Advanced analytics and innovative methods for complex market research decisions.

Choice modeling is a quantitative research method that measures consumer preferences by asking people to choose among realistic product or service alternatives. Respondents complete a series of choice exercises. In each exercise, they see a small set of options that differ across the attributes and levels included in the study.

By observing which options respondents select across many exercises, the model estimates how much each attribute and level contributes to overall preference. This helps teams understand how product features, pricing, and competitive context work together when customers make decisions.

What choice modeling delivers

The standard choice modeling deliverable is a market simulator with a what-if scenario page, importance scores, and sensitivity analysis.

Additional modules can be added when the engagement requires more.

• Planned runs to automate the scenarios clients already know they need.
• Willingness-to-pay to estimate the implied value of a specific product feature or attribute level.
• Price elasticity to quantify how demand responds to price changes.
• Optimization to identify the product configurations or portfolios that best fit the decision criteria.

When it is the right fit

See the choice modeling methods page for a full discussion of study design, estimation approaches, and how choice modeling compares to monadic tests and price sensitivity methods.

MaxDiff is a quantitative research method used to prioritize a large set of items. Respondents complete a series of choice exercises. In each exercise, they select the item they prefer most and the item they prefer least from a small set.

By observing these choices across many exercises, the model estimates how strongly each item is preferred relative to the others. This creates stronger separation than standard rating scales, where many items often receive similar ratings.

What MaxDiff delivers

The standard MaxDiff deliverable is an item prioritization table with subgroup comparisons and individual preference scores.

Additional modules can be added when the study requires more.

• Statistical testing to formally evaluate differences between items or groups.
• MaxDiff simulator to recalculate or filter preference scores across subgroups.
• Preference-score segmentation to group respondents by what they value most.
• TURF analysis to identify item combinations that maximize reach.

Study variations

Traditional MaxDiff is the standard approach. Each respondent sees all items, with each item appearing multiple times across tasks. It produces reliable preference scores across the full item set.

Express MaxDiff is designed for studies with a large number of items. Each respondent sees only a subset of items, reducing burden while keeping the study manageable. It trades some precision for practicality.

Sparse MaxDiff also addresses large item sets but takes a different approach. Every respondent sees every item, but each item appears only a small number of times. This maintains full item coverage while limiting task burden.

Express and Sparse MaxDiff solve the same problem in different ways. The right choice depends on the item set size, the study objectives, and how important full item coverage is for the analysis.

Anchored MaxDiff adds a follow-up question that establishes an absolute threshold for each item, independent of how items rank against each other. The threshold is defined by the anchor question, which might ask which items are must-haves, which are acceptable, or which meet some other criterion. A traditional MaxDiff always produces a ranking, but the anchor reveals what that ranking actually means. If all items clear the threshold, even the bottom-ranked items carry strategic importance. If few items clear it, even top-ranked items may not warrant investment.

When it is the right fit

See the MaxDiff methods page for a full discussion of study design and scaling approaches.

How it works

Respondents complete a series of choice tasks, each presenting a set of product or concept alternatives that vary across a defined set of attributes and levels. By observing which alternatives people choose across many tasks, the model estimates how much each attribute and level contributes to overall preference.

The output is a set of part-worth utilities, scores that quantify the relative value each respondent places on each attribute level tested. These utilities are the foundation for feature valuation. They help clients understand what each product feature is actually worth to their customers. They also power market simulation, segmentation, pricing analysis, and portfolio optimization.

Most choice studies include a none or other option, allowing respondents to opt out of the presented alternatives. Including a none or other option extends the model from relative preference share to overall demand estimation. Without it, the model measures only relative share among presented alternatives, not overall demand.

How it compares to other approaches

Monadic concept tests are a common alternative. Researchers typically field a series of monadic tests, each exposing respondents to a single concept and measuring reaction directly. Clients like them because the results are straightforward to interpret. Monadic tests cannot tell you how a concept performs against alternatives or what drives preference at the attribute level.

Price sensitivity methods like Van Westendorp and Gabor-Granger measure acceptable price ranges and purchase likelihood at specific price points. These approaches treat price in isolation. Choice modeling embeds price alongside other attributes, producing more actionable estimates of willingness to pay.

When fielded alongside a choice study, Van Westendorp results can define each respondent's consideration set in the simulator. This keeps the price range grounded in what each respondent considers realistic.

