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Marketing technology (martech) is growing increasingly sophisticated, with the availability of new tools and data. But data grows with every individual customer interaction. And, historically, marketing hasn’t been able to gain insights into real-time data on customers like contact centers can. So, they miss out on customer intent, behavior and ever-changing preferences.
Gain insights with access to all data
There’s still a long way to go. Only 9% of marketers have an “excellent” understanding of what multi-touch attribution is. This translates to missed opportunities or even wasted budget on technology that they’re not taking advantage of.
Some of this shortfall is due to siloed data sources. This is especially problematic when it comes to the more advanced technology marketers want today. For example, they want to apply machine learning and artificial intelligence (AI) to drive better business decisions and marketing campaigns that contribute to higher sales.
The question is how to access more relevant customer data that can be used to unearth relationships, context, and insights within that data. It’s about integrating data and it’s also about the data itself.
Bridge contact center engagement and marketing
There’s a new bridge forming between digital marketers in the martech area and traditional contact center technology. Integration with data providers like LiveRamp and other platforms let marketers access data derived from the contact center and get it into martech ecosystems, such as CRM systems. And that data is portable to many platforms.
No matter which channel the customer engages on, marketers can discover:
Think of the advantages for demand generation. When trying to target prospects in ads, you might use data providers to build campaigns. If a prospect responds to an ad, all the data from that journey—even the touchpoints that happened before the prospect visited your site—are available to programs like Genesys Altocloud, which identifies specific moments in the customer journey, so you can step in and optimize business outcomes.
Genesys pulls together all the conversational data and interaction data that never makes its way to your CRM system and expands its use into a machine learning engine that powers predictive lead prioritization, predictive prospect engagement and predictive routing to sales resources. Together, these technologies address the front side of the customer journey more than ever before.
Get smarter about data
Accessing this wealth of data is just one benefit. Not only can you engage with customers predictively, but you can also more intelligently match prospects with the employee or agent in the contact center. Then, use AI technology and machine learning with predictive routing to match them.
It’s a different use case for predictive routing in the contact center, which is typically around speeding service and reducing handle time. This use case is about accessing all that rich data that happens before the customer comes to your site—and combining it with predictive routing and Genesys Altocloud.
Rich data resources create many opportunities to boost results. You can predict the best leads to send to the right sales resource based on the probability of purchase intent by leveraging the power of AI and machine learning. And you can personalize the automation of content and offer delivery to each prospect based on their prior customer journey and purchase intent data, such as keywords they used to get to your site.
Build more meaningful campaigns
If you’re a marketer, you might have wrung out all the value of the data you collect, and the underwhelming result is often flat conversion rates. But now there’s more data available that gives you a common view of all interactions across workflows and channels—from bots, other systems or humans. This untapped “secret” data that’s sourced from all contact center interactions can be used to create more meaningful campaigns—and it’s available now.
Learn how businesses are leveraging artificial intelligence and getting the most from their AI investments.
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