Coordinating AI agents, tools, data sources, and handoffs to complete a task. Orchestration determines which work happens when, what context each step receives, and how the results come together.

An AI system that observes context, selects actions, and carries them out with some degree of autonomy to pursue a goal. Agents may use tools and adapt their next steps to the results; their level of autonomy varies.

Connecting an AI response to relevant sources, data, or tool results. Grounding helps make an answer traceable and more reliable, but does not by itself guarantee that every claim is correct.

Identifying observations that differ from an expected pattern, such as an unusual sales decline or a sudden price change. An anomaly is a reason to investigate; it is not, on its own, an explanation of the cause.

Choosing a product range to improve an objective such as profit, sales, or shopper choice within practical limits. It considers how adding or removing a product affects the rest of the range, alongside space and operational constraints.

In commercial analytics, assigning credit for an outcome to activities, touchpoints, or drivers, such as advertising, price, or promotion. The allocation depends on the method used; attribution alone does not establish a causal effect.

Sales value divided by units sold for a defined product set and period. Changes in average selling price can reflect discounts, product mix, or pack mix as well as changes to individual prices.

A reference forecast produced under a stated set of assumptions, against which alternatives are compared. Whether it includes planned promotions or other activities must be specified; a baseline is not necessarily a no-action forecast or a sales target.

A reference used to assess performance, such as a prior period, peer group, market average, or simple forecasting method. A useful benchmark compares like-for-like measures and makes differences in scope explicit.

The value associated with a brand’s name and the awareness, associations, perceptions, and loyalty it creates. It can strengthen or weaken customer response to an offering and is distinct from a monetary valuation of the brand.

A structured argument for a proposed decision that connects expected benefits, costs, assumptions, risks, and required actions. It explains why an option is worth pursuing and how its results will be assessed.

Sales gained by one product at the expense of another product in the same company's portfolio. A launch or promotion can increase sales for the featured product while creating a smaller gain for the portfolio as a whole.

In retail, managing a group of related products as a business unit to meet shopper needs and improve category performance. It coordinates assortment, pricing, promotion, and placement. Procurement also uses the term for managing categories of organizational spending.

Methods for estimating what difference an action makes to an outcome. Causal inference compares what happened with a credible estimate of what would have happened without the action, using experiments or explicit observational assumptions.

Molsaro uses this term for a connected approach to understanding business conditions, evaluating commercial options, choosing actions, coordinating execution, and learning from results. It brings analytical methods, AI, and business context together to support commercial teams.

The systematic collection and interpretation of information about competitors, including their products, pricing, positioning, channels, and strategic moves. Its purpose is to understand implications for a business decision.

A limit or requirement that a proposed choice must respect, such as budget, capacity, minimum margin, or timing. An objective defines what to improve; constraints define which options are feasible.

Sales revenue minus variable costs, measured in total or per unit. It is the amount available to cover fixed costs and contribute to profit. The contribution margin ratio expresses this amount as a percentage of sales revenue.

An outcome that would occur under an alternative condition, such as sales without a promotion that actually ran. Because that alternative is not observed for the same unit at the same time, estimating it requires an experimental design or defensible modeling assumptions.

The percentage change in quantity demanded of one product divided by the percentage change in another product’s price, with other factors held constant. Positive values generally indicate substitutes; negative values generally indicate complements.

Grouping customers by shared needs, behaviors, economics, or characteristics so decisions can be more relevant. The best segmentation for pricing may differ from the one used for communication or account planning.

The roles, policies, and processes that define how data is owned, managed, accessed, and used. Governance makes responsibilities and permitted uses clear throughout the data lifecycle.

A record of where data originated, how it was transformed, and which outputs depend on it. Lineage helps people trace an unexpected result back to its sources and understand the impact of changes.

How well data supports its intended use. Common dimensions include accuracy, completeness, consistency, timeliness, and validity; a dataset can be suitable for one decision and insufficient for another.

A data management system designed for reporting and analysis, typically integrating organized historical data from multiple sources. It supports analytical queries across subjects such as products, customers, markets, and time.

Estimating future demand over a defined horizon using historical patterns and relevant information. A forecast describes expected demand and uncertainty; it is different from a sales target or a committed supply plan.

Estimating how demand relates to factors such as price, promotion, distribution, product attributes, and market conditions. A demand model can help evaluate commercial changes when those responses are supported by the data.