What you can do with it

Estimation approaches

How the model is estimated matters as much as how the study is designed. The three main approaches differ primarily in how they handle preference variation across respondents. Preference variation, also known as preference heterogeneity, describes how differently people value the same attributes.

How it works

Each MaxDiff task presents a subset of items drawn from the full set. Respondents identify their most and least preferred item within that subset. Each response generates two simultaneous observations. Both are modeled as separate multinomial logit choices and estimated jointly. This dual-observation structure makes MaxDiff considerably more statistically efficient than ratings or ranking tasks of equivalent length.

MaxDiff does not measure each item in isolation. Each score reflects how an item performs when it competes against other items in the design. The competitive structure produces more separation between items than direct rating scales.

The scores

Each item receives a score reflecting how strongly it is preferred relative to the others in the study. The scores capture both rank and magnitude. A score of 40 means an item is twice as preferred as an item scoring 20. The relationship is precise. A wide gap between two adjacent items is meaningful. A narrow gap means the items are effectively tied, regardless of their rank order.

Scores are calibrated the same way for every respondent. They can be averaged across the sample and compared meaningfully across groups. MaxDiff scores are well suited for segmentation, TURF analysis, and preference simulators.

Estimation

Precipio estimates MaxDiff models using hierarchical Bayes by default. This produces individual-level utilities for every respondent. The same framework is used in choice-based conjoint and supports segmentation analysis and respondent-level scoring. Full detail on HB estimation is on the HB methods page.

How it works

Hierarchical Bayes is a multi-level Bayesian estimation framework. Precipio uses HB across multiple model types, including conjoint, MaxDiff, and regression, to produce individual-level estimates.

Individual-level estimation presents a data challenge. Each respondent contributes only a limited number of observations to the model, which is rarely sufficient to produce stable estimates. HB addresses this by combining each respondent's data with patterns observed across the total sample.

In conjoint and MaxDiff applications, the model uses two levels.

• Lower level refers to respondent preferences
• Upper level refers to population preferences

These two levels interact through partial pooling. Respondents with clear, consistent answers are estimated mostly from their own data. Respondents with noisier answers are pulled toward the population distribution.

The result is a preference profile for every respondent, reliable enough to support segmentation, simulation, profiling, and pricing analysis.

What you can do with it

Choosing the right model

HB is not the only approach. Aggregate MNL estimates a single average utility model for the sample, and latent class identifies a small number of distinct preference segments. Both are well-suited to studies with smaller sample sizes, where HB's individual-level estimation may not be stable. When the sample supports it, though, HB produces the most complete picture of preference variation across your market.

It is worth noting that a simpler model does not mean a faster or cheaper path to results. Aggregate and latent class analyses often require more analytical iteration to produce outputs that are ready for business decisions.

What this work covers

Segmentation studies identify meaningful groups within a population. People in different segments think, behave, or prioritize differently from one another.

The engagement covers study design, statistical modeling, iterative refinement, and the classification tools needed to apply the solution.

The primary segmentation method is latent class analysis. It finds segments by modeling how people respond, producing a solution grounded in behavioral and attitudinal reality.

How an engagement works

Segmentation work starts with a conversation about which questions to include in the survey. The variables fed into a segmentation model shape everything that follows. Getting this right is part of the analytical work.

Once data is collected, Precipio runs latent class models across a range of segment solutions. Results are delivered in a structured output file containing indexed profiles, model parameters, respondent assignments, and fit statistics. Each candidate solution is reviewed with the client until the right one emerges.

Selection criteria are both statistical and practical. A useful segmentation is actionable.

Once a solution is agreed upon, Precipio builds a classification algorithm using a targeted subset of survey items selected through a stepwise procedure. This becomes the basis for the typing tool, delivered as a spreadsheet classifier or as code.

Segmentation approaches

Segmentation studies differ in what they measure and why. Early in an engagement, Precipio helps clients identify which framing fits their business question.

Attitudinal segmentation groups people by beliefs, perceptions, or values. It works well for messaging strategy and brand positioning.

Needs-based segmentation groups people by what they are trying to accomplish. It is most useful for product development and portfolio decisions.

Behavioral segmentation groups people by what they actually do. Purchase patterns, usage habits, and channel behavior are common inputs. It is grounded in observed data rather than stated preferences.

Occasion-based segmentation groups by context of use or purchase rather than by person. It fits categories where the same individual behaves differently depending on the situation.

