Adaptive Pricing (AP) enables & sustains massive revenue lift in hyper-competitive e-commerce channels by discovering individual consumers’ latent pricing and promotional elasticity patterns.
Multiple Fortune 500 companies use Adaptive Pricing to achieve untapped margin and growth potential, with an average revenue lift of 10-20%.
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Refines prices and promotions in real-time according to key visitor signals.
Sifts through data like location, behavior, and historical purchase patterns.
Can be implemented by any online business with pricing and checkout functionality.
By sifting through geo-location, behavioral, demographic, transactional and product data, AP uncovers consumers’ elasticity to various incentives, including pricing changes.
AP captures untapped revenue, margin and growth potential by tailoring optimal pricing and promotional actions in real-time without cannibalization nor a competitive response.
AP acts as a personalization layer sitting on top of revenue management’s ongoing pricing optimization without any change or disruption to current practices or IT system.
Our end-to-end technology platform allows us to rapidly deploy the concept, capture high-granularity data and iteratively enhance the capabilities.
Elasticity learnings have been cross-utilized in pricing, revenue management and direct marketing areas for much wider impact within our client organizations.
Our analytics reveal the future responsiveness of each individual customer to marketing offers.
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Based on predicted future customer sensitivity and engagement propensity impacted by offer.
Optimized to produce maximal response based on each individual’s profile.
Determines and targets optimal re-engagement window for each customer.
Based on historical outcomes.
Hand-assembled based on organizations habits and standardized historical templates.
Standard schedule that applies to all customers.
See our highly tailored project approach that produces significant and sustainable competitive advantage for our clients.
By sifting through geo-location, behavioral, demographic, transactional and product data, AP uncovers consumers’ elasticity to various incentives, including pricing changes.
AP captures untapped revenue, margin and growth potential by tailoring optimal pricing and promotional actions in real-time without cannibalization nor a competitive response.
AP acts as a personalization layer sitting on top of revenue management’s ongoing pricing optimization without any change or disruption to current practices or IT system.
Our end-to-end technology platform allows us to rapidly deploy the concept, capture high-granularity data and iteratively enhance the capabilities.
Elasticity learnings have been cross-utilized in pricing, revenue management and direct marketing areas for much wider impact within our client organizations.
AP conducts millions of pricing experiments to assess discount impact & discover elasticity patterns.
While pricing most directly impacts revenue, indirect drivers of revenue include:
Generally, traditional pricing only considers product attributes and temporal trends to set a flat price for all customers.
AP is able to incorporate customer attributes and behavioral patterns to determine elasticity and recommend pricing for each customer
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See our highly tailored project approach that produces significant and sustainable competitive advantage for our clients.
By sifting through geo-location, behavioral, demographic, transactional and product data, AP uncovers consumers’ elasticity to various incentives, including pricing changes.
AP captures untapped revenue, margin and growth potential by tailoring optimal pricing and promotional actions in real-time without cannibalization nor a competitive response.
The approach fundamentally avoids traditional challenges of pricing optimization under revenue management: explicit forecasts, inaccurate market elasticity assumptions, planning layers as input to improve correctness, response to abrupt changes, etc.
DCP can be used for any sales channel, online or offline, manual or automated.
For each product and location combination, DCP determines the optimal daily price based on a causal analysis of N-day (eg 30 days) forward revenue for each price option.
Every day pricing outcomes are fed back to DCP to refine predictive modeling and improve price recommendations.
DCP executes under complete self-learning and self-reinforcing machine learning automation; thus no manual demand forecasts nor human adjustments are required
In this example, DCP has determined $90 price would sell quickly short-term but lower inventory would miss the demand surge in two weeks; thus, $110 is the recommended price.
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DCP naturally identifies deficiencies in revenue capture and opportunities for additional revenue capture on a daily basis.
Complete self-learning and self-reinforcing machine learning automation leveraging real-time feedback of revenue outcomes
Minimal manual intervention required, though client can set basic pricing parameters and rules
Continuous daily test and control experiments to adjust pricing strategy to changing customer, competitor, and macroeconomic conditions
Infrequently-updated demand forecasts are fed through an analytical tool with the pricing outputs being substantially adjusted manually
High-cost, complex, and bureaucratic manual review of pricing leading to slow turnaround times and leaving revenue on the table
Infrequent, project-based updates to pricing logic only after the previous strategy’s performance has diminished
See our highly tailored project approach that produces significant and sustainable competitive advantage for our clients.
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