A growing variety of brands are using AI to enhance customer experience with loyalty programs and streamlined operations.
These brands are using the technology to supply more personalized experiences and automate highly repetitive, manual processes.
The technology is a game-changer, particularly for customer analytics. Loyalty program leaders have been in a position to segment customer data for years, allowing them to focus on offers to specific groups, said Patricia Camden, EY Americas loyalty leader. “But with those segments, you are still just talking to a broad, generalized group that you have put right into a bucket.”
AI, then again, takes it a step further. The technology enables brands to focus on offers to specific individuals by helping them understand “what each human values” as a substitute of “pushing the identical reward to everyone” or those inside a selected segment, Camden said.
AI also allows loyalty programs to actively shape consumer behavior and habits while deepening a brand’s relationship with a customer, Camden said.
“It really allows the brand to tailor the rewards, messaging, offers and experiences to individual preferences and behaviors in real time,” Camden said. “That’s probably probably the most powerful thing about AI and the way it may possibly improve loyalty.”
A quick casual restaurant, for instance, could send a customer unique offers for brand spanking new items to assist “unlock a secret reward” designed for that customer or provide them additional points for his or her loyalty, Camden said.
“It’s really loyalty gamified but in a way that feels personal, not gimmicky,” Camden said.
Those changes have the potential to disrupt existing customer relationships and establish recent ones.
“Loyalty will change into less about what a brand desires to push and more about how consumers want to have interaction,” Camden said. “AI goes to completely rewire the role loyalty plays in the shopper experience.”
Making loyalty programs more efficient
AI will help loyalty program leaders stretch their limited resources by helping create content for hyperpersonalized offers and optimizing campaign spend.
“It really saves marketing teams time and budget,” Camden said.
Instead of assigning staff such tasks as exchanging and reconciling transactions between program partners or providing individual customer preferences to hotels and retailers, AI can manage such tedious tasks, said Brendan Boerbaitz, senior manager at Deloitte Consulting.
AI may also improve predictive analytics. One EY client, for instance, uses its loyalty program to be certain that customers renew their relationship with the brand every year and now uses AI to discover and goal offers to customers who’re prone to churn, Camden said.
More and more loyalty programs are using AI for fraud detection, too. Unlike humans, AI can quickly “connect dots at scale” to make sure points and advantages are issued accurately, said John Pedini, principal analyst at Forrester.
“It will help flag unusual patterns before they change into expensive problems,” Camden said.
Developing strong use cases
However, before integrating AI into their customer loyalty programs, brands must “develop use cases that provide clear and measurable value,” including personalization, segmentation, variant testing and low- or no-code campaign development, Pedini said.
It’s best to give attention to applications of AI that take an existing process and make it higher, more efficient or cheaper, Pedini said.
Starbucks, as an example, uses its proprietary AI platform dubbed Deep Brew “to drive automation, operational efficiency and loyalty engagement by identifying and incentivizing specific members with personalized offers and rewards,” Pedini said.
But not all use cases need AI. Forcing AI on business problems that might be solved via more conventional, lower-cost solutions is a “big pitfall,” Boerbaitz said.
When deciding whether to implement AI, Boerbaitz urges brands to think about the next questions:
- If AI were stripped from the document, wouldn’t it be clear what problem is being solved?
- Do I really understand the specifics of the issue we’re solving right down to the extent of the user?
- Is this problem underserved by other tools and techniques?
Data, cross-department collaboration are vital to success
AI adoption is a “team sport” that requires cooperation to avoid redundant work and conflicting initiatives, and construct data sets, tools and models for multiple applications, Boerbaitz said.
“It takes engineering, architecture, strategy, change management, data and loyalty teams all coming together to make AI programs in loyalty successful,” Boerbaitz said.
It’s also necessary to not rush the method.
Brands should avoid launching an AI model too soon since the technology is determined by high-quality data to deliver on its guarantees. Launching a model with outdated or incomplete data could lower accuracy and create “more issues than it solves,” Pedini said.
“The worst thing you’ll be able to do is have incomplete data sets,” Camden said. “If the AI makes assumptions based on what it knows, you’ll be able to find yourself sending something that is just not appropriate or not what the client expects to see.”
That can take away the “emotional element” of loyalty programs, Camden said.
“If a brand lets AI take the wheel without real human guardrails, the shopper experience could begin to feel impersonal, off base and overcurated,” Camden said.
Loyalty program managers should ensure their data is comprehensive, including all channels and touch points, and properly labeled, Pedini said. Sound data governance of policies, standards and procedures can also be vital to making sure privacy, stopping bias and complying with regulations, Pedini said.
It’s also essential for humans to be involved in loyalty programs because first-party data will help businesses improve their product strategy, brand positioning and repair design.
However, that won’t occur “if the machines take over,” Camden said. “AI mustn’t be used to switch our pondering.”
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