The Challenge
A major telecommunications company hypothesized that by leveraging real-time propensity-to-buy
scores—assigned to visitors based on their interactions and historical data—they could significantly
increase conversion rates if high-propensity callers were routed directly to dedicated sales teams
rather than through the standard routing process.
The underlying belief was that targeted engagement with these qualified leads at the moment of expressed
purchase intention would also reduce the number of dropped or abandoned calls as well as more effectively
drive completed sales.
The traditional call routing system, already in place to provide a baseline, often resulted in dropped or
abandoned calls, especially for those customers with a high propensity to buy.
To validate this hypothesis, the telecommunications company expressed a willingness to implement this new
approach for those customers who had consented to tracking (45% of callers), carefully observing resulting
changes in conversion and sales performance, using measurement frameworks such as control groups to
accurately assess the impact of the dedicated routing strategy vs. existing call routing and generic
inbound calls.
The Solution
To address this challenge, the telecommunications company implemented a dedicated routing strategy based
on the customer’s propensity scores on incoming calls. When a customer with a high propensity score
decided to call to potentially complete their purchase, they were automatically routed to a specialized
sales team with context, rather than going through a normal call routing process.
Key technical components of this solution included:
- Tealium and Score Updates: The propensity score model utilized real-time data from
Tealium routed into the in-house built AWS modelling system, which updated scores based on customer
interactions and historical data. - Analytics Integration: Contact and interaction points were monitored using Adobe
Analytics, ensuring that all incoming calls were accounted for and scored properly, allowing for
reporting of each routed population’s performance. - Real-Time Propensity: At the point of call, Tealium makes available and passes the
customer’s propensity score into the call flow, along with additional contextual data-points, to allow
the necessary routing to take place and also updates Adobe Analytics with the appropriate flags for
reporting. - Tracking Consent Limits: The model was only applicable to the 45% of customers who
consented to tracking, as 55% of calls were made using standard phone numbers without tracking consent.
This approach was expected to enhance the likelihood of closing sales by ensuring that the right team
engaged with high-potential customers as soon as they expressed interest by calling.
The Results
The results from implementing the propensity to buy model were positive and able to quickly validate
the original hypothesis with statistical confidence of 100% within a week, with a control group test.
The telecommunications company experienced:
- Increased Conversion Rates: The new system led to fewer dropped and abandoned calls,
significantly enhancing customer engagement. - Reduced Average Handle Time (AHT): The additional real-time customer context made
available to call agents saved time and reduced call handling time. - Quantifiable Sales Growth: The implementation resulted in a significant number of
additional orders per week, marking an overall uplift of 57.97% compared to standard
routing methods and a 37.1% increase over traditional call tracking-based routing systems. - Broader Application: Similar improvements in conversion rates are expected for
non-product hotlines, typically associated with service pages, demonstrating the effectiveness of this
model beyond direct sales. Average Handle Time (AHT) and First Call Resolution (FCR) are the key metrics
being monitored here.
This case study highlights how effectively utilizing customer data can lead to strategic outcomes that
not only enhance customer experiences but also drive significant business growth.
