Staying true to our mission of making data-driven growth a reality for all B2C companies, we're constantly developing more ways to help optimize your growth initiatives with actionable consumer insights and predictions.

From enhanced customer analytics to bespoke predictive modeling, understanding how all these insights and predictions work individually and together can be a little overwhelming at times.

That's why we're introducing Faraday Solutions — a better way to think about how the Faraday AI Platform empowers you to operationalize an insight-driven growth strategy.

Customer Insight Discovery

Customer Insight Discovery (CID) takes a qualitative approach to helping you understand who your customers are as real people — beyond clicks and transactions. It can help you identify which messaging will really resonate, understand which channels are most impactful throughout your customer lifecycle, and ultimately, help you develop stronger brand.

You can start generating your own insights by building, analyzing, and comparing custom segments with Explore. Additionally, Faraday's Data Science team can surface and curate meaningful insights from your enhanced data, which are presented in the form of a Customer Insights Report (CIR).

Optimized Customer Acquisition

Optimized Customer Acquisition (OCA) takes a predictive approach to finding and converting more customers. Bespoke predictive models rest at the core of this solution, enabling you to identify and engage with likely buyers and likely high-LTV customers throughout your acquisition initiatives.

Lead generation, lead conversion, and LTV predictions are generally applied on an ad hoc basis with Reach, or in real time with Inform.

Maximized Customer Value

Maximized Customer Value (MCV) takes a predictive approach to maximizing your customer lifetime value (CLV) through optimized engagement and retention.

Predictive up-sell, cross-sell, and churn models help you identify opportunities to increase sales to your existing customer base and re-engage risky customers who haven't hit their full CLV potential.

Monitor is often used to generate and deploy these predictions on a recurring schedule, enabling you to capitalize on CLV growth opportunities as they emerge.

Personalization at Scale

Personalization at Scale (PAS) takes a computational approach to persona development. Rather than relying on educated guesses or pre-computed personas (e.g. Claritas Prizm Codes), Faraday's Personas system computes your personas by employing machine learning algorithms against your enhanced customer data.

The resulting personas can then be assigned to your prospects, leads, and customers on an ad hoc basis with Reach, in real time with Inform, or on a recurring schedule with Monitor.

Location and Market Intelligence

Location and Market Intelligence (LMI) takes a predictive approach to geospatial analytics.

Faraday's data science team employs a combination of geospatial analysis techniques to help you identify high-opportunity markets for growth, pinpoint ideal locations for physical stores or out-of-home campaigns, and identify optimal catchment areas for geo-targeted campaigns designed to drive more consumers to existing stores.

You can visualize market opportunities with Explore and deliver high-opportunity zip codes for geo-targeted campaigns with Reach.

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