How marketers can improve their decision-making processes using automation.
Without actionable data, customer experience strategies are doomed to fail. Lisa Loftis, Principal of Customer Intelligence Solutions at SAS, discussed some interesting CX findings from Futurum Research in her presentation at our MarTech conference.
“One of their most significant findings was that the future of CX is in real-time data collection analysis and being able to tune these activities so that you can proactively meet and exceed customer requirements,” she said.
She added, “In our philosophy, data does not change the organization — decisions do.”
Marketers have a responsibility to add more data into their decision-making processes, especially given the technologies available. Marketing automation platforms have made decision-making more effective by streamlining tasks that used to take up much of marketers’ time.
“Automating decisions is not a new focus for marketers and CX leaders,” she said. “The issue is that the pandemic-induced digital behaviors that we’ve been talking about have ratcheted up the importance of automating decisions in CX.”
Here are some reasons why successful CX strategies require data-driven decision-making.
Data adds customer context
Data drawn from analytics and CRM systems can provide marketers with much-needed context to make better campaign decisions. What’s more, these tools can create the foundation brands need to automate these choices going forward.
“You can begin to understand how relevant the company is to the customer,” Loftis said. “Do they have products? Do you have products that they want or need and how do they feel about their past interactions with you? This information falls almost exclusively in the CRM category, and it can be used to understand things like segment behavior and offer personalization.”
She added, “We can start to understand what motivates an individual and what their influence value can be. The data that makes up personal context comes from a mix of the third-party purchased information and social media activity.”
Automation determines the next best actions
Loftis provided an anonymous case study of a large bank that used automated decisioning, helping illustrate the benefits of automation. She described how the campaign yielded significant benefits for this bank, generating 6 million leads annually and 80,000 to 100,000 new accounts per year in marketing ROI over 100% in the first few years.”
She also laid out the process by which successful marketing teams work with these technologies: “Marketing groups generate individual targeted lead lists which are submitted to a central decision engine. The engine uses a combination of predictive analytics and machine learning, business rules, and predetermined constraints to develop a list of potential offers for each customer.”
She added, “So when the customer…visits an included channel, the channel contacts the decision engine for a list of possible offers.”
Marketers would be wise to vet their chosen automation system, ensuring its decisioning process aligns with organizational goals. When deployed correctly, these technologies can optimize customer offers in real-time to provide the best possible CX.
Streaming data fuels decisioning
Many organizations have turned to data streaming, a solution designed to address data pipeline issues, recognizing its value in the decisioning process.
“Streaming data isn’t new, but the way that we handle it has changed pretty significantly in the last few years,” Loftis said. “In effect, it was a smaller version of the data warehouse, another data silo, and it existed for one reason: to store data.”
“The problem was that there was almost always a lag in the process,” she added.
Data streaming can help marketers capture and aggregate large quantities of customer data, which can be used to fuel automated marketing processes. This is also all done in real-time to ensure customers enjoy seamless experiences.
“To meet customer expectations, streaming data has to be analyzed and acted upon as soon as it comes into the stream, not hours later,” said Loftis. “The data and analysis results can always be stored for later usage if the nature of the actions does not call for real-time delivery. But the digital engagement models today mean that we have to apply analytics to the data as it is moving through the stream.”
She added, “The goal of a true streaming data platform is to apply high-end analytics directly to the data.”
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