Optimize lead scoring, customer engagement, and segmentation with external data

eCommerce machine learning
Consumer buying behavior continues to shift online.
As consumers increasingly shop online, there is huge growth potential for eCommerce businesses. Online shoppers are more responsive to advertising and promotions than consumers who do not transact online. Enriching internal data with external signals such as social media, demographic, and geospatial can boost eCommerce sales. This is an efficient and effective way to gain new customers at  lower customer acquisition costs while creating brand loyalty and higher customer LTV.

Explorium's automated data science platform

Challenges

Consolidating external data for analytics is resource and time consuming
Explorium’s External Data Platform centralizes access to public, premium, and proprietary external data for business analysts, data scientists, and business leaders

Transforming, matching, and integrating external data is tedious work
Explorium’s External Data Platform ML capabilities automatically match and integrate external data with your internal data accelerating time to value

Assessing the value of external data is inexact
Explorium’s External Data Platform identifies the relevant external data signals and calculates the uplift in your ML models before deployment

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Benefits

The right external data is a competitive advantage. 

Understanding the broader trends in the market and including them in predictive models improves their performance. Enrich your internal data with external data signals to gain better insights and improve business outcomes.

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Understand Customer LTV
Consumers have more options than ever. Predicting user behavior early on in the funnel and identifying those users most likely to have a high LTV is key. This informs and transforms customer acquisition and retention marketing campaigns. Redefine LTV models by enriching internal data with external data signals such as payment history compared to socioeconomic and income status, social media activity, reviews of similar products, and income history. Use the prediction to better optimize Google and Facebook ad campaigns.

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Optimize marketing efforts through improved customer segmentation
One of the biggest challenges marketers face is understanding who their customers are and why they buy. Data scientists and market researchers use data to look for patterns, which can help inform new segmentation strategies based on predicted behaviors. Using external data to enhance internal data and website analytics provides a richer understanding of customers’ needs and enables better targeting and communication with prospects.

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Improve Ad Retargeting
Converting an abandoned cart to a completed purchase is a big challenge for eCommerce companies. Retargeting is a powerful marketing strategy, especially when coupled with product catalog based ad types such as Facebook Dynamic Product ads and Google RLSA. Understanding which behaviors, actions, and interactions could indicate a potential customer drives where to best allocate marketing dollars. Enriching internal customer data with external sources such as location based data (zip codes and IP addresses), geospatial financial indicators, and time series data on repeat visitors significantly improves your models to retarget customers.

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CASE STUDY
GlassesUSA Thumb

machine learning solutions by explorium

“The ease of use and flexibility of Explorium allows us to launch new use cases on a monthly basis with minimal effort but maximum impact.”

Nadav Yekutiel, Head of Product Analytics & Data Scientist, GlassesUSA.com

DATA

Unlock new business value with external data.

Explorium’s External Data Platform improves analytics and machine learning. Explorium enables organizations to automatically discover and use thousands of relevant data signals to improve predictions and ML model performance. Explorium’s  External Data Platform empowers data scientists and analysts to acquire and integrate third-party data efficiently, cost-effectively and in compliance with regulations.

machine learning insurance
Example data signals for eCommerce

Review based information:

  • Business rating and reviews
  • Health score
  • Noise level
  • Average pricing of businesses in area

Interests and habits:

  • Hobbies
  • Interests by industry: apparel, groceries, music, sports, gifts, art, culture, and more

Purchasing behavior:

  • Number of businesses nearby by category
  • Foot traffic information: number of visits in the area, places people visited on the same day, etc.
  • Tourist attributes: hotels and attraction characteristics (number of hotel rooms in an area, etc.)

Trends behavior:

  • Trending keywords and subjects
  • Web traffic

View our Data Gallery to see more examples and learn more.

RESOURCES
Take Control of Your Data - An Essential Guide for Marketers
Drive eCommerce Conversions with Predictive Models Fueled by External Data
Optimize Your Analytics — Why You Need a Data Acquisition Strategy
Boost eCommerce Conversions Using Predictive Models Without a Data Scientist
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