort.
• Buell’s 2019 synthesis in the Harvard Business Review generalised the finding under the name operational transparency, across services from kitchens to government offices [15].

For a quote engine, the reading is a short, narrated pause in which the interface names the operations it is actually performing, and the discipline is that every named step has to be real.

• The steps worth naming are the ones a buyer cannot see and would otherwise assume away: checking production or delivery capacity, applying the volume tier, verifying option compatibility, computing the periodic breakdown.
• A pause of one to two seconds is a design choice derived from the finding, not a measured optimum; the papers establish the direction of the effect, not a timing curve.
• Naming a step the engine does not perform converts transparency into theatre, and a buyer who discovers it loses the trust that section 5 shows is the engine’s main asset.
• The engine itself belongs to the family of deterministic workflows in which an agent is confined to analysis and drafting: the pricing rules run as code, and what the pause displays is that code at work.

5. How the number is cut changes what the buyer compares it to

Three separate lines of research show that the same total, cut differently, is evaluated against different reference points, and each line comes with a measurement and a boundary.

Bhavya Mohan, Ryan Buell and Leslie John studied cost transparency, the voluntary disclosure of what a product costs the firm to make, in six laboratory and field studies published in 2020 [16].

• In a preregistered field experiment, diners were 21.1% more likely to buy a bowl of chicken noodle soup, priced at $4.95, when the sign listing its ingredients also showed the cafeteria’s cost per component, labour included, at a gross margin of about 17%.
• A second figure circulates from the same paper, a 22.0% rise in sales of leather wallets after an online retailer added a cost infographic, and the authors themselves file it under “anecdotal evidence” from a natural experiment; the two numbers are close, but only the first carries the weight of a preregistered design, and a reader who quotes “21 to 22%” as one range is merging two different levels of proof.
• The mechanism is trust: participants rated cost disclosure as an intimate disclosure, and that perception raised purchase interest, with a mediation test confirming the path.
• The effect appeared at margins from 17% to 55%, and purchase interest rose even when the price was surprisingly low, which rules out a reading limited to justifying a high price. Whether the effect survives extreme markups or luxury positioning is listed by the authors under future research and was not tested, so a quote engine should not claim that boundary as a finding.
• The first author completed the doctorate this work comes from at Harvard Business School in 2016 and was at the University of San Francisco by publication; the two co-authors are at Harvard Business School.

Marco Bertini and Luc Wathieu examined what partitioning a price does to attention, in a 2008 research note and a 2010 Harvard Business Review article [17] [18].

• Buyers who see a single total compare it downward against other totals, a fixation that turns any offer into a commodity.
• Splitting the price into components tied to specific benefits redirects attention to those benefits and raises the weight they receive in the evaluation.
• For a quote engine, the split follows value lines a buyer recognises (support hours, extended warranty, express lead time, certification), each priced separately and each removable, so the comparison becomes “do I need this” rather than “who is cheapest”.

John Gourville measured the effect of temporal reframing in 1998, building on Richard Thaler’s mental accounting, and the two together explain why the period a price is expressed in changes its psychological category [19] [20].

• An annual figure such as €3,650 a year is filed as a major expenditure and triggers loss aversion; the same amount expressed as €10 a day, or about €304 a month, is compared with small recurring expenses already accepted, and resistance falls.
• Gourville and Dilip Soman extended the logic in 2002 to how the timing of payments changes consumption and renewal, a reminder that the reframing works on both sides of the contract [21].
• In a business purchase the finance function will re-aggregate the number anyway, so the honest design is a display switch (annual, monthly, per unit, per user) rather than a hidden total, which keeps the benefit of the reframing without the appearance of concealment.

6. Where the cursor starts decides what feels like a loss

C. Whan Park, Sung Youl Jun and Deborah MacInnis compared in 2000 two ways of configuring the same product: additive, starting from a base model and adding options, and subtractive, starting fully loaded and removing them [22].

• The subtractive frame led participants to keep more options and to pay a higher total, across option price levels and product categories.
• The explanation is the reference point: an option already included is coded as possessed, and removing it is a loss, whereas adding the same option from a bare model is coded as spending.
• The same study found a limit: when commitment to the product category is low, a fully loaded start can demotivate the purchase altogether.
• Loss aversion supplies the coefficient: Daniel Kahneman and Amos Tversky formalised it in 1979 [23], and their 1992 cumulative version estimated that a loss weighs about 2.25 times an equal gain [24].

