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Unscripted by Perx · Episode
From Loyalty Points to Revenue Intelligence: How AI Is Rewriting Customer Retention for Banks
Perx is redefining what a loyalty platform can be, from campaign execution to Autonomous Revenue Intelligence. In this episode, Nikita Shaha and Amrith G break down why banking loyalty spend keeps rising while retention does not, and what changes when engagement is built on behaviour instead of points.
Hosts: Nikita Shaha, Amrith G
Duration: 24:34
Published: Jul 7, 2026
Topic: Customer retention in BFSI, agentic loyalty
In Brief
- Banks keep investing in retention, yet McKinsey’s 2025 Global Banking Annual Review found only 4% of new checking account applicants now choose their existing bank without shopping around first, down from 25% in 2018, while customer churn is estimated to cost US banks $195 billion a year (CoinLaw, 2026).
- Net Promoter Score is the wrong primary signal for loyalty health: a customer can give a nine on an NPS survey weeks before moving their primary account elsewhere, because NPS measures how someone felt about a past interaction, not what they are doing with their money right now.
- Revenue Intelligence is described as a closed loop of input, engine, and output: full-dimension customer behaviour feeds a growth engine that decides the best next action per customer, and execution closes back into measurable proof that the intervention changed spend or lifetime value.
- The gap holding most organisations back is not technology, it is data: CleverTap’s 2024 Banking on AI report found 57% of banking executives have not achieved a unified customer view due to data silos, and 41% are not using real-time segmentation, which leaves loyalty systems working with customer behaviour that is days or weeks old.
- Readiness for an agentic, behaviour-led approach comes down to four things: real-time data connectedness, revenue-defined measurement clarity, governance confidence for autonomous decisions, and an organisational shift from managing campaigns to defining outcomes.
What's Covered in This Episode
| Timestamp | Chapter |
|---|---|
| 00:00 | Introduction |
| 00:31 | The retention economics nobody argues with — and the loyalty results that do not add up |
| 03:39 | What Revenue Intelligence actually means: input, engine, output |
| 07:42 | The data problem and the lag that makes it worse |
| 11:57 | Speed to respond: why the intervention window is narrower than you think |
| 15:07 | The measurement debate: why NPS is the wrong scoreboard |
| 18:27 | What ready actually looks like |
| 21:38 | What we are seeing and where this is heading |
FROM LOYALTY POINTS TO REVENUE INTELLIGENCE: HOW AI IS REWRITING CUSTOMER RETENTION FOR BANKS
A Perx Unscripted Podcast Episode Hosted by Nikita Shaha, Head of Product and Technology, and Amrith, Sr. VP Customer Value Creation and Performance Analytics, Perx Technologies
CHAPTER 1 — THE RETENTION ECONOMICS NOBODY ARGUES WITH, AND THE LOYALTY RESULTS THAT DON'T ADD UP
Nikita: Welcome to Unscripted by Perx, the podcast where we get into the conversations that do not usually make it into the slide deck. I am Nikita Shaha, Head of Product and Technology at Perx Technologies. And joining me today is someone I have the pleasure of thinking out loud with on a fairly regular basis.
Amrith: Okay, so — welcome everyone. We were just having this exact conversation in the corridor before we sat down, which honestly is probably how this episode should have started anyway.
Nikita: Honestly, yes. Some of the best thinking happens before the recording starts.
Amrith: Okay, let me kick us off with something that I find genuinely fascinating.
There is a stat from Bain and Company — Frederick Reichheld, who actually invented the Net Promoter Score, found that a 5% increase in customer retention produces a 25% to 95% increase in profit. Not revenue. Profit. And the flip side of that is that customer churn costs US banks an estimated 195 billion dollars annually. These are not controversial numbers.
Nikita:For sure – No one argues with those. At least no CFO in financial services pushes back on them.
Amrith: And yet — this is the part that gets me — McKinsey’s Global Banking Annual Review in 2025 found that only 4% of new checking account applicants now choose their existing bank without shopping around first.
Nikita:Wait, really? Just 4%? If I recall correctly, that’s a dip from 25% in 2018. Such a dramatic shift in such a short time.
Amrith: A Yes! So the investment in retention is going in. The loyalty is not coming back at the same rate. And I think the question we want to spend today exploring is — why? Because it is not for lack of trying. Banks are running loyalty programmes. They are spending on engagement. They are measuring NPS. And yet.
