www.foodfirst.com an e-commerce site specialized in products for sports nutrition for everyone has joined the Affinyze network on Monday July 23.

www.maxbodyfood.ch, an e-commerce site specialized in products for diet, Health & Wellness, has joined Affinyze network and is using its predictive recommendations to further increase sales.

(versão em português segue abaixo / Portuguese version below)

TchêOfertas: Swiss targeting technology Affinyze Network personalizes group buying sales offers for visitors of tcheofertas.com.br and tcheviagens.com.br

Porto Alegre/Brasil, Lausanne/Suiça - 09 de novembro, 2011:

The Brazilian group buying websites TchêOfertas and Tchêviagens have partnered with FinScore to leverage Affinyze Network recommendations with its advanced technology which allows to increase sales on group buying and e-commerce website.

Affinyze Network tracks visitor behavior on all associated websites and thereby identifies visitor interest profiles. Based on those profiles Affinyze generates recommendations for relevant content individually tailored to each visitor. Affinyze applies advanced data mining technology to achieve significant click rate and purchase rate increases. The integration of the website with Affinyze is very easy and straightforwards.

 Affinyze Network is not only used to personalize the offers and the order in which the are displayed for each visitor on the website; it is also used to optimize and prioritize offers included in daily emailings. In this way both channels, inbound website visits and outbound emails are optimized to maximize returns.

Each visitor receives the most relevant content, the website increases sales and customer satisfaction.

According to Adriano Kalil, responsible for TchêOfertas, the integration of the website with Affinyze Network was really simple, and the partnership with FinScore enables the company to gain access to a new generation of intelligent business process optimization and customer relationship ehancements via personalization. Frank Block, CEO of FinScore said "Affinyze Network finally makes advanced targeting technology available to all those interested in getting the benefits from advanced analytics to increase the website and internet sales processes."

Read more about Affinyze Customer Online Targeting

TchêOfertas: tecnologia suíça de targeting Affinyze Network personaliza ofertas de compra coletiva para visitantes dos sites tcheofertas.com.br e do tcheviagens.com.br

Porto Alegre/Brasil, Lausanne/Suiça - 09 de novembro, 2011: O site gaúcho de compras coletivas TchêOfertas fechou parceria com a FinScore para utilizar Affinyze Network com a sua tecnologia avançada que aumenta as vendas em sites de compra coletiva e de comércio eletrônico.

O Affinyze Network monitora a navegação nos websites associados para descobrir o perfil dos visitantes e sugerir o conteúdo apropriado a cada visitante usando métodos de data mining e de modelagem estatística avançada. A integração é simples e os benefícios são imensos.

O Affinyze Network não somente é usado para personalizar as ofertas mostradas quando o visitante navega pelo site mas também para priorizar as ofertas incluídas nas campanhas de e-mail.

Como cada visitante recebe o conteúdo correto, o site vende mais, aumentando as taxas de conversão e de satisfação dos clientes.

Segundo Adriano Kalil, do TchêOfertas, a integração do site no Affinyze Network foi muito simples, e a parceria com a FinScore permite ao site de compra coletiva o acesso a uma nova geração de otimização de negócios e de relação com os seus clientes e visitantes. Segundo Frank Block, da FinScore, o Affinyze Network finalmente põe à disposição de todos websites e e-commerce as tecnologias mais avançadas e poderosas de data mining, permitindo assim aumentar a eficiência de processos de venda na internet.

Leia mais sobre Affinyze Customer Online Targeting

About FinScore
FinScore SA is a unique provider of software and services for data quality, customer and web intelligence, and reporting. Our clients are active in banking, telecommunication, insurance, e-commerce, and other domains. FinScore’s software processes huge amounts of transactional, behavioral and socio-demographic data to offer a visual and interactive experience that helps companies learn more from customer interactions and anticipate customer needs to better serve them. Our software can be seamlessly integrated with today’s information systems.

Contact
FinScore SA : Dr. Frank Block, CEO, FinScore, Chemin de la Rueyre 116-118, CH-1020 Renens, Switzerland, www.finscore.com, contact form

Affinyze Network is now being launched for accomodating up to hundreds of websites on a single tracking and predictive targeting network. If you represent an e-commerce website wishing to increase its purchase rates please let us know and we will explain how your site can be connected to the network in one day and start profiting from its powerful recommendations. Soon more...

Frank Block, FinScore, has co-authored an article on JSR due to publication in fall 2010 with the title "Analytics for Customer Engagement" on the current state-of-the art of marketing analytics including important topics such as

• opportunities and organizational aspects with respect to data collection for customer engagement
• how key behavioral manifestations of customer engagement (WOM, cocreation, complaining behavior) can be included in customer engagement models
• how to overcome existing barriers in marketing practice to introduce analytical models for customer engagement

Here is the summary of an article that appears in the Journal of Service Research:

The quantification of marketing actions and their results is an essential part of the marketing function. So far, analytics have concentrated on direct (tangible) customer outcomes, such as current and future transactions with the firm, and neglected the additional value of customer engagement. Beyond direct customer outcomes, customer engagement includes behavioral manifestations that have rather indirect impacts on firm performance, such as word-of-mouth (WOM) referrals, participation in the firm’s activities, suggestions for service improvements, customer voice, participation in brand communities, or revenge activities; all of these actions affect the brand or firm in ways separate from the influence of purchase. Neglecting behavioral manifestations of this kind can lead to highly biased perceptions of a customer’s contribution to a firm. For example, failing to incorporate WOM in the customer lifetime value (CLV) calculation could lead to an underestimation of the CLV by up to 40%. Thus, it is essential to establish measures and models that account for key behavioral manifestations of customer engagement.

The main objective of this article is to discuss how existing knowledge and modeling approaches from transaction research may be leveraged to build a model in the extended context of customer engagement. Moreover, taking into consideration the increasing ease of interacting quickly online and the resulting customer engagement opportunities (e.g., customer cocreation) requires a clearer view of the capabilities of analytical methods to deal with large data sets. Therefore, this article:

1. Reviews opportunities and organizational aspects with respect to data collection for customer engagement. Greater database size and complexity requires aggregated data, working with subsets, or the adaptation of methods from computer science. Real-time computation and new data sources will prompt the implementation of large-scale text mining techniques.
2. Offers a brief overview of “traditional” models for dealing with customer transactions to discuss how key behavioral manifestations of customer engagement (WOM, cocreation, complaining behavior) can be included in these models.
3. Discusses how to overcome existing barriers in marketing practice to introduce analytical models for customer engagement. The core aspects pertain to (a) data quality, the size of the databases, and new types of data; (b) data ownership, such that clear responsibility drives data quality and access to data; (c) model complexity, mitigated by standardization; (d) ownership of modeling tools; (e) usability of the results, which requires fast delivery of results together with clear interpretations of findings; and (f) integration into company processes.

For more information, click here.