“Adobe captures this event-level longitudinal data across product usage, marketing, and customer support to build various types of predictive models,” Padmanabhan says. These include paid conversion and retention models, customer retention models, automated feature extraction and segmentation, upsell and cross-sell models, and optimal allocation and segment-based forecasting models.
The tools the company has used for its machine learning efforts include Python Scikit-learn, Spark ML, SAS, and proprietary in-house methods.
Machine learning methods have helped the company build individual-level, high-dimensional models, Padmanabhan says. “Previously, Adobe leveraged statistical tools for building more aggregated models that would ignore individual-level heterogeneity altogether,” he says.
Among the key benefits of machine learning for Adobe is a greater understanding of the marginal impact of paid media, which has resulted in the improved allocation of media touchpoints across various selling channels; and the ability to understand individual customer propensities and lifecycle stages, which helps drive marketing campaigns.
The company has also seen improved customer engagement through a better understanding of how individual products are used and through responses to marketing campaigns, which has led to more customized products and customer support experiences. That, in turn, has helped with customer retention.
In addition, Adobe has seen improvements in enterprise sales and territory planning, which drive higher sales efficiencies; and the development of a consistent way of defining and analyzing key performance indicators across the business, which has allowed the company to evaluate all campaigns in a common framework.
Given the success so far, the company is looking for other options to take advantage of machine learning. “There is a strong push within Adobe to leverage machine learning in managing all aspects of the customer experience,” Padmanabhan says.
Managing risk for customers
At LexisNexis Risk Solutions (LNRS), a provider of financial risk management services, machine learning helps customers protect against identity theft, money laundering, benefit scams, health care fraud, bad debt, and other risks.
LNRS began using machine learning several years ago to analyze and extract information from extremely large and heterogeneous data pools, to create graphs and make predictions about events, says Flavio Villanustre, vice president of technology architecture and product at LNRS.
The company uses mostly homegrown machine learning tools based on HPCC Systems, an open source, massive parallel-processing computing platform for big data processing and analytics.
The platform “gives us advantages when dealing with complex models and needing scalability to apply to very large and diverse data sets,” Villanustre says. On top of the HPCC platform, LNRS designed its own domain-specific abstractions in the form of domain-specific languages such as Scalable Automated Linking Technology, a sophisticated record linkage tool, and Knowledge Engineering Language, which combines graph analysis with machine learning capabilities.
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