Nikita:You know, I think the honest answer is that most loyalty programmes are measuring the wrong thing. And I do not say that to be provocative — I say it because I think it is genuinely what is happening. If you are measuring enrolment rates and NPS scores, your programme can look completely healthy while the revenue picture is quietly deteriorating underneath it.
Amrith:You’re absolutely right! For the benefit of our listeners, could you expand more on that?
Nikita:So, a customer can give you a nine on an NPS survey three weeks before they move their primary account to a competitor. They liked the last interaction. They have already decided to go elsewhere for the next thing they need. The score looked fine. The revenue was already leaving. And the loyalty programme had no way of knowing the difference between those two things — because it was not looking at the right signals.
Amrith: Which is exactly the gap. And it is that gap — between what retention is worth and what existing programmes are actually producing — that leads us into the main thing we want to talk about today. Which is Revenue Intelligence. And what it actually means for the way loyalty works.
Nikita:Yes. And I want to be clear from the start — we are not talking about a feature or a dashboard. We are talking about a fundamentally different way of thinking about what a loyalty programme is supposed to do. And I think the best way to describe it is as a loop.
Amrith: A closed loop at that!
Nikita:A closed loop. Exactly. And let us walk through what that looks like.
CHAPTER 2 — WHAT REVENUE INTELLIGENCE ACTUALLY MEANS: INPUT, ENGINE, OUTPUT
Nikita: Okay so the loop has three parts. There is the input. There is the engine. And there is the output. And what makes it a loop — not just a pipeline — is that the output feeds back into the input. It is continuous. It does not stop when the campaign ends.
Nikita: The input is customer behaviour in its full dimension. And I want to be specific about what I mean by full dimension, because this is where I think most programmes fall short. It is not just reward redemptions. It is not just points balances. It is every card transaction. Every product feature used — or ignored. Whether a reward was redeemed or left to expire.
Amrith: Whether a customer logs in at 11pm or 9am. Whether their savings balance is growing or shrinking. Whether they called support once or seven times in a quarter – It is the whole picture.
Nikita: It is the whole picture. And individually, those are just data points. But collectively they are a precise portrait of who this customer is, what they value, and where their relationship with the enterprise is heading.
Amrith: And from my side — from an analytics and customer value perspective — what I find so compelling about that input layer is that it tells you things the customer will never tell you directly. Someone who is beginning to shift their primary spending to a competitor is not going to call you to let you know. They are not going to fill in a survey about it. But their transaction data will show it. Their product engagement will show it. The signal is there. The question is whether the system is built to receive it.
Nikita: Absolutely. And that is where the engine comes in. Because the engine is what converts that raw behavioural input into individual-level strategy. Not segment level. Not cohort level. Each customer. What is the best next action for this specific person, given everything we know about their behaviour and their value trajectory? That is the question the growth engine is answering, continuously, for every customer.
Amrith: And the scale of that is what changes things. Because a human campaign team can make that judgment for a handful of customer segments. The engine makes it for every customer. Simultaneously.
Nikita: Right. And then the execution — acting on those decisions at speed, without a human approving each individual action — is the direction we are building toward. The idea is that an autonomous capability can observe a spend decline on day one, look at what this specific customer has responded to historically, select the right mechanic, and act within hours. Not weeks. Not after the next campaign planning cycle. Hours.
Amrith: And that speed is not a nice-to-have. We are going to come back to that. But the output. Tell them about the output. Because I think this is the part that changes the board conversation.
Nikita: Yes. So the output is Revenue Intelligence again — but now as proof rather than input. Did those hyper-personalised interventions actually change what customers did with their money? What did each customer’s spend trajectory look like after the intervention versus before? Can we draw a direct causal line between the loyalty investment and the commercial return?
Amrith: That is the question every CFO is actually asking. They are not asking how many customers opened the email. They are asking — what did it produce?
Nikita: Exactly. And that is what Revenue Intelligence as an output means. It is not a dashboard you look at. It is commercial proof. And — this is the part I think is genuinely distinctive — Revenue Intelligence is not something you add on top of loyalty. It is embedded in the way loyalty works. The input is behavioural intelligence. The output is commercial proof. Loyalty is the mechanism that runs the loop. Everything in between — the growth engine, the strategy, the execution — is the platform working. It is one ecosystem.
