Find-it-First® (FiF®) is a proprietary digital transformation technology platform built by agencyQ to manage complex structured and unstructured data, content and assets for dissemination through content-rich websites and other digital and offline channels.

Find-it-First

FiF Logo-R

Intelligent Automation saves time and cost

Find-it-First more efficiently ingests, analyzes and categorizes complex data and content. 

Find-it-First more effectively displays relevant content personalized for different users based on their preferences.

Find-it-First is a unique combination of agencyQ’s proprietary APIs and software designed to integrate seamlessly into existing client IT infrastructures, Sitecore® Experience Platform™, and Marketing Automation Systems.

Find-it-First is a Finalist for Technology Innovation at the ACT-IAC Igniting Innovation 2019 Awards!

Challenge

Any organization that wants to enhance Customer Experience and reduce content processing time and cost will benefit from Find-it-First (FiF).

Organizations with large amounts of complex content benefit from reduced demands on staff time and improved Customer Experience for users who engage and interact with the content.

Organizations that benefit have:

  • Large quantity of structured and/or unstructured data
  • Content that needs detailed categorization and organizing
  • Data and content that needs to be more explorable and discoverable
  • Need for more effective content personalization for different audiences and stakeholders.

Content Analysis
Efficiency

FiF uses the power of Artificial Intelligence, Natural Language Processing, and Deep Learning algorithms to create custom classifiers and classes to index and catalog content rapidly. FiF is innovative in the way it tackles the challenges of ingesting, analyzing, categorizing complex, technical, and specialized scientific data, and content using an integrated system of AI, natural language processing, visual recognition (for images), search functionality and content management tools tied together, and automated through agencyQ’s FiF software. FiF’s Natural Language API gains insight from structured and unstructured content using a unique Semantic Analysis process that analyzes and categorizes content via dynamically-inferred context & concepts rather than relying on traditional keyword methods. agencyQ’s FiF platform incorporates a data driven proprietary algorithm that creates a closed loop system between the back end Content Management Systems, Customer Relationship Management systems, and any front end digital engagement touch point – including the internal and external websites, social channels, email, and mobile.

Content Relevance
Effectiveness

Better targeting to different audiences. Faster personalization and optimization. Content easier to understand and navigate. Architected and built to create a quicker and more fulfilling Customer Experience, FiF emphasizes speed, accuracy, and adaptability by leveraging proprietary algorithms and analytics to enrich data for warehousing in a central content repository. FiF’s innovation stems from the proprietary algorithms developed by agencyQ integrated with the intelligent automated analysis and recommendation engines that power FiF. These drive the critical information and strategic insights necessary to deliver the highest value, most relevant personalized content for each and every target audience member. The agencyQ algorithm continually measures and optimizes content elements based on a user’s observed engagement with every piece of content across all digital channels. FiF determines the “intentionality” they exhibit and then refines the content so that it is tailored specifically to each different persona to increase interest and engagement with that persona.

Persona Driven Experience

Different types of users will see different types and styles of content automatically displayed on the website and in search results based on an analysis of how they engage with and search for specific information.

Built-in
Machine Learning

Built-in Machine Learning allows the FiF platform to more accurately identify and tag content over time as more data is available to analyze and categorize. Due its built-in automation, the analysis and categorization of data and content by FiF is faster, more efficient and more accurate so that end-user can spend more time on high value analysis, conclusions, insights, and actions from data rather than spending time manually doing administrative and rote tasks better accomplished through Intelligent Automation.

Specific Insights and Targeted Content Actions

FiF combines persona development and customer journey mapping to automatically guide desired interactions with users and create personalized content based on the most likely persona and provide trigger signals for marketers to take human interaction when desired. The secret sauce in FiF is a system of proprietary and repeatable algorithms that continually looks at known user data (demographic, firmographic, etc.) and intentionality data in near real time to continuously “nudge” users back into the Customer Journey path to create the best chance of desired outcomes. FiF is guided by an overlay of Customer Journey mapping and can be customized to any Customer Experience use case where there is (1) a mappable desired outcome, (2) a system of digital interactions, and optionally, (3) a system of desired human interactions.

Customized Analytics Dashboards

 

Content editors can analyze how particular pieces of content are performing and review user search and engagement patterns. Dashboards can be adapted for use in any organization. The Complementary Engagement Analytics tool built into FiF provides ongoing engagement KPIs and metrics to accurately measure engagement and conversion rates that determine how each end-user is interacting with different pieces of content across various channels.

  • Accounts for every content offering per item preview or link
  • Foundation for bridging gap between content NLP metadata and Persona Engagement
  • Provides tool for content editors to further boost and manipulate content metadata based upon queryable trends
  • Content editors have the ability to boost and override content retrieval to further refine page content

 

Related Content can be analyzed based on combination of content age, NLP metadata, and content editor boosting: 

  • Most searched content per persona
  • Most related content per NLP metadata per persona
  • Most recent content in ‘news section’
  • Most popular content per persona
  • Read time – estimated time to read content at top of article

 

FiF Infographic Q Design-v05

Find-it-First® and FiF® are Registered in the U.S. Patent and Trademark Office.

The use of DoE logos or images do not imply endorsement.