MaxDiff-based segmentation derives segments from stated priorities measured through a MaxDiff exercise. It produces groups defined by what people value most, with less susceptibility to scale-use bias than rating-based approaches.

Ensemble segmentation combines multiple segmentation views when a study needs one solution to reflect several distinct topics or modeling approaches.

What segmentation delivers

Every segmentation engagement produces a consistent set of outputs.

• Study design consultation to identify which questions to include and how to structure the survey. Variable selection shapes everything that follows.
• Segmentation output file with indexed profiles, model parameters, respondent assignments, and fit statistics. Delivered in Excel or Google Sheets.
• Typing tool for assigning new respondents to segments in future surveys. Delivered as a spreadsheet classifier or as code in Python, JavaScript, or pseudocode.
• Profiling data with respondent assignments and indexed variables for client-side profiling and reporting.

When it is the right fit

Segmentation is most valuable when the population is genuinely heterogeneous and a single message or product cannot serve everyone well.

How it works

Segmentation methods differ in how they define similarity, estimate group structure, and assign respondents to segments. At Precipio, segmentation work starts with latent class analysis.

Latent class models derive segment structure from the data and assign each respondent to the group that best fits their response pattern.

Precipio runs models across a range of candidate solutions, evaluates each on fit, segment size, and differences across groups, and works with clients to select the solution that serves the business question.

Latent class segmentation

Latent class is a finite mixture model. It assumes the population consists of a fixed number of distinct groups, each with its own response pattern. The model estimates the underlying structure of the data. Segments emerge from that structure.

Models are estimated across a range of segment counts. Each solution is evaluated on statistical fit and practical interpretability. A statistically superior solution may produce segments too small or too similar for practical use. A simpler solution may serve the business better. The right answer balances both.

Ensemble segmentation combines multiple candidate segmentations into a consensus solution. This can improve stability and sometimes outperform any single candidate segmentation. Strong ensemble solutions depend on component models that capture different patterns in the data. In practice, Precipio may develop smaller topic-specific segmentations and combine them into a broader solution when no single variable set should dominate the final segmentation.

Respondent classification and typing

Once a segmentation solution is finalized, a classification algorithm is built so new respondents can be assigned to segments in future surveys. Running the full latent class model each time is not required. The typing tool handles assignment using a small set of discriminating questions identified through a stepwise selection procedure.

The result is a compact, portable tool that works independently of the original model and can be deployed in whatever format the engagement requires.

MaxDiff-based segmentation

When a study includes a MaxDiff exercise, the resulting utility scores can serve directly as inputs to latent class segmentation. The segments that emerge are typically more useful than those from ratings-based approaches.

Ratings tend to compress results. Many respondents rate many items as important, which reduces the differences between people and makes segments harder to separate. MaxDiff forces tradeoffs. Each choice reveals relative priority. The resulting segments are defined by what people value most, with clear separation between groups.

The resulting segments are actionable, with clear definitions that make them easy to interpret and name. Utilities are rescaled before or during estimation to account for individual-level scale variation in the estimates.

Classifying new respondents into MaxDiff-based segments presents a practical challenge. A full MaxDiff exercise is usually too long to include in future studies solely for typing purposes. A shorter assignment model can be built instead. The MaxDiff items are broken into pairs, the pairs that best distinguish the segments are identified, and those pairs form a compact typing battery. This preserves the logic of the original segmentation while keeping future classification practical.

The platform

Precipio builds TURF and reach analysis systems that help teams move from item rankings to portfolio decisions. The work combines reach optimization, diagnostic interpretation, and evaluated alternatives, delivered in a format matched to the client workflow.

Delivery can take the form of Excel tools, Google Sheets tools, web-based interfaces, or API-backed systems. For teams that need interactive scenario testing and exploration across assumptions, a full decision-support system is available as a separate capability.

Smaller TURF projects may only need a spreadsheet-based analysis. The platform scales to match what the problem requires.

What TURF delivers

The standard TURF deliverable is a simulator that lets clients evaluate item combinations and maximize unduplicated reach. The simulator is tailored to the scale, workflow, and decision needs of the engagement.

Optimization and diagnostics

Reach optimization identifies which combination of items maximizes unduplicated reach at each portfolio size. Standard outputs include reach curves by bundle size, optimal combination rankings, incremental reach by item, and evaluated alternatives when near-optimal bundles are worth examining.

