Yuliya Malinina | January 16th, 2020
I found out that marketing campaigns with intent data using became more and more popular. I was very interested in it and decided that I’d love to do research to find out which way could be more effective for companies with cashback and coupons business because the past several months I took part in such a platform launch . Would benefits of using intent data be higher than other efforts for user acquisition? I’ll try to compare the benefits of using intent data to benefits of other actions such as UA based on social networks targeting and Browser Extension development like Rakuten’s cashback button, that finds deals, coupons, cashback at all connected with Rakuten stores and shows SERPs during their members browse something on the internet For the beginning let me share with you a bit more details what Intent data means for marketers (I’m pretty sure you know, but anyway, just in case.. ) Intent data shows which leads or accounts are actively conducting research online in other words it’s a behavioral information collected about an individual’s online activities, combining both topic and context data. So you can get a rich source of data regarding the interests of the buyer and can create the basis for predicting a future purchase. There are two types of Intent data collection: First party intent data – it also called engagement data and marketers have been using it for a long time, so there is nothing new here Third party intent data – most interesting and newest one because this data comes from external sources. While marketing automation tracks our own web properties, third-party intent data providers can track everyone else’s. A potential buyer often makes a small review before making a purchase. He can do this review directly on the seller’s website – read a blog, download whitepapers, analyze reviews of previous customers. Or on a third-party site – that is, watch content that is related to the product. The conversion to a purchase from such an advertisement is higher than in an advertisement using data collected about users on social networks. According to various estimates, by 200-400%. 3.5% – conversion from Google search (alternative sources say 1.7%) versus 0.7% in social networks, averaged data on the US market. The entire search engine business is built on intent data. The opportunity to use their knowledge about the intentions of users they sell to advertisers (through Google Ads, for example) After analyzing this information, using targeting based on data from social networks for advertising no longer looks as interesting as before. Therefore, I decided that I would choose between advertising based on intent data and a browser extension development. To answer this question, first I’ll try to find out if the browser extension is really effective and how much the cost of intent data providers services. Browser extension: First, let’s figure out why it is needed at all (if you’re not Rakuten and don’t have millions of your website visitors 🙂 ). First of all, to increase conversions in paying users. A plugin recognizes a specific product that user views and instantly displays all available coupons and discounts on it. Such triggers in 90% of cases motivate a person to buy and conversion rate increases. Also, if users allow browser plugins to view the contents of the pages they read it can become one of the sources of Intent data as well! For example if I create a browser extension for my site it turns out that I automatically begin to collect this valuable information and in the future I can share it with advertising networks by acquiring another source of income? (Not sure but it would be good to know.. ) And the most interesting thing is that users who install the plugin also have benefits from its use, primarily by saving their time. They no longer need to constantly look at the offers of stores in order not to miss the discount, because the plug-in will notify them about it as soon as a favorable offer appears in the store. Using SimilarWeb (also, by the way, a browser extension) I found Cashback and Coupons sites with monthly number of visitors less than 1M (To comparison: Rakuten has over 70M visitors monthly) – https://dealhack.com, https://www.rebatesme.com. So, less than 1M users visit them every month but they all have a plugin which they actively promote on their sites. Does this mean that their plugins are effective? I can evaluate this only by indirect signs, such as plugin updates in their Chrome Store stores. All companies have the latest plugin version update released no later than November 2019, which means that they support them. Would you start spending money on supporting something that doesn’t bring you income at all? I don’t think so. I certainly wouldn’t. I assume that the plugin will help increase conversions and it would not be bad to develop it. Intent Data Providers: Google search helped me to find a list of B2B Intent Data Providers very quickly , https://datarade.ai/data-categories/b2b-intent-data, but none of them published their prices openly, so it was not possible to quickly evaluate the financial side of the campaign. I’m going to communicate with each of them and share my results with you as soon as I get them.
Yuliya Malinina | December 17th, 2014
The Idea Unified eCommerce Product Catalog is an eCommerce system based on Java. It was originally developed as NoSQL-based eCommerce data model repository for processing large amount of eCommerce-specific data: product, category, price, and inventory. Unified eCommerce Product Catalog is organized on the concept of one aggregated product entity connected with multiple supplier/vendor offers (inventory, prices, shipping options) and multiple content providers (product description. attributes, languages and etc) Additionally it provides a built-in highly-optimized object-oriented Java API for easy and seamless integration with third-party solutions A high level overview The main goal of the UCS system is to provide platform for building E-Commerce Catalog-based services with high entity management operations performance (~above hundred millions transactions/operations per second in average) It is oriented for eCommerce systems usually use search/query/get much frequently than update/modify operations with data stored in their persistence to get more benefit from in-memory storage Main insights all data is stored in RAM all changes are fleshed into persistence store few predefined set of entities and their relations are supported: Product, Product Content, Product Identity, Product Variation Family, Attribute, Category, Category Tree, Offer, Channel, Owner data persistence can be done by traditional RDBMS or non-RDBMS database (like PostgreSQL or MongoDB) access through object-oriented API transactions only on single operation level Architecture