Amrith: I love that framing. One ecosystem. Because I think too often people hear “revenue intelligence” and think — oh, that is the analytics team’s job. That is separate from what the loyalty team does. And what you are describing is the opposite of that.
Nikita: It is completely the opposite. The two things are inseparable. You cannot have one without the other.
CHAPTER 3 — THE DATA PROBLEM AND THE LAG THAT MAKES IT WORSE
Amrith: Okay. So if that loop is the goal — and I think most people listening to this would agree it sounds like the right goal — the obvious question is: why are most organisations not running it? And I want to give an honest answer to that. Because I think the honest answer is more useful than a polished one.
Amrith: The reason is a data problem. Not a technology problem. A data problem. Research published by CleverTap in 2024 found that 57% of banking executives have not yet achieved a unified customer view due to data silos. And 41% — almost half — are not using real-time segmentation capabilities. These are not edge cases. This is the majority of the industry.
Nikita: That number always stops me. 57%. More than half of banking executives cannot see a complete picture of their own customers.
Amrith: Right? And when you dig into why, the answer is structural. It is not that the data does not exist. It exists. It is just scattered.
Nikita: It is completely scattered. Transaction data lives in the core banking system. Engagement data lives in the CRM. Redemption data lives in the loyalty platform. Product usage data lives in the app analytics tool. And in many organisations, none of these systems talk to each other in real time. They batch-sync. Daily at best. Weekly in many cases.
Amrith: Which means the loyalty system is always working with a version of customer behaviour that is somewhere between one and fourteen days old.
Nikita: At minimum. And the consequence of that lag is really specific. For example, a customer whose card spend drops 35% over a three-week period — that signal appears in the core banking system on day one. It reaches the loyalty platform two weeks later, via a batch sync.
Amrith: And by the time a campaign team has seen it, designed a response, got it approved, and launched — you are at week four. Week five, realistically.
Nikita: And the intervention window — the period when this customer is still persuadable, still reachable, when a relevant offer from you still lands as a reason to stay rather than a cynical attempt to win them back — that window is often just a few days wide. Not weeks.
Amrith: By week four you are not retaining a customer. You are chasing one you have already lost.
Nikita: Yeah.
Nikita: And here is the thing I want to be clear about, because I think it is easy to hear this as a technology problem and immediately start talking about replacing the core banking system or building a new data warehouse. But the lag is not a technology failure. It is an architectural choice. Loyalty was built as a campaign delivery mechanism. You do not need real-time data to send a monthly points statement. That design made complete sense for what loyalty used to be. It just does not make sense anymore for what we are asking it to do.
Amrith: That is such an important distinction. Because replacing everything is not the answer. The organisations we see beginning to close this gap are not doing it by ripping out their infrastructure. They are doing it by connecting the loyalty intelligence layer directly to live transaction feeds. One architectural change. Treating customer behaviour as a real-time signal rather than a batch report. And that single shift is the prerequisite for everything else in this conversation.
Nikita: Without it, the loop cannot run. The engine cannot act on intelligence that arrives two weeks late.
Amrith: And I would add one more dimension to this, which is the human dimension. The data silo problem is not just technical. The team that owns the core banking data and the team that owns the loyalty programme often sit in completely different parts of the business. Different reporting lines. Sometimes different definitions of what a customer even is. Getting those teams to work from the same customer record, in real time — that is as much a governance and alignment challenge as it is an engineering one. The organisations that have solved it did not do it by buying better software alone. They did it by deciding that customer data is a shared asset. Not a departmental one.
Nikita: And that decision — who owns the data, who has access to it, in what form and at what speed — is ultimately a leadership decision. Not a technology decision.
Amrith: Completely. And it connects directly to the readiness conversation we are going to have later. Because the organisations that have made that decision are the ones who are genuinely positioned to move.
CHAPTER 4 — SPEED TO RESPOND: WHY THE INTERVENTION WINDOW IS NARROWER THAN YOU THINK
Amrith: Okay so I want to linger on the speed question for a moment, because I think it gets framed wrong a lot of the time. In most organisations, speed of response is treated as a marketing efficiency question. How do we get campaigns out faster? How do we reduce turnaround time? And those are reasonable operational goals. But they completely undersell what is actually at stake.
Nikita: How do you mean?
Amrith: So — the value of an intervention is not just whether it is the right intervention. It is whether it arrives at the right moment. And those are two completely different problems. A win-back offer sent at week four, to a customer who made their decision at week two — that is not a loyalty intervention. That is a post-mortem. You are acknowledging that the programme noticed, eventually. The customer has already moved on mentally.
