Reach Analytics’ response modeling identifies the consumers who are most likely to respond positively to an advertising message based on a consortium of data sources. The self-service marketing platform automatically identifies new prospects and updates lead scoring by pulling in data from their customers’ past campaigns, as well as in conjunction with third-party data sources.
Email marketers don’t need to worry about maintaining a clean email list because Reach Analytics streamlines the data management process automatically by cleaning data, deleting duplicates, and appending data. Marketers simply need to upload a list of names and corresponding emails, or physical addresses, and Reach Analytics organizes the data so marketers can begin building data models.
Marketers can also quickly upload engagement metrics from past campaign results and Reach Analytics will automatically assign value to engagement factors such as channel, promotional offer, or creative content, to help its customers better personalize marketing communication in the future.
Reach Analytics’ response modeling feature is designed to complement the platform’s look-alike modeling feature, first launched in April 2016, that identifies prospects most likely to become new customers. Reach Analytics also launched a redesigned user-interface that incorporates customer and prospect profiles so marketers can get insights and take action quickly. The platform is designed to be collaborative, so customer profiles are easily sharable and include note-taking capabilities.
Based in Silicon Valley, Reach Analytics primarily serves agencies, brands and non-profit organizations with data enrichment and predictive marketing solutions for customer acquisition and retention. The company was formed in 2013 from the merger of DMRA and RapidBuyr.