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Scott Gifis, vice president & managing director, North America, AdRoll, believes that his company helps clients drive customer loyalty through CX-related initiatives.
“Where a lot of our customers will leverage us with respect to CX-related initiatives is to deliver targeted, sequential messaging to inform customers at appropriate stages in their lifecycle,” he explained to Loyalty360. “For example, if you are promoting customer loyalty programs as a retailer, you might leverage AdRoll’s technology to synchronize personal messaging across email, display, and on your website. If you are a technology company, you might leverage AdRoll’s Account Based Marketing technology to deeply integrate into your CRM system and automate messaging across your customer touch points across web, email, onsite, even inside your product application, to drive adoption of specific features that drive retention or upsell to more premium offerings, these messages might include recommend studies or customer testimonials, and deliver them over time at the right time.”
AdRoll is a leading performance marketing platform used by more than 35,000 clients worldwide. Its suite of high-performance tools works across devices, helping businesses attract, convert, and grow their customers. The company is home to the world’s largest opt-in advertiser data co-op, the IntentMap™ with more than 1.2 billion digital profiles and has driven more than $7 billion in sales for its customers.
AdRoll’s goal is to build the most powerful marketing platform through performance, usability, and openness. AdRoll is based in San Francisco, with offices in New York, Chicago, Tokyo, London, Dublin, and Sydney.
AdRoll has built the world’s most widely used retargeting platform that has made officials keenly aware of the importance and value of data. The company’s mission is to help every marketer collect, analyze, and act on their customer data to deliver high-performance marketing campaigns.
Gifis believes there are inherent challenges brands have with the measurements that impact customer loyalty.
“It depends on the customer you’re talking with,” Gifis explained to Loyalty360. “Retail customers are a bit more straightforward. Make sure things are relevant and the testing environment is clean. Streamline that process and look at measurement and optimization. With sales/B2B companies, their point of engagement is rarely their point of sale. A marketing qualified lead, for that you need to be deeply integrated into their CRM in most cases. AdRoll’s Account Based Marketing platform does just that and it gives our B2B customers a very clear view of the true impact and value of their advertising strategy including their media dollars.”
When you have consumer brands that have services, Gifis noted, “there tend to be different touch points with customer service. There are ways we can integrate into their systems of records. All of that can be implemented in targeting features that they can run different marketing campaigns for.”
Adroll’s systems and reporting help clients understand what content resonates the most and which pieces drive the better outcomes.
“Our proprietary machine learning can then optimize messaging on their behalf and recommend which pieces of content they should do more or less of based on those things,” Gifis noted. “We integrate deep into their platform. If they have a web-based application or if they’re a B2B software company, they usually have a mobile and desktop application and integrated all the way through into their CRM systems. Wherever their system of record lies around the customer, we can incorporate all the different points and features within our system and we marry that with web, or mobile, or wherever they are online to map behavioral data together with the customer data they should help inform targeting, price, and the intent modeling.”
Gifis described three pillars for measuring incrementality:
One is you need a clean, intellectually defensible testing framework.
The second is you need the reporting and insights to be able to understand actionable insights around what is and isn’t actually working.
The third is building a technology that optimizes based on those signals autonomously around incremental lift. The last piece is very hard to do.
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