Nikita: That is a really good way to frame it actually. It is a post-mortem, not an intervention.
Amrith: Right? And the right moment is not when the campaign team is ready. It is when the customer’s own behaviour signals that they need something. And in banking especially, that window can be very short. Customers who begin mentally shifting their primary financial relationship often make that decision in a two to three week period. After that window closes, the same offer lands completely differently. It does not feel like care. It feels like you noticed they were leaving and panicked.
Nikita: Which is essentially what happened, to be fair.
Amrith: Which is essentially what happened! And customers can feel that.
Nikita: So from a product perspective, this is exactly where the autonomous execution direction becomes really concrete. Because the question stops being — can we send a better campaign faster? And it becomes — can the system see the signal when it appears, on day one, and act on it before the window closes? Without a human having to initiate every step of that process.
Nikita: And the way we think about that is through the idea of agents. When a system can observe a spend decline as it is happening, assess what that specific customer has historically responded to, select the optimal mechanic — a spend challenge, a personalised offer, a category bonus — and deploy it within hours, not weeks, the nature of what a loyalty programme is changes. It stops being a campaign delivery mechanism running on a schedule. It becomes a continuous response system running on signals.
Amrith: And the commercial difference between day three and day seventeen is measurable. Not in open rates. Not in NPS points. In spend trajectory. Did that customer’s monthly transaction volume with you go up or down after the intervention? Did they open a second product in the following quarter? The timing is not a customer experience variable. It is a revenue variable.
Nikita: Absolutely. And the cross-sell version of this is equally important. A customer whose transaction data shows they are beginning to use a competitor for something your product could serve — a savings product, a credit facility — that signal is in the data right now. The window to act on it is days. If the loyalty system receives that signal two weeks later, you are not cross-selling. You are reacting to a decision that was already made without you.
Amrith: And I think the organisations that figure this out — genuinely, in practice, not just in strategy decks — will have a fundamentally different relationship between their loyalty investment and their revenue outcomes. Because every intervention that lands in the right window, for the right customer, with the right mechanic, closes directly into a measurable commercial result. That is the Revenue Intelligence output. That is what the loop is for.
CHAPTER 5 — THE MEASUREMENT DEBATE: WHY NPS IS THE WRONG SCOREBOARD
Amrith: Okay. I want to say something that I know will generate some disagreement. And honestly, good. If it does not generate disagreement then it is not worth saying.
Nikita: I know what you are going to say.
Amrith: Do you?
Nikita: I have a feeling.
Amrith: NPS is the wrong primary metric for measuring whether your loyalty programme is working.
Nikita: There it is.
Amrith: And I say that with full respect for Frederick Reichheld, who we already mentioned, who literally invented it, and who has contributed enormously to how this industry thinks about customer relationships. But here is the specific problem. NPS measures how a customer felt about their last interaction. It does not measure what they are doing with their money right now. And those two things can point in completely opposite directions.
Nikita: Give our listeners the example.
Amrith: So — a customer gives you a nine NPS score. Three weeks later they move their primary account to a competitor. They genuinely liked the last thing you did. They have decided to go elsewhere for the next thing they need. The score looked healthy. The revenue was already in motion. And if NPS is your primary signal for loyalty health, you did not see it coming.
Nikita: And from a platform perspective, the NPS problem is actually deeper than just the metric itself. It is a loop problem. If the primary signal your enterprise uses to assess loyalty health is a survey that runs quarterly, you are working with information that is three months old, collected from customers who agreed to take a survey, about a feeling they had at a specific moment. That feeds back into your strategy on a three to six month cycle. Meanwhile the actual behaviour of your actual customers is happening every single day — in the data, in real time — and it is not reaching the decision-making process.
Amrith: Exactly. And the metric that closes that gap — the one that actually connects loyalty investment to commercial return — is what we call the Revenue Attribution Loop. And the question it is answering is very specific. Did this intervention change this customer’s revenue trajectory? Not how many people received the campaign. Not what the open rate was. Did this specific action, at this specific moment, produce a measurable change in this specific customer’s spend or lifetime value? That is the line the CFO needs to see drawn.
Nikita: And I want to add a nuance here, because I do not want this to sound like a binary. We are not saying throw out NPS. The experience signal genuinely matters. Customers who feel good about their relationship with a brand do behave differently over time. That is real.