In retail measurement, the extent to which a product is available across outlets in a defined market. Numeric distribution is the percentage of outlets carrying it; weighted distribution accounts for those outlets’ sales importance. This differs from physical distribution, the movement of goods through a supply chain.

Breaking a result or change into contributions associated with different drivers, such as seasonality, price, or distribution. Contributions depend on the model and reference point; they should not automatically be interpreted as causal effects.

In Molsaro, connecting internal business data, documents, institutional knowledge, and operational context to support business questions and decisions. This describes Molsaro’s use of the term; its scope varies across organizations and vendors.

Information used to support or challenge a conclusion, such as observed data, research, documents, or experimental results. Its usefulness depends on relevance, source quality, and the strength of the connection to the claim.

The ability to provide understandable explanations of a system’s mechanisms or outputs for an intended audience. Explanations may describe influential inputs, model behavior, and limitations; an explanation does not by itself establish that an output is correct.

An input variable or measurable characteristic used by a statistical or machine-learning model. A feature may be observed directly or derived from other data, such as price, customer tenure, or a moving average of sales.

How closely forecasts match subsequently observed outcomes. Accuracy depends on the metric, horizon, aggregation, and comparison period, so it should be assessed against a suitable benchmark under consistent conditions.

A systematic tendency to forecast above or below observed outcomes. It is often assessed using mean signed error. A bias near zero does not imply high accuracy, because large positive and negative errors can cancel.

How far into the future a forecast extends. Forecasts for next week and next year support different decisions and typically have different levels of uncertainty.

AI that produces new content, such as text, images, or code, based on patterns learned from data and the context it receives. Generated content can be useful, but needs appropriate grounding and verification for business use.

Usually, gross profit expressed as a percentage of net sales: (net sales − cost of goods sold) ÷ net sales × 100. The term is sometimes used for the gross profit amount, so reporting should distinguish the monetary amount from the percentage.

The authority and practical ability for people to review, direct, correct, or stop automated work. Effective oversight specifies which decisions require review and gives reviewers enough information to act.

The difference an action makes compared with what would have happened without it. A sales increase during a promotion is not necessarily all incremental, because some sales may have happened anyway or shifted from other products or periods.

An interpretation that explains why an observation matters to a business question. An insight connects evidence with context and implications, rather than simply restating a metric or chart.

Combining AI with process and task automation to perform work and support decisions. It can bring together machine learning, workflow management, and robotic process automation, with people handling oversight and exceptions where needed.

A collaborative planning process between a supplier and a customer, often a retailer. It aligns growth objectives, category priorities, investments, actions, and measures of success across both organizations.

A selected measure used to assess progress against a business objective. A KPI needs a clear definition, scope, and owner so that teams interpret and act on it consistently.

A representation of entities and the relationships between them, such as products, brands, customers, markets, and documents. A knowledge graph helps connect information and preserve the context of those relationships.

A language model trained at large scale to learn statistical patterns in text and generate or process language. Modern LLMs are usually neural networks. They can support tasks such as summarization and question answering, but fluent output can still be factually wrong.

The collection and analysis of information about customers, competitors, products, channels, and market conditions to support business decisions. It often combines ongoing monitoring with research and multiple internal or external sources.

The planned collection and analysis of information to answer a specific market, consumer, or customer question. It can use primary methods such as interviews and surveys, or secondary sources such as existing reports.

A company's, brand's, or product's sales as a proportion of a defined market's total sales. Comparisons must specify whether share is based on value or volume, and use the same market boundaries and period.

An observed event, pattern, or change that may matter to a business, such as a competitor launch or a change in shopper behavior. A signal draws attention to something that needs interpretation.

Statistical modeling, usually using aggregated data over time, to estimate the effects of marketing activity on outcomes such as sales while accounting for other influences. It informs budget allocation; causal interpretation depends on assumptions about confounding, model structure, and data quality.

The natural-language interface and orchestration layer of Molsaro. It assembles authorized business and market context, invokes analytical engines and agents, and brings their results together to help commercial teams make decisions.

Repeatedly evaluating a model with inputs randomly sampled from probability distributions to estimate a range of possible outcomes. The results depend on the chosen distributions, relationships between inputs, and the model; they do not remove uncertainty.

Gross sales less sales returns, allowances, and discounts. It is a revenue measure, not profit: cost of goods sold and operating expenses have not yet been deducted. Comparisons should use a consistent treatment of applicable customer rebates and trade terms.