The platform classifies each item by the role it plays in the solution. Broad anchors have strong individual reach and appear consistently in high-reach bundles. Hidden extenders rank below the top items on their own but add unique audience coverage that the obvious items miss. Flexible substitutes perform well in some bundles but overlap with other strong items, making them useful alternatives when constraints change the final decision.

Overlap analysis identifies whether item pairs reach similar or distinct audiences. Saturation diagnostics show where adding items begins to contribute diminishing reach. Both help teams understand the reasoning behind the recommendation.

For segment-level work, the platform can compare optimal bundles across respondent groups and identify where a single recommendation holds and where different audiences need different solutions.

When it is the right fit

TURF is appropriate when the decision involves selecting a subset from a larger item set and the objective is how many people that subset collectively serves.

How it works

TURF stands for Total Unduplicated Reach and Frequency. Reach is the proportion of respondents who find at least one item in a selected set acceptable. Frequency is the average number of items in that set each respondent finds acceptable. Reach is the primary optimization target in most studies. Frequency provides secondary context. A portfolio with high reach and high frequency may face more internal substitution than a portfolio with high reach and lower frequency.

The analysis begins with a respondent-by-item matrix. Each cell indicates whether a given item reaches a given respondent. The binary reach matrix can be derived from survey selections, rating thresholds, purchase behavior, product ownership, claimed consideration, or modeled probabilities converted to binary values.

TURF finds the K-item combination from a set of N items that produces the highest unduplicated reach. The right algorithm depends on the scale of the problem. Algorithm selection and tradeoffs are covered on the optimization methods page.

Input data and thresholds

When input data is continuous or ordinal, a threshold converts it to binary reach. A common example is top-2-box intent. Respondents who rate an item 4 or 5 on a 5-point scale count as reached. With MaxDiff data, the threshold is applied to rescaled utilities. Respondents whose utility for an item exceeds the threshold count as reached by it.

The unit of analysis is typically a survey respondent but can also be a customer, store, or account.

Item role classification

Item role classification explains why items matter in a solution, beyond the reach number alone.

Broad anchors have high individual reach and appear consistently across strong solutions. They are usually the primary drivers of total portfolio reach.

Hidden extenders have lower individual reach but add unique audience coverage. A hidden extender is more valuable than its ranking suggests because it reaches people not already covered by the obvious items.

Flexible substitutes perform well in some bundles but overlap with other strong items. They may not appear in the optimal solution, but they are useful when business constraints or stakeholder requirements change the final selection.

Some items have no clear role in optimal solutions. These can be flagged for monitoring, niche segment review, or strategic consideration outside the reach model.

Overlap and saturation

Overlap analysis evaluates whether pairs of items reach similar or distinct audiences. High-overlap pairs indicate items competing for the same respondents. Complementary pairs cover distinct audiences and tend to build total reach more efficiently.

Saturation diagnostics track incremental reach as bundle size increases. The reach curve typically rises steeply at first and flattens as the portfolio grows. Saturation analysis identifies the point where additional items contribute diminishing returns, helping teams determine when a portfolio is large enough.

How it works

Optimization methods find the best combination of elements from a defined set, subject to whatever constraints apply. The right approach depends on the size of the problem and the confidence the decision requires. Exact methods evaluate the full solution space or use pruning logic to prove no better answer exists. Heuristic methods search efficiently and return strong solutions without guaranteeing optimality.

The tradeoff applies across problem types, from TURF item selection to choice model product design. When the output will directly drive a significant business decision, exact methods or near-exact heuristics with multiple runs are preferred.

Exact vs. heuristic

Exact methods become computationally expensive as the problem grows. Heuristic methods scale well but cannot confirm the solution is the best possible. The practical difference matters, and the sections below describe each approach in detail.

For most research applications, item sets are small enough for exact methods. Heuristic methods become necessary when item sets are large or portfolio configurations are complex.

Exact methods

Full enumeration tests every possible combination in the solution space. It guarantees the optimal solution and is simple to verify. For a 20-item set with a target bundle of 5, there are 15,504 combinations. This is fast by any standard. As the problem grows, the combination count grows quickly and enumeration becomes impractical.

Branch-and-bound search finds the optimal solution more efficiently by eliminating branches of the search tree that cannot beat the current best result. When the algorithm determines a partial solution cannot lead to a stronger outcome than the best complete solution found so far, it prunes that branch and moves on. For many structured problems, branch-and-bound reaches the global optimum substantially faster than enumeration. Performance depends on the structure of the problem and how early strong solutions are found.

