Customer Engagement Platform - AI driven Merchandising

The ODOSCOPE Customer Engagement Platform specializes in AI-driven merchandising by deciding which products should be presented to which users and when, taking into account business interests such as margins, return rates or stock sales. In addition, business users gain deep insight into their product sales and conversion rates, as well as which products are sold to which customers, in which situations, and at what prices, for granular assortment control.

When it comes to presenting relevant products in online stores, on websites, in apps or at other digital touchpoints in a largely cookie-less world, ODOSCOPE CEP relies on "situationalization" as the next level of personalization. In addition to personal profile information and historical purchase behavior, situational data such as current usage behavior, referrer, device type, time, day or location, but also "external" parameters such as weather, population density, local events or other available contextual information are used to anticipate and predict the needs of the current - in many cases unknown - user on a user-specific and situation-specific basis using a highly innovative real-time AI technology.

Areas of application include various touchpoints such as product lists, news feeds, landing pages, newsletters, display banners, as well as personalized recommendations in e.g. shopping carts, on product detail pages, during the checkout process, etc. All of these touchpoints can be optimized in terms of various metrics such as conversion rate, revenue, bounce rate, session length, average order value, margin, return rates, campaign efficiency, advertising costs, assortment sales, etc. The business user of an online shop, website or digital touchpoint can define different playout strategies and test them against each other using A/B/n tests.

The ODOSCOPE Customer Engagement Platform combines the benefits of a Customer Data Platform (CDP), which stores all information about an individual customer, with detailed product data stored over time and situation-aware real-time decisions. This helps align the interests and needs of the customer with the interests and needs of the business.

Visual interfaces for manual customization of AI output help take into account specific business metrics and needs when matching relevance to individual users. Specifically, this means that output is optimized not only by conversion rates, but also by absolute profit. The business user can also set up inventory sales for specific product lines or brands.

Using innovative real-time AI modules, each user, known or unknown, receives their own individual product listings and recommendations based on profile information and anonymous contextual information such as device, location, weather, referrer, time, etc. In addition, each click by the user can be immediately considered for the next relevance decision. For example, a user filtering for dresses by size will not only get a filtered list of dresses for that size, but that list will also be sorted by relevance for that size.

AI-driven merchandising is not just about making the process more efficient, it is about enabling companies to analyze their customers' behavior and preferences in a way that has never been possible before. With this information, companies can make more informed decisions about which products to stock, which products to feature, and how to price them. The result is a more effective product offering and maximized sales.

Since 2022, the ODOSCOPE CEP includes a fully customizable CDP based on the open source ClickHouse database. ClickHouse has the advantage of combining all customer profile information with all product data over time and all user behavioral data.

ODOSCOPE also offers this data layer based on ClickHouse as a managed data lakehouse, i.e. the data lakehouse is located in the customer's data center but managed by ODOSCOPE. This has the great advantage that the customer owns the data and the database and can use this data lakehouse as their own data science pool to run their own machine learning tools on top of it. New additional data points created by the data science team can then be used by the ODOSCOPE CEP for relevance calculations.

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Please find evidence, positive test results and testimonials here in our case studies:

https://www.odoscope.com/insights/case-studies/

Please contact info@odoscope.com for getting case studies without registering.

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