New mobile apps to keep an eye on
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What new social media mobile apps are available in 2023?
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Use new social media apps as marketing funnels
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Try out Twitter Spaces or Clubhouse on iPhone
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What app are you currently experimenting on?
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In the realm of Predictive AI, structured data has long been the primary fuel for building accurate models. However, a wealth of valuable information often lies untapped in unstructured, free-text data. This article explores how text normalization techniques can affect AI modeling building and the accuracy of your Predictive AI model, particularly in the areas of lead scoring, account scoring, churn prediction, and automated data science.
The Challenge of Free-Text Data
Free-text fields in forms and databases often contain crucial information that could significantly enhance predictive models. However, the lack of standardization in these fields presents a major hurdle. When individuals input data in their own words, it creates a diversity of expressions that traditional AI models struggle to interpret effectively.
For instance, job titles, company descriptions, or customer feedback can vary widely, making it difficult for AI models to extract consistent, meaningful insights. The challenge lies in normalizing this data to make it analyzable without losing its nuanced information.
Let's explore some use cases of how unstructured free text form data can influence affect your model:
Use Case 1: Job Title Analysis
Consider the variability in job titles across different organizations. A "Director of Marketing" "Head of Growth," and "Product Marketing Lead" might all describe similar roles. By normalizing these titles, AI models can:
- Accurately assess decision-making authority: Identify leads who are likely to have purchasing power or influence.
- Identify high-value prospects: Recognize titles that are typically associated with higher conversion rates or larger deal sizes.
- Analyze organizational structures to tailor pitches: Understand where a lead fits in their company's hierarchy to customize your approach.
- Improve lead qualification: Better match leads to your ideal customer profile based on standardized job functions.
For instance, a B2B software company could use this technology to analyze historical sales data, identifying which normalized job titles most often lead to successful deals. They could then adjust their lead scoring model to prioritize prospects with similar titles, thereby focusing sales efforts on the most promising leads and improving overall conversion rates.
Use Case 2: Email Domain Analysis
Email addresses often contain valuable information about an individual's professional context. By categorizing email domains, predictive models can uncover insights such as:
- Patterns in how different sectors (government, education, corporate, personal) interact with a business
- Variations in customer behavior based on professional affiliations
- Potential for targeted marketing campaigns based on domain types
A software company, for example, could use this analysis to predict which types of organizations (based on email domains) are most likely to convert from free trials to paid subscriptions, allowing for more efficient allocation of marketing and sales resources.
Most importantly they would be able to add another field to learn through rather than have this valuable date lost due to the unstructured nature of the field. As the data is the cornerstone of your accuracy.
Forwrd launches Segmented Fields: Automatically segment unstructured data for your review.
The reasons and use cases above are why Forwrd has decided to develop Segmented Fields. Segmented fields are basically an algorithm based learning process to identify what the text in free form fields means, group the field entries by their segmentation and incorporate this previously lost data into your model build.
Forwrd’s machine learning identifies fields compromised by unstructured data, automatically groups them into a category identified by our semantic engine as relevant and allows the user to review the segmentation and approve or decline.
It allows our clients to tap into previously non-utilized data sources, extracting valuable insights that were once hidden in the noise of unstructured text.
In a world where data is king, the ability to effectively analyze and predict based on free text inputs sets Forwrd apart.
🥂 Here’s to new fields which can now be used to build your operational AI models.
