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.
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
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.
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.
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.
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.
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.
Information that is available from government records and other sources available to the general public.
We partner with established tier 1, 2, and 3 data aggregators and niche data providers to source premium data. Data aggregators provide raw and modeled data.
Data we acquire, process, and standardize to create derived data. Derived data can be used to enrich internal data and build new or existing ML models
Example data signals for eCommerce
Review based information:
Business rating and reviews
Average pricing of businesses in area
Interests and habits:
Interests by industry: apparel, groceries, music, sports, gifts, art, culture, and more
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.)
Trending keywords and subjects
View our Data Gallery to see more examples and learn more.
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