This global financial services corporation is currently the world’s largest financial services network and a top five foreign exchange (FX) trader. The company has approximately 16,000 offices worldwide in more than 140 countries. It employs approximately 260,000 staff and holds over 200 million customer accounts.
Sales personnel had been finding it difficult to get an accurate picture of a client’s position due to disparate sales data stored in various product-based silos.
Customer data previously dispersed across the accounting, credit, CRM, FX transactions and option maintenance departments has now been consolidated to provide a comprehensive business intelligence and performance management application, providing users with a 360-degree view of their customers, known as their ‘Golden Source’ for all FX trading data.
The solution identifies customer behavior patterns and uses predictive outcomes to enable sales personnel to better target their customers and increase sales.
Integration with the telephony system provides instant access to this information and ensures FX sales personnel can provide customers with first-class service, personalized and tailored to their individual needs.
The ability for salespeople to fully understand client buying patterns and to proactively approach clients has led to a quantifiable improvement in sales performance across the board. The use of the predictive models to identify clients who were the most likely to buy new or additional products has proven to be a staggering 800 percent more effective than random calling.
Reducing Customer Churn
Client coverage analyzes customer buying patterns and recommends whether they may be better served through other, more cost-effective engagement channels such as e-trading. The ability to identify clients who vary from expected buying patterns early in the buying cycle has led to a reduction in customer churn, allowing traders to proactively approach those clients at risk and rectify the situation.
Predicting customer behavior
After analyzing historical trading patterns, data mining algorithms are used to deliver new insights and predict customer behaviors. This can optimize coverage in multiple areas:
Forecast client transaction patterns and volumes.
Predict cross-sell opportunities and identify a customer’s propensity for cross-sell.
Identify which customers are at risk of switching to a different vendor, based on patterns of activity or volume.
Analyze client engagement methods and their impact on trading volumes to determine the best way to engage with a particular client.
Identify the best methods for reaching clients (e.g., voice, electronic communications).
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