Show your products and services to your customers.
Present automatically downloaded content on the website.
Recommendation frames analyze data about the customer's previous behavior - such as shopping, browsing products.
Based on this analysis, they provide personalized suggestions for content or products that may interest a given user.
Expand the spectrum of products you show to customers in the feed. From now on, these won't be products shown "rigidly" according to what's in the feed file, but (after Google learns your data) suggestions relating to the specific activity of the site's users.
Integration with Vertex AI is recommended in e-commerce, where it opens up opportunities to show products in several recommendation models:
Predicts the next product the user is most likely to be interested in (or even convert). Prediction based on the user's purchase and browsing history.
Predicts products frequently bought together in one shopping session. A useful recommendation when a customer is about to buy a specific product and you want to recommend complementary products.
Predicts another product that the customer is likely to buy or at least be interested in. This recommendation is usually used on the home page.
Predicts other products with attributes similar to the product under consideration. The recommendation is useful on product pages, especially when the customer's chosen product is out of stock.
Encourages repeat purchases based on previous repeat purchases. This personalized model predicts products that have been bought at least once and that are bought regularly.
Personalized model based on promotions. It can recommend products on sale.
Includes identifiers of products that the user has recently interacted with.
Extends recommendations from optimizing for a single set of recommendations at a time to optimizing for an entire page with multiple panels, selected by the model automatically.
Adapt marketing content and messages to the individual preferences of the recipient.
Offer customers products or services selected based on previous interactions and preferences.
Generate individual promotional offers or discounts depending on customer behavior.
Suggest the user the next steps to take in the process of using your services.
Suggest content such as blog articles, how-to videos or additional courses tailored to what your customer has previously searched for/bought.
iPresso is a CDP (Customer Data Platform) - it collects information about your customers in the form of attributes and activities. You will use the data for multi-channel, automated communication (email, SMS, web push, mobile push, on-site). You reach your recipients with the right message at the right time.
A simple and intuitive frame creator. You can use ready-made sets that you can immediately adapt to your preferences and save them as templates for future use. We created the frame creator with care to ensure that they look good and do not require the involvement of an IT team to show them on your website.
Recommendation frames are actually the dot on the i, which perfectly complements our original functionality, which is Feed Manager. Now you can make even better use of the potential of feeds, which you will use not only in e-mail creations, text messages, web pushes, but now also as an element of a website and pop-ups. Thanks to frames, you gain a broad context for presenting your products and services.
We generate data for reports based on the activity of contacts who interacted with a given frame. Thanks to this, we know to whom a given frame was displayed and how many clicks there were.
Product recommendations not only increase conversions, but also build lasting relationships with customers - everyone likes to receive offers tailored to their needs.
Dynamic product suggestions stimulate additional purchases, promote new products and increase the average basket value.
Recommendation frames in the financial sector may include suggestions for various financial products, such as loans, savings accounts, investments or insurance, taking into account the client's current financial situation.
Additionally, recommendations can be used to promote financial management tools, investment portfolio analysis or retirement planning.
By analyzing student activity data, such as test scores, preferred topics or learning rate, recommendation frames can generate recommendations tailored to each student's individual needs and learning style.
Recommendations in education may include suggested courses, additional materials, online lessons, homework assignments, or diagnostic tests.
Recommendations in the travel industry may include suggestions for destinations, hotel offers, tourist attractions, restaurants and recreational activities. Thanks to this, travelers receive personalized suggestions that highlight their individual tastes and expectations, which translates into a more satisfying travel experience.
Additionally, recommendation frames can be used to promote loyalty programs, special offers or additional services that may be of interest to a specific traveler.
By analyzing data related to purchase history, preferences, and online activity, recommendation frames provide personalized product and service suggestions. This tool allows you to effectively adapt the store's offer to the individual needs and tastes of customers, thereby increasing their commitment and loyalty.