A fully loaded start is the most profitable anchor on paper and the riskiest with a professional buyer, which is why the better-documented configurators settle on a middle position.

• A purchasing manager who opens a quote pre-filled with every option reads the frame for what it is, and the reactance that follows costs more than the options retained; this is an extrapolation from the consumer studies, stated as one, since no equivalent business-to-business measurement was found.
• Daniel Goldstein, Eric Johnson, Andreas Herrmann and Mark Heitmann argued in 2008 for defaults set to what the customer would most plausibly want, and a 2011 study of online car configurators by Herrmann and six co-authors measured how default options steer configuration choices [25] [26].
• The resulting architecture is a smart default: a configuration profiled on the buyer’s segment and stated use case, presented as the professional standard for that case, with unrestricted addition and removal shown transparently in the price.

Criterion	Additive (build-up)	Subtractive (strip-down)	Guided hybrid (smart default)
Initial anchor	Bare model at the floor price	Fully equipped model at the ceiling price	Balanced configuration profiled on the segment
Action required	Tick extra modules, each with a surcharge	Untick pre-installed modules to lower the bill	Adjust modules around a recommended base
Status of an option	A potential acquisition	Something already possessed	A standard adapted to the use case
Emotional coding	Each option kept is spending	Each option removed is a loss	Measured trade-offs between performance and savings
Typical total	Low to middling, frequent under-equipment	High, substantial retention of features	Fitted to real needs and to margin

7. Six design rules for a quote engine, each tied to what was measured

Put together, the studies above describe a quote engine as a trust device rather than a form, and each rule below names the finding it rests on and the failure it guards against.

Design rule	Evidence	Execution risk	Prescription for the simulator
Filter first, then adjust	Häubl and Trifts (2000) [3]	Overload and abandonment in front of too many attributes	A two-step path: qualify the use case, then adjust parameters in a live comparison matrix
Let the buyer build, and let the build finish	Norton, Mochon and Ariely (2012) [9]; Franke, Schreier and Kaiser (2010) [12]	Incomplete or blocked configurations destroy the ownership effect	Immediate feedback, no blocking error, always a valid path, a visual recap of the co-designed project
Show the work	Buell and Norton (2011) [14]; Buell (2019) [15]	A delay read as an infrastructure defect	A short narrated pause naming the real capacity, pricing and compatibility checks
Cut the price along value lines	Bertini and Wathieu (2008, 2010) [17] [18]	Commoditisation and downward comparison of totals	Components tied to identifiable benefits, each priced and each removable
Reveal what it costs you	Mohan, Buell and John (2020) [16]	Inconsistent or selective disclosure that breaks trust	A stable breakdown of materials, labour and logistics behind the price, identical across quotes
Reframe the period, keep the total visible	Gourville (1998) [19]; Thaler (1985) [20]	Loss aversion in front of a single aggregate	A display switch across annual, monthly, per-unit and per-user views, with the anchor set by a profiled default

The quote as the meeting point of two models

An interactive quote is where the seller’s economic model and the buyer’s cognitive model meet on one screen, and the research above says that the meeting goes better when the interface respects how the second one works.

• The value of the quote has left its production, which agents now draft under the seller’s rules, and settled in the four moments the buyer experiences: filtering, building, watching the computation, and reading the number.
• Each of those moments has a measured effect and a measured boundary, and the boundaries (successful completion, real steps, consistent costs, a default the buyer can read as fair) are the part a rushed implementation skips first.
• The same discipline that this site describes for deciding with company data applies here: the engine’s numbers must be reproducible by the finance function on the other side, or the trust the interface built is lost at the first audit.
• For a mid-sized company, the first configurator to build is the one for the offer that is renegotiated most often today, because that is where the gap between a static quote and a co-built one will show up in the closing rate first.

Book the Junyr AI maturity audit: a free, no-commitment 30-minute video call to choose which commercial artefact to make interactive first.

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By Paul-Antoine TUAL, AI Transformation Leader, Croissance et Transitions, September 2026.

Sources

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