Industry Use Cases

 

Here’s a selection of real-world machine learning problems that we can help you to solve in a more open and customizable way. These examples are by no means exhaustive, so get in touch if you’re working on another use case.

E-Commerce

Product Recommendations

Personalise the entire shopping experience to increase revenue and customer satisfaction.

  • Select from a suite of industry-leading recommendation algorithms including matrix factorization, content based, activity similarity, basket analysis.
  • Cross sell related products.
Next Best Action
  • Identify the next customer actions to maximise revenue.
Predict LTV (Lifetime Value)
  • Identify in real time customers who are likely to generate revenue.
Churn
  • Identify customers who are likely to be lost to prioritise proactive re-engagement.

Finance

Risk Analysis
  • Predict whether a customer will be low or high risk.
  • Supplement slow-moving credit scores with real-time insights based on customer behaviours.
Identify Emerging Trends
  • Identify new markets, trends or products from news and social media.
  • Provide early investment opportunities by spotting emerging trends.

Insurance

Recommend insurance products
  • Predict which insurance products are appropriate for a new customer and upsell the most relevant policy.
  • Utilize social data and contextual data to classify users.
Fraud Detection
  • Identify fraudulent applications and commercial behaviour with pattern recognition and anomaly detection.
  • Reduce false alarms.

Media

Content Recommendation

Show personalized up-to-the-minute content for each user to allow them to engage with the latest published articles or streaming media.

  • Optimize engagement and clicks through targeted recommendations for each user.
  • Select from a suite of industry-leading algorithms including cluster based, matrix factorization, content based, activity similarity.
  • Combine, A/B and optimize test sets of algorithms.
  • Integrate multiple recommendation areas per page.

Advertising

Ad Personalization
  • Personalise the format of an advert – for example, customise the product, call to action and design.
  • Predicting affinity for brand or interest.
Ad Targeting
  • Deciding which audience to show which adverts
  • Personalised audience retargeting.
User Segmentation and Clustering
  • Gain actionable insights on your customers and audience by analysing the characteristics of the clusters.
  • Include segments defined by machine learning-based models into your marketing workflows.

CRM

Customer Support Triage
  • Classifying and prioritise customer support requests to reduce the overhead of manual triage roles.
Score Sales Leads
  • Build on your sales history to build a predictive model to identify the most profitable sales leads.
Real-time marketing
  • Optimize the customer journey to by orchestrating personalised multi-channel communication.

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