Leading organizations leverage Big Data analytics and AI technology to drive smarter, faster and more accurate decisive actions in every part of their business. We serve as strategic partner to them where we consult and deliver a wide range of analytics services for centralized analytics teams or individual business units/ departments.

Below are the Use Cases across industries, by function:

  1. Digital Marketing: Predict virality, Influencer Marketing,PR & SEO(extract company mentions,Extract backlinks,Refine results by traffic rank,sentiment, spam score, thread participant count, or social filters), Targeted Advertising and re-targeting, web data coverage,Brand monitoring systems, reputation tracking platforms and Social Media Data Mining trends.
  2. Cybersecurity
  3. Recommender Systems
  4. Image Recognition
  5. Speech Recognition
  6. Pricing Comparison
  7. Dynamic Pricing
  8. Route Planning
  9. Fraud and Risk Detection
  10.  Predictive car maintenance
  11.  Server monitoring
  12. Aggregator systems – News, Poll, Reviews, Search, Social media, Video, Blogs, etc
  13. Text categorization, summarization, topic detection
  14. Predicting adherence, risk default and churn.
  15. Market basket analysis
  16. Segmentation and Clustering
  17. Marketing – Predicting Lifetime Value (LTV), what for: if you can predict the characteristics of high LTV customers, this supports customer segmentation, identifies upsell opportunties and supports other marketing initiatives , usage: can be both an online algorithm and a static report showing the characteristics of high LTV customers Wallet share estimation working out the proportion of a customer’s spend in a category accrues to a company allows that company to identify upsell and cross-sell opportunities usage: can be both an online algorithm and a static report showing the characteristics of low wallet share customers
  18. Churn – working out the characteristics of churners allows a company to product adjustments and an online algorithm allows them to reach out to churners , usage: can be both an online algorithm and a statistic report showing the characteristics of likely churners
  19. Customer segmentation – If you can understand qualitatively different customer groups, then we can give them different treatments (perhaps even by different groups in the company). Answers questions like: what makes people buy, stop buying etc
  20. Product mix –What mix of products offers the lowest churn? eg. Giving a combined policy discount for home + auto = low churn, usage: online algorithm and static report
  21. Cross selling/Recommendation algorithms – Given a customer’s past browsing history, purchase history and other characteristics, what are they likely to want to purchase in the future? usage: online algorithm
  22. Up selling – Given a customer’s characteristics, what is the likelihood that they’ll upgrade in the future? usage: online algorithm and static report
  23. Channel optimization – what is the optimal way to reach a customer with cetain characteristics? usage: online algorithm and static report
  24. Discount targeting – What is the probability of inducing the desired behavior with a discount – usage: online algorithm and static report
  25. Reactivation likelihood –  What is the reactivation likelihood for a given customer , usage: online algorithm and static report
  26. Adwords optimization and ad buying  – calculating the right price for different keywords/ad slots
  27. Sales – Lead prioritization – What is a given lead’s likelihood of closing revenue impact: supports growth usage: online algorithm and static report
  28. Customer support -Call centers Call routing (ie determining wait times) based on caller id history, time of day, call volumes, products owned, churn risk, LTV, etc. Call center message optimization Putting the right data on the operator’s screen Call center volume forecasting predicting call volume for the purposes of staff rostering
  29. Risk -Credit risk, Treasury or currency risk, How much capital do we need on hand to meet these requirements?
  30. Fraud detection -predicting whether or not a transaction should be blocked because it involves some kind of fraud (eg credit card fraud)
  31. Accounts Payable Recovery – Predicting the probably a liability can be recovered given the characteristics of the borrower and the loanAnti-money laundering – Using machine learning and fuzzy matching to detect transactions that contradict AML legislation (such as the OFAC list)
  32. Anti-money laundering : Using machine learning and fuzzy matching to detect transactions that contradict AML legislation (such as the OFAC list)
  33. Logistics Demand forecasting – How many of what thing do you need and where will we need them? (Enables lean inventory and prevents out of stock situations.
  34. Revenue impact: supports growth and militates against revenue leakage usage: online algorithm and static report
  35. Human Resources – Resume screening, scores resumes based on the outcomes of past job interviews and hires, Employee churn – predicts which employees are most likely to leave, Training recommendation, recommends specific training based of performance review data, Talent management, looking at objective measures of employee success.

 

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