Filtered enumeration reduces the search space before optimization begins. The analyst specifies which combinations are infeasible or off-limits, and the search runs only over the remaining space. When the filtered space is small enough, enumeration is exact over all feasible combinations. When it is still too large, filtered enumeration can be combined with heuristic methods to search the reduced space. This approach is most valuable when the full solution space is too large for unconstrained enumeration but the analyst can identify infeasible combinations in advance.

Heuristic methods

Stepwise plus swaps builds a solution incrementally by adding the element contributing the most at each step, then tests whether swapping any included element for an excluded element would improve the result. The swap phase makes this more robust than a pure greedy approach. It is practical for large problems and consistently produces strong solutions.

Beam search retains multiple promising partial solutions at each step rather than committing to a single path. The number of paths retained is the beam width. A wider beam reduces the risk of locking in a suboptimal direction early but increases computation time. Beam search is useful when multiple near-optimal solutions may exist and the search should explore more of the space than a single greedy path would cover.

Monte Carlo plus swaps samples many candidate solutions randomly, evaluates each, and applies local swap improvements to the strongest candidates. Because results depend on random sampling, a seeded implementation is important for reproducibility. This method is well-suited to very large or difficult search spaces where structured traversal is impractical.

Genetic algorithms maintain a population of candidate solutions and improve them over successive generations. Each generation evaluates the fitness of each candidate, selects high-performing candidates as parents, creates new candidates by combining elements of parent solutions, and introduces random mutations to maintain diversity. Because the algorithm maintains a population rather than following a single path, it explores multiple regions of the solution space simultaneously and is less likely to converge on a local optimum. Genetic algorithms are well-suited to large, complex search spaces where the solution involves multiple interacting dimensions.

Greedy forward search selects the strongest element first, locks it in, then selects the next-strongest incremental contributor given the current solution, and repeats until the target size is reached. It is fast, transparent, and easy to explain to nontechnical audiences. It is also the least reliable when overlap or interaction effects create situations where the locally optimal choice at each step leads to a weaker final solution. Best used when speed and transparency matter more than search depth.

Method selection by problem type

TURF and reach optimization selects K items from a fixed set of N. The search space is the set of all possible K-item combinations. For most research applications, item sets are small enough for exact methods. Full enumeration and branch-and-bound handle the typical problem size without difficulty. When item sets are larger or the project requires fast scenario turnaround, stepwise plus swaps and beam search produce strong results. Greedy forward search is an option when transparency and speed are priorities.

Choice model product optimization designs product configurations by selecting attribute levels and then optimizes which configurations to offer as a portfolio. The search space is defined by all possible attribute-level combinations across all products, and many of those combinations may be infeasible. Genetic algorithms and Monte Carlo plus swaps are effective because they explore diverse configurations without requiring structured traversal of the full space. Filtered enumeration reduces the space to feasible configurations before the search begins, making exact or near-exact methods practical for problems that would otherwise require heuristic search.

What this work covers

A decision-support system (DSS) is a custom application built on completed analysis. Each DSS is built specifically for the project. The underlying method, the decision context, and the required outputs all shape the design. A DSS can be built from a conjoint model, a MaxDiff study, a TURF analysis, or a combination.

Architecture ranges from a standalone spreadsheet to a three-tier cloud application, depending on the requirements of the engagement.

What a DSS can include

The functionality built into each tool depends on what the engagement requires.

Simulation and scenario testing

• Scenario simulation to test how changes to a product, price, or lineup affect preference share or demand.
• Planned scenario runs built around the specific decisions the client knows they will need to make.
• Price sensitivity and willingness-to-pay to quantify how demand responds to pricing changes and estimate the implied value of product features.

Optimization

• Product and portfolio optimization to identify the configuration or lineup that best meets a defined objective or constraint.
• Cannibalization analysis to evaluate how products within a lineup compete with each other.
• Reach and coverage optimization to identify item combinations that maximize unduplicated audience coverage.

Analysis and filtering

• Segmentation and filtering to apply segment-level cuts across all outputs without re-running the analysis.
• Multi-method consolidation to bring outputs from two or more analytical models into a single interface.
• Custom metrics calculated from model outputs against client-defined KPIs.

When it is the right fit

Studies requiring extensive calculation are well served by a cloud-based DSS. Multi-method engagements may benefit from having all outputs in one place. Optimization work is a consistent fit.