Finding values of decision variables that maximize or minimize an objective while satisfying constraints. In commercial planning, examples include maximizing profit within a budget. A method’s solution quality and guarantees depend on the problem and algorithm used.

Deciding the roles, investment, positioning, development, and lifecycle of products across a business. It considers how products work together, compete for resources, and contribute to the company's growth objectives.

A range intended to contain a future observation at a stated coverage level. It communicates uncertainty around a forecast and is different from a confidence interval for an estimated model parameter.

Here, price elasticity of demand: the percentage change in quantity demanded divided by the percentage change in the product’s own price, with other factors held constant. It is usually negative; elastic and inelastic demand are commonly compared using its absolute magnitude.

The design of pack sizes, formats, benefits, and price points to meet different shopper needs, occasions, and channels. It considers the value proposition and the role of each pack together.

The approach a business takes to setting prices in support of its objectives and positioning. It considers customer value, willingness to pay, competition, costs, channel economics, and the expected demand response.

How well a promotion achieves its objectives after accounting for its costs and wider effects. Evaluation can include incremental sales, margin, cannibalization, timing shifts, and customer response.

Sales during a promotion minus a specified baseline. Percentage uplift is this difference divided by baseline sales, multiplied by 100. The result depends on the baseline and is not automatically a measure of causal incrementality or profit.

A method that retrieves relevant information and supplies it as context to a generative AI model. It can ground responses in specific sources, while still requiring checks on source quality and the accuracy of the resulting answer.

A commercial discipline for growing profitable revenue through coordinated decisions about pricing, promotions, assortment, and trade investment. It uses consumer, customer, and market understanding; pack architecture and product mix are common areas of application.

An access-control model in which permissions are assigned to roles and users are assigned to those roles. A user obtains the permissions of their authorized roles, rather than having every permission assigned individually.

A description of a possible future situation or a set of assumptions used to explore outcomes. It may include external conditions, proposed actions, or both. A scenario is not necessarily a prediction; models can be used to evaluate its implications.

Exploring alternative future conditions and choices before committing to a plan. It helps teams test assumptions, compare options, and prepare responses to uncertainty; quantitative simulation can support that exploration.

A recurring pattern linked to a regular calendar cycle, such as weekdays, months, or holidays. Separating seasonality from other changes helps teams interpret performance and create more useful forecasts.

Sales from a supplier to a retailer, distributor, or other channel customer. Sell-in can differ from final customer demand because it also reflects inventory movements and the timing of orders.

Sales from a retailer or channel to the end customer, often measured through point-of-sale data. It records purchases made, rather than supplier shipments into the channel. Observed sell-out can understate demand when products are unavailable.

Examining how a result changes when selected inputs or assumptions change. It helps identify the drivers that matter most and the conditions under which a decision could become less attractive.

Using a model to represent how a system behaves under specified conditions. It can explore existing operations, alternative actions, or uncertain futures. Commercial simulations may compare demand, revenue, margin, and portfolio outcomes under different assumptions.

An assessment of where a product's or promotion's sales come from, such as competitors, other products in the same portfolio, category expansion, or purchases shifted across time. The supported breakdown depends on the method and available data.

A distinct product variant tracked in inventory, and commonly the organization’s internal identifier for that variant. Size, flavor, color, or pack differences can define separate SKUs. An SKU is not necessarily a globally standardized identifier such as a GTIN.

The movement of demand between alternatives when prices, availability, or product features change. Substitution can occur within a company's portfolio or between competing products.

Commercial spending or terms agreed with channel customers to support sales and distribution, such as discounts, promotional funding, or listing support. Its value depends on the incremental benefit and the full customer economics.

Selecting promotion mechanics, depth, timing, and investment to improve an objective within business constraints. It uses expected demand response and customer economics to compare alternative plans.

Incomplete knowledge about a quantity, event, or outcome, arising from variability, limited information, or model limitations. It may be expressed through ranges, probabilities, or alternative scenarios; not every form of uncertainty can be reliably quantified.

The percentage of sales in a defined market represented by outlets carrying a product. The numerator is sales of those outlets; the denominator is sales of all relevant outlets, using the same sales measure and period. ACV weights use total outlet sales; PCV weights use category sales.

The maximum amount a customer would be prepared to pay for an offering under particular conditions. It can vary by customer, occasion, alternatives, and perceived value, so it is not a single fixed market price.

An organized flow of tasks, decisions, and handoffs used to complete work. It can include branches, parallel steps, and repeated steps, and may combine people, software, and AI agents.