This glossary provides definitions for key terms and concepts used throughout the Contextual Commerce knowledge base.
| Term | Definition |
|---|---|
| Average order value | The average total amount spent by shoppers per transaction, which can be influenced by campaigns that encourage higher spending. |
| Billing period | The phase after the Proof of Value, where the client starts being billed based on the additional revenue generated by Contextual Commerce. Billing is tied to the demonstrated impact of campaigns, ensuring that clients only pay based on results. |
| Campaigns/Concepts/Messages | UI elements that Contextual Commerce injects into the app to engage shoppers, typically shown as sliders or inline campaigns. These elements encourage shopper actions, like completing a purchase. Campaigns may include text, images, and calls-to-action based on predefined strategies. |
| Contextual Commerce | A system that monitors shopper behavior and displays personalized messages (called campaigns) to increase the chances of completing a purchase. The decisions on when to show them are based on both behavior tracking and data provided through the SDK. |
| Contextualization | Using a shopper's real-time behavioral data and contextual signals like location, time of day, pricing, inventory, etc. to deliver tailored content, offers, recommendations, and experiences matched to their immediate context. |
| Conversion rate | The percentage of shoppers who complete a desired action (e.g., making a purchase) after interacting with a campaign. This is a key metric for assessing campaign effectiveness. |
| Decision engine | The core machine learning system that processes data from shopper interactions to decide when, where, and which campaign to display to maximize conversion. |
| Design | Defines the visual and stylistic elements of a campaign, including layout, colors, and fonts, to ensure that campaigns align with the brand's voice and fit seamlessly within the site’s design. |
| Group/Group assignment | When a shopper session begins, they are assigned to a group—Test (where campaigns are shown) or Baseline (where campaigns are hidden). This assignment helps measure the impact of campaigns and is essential for billing, as revenue is compared between groups. |
| Incremental revenue | The additional revenue generated as a result of Contextual Commerce campaigns. It is measured by comparing the revenue of the Test group (who see campaigns) to the Baseline group (who don’t see campaigns). |
| Pages/Views | Individual screen views within the app (e.g., Home, Product pages). Each rendering of a new page type (or product variation, depending on the UI design) is tracked to provide decision data to the Contextual Commerce decision engine. |
| Placement | Refers to both the page type where a campaign is shown and the specific UI component (Slider, Billboard, Module, Badge, or Custom) that displays it. Placements are configured to fit seamlessly within the shopper’s journey. |
| Product | A distinct item within e-commerce, which may have multiple variations (e.g., sizes, colors). Each Product has a unique productId, and variations are tracked by individual skus. Contextual Commerce tracks both the overall item and its variations. Each variation (or SKU) is unique and must be identifiable across the system, not just within the parent product. |
| Proof of value | The initial phase of Contextual Commerce activation on a client’s site where A/B tests assess the impact of campaigns. This phase is intended to demonstrate the effectiveness of campaigns before full rollout. Statistical significance is required to move to the Billing Period. |
| Revenue per session | The average revenue generated per shopper session. This metric is used to evaluate the financial impact of campaigns. |
| Sessions | A continuous period of shopper activity within the app. Sessions automatically expire after inactivity (typically 30 minutes), after which a new session is created. Contextual Commerce transfers key shopper data (such as shopperId and cart contents) between sessions to ensure continuity. |
| Statistical significance | Ensures that the observed results of campaigns are intentional and not due to random chance. Statistical significance is critical in determining whether the impact of a campaign is real and replicable. |
| Technique | The strategy behind the presentation of a campaign. Techniques determine what type of message (e.g., urgency, social proof) will be displayed, using data to match the message to the shopper's behavior. Techniques are categorized by overarching strategies, such as increasing conversions or re-engaging inactive users. |
| Traffic split | The strategic division of site traffic (users or sessions) randomly assigned between the Contextual Commerce (Test) and Baseline (Control) groups. This ratio (for example, 90/10) aims to drive incremental revenue by allocating the majority of traffic to the Contextual Commerce group, while ensuring the Baseline group remains large enough to maintain statistical significance, even during low-traffic periods. |