What custom decision-support systems deliver

The deliverable is a working application. Architecture, functionality, and interface are all built to the requirements of the engagement.

How it works

A DSS is built on top of a completed analytical model (or models). Development is sequential. The underlying study must run and the model must be complete before the DSS can be finalized.

Some work can start earlier. Interface design and structural planning can proceed while fieldwork is in progress or analysis is underway. The tool cannot be assembled and tested until the model outputs are locked.

Once the model is ready, the DSS is built, tested against the model outputs, and delivered as a working application.

Architecture

Architecture is determined during scoping. Application type is matched to the calculation requirements and the number of methods the engagement includes.

Simple applications live natively in a spreadsheet. The tool is self-contained and requires no external infrastructure.

Three-tier applications consist of a user interface, a separate data store, and a cloud-based calculation engine. The interface handles user interaction. The data store holds reference data. The calculation engine runs the computation. Three-tier systems are used when data volume or calculation complexity exceeds what a spreadsheet environment can support.

Three-tier systems incur hosting costs. Fees may apply for the interface (if web-based), the database, and the compute layer.

Privacy and access

Access to three-tier systems is invite-only. Each project uses a dedicated data store with project-specific credentials validated server-side before any data is accessed. Access can be granted or revoked at any time.

Most often, the server-side data store holds analytical data only. Client-specific labels and calculated outputs exist only within the user interface, which limits the business-context value of what the backend retains. Some applications benefit from storing calculated outputs in the data store. Optimization work, for example, can reduce compute time and cost by preserving intermediate results. These situations are addressed on a project-by-project basis and discussed in detail with the client.

What this work covers

Research problems that fall outside standard methods take several forms. Some require study structures too complex for an off-the-shelf design. Others call for a specialized measurement approach built around the specific research question. A third type involves synthesis of large quantitative outputs into a clear, defensible recommendation.

Each engagement begins with the research question. The analytical approach is designed to fit the problem.

Purchase Funnel CBC

Purchase Funnel CBC is a choice-based method that estimates demand at each stage of the buying process within a single competitive exercise.

A standard conjoint exercise measures preference. It identifies which products respondents favor under competitive conditions. Purchase Funnel CBC goes further, connecting each competitive choice to a position in the buying process. The result is a model that distinguishes products that generate interest from products that generate demand.

This distinction matters when a product can be attractive but still face barriers to adoption. A respondent may prefer a concept in the choice task but only be willing to learn more, consider it later, or stay with their current setup. The method is especially useful for new product launches and for categories where trust, switching costs, or ecosystem fit affect conversion. Technology, financial services, insurance, and B2B products are common applications.

Funnel-stage estimates map naturally to a TAM/SAM/SOM framework. TAM represents broad latent interest. SAM narrows to realistic consideration. SOM identifies near-term capturable demand. This connects observed choice behavior directly to market sizing.

The method also supports portfolio analysis. When a new product enters the competitive set, the design estimates whether it expands the portfolio's reach or draws demand from existing products, and at which funnel stage that movement occurs.

Purchase Funnel CBC can also be connected to time series forecasting. This makes it possible to track how buyers move through the funnel as market conditions, competitive launches, and pricing changes affect demand at each stage over time.

Attention-Balanced CBC

Attention-Balanced CBC is a conjoint design approach for studies where brand and price may otherwise dominate the choice decision.

Brand and price are high-fluency attributes. Respondents process them quickly. When a study includes many features, claims, or benefits, respondents naturally simplify. Brand and price become the primary anchors, and strategically important attributes may receive less attention even when they are central to the business question.

A low utility estimate for an attribute can mean it is genuinely unimportant. It can also mean the attribute did not receive enough attention to generate a clear signal. Those are different findings and should not be treated as the same result.

The method addresses this by controlling how much attention each part of the offer receives across the choice tasks. Strategically important attributes get adequate exposure independent of brand and price. The result is stronger, more reliable estimates for the attributes that matter most to the decision.

The approach is especially useful for studies evaluating large feature sets in digital products, subscription plans, service bundles, and technology platforms where brand recognition and price are strong but the business question is about the value of specific features.

Capabilities

Precipio's work spans preference measurement, segmentation, reach analysis, decision-support systems, and specialized quantitative methods. Each engagement is designed around the decision it needs to support.

Methods

Each methods page documents study design, estimation, and technical detail for analytically sophisticated readers.