Feature Engineering, Data and Predictive Modeling Success

Feature engineering represents the artistic and intuitive side of data science. Here, we define feature engineering and its role in predictive modeling and highlight related data services that generate more valuable insights for your business than in-house resources alone. 

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What is Feature Engineering?

Feature engineering is a crucial part of predictive modeling success. The goal of predictive modeling is to identify how likely a subject (such as a customer or prospect) is to perform a desired action. A predictive model is a combination of attributes (also known as features) that predicts the likelihood of an outcome. 

Feature engineering is the process of refining raw data and identifying the most predictive attributes to use in modeling. When applied to marketing use cases, feature engineering supports the creation of predictive models that produce actionable insights around high-value customers, such as propensity to churn or acceptance rates for a product offer. 

The Predictive Modeling Process (1)


How is Feature Engineering Performed?

Feature engineering is performed manually by data scientists. Guided by intuition, it’s an overtly human step in a machine-centric field. Since a given data set may have a countless number of possible features, it’s not reasonable for even the most sophisticated algorithms to compute and evaluate them all. Human intervention guides the algorithms towards the most meaningful ones. 


Feature Engineering Challenges

1) Limited Data

Limited data is one of the most pervasive challenges in feature engineering: the smaller the data set, the fewer features may be drawn from it. Insufficient features can lead to models that are less actionable and insightful. Data scientists that support marketing teams often face this problem when attempting a predictive modeling exercise using first-party data alone.

2) Human Errors

Due to the manual nature of feature engineering, the process (which often contains repeated steps and handwritten code) is prone to human error. Experience, training, and self-discipline are key methods for reducing mistakes over time, but they do take patience. 

3) Time

With the increased push toward AI strategies in the post-big data world, data scientists are under mounting pressure to produce insights from increasingly complex information. There simply isn’t enough time for even the most efficient individuals to thoroughly vet features from burgeoning data. 

That said, there are some automated feature engineering tools on the market that are meant to fight this problem, but they are limited, and may not be a viable solution for teams with limited resources.


Data Enrichment & Feature Engineering Services

Data Enrichment 

The problem of limited data is overcome through data enrichment. Merging your first-party data with third-party data from a trusted data provider fills in the gaps in your knowledge of your audience. By expanding beyond the information you’ve gathered in-house, data enrichment helps you better predict the behavior of people outside your current customer base. 

If you want data enrichment services that make your AI smarter, work with a data provider that offers scale as well as feature-rich data sets to increase the breadth and depth of your existing data.

Learn more about data’s relationship with machine learning:

Consumer Analytics Services

You can outsource data enrichment, predictive modeling and related services so it’s possible to benefit from AI-driven insights even if your in-house data team is small or nonexistent.  Mobilewalla offers data enrichment, consumer analytics, and other data-related services, allowing you to devote your internal resources to other critical projects.  

80% of marketers worldwide consider third-party data providers to be the most trustworthy source of consumer information. Additionally, 56% of US companies are either evaluating AI and machine learning tools for use in their marketing departments or plan to in the future. If you’re in either group, consumer analytics services from a trusted data partner gives you access to all of these tools. 

What types of brands benefit from these services? Learn more in the following case studies:


About Mobilewalla

Mobilewalla is a leading consumer intelligence provider, boasting the most comprehensive consumer data repository in the digital ecosystem. We aggregate data from multiple sources, then apply data cleansing techniques, fraud detection measures, and a combination of deterministic, artificial intelligence, and machine learning techniques.

Data analysts, researchers, and marketers leverage our highly-accurate consumer data sets for richer, more robust customer profiles including information about their competitors’ customers. With app usage, location, and behavior-based data, enterprises can build a complete picture of current and potential customers to connect with them when and where they are ready to engage.

Connect with our consumer intelligence experts by filling out the form to learn how enriching your data can enhance feature engineering. 

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