DataQuarks are an analytics start-up using AI to take data directly to business decisions. While use cases of our platform for other verticals are in the pipeline, we are currently focusing on the e-commerce retail sector.
35-year old Oliver from Twickenham just bought a striped Cashmere jumper
DataQuarks' Customer Segmentation analysis can predict whether Oliver will be a high value, loyal customer based on the characteristics of similar customers. Now you can personalise your marketing to help him stay happy and fashionable and to maximise the value of your future customer engagement.
Mother of two, Amanda bought a Hallowe'en costume for her five-year-old last October
DataQuarks' Recommendation Engine can provide you with a timely reminder to send her a coupon to redeem for this year's Hallowe'en purchase, to help retain and maximise the lifetime value of existing customer.
22-year old Emma usually buys the latest fashion items from your store in first week of every month after pay day - but not this time
DataQuarks' Churn Detection can help you identify customers who are about to leave for your competitors. Now you can customise a campaign to bring her back for good and better ensure future customer retention.
DataQuarks leverages Machine Learning to recommend business decisions for e-tailers. With ‘one-click’ integration to your own e-commerce platform, our analytics tools will provide targeted insights to help you acquire new customers optimally, retain and sell more to your customers.
Our solutions help small and medium sized retailers leverage the power of Data Science without the large IT budget of Fortune 500 corporations. By using complex machine learning techniques but communicating in your natural business language, DataQuarks can help you make fast, data driven decisions without hiring expensive Data Scientists.
Insights To Enhance Customer Experience
Dig deep into customer buying behaviours to optimise your customer targeting and marketing activity
Improve cross selling opportunities by identifying which other products that customers are most likely to buy based on behavioural patterns
Identify ‘at risk’ customers before they leave in order to action to restore loyalty and ensure customer retention
Identify the best channels and marketing tools to attract loyal, high value customers
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