Amrith: Completely real.
Nikita: But NPS as the primary metric for a loyalty programme creates a dangerous proxy. It tells you what happened to sentiment. It does not tell you what is happening to value. And the enterprises we think are ahead of the curve are using both — but they have added the revenue attribution layer alongside the experience metrics. So they can walk into a board conversation with the full picture. The experience story and the commercial proof together.
Amrith: And that combination — that is what changes how loyalty is perceived inside an enterprise. From a cost centre to a growth lever. That is the conversation we want to be having with leadership teams. And it becomes possible when the measurement is right.
CHAPTER 6 — WHAT READY ACTUALLY LOOKS LIKE
Nikita: So let us talk about readiness. Because I think there is a tendency to frame the Revenue Intelligence conversation as something that is coming — a future state that organisations need to prepare for. And that is partly true. But the more important question is not whether the technology direction is clear. It is whether the organisation is ready to use it when it is. And that readiness has four dimensions.
Nikita: The first is data connectedness. Transaction signals reaching the engagement layer in real time. Not via batch processing. If your loyalty system finds out what your customers did last week on Monday morning — the loop is broken before it starts. Real-time or near-real-time connectivity between the core transaction layer and the loyalty intelligence layer is the foundational prerequisite. Without it, everything else is academic.
Amrith: And that sounds technical but it is actually a strategic decision. Which data does the loyalty system need to see? At what frequency? Who owns the connection between those systems? Those questions get answered in a leadership meeting, not a data engineering meeting.
Nikita: Exactly. The second dimension is measurement clarity. Leadership has defined what success looks like in revenue terms. If the loyalty team cannot answer the question — what did last quarter’s programme contribute to revenue, with a specific number and a causal attribution — the measurement architecture is not there yet. And that is not a technology gap. That is a goal-setting gap. The organisation has not yet decided what it is actually trying to prove.
Amrith: The third is governance confidence. And this one is particularly important in financial services. Compliance and risk teams have a framework for what the system can decide autonomously versus what requires human escalation. Autonomous capability does not mean unregulated. It means the guardrails are built into the system rather than bolted on after the fact. The enterprises that are ahead of the curve have had the compliance conversation first. Not as an afterthought. First.
Nikita: And the fourth — and I genuinely think this is the one most organisations underestimate —
Amrith: Organisational readiness.
Nikita: Organisational readiness. Yes. The shift from campaign management to goal definition and outcome governance. Marketing teams have been built around the campaign as the primary unit of work. The brief, the execution, the report. When the platform is executing autonomously, that primary unit of work changes. It becomes: what outcome do we want to optimise for? What are the guardrails? Are the results we are seeing consistent with the strategy we defined?
Amrith: It is a different skill set. And in many ways a more interesting one — you are working at the level of strategy rather than execution.
Nikita: Exactly, it is a completely different skill set. But it requires a cultural shift that the technology alone does not create. And that shift takes longer than people expect.
Amrith: The organisations that have these four things in place — connected data, revenue-defined success metrics, governance frameworks, and teams willing to work differently — those organisations are not waiting for the technology direction to clarify. They are building the foundation right now.
Nikita: Yes, and they are the ones who will move fastest. Not because they were lucky. Because they prepared.
CHAPTER 7 — WHAT WE ARE SEEING, AND WHERE THIS IS HEADING
Amrith: But you know what I find genuinely encouraging? Something is shifting in the market that I think is worth naming.
Nikita: You think so? Don’t keep us in suspense – spill!
Amrith: Twelve months ago, the question we were getting from the organisations we work with was: should we be thinking about this? Is Revenue Intelligence something that applies to us, or is it more relevant to other industries? Today the question is: how do we sequence the transition? What do we need to have in place before we can move to the next stage? That shift — from whether to how — is the most meaningful indicator of market readiness I have seen in this space.
Nikita: That is a big shift, actually.
Amrith: It is a big shift. And we see it across very different types of organisations. We have banking clients across Southeast Asia who are actively restructuring how their loyalty data connects to their core transaction infrastructure. Not piloting it. Not exploring it. Making it a strategic infrastructure decision at a senior level, with budget and timelines. And we have a luxury retailer in Thailand who has fundamentally rethought how they define loyalty outcomes — moving from redemption rates and member counts to revenue contribution per member. Completely different industry, completely different context, same underlying shift. They have decided that the measurement framework needs to change before anything else can.
