Predictive AI

InsideUp’s Predictive AI Advantage

The heart of InCapture’s technology is its Predictive AI engine—a proprietary system designed to pinpoint high-value sales prospects and enhance campaign ROI through precise, data-driven insights. Our Predictive AI system uses historical and real-time data to generate predictive scoring that helps clients reach decision-makers faster and optimize conversion rates. Core features include:

Targeted Predictive Scoring: Our AI model evaluates both account and contact levels, delivering a ranked list of leads based on purchase likelihood, ensuring efficient use of marketing resources.
Automated Data Pipeline: By automating data ingestion, processing, and analysis, our Predictive AI minimizes human intervention and accelerates time-to-value for each campaign.
Continuous Learning & Improvement: Built on a neural network model, our AI system continuously retrains and updates using client feedback, maintaining accuracy and relevance across multiple campaigns.
Enhanced First-Party and Third-Party Data Integration: The platform leverages client first-party data and seamlessly integrates third-party sources to create a holistic, actionable view of each prospect.

InsideUp’s Predictive AI platform enables our clients to scale demand generation efforts and drive measurable business outcomes by identifying, scoring, and targeting the most qualified prospects in real time.

Second-Party Data Captured from Campaigns Completed Across Many Cloud Technologies

By partnering with InsideUp, you gain access to a steady stream of second-party data. The Predictive AI module at InsideUp has ingested tens of millions of separate data records, gleaned from literally hundreds of cloud technology campaigns executed over many years. In addition, insights gained from related cloud categories can be applied to benefit a solution in another category that would not have been otherwise uncovered solely from the first-party data held by your company.

An Institutionalized and Industrialized Data Hygiene Process and Toolset

InsideUp has invested in a variety of data hygiene tools, integrated with our InCapture platform, that access an array of web-based and third-party resources to continually verify and append data. Our dedicated team of data analysts and scientists continuously updates data sets used for client campaigns and monitors data quality to refine our Predictive AI model. Clean data allows Predictive AI models to generalize well to new, unseen data.

Robust Selection of Data Attributes Within a Proprietary Data Set

InsideUp tracks a multitude of data attributes associated with our proprietary multichannel engagement data as well as from various data sources. This data is used in our Predictive AI system to uncover a large pool of likely buyers. Our system can ingest both contact and account attributes including buying committee data, online activity, engagement data, firmograhics, technographics and company milestones.

Training of Predictive AI Models Incorporates Historical Phone Conversational Data

Our Predictive AI system leverages the content of conversations (along with call metadata), while being in compliance with data privacy regulations. Phone engagement data reflects nuanced and detailed information, including vocal cues such as tone, pitch, and inflection, that uncover insights into the contact’s satisfaction, frustration, or level of enthusiasm. We can ask personalized questions, personalize our communications with Generative AI, and adapt our responses based on the conversation.

Predictions Derived at the Contact-level, not just the Company-level

Our Predictive AI system scores each contact differently than other contacts within the same company. With InsideUp, you gain the opportunity to present your solution to contacts in out-of-market accounts who are predicted to be interested in your solution for reasons that only our Predictive AI model can uncover. Our solution rises above prediction models (tied to keyword-based research done by anonymous employees) that do not indicate who might be part of the buying committee for a solution.

During AI Model Development Pre-Built Libraries Were Used to Meet Aggressive Timeline

To develop our model, we integrated key open-source Python libraries (notably scikit-learn) for efficient preprocessing, pandas for streamlined data manipulation, and open-source gradient boosting frameworks that are more effective with large datasets. Along with model monitoring and maintenance components to ensure ongoing performance scrutiny, we also established a feedback loop involving human experts to enhance the model’s adaptability. An autoML capability, leveraging other open-source libraries, was incorporated to handle dynamic model adjustments.

Our Predictive AI Model was Trained and Deployed using a Phased Approach

During data collection, robust datasets were compiled from diverse sources and combined into a data warehouse. To train the model, curated data was fed into a series of pre-built machine learning libraries to optimize accuracy. During the evaluation phase, our team rigorously assessed model performance using “hold out” data sets. Model deployment included packaging the best performing model into production-ready APIs. Continuous monitoring tracks prediction quality and checks for data drift supported by periodic retraining of the model.

Predictive AI Insights are Operationalized within a Workflow Automation Platform

We integrated our trained Predictive AI model with our existing workflow automation platform, InCapture. Therefore, the insights produced by the Predictive AI model are operationalized at scale for every campaign we conduct going forward. Since our existing software platform is also hosted on a public cloud infrastructure, we were able to easily establish custom Application Programmable Interface (API) endpoints to accept fresh input data and return predictions in real time.

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