Common Use Cases
Consumer-facing companies must be able to gather and manage the right data, develop intuitive features, and apply analytics that generate the insights effective to their action and business plan. We help manage diverse sources of information producing clean systems handling web traffic, sales, CRM, click logs, ad campaigns, and behavioral and social data helping companies improve targeting, reduce churn, or increase conversion rates.
SFL Scientific leverages data science tools to create custom platforms and systems that monitor, learn, and inform our clients. With many new tech areas emerging into the market, we're here to minimize the risk with innovation and generate campaign ROI.
Move beyond traditional business intelligence and business monitoring stages with data and analytic investment. Apply analytics to solve customer-centric business problems using data science to understand and quantify behaviors, preferences, patterns, interests, and predict their value.
Common use cases include:
Reducing customer attrition
Improving cross-sell and bundling of services
Increase customer satisfaction
Improving customer acquisition
Prevent customer lapse and targeted win back
We can help build actionable customer analytic profiles for your use case.
Develop monitor and learn systems that pull information about campaign performance across all teams to reveal trends, insights, and customer activity. Optimize campaign spend, testing, and decision-making using machine learning recommendations.
Real-Time Bidding: Develop price optimization, bidding strategies, and optimize ad placement and targets.
Ad Optimization: Develop models for ad types, placement location, personalized ad management, and shopping cart abandonment or next-best-product analytics.
Click & Engagement: Track and monitor click and paths of consumers, optimizing shopping cart and recommendation systems for a personalized consumer experience.
In relation to ad tech, the applications of data science are nearly endless and must be targeted to specific business goals. The fundamental questions here are not changed, however, we now have the tools to build automated systems, that work in real-time to mine and utilize these resources efficiently.
For example, by leveraging big data collection and social information, we can identify not only which ads are being clicked by what households, but how ad placement appeals to which user segments, and should be bundled with what complementary services and products.
We build data science tools and platforms, approaching profitability improvement in broad categories:
Product | Portfolio Assessment
Advertising Pricing | Yield Optimization
Consumer Acquisition | Retention | Yield | Value
Customer Experience | Recommendation Systems
Market | Channel Segmentation & Analytics
Cross and Upsell Analytics