> For the complete documentation index, see [llms.txt](https://docs.flowlity.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.flowlity.com/concepts/product-similarities.md). # Product similarities When a product is new, has sparse demand history, or is replacing another product, forecasting from its own data alone may not be enough. **Product similarities** let you link related products together so that their demand history feeds into the forecast of the target product — giving the engine more data to work with and producing better predictions. ### Why it matters Accurate forecasting depends on having enough historical demand. In practice, many products lack this: * **New product launches** — No past demand exists yet. * **Product replacements** — The old product's history is highly relevant to the new one. * **Seasonal or slow-moving items** — Limited data points make patterns hard to detect. By linking a similar product, you transfer its demand history to the target product. The Forecast Agent then uses this enriched history alongside the product's own data to generate a more reliable probabilistic forecast. ### How similar products are linked There are two ways to establish product similarities in Flowlity: #### 1. AI recommendations (Product Similarity Agent) The **Product Similarity Agent** automatically identifies the most similar products for each item in your catalog. It uses three complementary distance metrics, configured at the site level: * **Category-based similarity** — Compares categorical attributes such as product name, description, or tag values. Products that share the same attributes are considered similar. * **Text similarity** — Measures semantic closeness between text fields (name, description) using language models. For example, "red sports car" and "crimson racing vehicle" would score highly. * **Numerical similarity** — Compares numerical values (e.g. average price) within a configurable threshold. A **price penalty threshold** defines the acceptable range — products outside this range receive a lower similarity score. The agent combines these metrics and returns up to **5 recommended products** per item, each with a **similarity coefficient** (displayed as a percentage). Only recommendations above 30% similarity are shown. The agent runs weekly and updates recommendations automatically as your catalog and data evolve. #### 2. Manual linking You can also add similar products manually from the **Demand module** using the **Product strategy** sidebar. This gives you full control and is especially useful for product replacements. When linking products manually, you can configure: * **Source product** — The product whose demand history will be used. * **Coefficient** — A percentage that controls how much of the source's demand is transferred. For example, if two products are being consolidated into one, you might set 50% for each. * **End date** (optional) — Makes the link active only until a specific date. Useful when a replacement is temporary or when the new product has accumulated enough of its own history. * **Customer mapping** (optional) — If your site uses the customer axis, you can map source and target customers to control which demand streams are transferred. ### Viewing similar products demand In the **Demand module**, you can visualize the impact of product similarities directly in the chart: * Open **Table settings** (right-hand side of the screen). * Enable the **Similar products past demand** line. This line shows the demand history that is being contributed by linked similar products. It appears alongside **Cleaned past demand** (what the model uses) and **Raw past demand** (the unmodified signal from your data), giving you full transparency into what feeds the forecast. ### Current product ratio Each product has a **current product ratio** setting (default 100%) that controls the balance between the product's own demand and the demand contributed by its similar products. Lowering this value gives more weight to the similar products' history. ### Where to find it | Location | What you can do | | -------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | **Demand module > Product strategy sidebar** | Add, edit, or remove similar products for a specific item. View AI recommendations. | | **Agents module > Product Similarity Agent** | See the agent's configuration: which similarity metrics are active, which attributes are analyzed, and the price penalty threshold. Use the "Try it out" feature to preview recommendations for any product. | | **Demand module > Table settings** | Toggle the "Similar products past demand" line in the chart. | ### Related pages * [Demand forecasting explained](/concepts/demand-forecasting.md) — How the probabilistic forecasting engine uses similar products alongside other inputs. * [Forecast events and demand adjustments](/concepts/forecast-events.md) — Other ways to refine the demand signal. * [Demand](/modules/demand.md) — Where you view forecasts and access the Product strategy sidebar. * [Agents](/modules/agents.md) — Monitor the Product Similarity Agent and its configuration. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.flowlity.com/concepts/product-similarities.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.