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One platform to efficiently Bid, Discover, Convert & Grow your e-commerce business

Algorithmic Bidding | Bid Optimization | Brand Discoverability | Keyword Analytics | Buyability | Conversion | Trends & Product Intelligence | Performance Tracking & Diagnostics

Mathematical Machine Learning Bid Optimization models that maximise Revenues and lower ACoS

Our mathematical models use complex ML algorithms to model the rate of change of impressions without the assumption of linearity. We use ‘real world’ variables including Historical AMS Performance data, Industry/Category, Day of the week, Time of the day and as model inputs.

Models inputs include first party proprietary data including “crawler data” such as keyword position, keyword page and unique Amazon metrics such as Best seller Rank.

Models are used to Predict the optimal bid value for each keyword using all this data.

Mathematical Machine Learning Bid Optimization models that maximise Revenues and lower ACoS

Our mathematical models use complex ML algorithms to model the rate of change of impressions without the assumption of linearity. We use ‘real world’ variables including Historical AMS Performance data, Industry/Category, Day of the week, Time of the day and as model inputs.

Models inputs include first party proprietary data including “crawler data” such as keyword position, keyword page and unique Amazon metrics such as Best seller Rank.

Models are used to Predict the optimal bid value for each keyword using all this data.

Tools to “Fine Tune” the Campaign

Negative Keywords

We have developed a negative keyword tool that automates the removal of Negative keywords that are irrelevant, unprofitable, having low click throughs (CTR) and low conversions (CVR)

Search Term Addition Tool

Sales on Amazon are tracked to the search term and “missing” search terms are welcome additions to the original Keywords

Missing ASIN Tool

On a daily basis, the tool identifies “missing ASINs” that could be particularly relevant to large portfolios where on some ASINs, no campaigns are running

ML approaches to optimize Discoverability, Keywords, Conversions & Reviews

Brand Discoverability & Keyword Analytics

To sell efficiently on Amazon, a Brand must first be efficiently found. Our Discoverability scorecard tracks 32 metrics across key drivers and provides periodic updates to track progress. Our Keyword analytics tool, uses our Proprietary crawlers to uniquely find the top ranked keywords for your Brands (and those of competitors) – and for each keyword tracks search volumes and the relevant page and position on Amazon. Keyword performance is a critical variable in our Bidding model.

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Buyability Analytics & Conversions

The success of a Brand or any Business on Amazon is determined by Conversion of Click throughs to Sales. Our Buyability scorecard provides a ML enabled scoring across your Brands (and those of competitors) for 34 content parameters including Images, Description, A+ content, Reviews, Q&A. Buyability scores are updated every week, changes monitored and alerts sent on any change.

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Buyability Analytics & Conversions

The success of a Brand or any Business on Amazon is determined by Conversion of Click throughs to Sales. Our Buyability scorecard provides a ML enabled scoring across your Brands (and those of competitors) for 34 content parameters including Images, Description, A+ content, Reviews, Q&A. Buyability scores are updated every week, changes monitored and alerts sent on any change.

Request for demo

Review Analytics

Reviews are the ‘trust badge’ that drive confidence – customers often use the no. of reviews and the ratings as a proxy to decode whether or not they can trust the claims that a Brand is making. Brand owners can use reviews – both the velocity and the quality to identify early warning signals or drive competitor insights. Our Platform enables bucketing of reviews into quality, price, packaging and efficacy customized to context and KPIs using NLP.

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Build traffic & convert traffic from e-commerce websites

We deliver complex customer engagement models for ecommerce sites. Customer funnels are customized for clients and their business context using a combination of reward and referrals. For example in this model, the customer is incentivized with referrals and discounts as soon as he/she is brought on the site and rewarded with additional discounts and reward points once the product is bought. The model is built on a framework rewarding instant reward and gratification and ensures the cycle continues with retention marketing campaigns and pushing the consumer for referrals

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