What is Touchpoint Studio?

Introduction 

Touchpoint is any place on your website or app where possible opportunities can happen between you and your customer or prospect. Touchpoint studio is a unique solution for managing personalized experience on any channel. It’s ideal for upselling, cross-selling, keeping customers in the checkout, promoting actions, or just providing information of interest to them.   

Overview

In the process of making a touchpoint in TP studio you first create a default strategy which will be shown to all customers. By adding new strategy (or strategies) you can choose a specific audience to which this new strategy will be shown. When you have two or more strategies connected to one touchpoint you can sort them by priority order. 

Strategy defines rules and recommender types according to which the products will be served to your customer.  

Touchpoint types

Smart Recommendations ensure that every user is equipped with relevant recommendations he comes across on every digital platform. Those recommendations are based on your business wisdom, data and artificial intelligence.

Recommender models are the essential part of personalized customer offer. They can be used to present every customer with a relevant recommendation or to find the right buyers for specific products.

Next generation recommender systems give more personalized recommendations, reach customers through multiple channels and give real-time recommendations. They aim to present the right items to a user and at the right time.

Used not only in e-commerce, but in other industries as well, as they find the recommendations powered by intelligence to take crucial part in reaching the goal of personalized targeting. These industries include retail, banking, telecommunications, real estate, etc.

• Similar products recommendations 

This service uses transaction history and returns N similar products for each product. It recommends the best-fit alternative products based on market basket content. Convenient for search banners and product pages where a specific product is not available or not found, in order to provide alternative and prevent cart abandonment.

• Related products recommendations 

This service uses transaction history and returns N similar products for each product. For each product, related products are selected using a certain association rule. This feature recommends products that frequently go together in the basket. Perfect for upselling and convenient for “add-to-cart” banners, POS recommendations, search banners, etc.

• Carousel

Choose a maximum of 15 products to be presented on your web page of choice which will serve recommender outputs according to the recommendation model or rules you have selected. Recommendation model and rules are defined by the goal you chose to be your strategy of this Touchpoint. Your goal can be to boost chosen products, decrease bounce rate or increase cross-sell & up-sell. Carousel is best for category pages, “add-to-cart" pop up screen, etc. 

Recommender types

Recommender models are the essential part of personalized customer offer. They can be used to present every customer with a relevant recommendation or to find the right buyers for specific products.

History-based recommender

A history-based recommender system, personalized to each customer, looks at products they frequently purchase or the first and last products they bought. Using this information, the system predicts and suggests products that align with their preferences, making their shopping experience more individualized and enjoyable.

Time-based recommender

A recommender system based on time patterns examines a customer's purchase history to detect recurring buying intervals, like purchases made at regular time gaps. Understanding these habits, the system predicts and suggests products that fit the expected timing of their next purchase. This ensures recommendations align with the customer's established purchasing schedule, enhancing their shopping experience by offering timely and fitting suggestions for their upcoming purchase.

Attribute-based recommender

An attribute-based recommender system is designed to provide tailored recommendations by considering both the attributes of the customer and the characteristics of the items. It takes into account specific traits or preferences of the customer, such as demographic information, past purchase history, and stated preferences. Simultaneously, it analyzes the attributes of the items available, which can range from product features, genre, style, or any relevant categorization. By aligning these attributes and preferences, the recommender system suggests items that best fit the customer's profile, ensuring a personalized recommendation that matches their unique tastes and requirements.

Community-based recommender

A community-based recommender system employs a collaborative filtering strategy to recommend items to customers. This approach taps into the collective wisdom of a community of users with similar preferences or purchase histories. By analyzing the behavior and interactions within these communities, the system identifies patterns and correlations among users. If a user shares interests or purchasing behaviors with others in their community, the system recommends items that those similar users have liked or purchased. This harnessing of collective preferences helps in suggesting items that align with a specific user's taste, leveraging the power of peer insights and recommendations.

Basket-based recommender

A basket-based recommender system focuses on understanding relationships within items in a customer's purchase basket and predicts related products based on those associations. By analyzing the items a customer has already bought, the system identifies patterns and connections between these purchases. It learns from the combinations of products in the basket, deciphering which items are often bought together. Utilizing this learned knowledge, the system can then suggest additional products that are likely to be of interest to the customer, complementing their previous purchases and enhancing their shopping experience.

Description-based recommender

A description-based recommender system leverages all available textual data and descriptions associated with items to identify products that align with a customer's preferences. By analyzing the text, including product descriptions, features, reviews, and other relevant information, this recommender system gains a comprehensive understanding of each item's attributes. It then matches these attributes with the customer's known taste and preferences, allowing the system to recommend items that are most likely to resonate with the customer. This approach ensures that recommendations are not solely based on purchase history but also take into account the specific qualities and details of the items, resulting in more tailored and fitting suggestions for the customer.