Nikita: And what those conversations tell us from a product perspective is that the market is not waiting for a perfect solution. It is looking for a direction that is credible and a partner who is taking it seriously. The organisations we work with are not asking us to hand them a finished capability. They are asking us to build toward it alongside them. Which — honestly — is the most encouraging thing we hear.
Amrith: Because that is how the best things get built.
Nikita: That is exactly how the best things get built. And the direction we are building toward — a platform where every piece of customer behaviour generates a signal, every signal generates an insight, every insight informs a strategy, and execution closes the loop back into measurement — that is not a future state we are describing from the outside. It is the direction we are moving in right now. Together with the enterprises who have decided this is where loyalty needs to go.
Amrith: I think the simplest way to put it is this. Loyalty was never really about points. It was always about understanding customers well enough to give them a reason to stay and grow. Revenue Intelligence is what that understanding looks like when it is embedded into the system. Not as a quarterly report. Not as a dashboard. As a loop that is running continuously, learning from every signal, and acting on every opportunity. That is the conversation the industry is ready for.
Nikita: And we are genuinely glad to be having it. Not because we think we have all the answers — we absolutely do not. But because the questions are getting better. And when the questions get better, the solutions follow. If you are working through any of this in your own organisation and you want to think out loud with someone, we would love to hear where you are. No pitch. Just a conversation.
Amrith: Good episode.
Nikita: Good episode. Thanks everyone.
Amrith: See you in the next one.
Your Hosts
Nikita Shaha
Head of Product and Technology, Perx Technologies
With over 10 years of experience across banking, telecommunications, and software, she focuses on product strategy, AI transformation, and large-scale technical delivery. She writes on how enterprises build intelligent, data-driven customer engagement in a mobile-first economy.
Amrith G
SVP and Head of Customer Intelligence and Performance, Perx Technologies
With over two decades of experience across MarTech, enterprise software, and B2B marketing leadership, he focuses on AI-enabled customer engagement, data monetization, and demand generation. He writes on how enterprises build loyalty and growth in a data-driven, mobile-first economy.
Questions This Episode Answers
Why doesn't traditional loyalty spend improve retention in banking?
Most loyalty programmes measure enrolment and satisfaction rather than what customers actually do with their money, so a programme can look healthy on paper while retention quietly erodes underneath it. McKinsey’s 2025 Global Banking Annual Review found that only 4% of new checking account applicants now choose their existing bank without shopping around, down from 25% in 2018, even as loyalty investment has continued to rise.
Is Net Promoter Score (NPS) a reliable measure of loyalty programme success?
Not on its own. NPS captures how a customer felt about a past interaction, not what they are doing with their money right now, so a customer can give a high NPS score weeks before moving their primary account to a competitor. Perx’s Nikita Shaha and Amrith G argue NPS still has value as an experience signal, but it needs to be paired with revenue attribution to show whether an intervention actually changed a customer’s spend behaviour.
What does a behaviour-led, agentic approach to loyalty change for a bank?
Instead of a campaign team designing and approving a response over several weeks, an agentic system can detect a behavioural signal, such as a spend decline, as it happens and act within hours using the mechanic that specific customer has historically responded to. This matters because the window in which a customer is still persuadable is often just days wide, not weeks, so speed of response becomes a revenue variable rather than a marketing efficiency metric.
How is Autonomous Revenue Intelligence different from a standard loyalty programme?
Revenue Intelligence is described as a closed loop rather than a bolt-on analytics layer: customer behaviour is the input, a growth engine decides the best next action per individual customer, and the output is commercial proof that the intervention changed a customer’s spend or lifetime value. In this framing, loyalty is the mechanism that runs the loop, not a separate rewards ledger sitting alongside it.
What does an organisation need in place before adopting an agentic loyalty approach?
Four things, per the episode: real-time data connectedness between core transaction systems and the loyalty platform, measurement clarity on what revenue success looks like, governance confidence so compliance and risk teams have defined what the system can decide autonomously versus escalate, and organisational readiness to shift from managing campaigns to defining outcomes.
Have a Question About What You Just Heard?
Talk to the Perx team about what behaviour-led loyalty could look like for your institution.
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An ISO/IEC27001:2013 and ISO 27018:2019 compliant cloud solution
An ISO/IEC27001:2013 and ISO 27018:2019 certified cloud solution