This is the final post in a five-part blog post series that delves into modernizing marketing strategies through the integration of advanced marketing platforms. I had the pleasure of interviewing Shaun Memon of Munvo for this series, where we discuss a wide range of topics, from fragmented MarTech stacks, first-party data use and cross-channel marketing to modern marketing journeys and complex decisioning.

Marketers today are sending more campaigns than ever. Why isn’t that translating into better customer experiences or outcomes?

Shaun Memon: A lot of it comes down to mindset and operating model. Many organizations say they’re customer-centric, but in reality, they’re still organized around channels, products or lines of business that compete for the customer’s attention. Each group has its own targets and stack, so the enterprise is optimized for “more marketing” instead of “better experiences” and lifetime value.

Teams send more emails, more notifications, more offers but they’re often overusing single channels instead of designing orchestrated, cross-channel journeys. Instead of the right next message at the right time, customers get overlapping campaigns that don’t reflect their context or needs.

That shows up as over-reliance on batch messaging rather than coordinated journeys. Customers get overlapping campaigns that don’t reflect their context. Modern best practice is to think in terms of next-best action, not just next-best offer. Sometimes the right action is educational or supportive, such as teaching a customer how to tackle a home project or helping a new grad understand credit and loans, not just pushing the highest-fee product.

Legacy, stitched-together stacks make that shift harder. Data, audiences and offers are synchronized manually across multiple tools, which creates technical debt and slows teams down. Journeys get simplified to what the plumbing can handle, not what’s best for customers. More messages that are less relevant just open the door to more agile competitors.

When leaders say they want to modernize, what usually gets in the way?

Memon: It’s rarely about intent. Most leaders genuinely want to serve customers better. The blockers are usually operational: legacy infrastructure, technical debt and the sheer complexity of making anything new work with what’s already in place. Marketing teams are often under pressure to “do more with what we have.”

Each new idea means another integration, another handoff, another dependency. Rules and offers get duplicated across platforms, and different channel teams end up working in their own stacks. This leads to extraneous tools and custom integrations onto a fragile stack instead of stepping back to rethink the model.

Over time, that creates brittle integrations, duplicated rules and siloed execution across channels. Different teams own different platforms with their own audiences, offers and reporting. That increases effort and risk of mistakes, and makes it difficult to deliver a coherent customer journey.

The result is a lot of hidden cost: overlapping licenses, custom connectors to maintain, manual processes, slow approvals and inconsistent execution.

All of this shows up directly in the customer experience:

• Slow time-to-yes and abandoned journeys.
• Customers re-explaining context as they move between web, app, contact center or in-store.
• Irrelevant or conflicting offers that erode trust and create fatigue.
• No ability to “rescue” in the moment when someone drops an application, abandons a cart, or signals interest in something different than what we planned.

Teams stay stuck in “good enough” mode, more sends, more campaigns, while more agile competitors with modern stacks test, learn and meet customers where they are with far less effort.

Why is complex, real-time decisioning so important if a company already has journeys and campaigns running?

Memon: Most organizations already use segmentation, models and journeys to decide who gets what offer. The implicit assumption is that a customer’s context stays roughly stable while they’re in that journey. In reality, intent shifts quickly. New signals from browsing behavior, life events and in-store interactions may not match what we originally predicted, even if our models were directionally correct.

Marketers shouldn't have to design every journey manually. As organizations mature by getting data in order, building profiles, refining segmentation and automating journeys, real-time decisioning becomes the next layer to reduce friction by avoiding offers that are technically “on model” but wrong for the moment. The future is self-optimizing decisioning. Instead of optimizing one campaign or one journey at a time, you’re optimizing for the customer’s overall lifetime value, building trust and attachment over time.

As we saw in Mary’s journey in the last post, the ability to pivot when you learn something new about a customer is what makes the experience feel truly personal and relevant in the moment.

Historically, that kind of adjustment happened in a branch, at the call center or in a store, with a human picking up on cues and adapting the conversation. Real-time decisioning engines are how we scale that human, in-the-loop judgment into a digital, omnichannel world while keeping messaging coordinated across channels.

Journey builders are among the latest product offerings in the MarTech world. However, most can only scale to a limited rule set. For most large enterprises, there are hundreds of micro moments customers have that signal their intent and are impossible to script. That’s where decisioning scales where journeys alone do not.

A real-time decisioning engine becomes the brain of the MarTech stack: the place where the next-best communication or offer is decided. Because it’s reading and writing against your centralized data, it can incorporate all available customer and streaming signals, update context as it changes, and ensure that when you do need to pivot away from what was pre-planned, every channel stays in sync around the new decision.

Get practical guidance on simplifying and modernizing your MarTech stack in our webinar From Fragmented to Connected: How to Build a Modern Stack.

We have heard a lot about CDPs over the last decade. Why are we now talking about customer engagement platforms instead?

Memon: Customer data platforms (CDPs) took off around 2014, when most organizations were still dealing with enterprise data warehouses, mainframes and slow batch jobs. Marketing teams couldn’t easily access unified customer data, so CDPs emerged to pull data from core systems into one place for identity resolution, segmentation and basic activation.

The trade-off was another data silo. You now have the warehouse and a CDP copy to maintain, sync and push back downstream. Over time, many CDPs added features – such as personalization, channel orchestration and deployment, AI/ML modeling – and started to look like bundled marketing stacks. Meanwhile, cloud data lakehouses like Snowflake and Google’s BigQuery changed the game. Done well, they let you centralize customer data once and let multiple systems read and write to the same environment in near-real time.

In that world, the question isn’t “where do we put the data?” anymore. It’s “how do we engage with the data we already have?” That’s where customer engagement platforms (CEPs) come in as the action/decision layer on top of your first-party data. They are the place where decisions, journeys and inbound/outbound orchestration come together. CEP isn’t in the same class as a CDP, event stream processing or data management platform.

Instead of being another data repository, a CEP is designed to:

• Use data where it lives (your lakehouse or customer data mart).
• Orchestrate omnichannel journeys across outbound and inbound, digital and physical, and owned and third-party channels from a single place.
• Apply real-time context signals and decisioning to choose the right message for the moment.
• Activate offers and experiences across channels with minimal integration work.
• Capture contact and response history in one model so you can measure lift and iterate.

To simplify this, we can think of the data warehouse or cloud lakehouse as the single source of truth and the CEP as the decision, coordination and action layer. The CDP is the middle layer that compiles customer data in a marketing-usable format. This layer becomes largely unnecessary as a separate entity with cloud lakehouses and medallion data architecture taking the place of standalone CDPs.

For leaders trying to move from their current stack toward CEPs and the next-best-action model, where should they start?

Memon: Customer experience platforms let strategy, segmentation, messaging and offers, channel activation, journey orchestration and measurement all run from a centralized layer. That reduces the number of integrations, keeps data movement to a minimum, and gives marketing, analytics and frontline teams a shared view of the customer so they can behave in a customer-centric way. CEPs that support continuous learning loops with offer management and metadata capture to support detailed lift analysis and insight generation will outperform static journey builders.

For leaders trying to move toward that CEP model, I’d start in this order:

1. Begin with the customer experience, not the tools. Get clear on what “good” looks like in two to three years:
   • Which journeys matter most (onboarding, activation, cross-sell, retention, service recovery, etc.)?
   • Which channels really matter for your brand: owned digital, in-person, contact center, social, paid media, app, SMS, push?
   • What kind of value do you want customers to feel – education, guidance, convenience, financial benefit – instead of just pushing more offers?
2. Align on data, governance and cloud strategy. Before buying more MarTech, make sure:
   • You are aligned with IT cloud and data strategy. Do you have (or are moving toward) a unified customer data mart or lakehouse that serves as one place to read and write customer data?
   • Channels can feed signals back in close to real time, so you’re not acting on a three-day-old shadow of the customer.
   • You can capture a unified contact and response history (who saw what, when, and how they reacted).
   • You account for governance. Next-best-action engines need auditable rules, traceable AI, and guardrails on what can be offered to whom.
3. Map current capabilities against four core domains. Look at what you already own and where the gaps really are:
   • Data – access, timeliness, golden record (individual/account/household).
   • Segmentation and modeling – can marketers easily build and use segments, models, and eligibility rules?
   • Orchestration and activation – can you coordinate offers and journeys across inbound and outbound channels from one place, or are you duplicating work per tool?
   • Real-time offer arbitration – can you change the message in the moment when context shifts, or are you locked into whatever the journey said on day one?
4. Design a lean CEP layer, not another pile of tools. Use a few principles to guide investment decisions:
   • Keep it simple – go for a small number of interoperable components instead of a lot of bespoke glue.
   • Centralize data – access from your cloud warehouse/lakehouse and avoid unnecessary copies.
   • Minimize overhead – favor SaaS/PaaS, automate operations, keep admin burden low.
   • Avoid tech debt – use standards-based, API-driven integrations and a composable architecture.
   • Design for privacy – consent, lineage, explainability for AI, and regulatory checks baked in.
   • Run lean for ROI – prioritize use cases with fast payback, such as digital sales, application rescue, churn saves, pre-approved offers.
   • Measure relentlessly – define North Star metrics (digital adoption, time-to-yes, CPA, LTV) and review them monthly.
5. Ask whether you’re solving use cases or buying a category. As you evaluate platforms, pressure-test them with concrete scenarios:
   • Can this help us run the journeys we care about, across the channels we actually use?
   • Does it reduce integrations, data copies and manual work or add more?
   • Will marketers and CX teams be able to run with it day-to-day, or does every change require input and work from IT or analytics?

Tools like SAS^® Customer Intelligence 360 and upcoming SAS 360 Marketing Decisioning can play a central role in that journey. Together, they give you a single place for segmentation, offers, decisioning and activation across owned, inbound, outbound and third-party channels with the ability to pivot in near-real time using AI and machine learning in a responsible, governed way.

That said, the best decisioning engine won’t fix a channel-centric operating model; you need strategy, process and technology pulling in the same direction.

If a marketer is feeling the pain of a fragmented stack today, what should they do next?

Memon: The real risk of sticking with fragmented stacks isn’t only operational, it’s strategic. Competitors with modern CEPs that learn and adapt faster will widen the gap over time. There’s a lot to consider and align on as an organization.

Figuring out how to get from “where we are” to “where we want to be” can feel daunting. It’s important to start by identifying gaps, sequencing changes and keeping overhead manageable so teams can stay focused on delivering value to customers.

Is your MarTech stack holding you back? Take our self-assessment to see where you stand on your marketing modernization journey.