Problems in customer engagement
Enterprises today face mounting pressure to retain customers who demand seamless, highly personalized interactions. However, they frequently stumble, resulting in disjointed experiences that erode brand loyalty. Solution Designers must look past the surface symptoms to address the structural root causes:
Relevance and real-time context
- Symptom: Inability to deliver 1:1 personalization, resulting in generic, mistimed, or tone-deaf interactions.
- Root Cause: Prioritizing internal business goals over customer context and failing to act on real-time intent due to outdated batch processing.
Omnichannel consistency
- Symptom: Disconnected experiences as customers cross different channels, products, and business units.
- Root Cause: The lack of a centralized intelligence layer to unify data, analytics, and decision-making across the enterprise.
Adaptive orchestration
- Symptom: Customer journeys that frequently break or hit a dead-end because they are mapped as predefined, inflexible paths.
- Root Cause: Decision-making is fragmented across siloed teams (marketing, service, risk) rather than orchestrated holistically.
Persona pain points and the diagnostic translation layer
When business stakeholders describe their pain points, they usually describe symptoms. The Solution Designer's job is to diagnose the underlying decisioning and architectural failures.
The marketing leader (CMO / VP of marketing)
Focuses on acquisition, retention, churn, and campaign ROI:
| Symptom | Root cause |
|---|---|
| "Our campaigns aren't converting like they used to." | Engagement is product-centric (pushing a specific product) rather than customer-centric (meeting a specific need). |
| "We need true 1:1 personalization, not just segments." | Relying on batch processing and audience segments instead of calculating the Next Best Action for the individual in real-time. |
| "We are sending tone-deaf offers to angry customers." | Marketing and Service systems are siloed; outbound offers lack the context of current inbound service issues. |
The customer experience (CX) / service leader (CXO / head of support)
Focuses on NPS, customer lifetime value, resolution times, and journey consistency:
| Symptom | Root cause |
|---|---|
| "Our customer journeys are broken or dead-end." | Journeys are hard-coded and predefined; the system cannot adapt dynamically when a customer does something unexpected. |
| "Customers hate repeating themselves when they switch channels." | Channels have their own isolated logic; there is no centralized brain remembering the customer's state across touchpoints. |
| "Our agents don't know what the customer actually needs." | Inbound channels lack systemic guidance; agents are reacting to the customer rather than being proactively guided by AI. |
The data and analytics leader (chief data officer / VP of IT)
Focuses on data utilization, model deployment, architecture, and technical debt:
| Symptom | Root cause |
|---|---|
| "We have a massive data lake, but we aren't using the data." | Data is trapped in storage; it is not operationalized or fed into a real-time decision engine. |
| "It takes our data scientists months to get a model into production." | Analytics and operational runs are disconnected; there is no MLOps framework to quickly deploy and test models at the edge. |
| "We can't react to customer signals fast enough." | The architecture relies on nightly batches rather than streaming events and real-time triggers. |
The line of business / growth leader (head of retail banking, wealth)
Focuses on revenue, share of wallet, cross-selling, and advisor efficiency:
| Symptom | Root cause |
|---|---|
| "We are missing obvious cross-sell and up-sell opportunities." | The system cannot calculate and balance the customer's propensity to buy against the business value of the offer in real-time. |
| "Our advisors/bankers are spending too much time prepping for calls." | Lack of automated, synthesized intelligence; humans are doing the analytical work that a decisioning engine should provide instantly. |
The "Ask Why" framework to uncover engagement root causes
In customer engagement, the most common trap that a Solution Designer can fall into is solving the problem for the channel (for example, buying a better email platform) rather than for the decision (for example, fixing the logic that chooses what to email). To identify a Pega-shaped problem, you must drill down past complaints about "low open rates" or "bad campaigns" to find the structural breakdown in enterprise intelligence.
The diagnostic drill-down
When a stakeholder says, "We need a new marketing automation tool to stop customer churn," use the five whys to shift the focus from the channel to the Centralized Brain:
- The request (the surface): "We need a new campaign tool to send more personalized emails and reduce churn."
- Why 1 (the performance gap): Why aren't your current emails stopping churn?
"Because customers ignore them; they feel irrelevant or poorly timed." - Why 2 (the logic gap): Why are the offers irrelevant?
"Because we send the same 'Save 10%' offer to everyone in the 'At Risk' segment, regardless of what they are actually doing today." - Why 3 (the context gap): Why are we using broad segments instead of their immediate context?
"Because our email tool can't see that the customer was just on our website reading a specific cancellation FAQ five minutes ago." - Why 4 (the structural root cause): Why is the email tool blind to the website data?
Root cause: You should treat this as an email problem rather than an omnichannel decisioning problem. The decision logic is siloed within the channel, and the stakeholder lacks a centralized intelligence layer to orchestrate context across the enterprise.
Core diagnostic questions for Solution Designers
Use these probing whys during discovery sessions to expose the need for a Centralized Brain, real-time triggers, and Next Best Action:
| Structural gap | Diagnostic question |
|---|---|
| Siloed logic | "If a customer calls in furious about a billing error, why does our mobile app still show them an aggressive cross-sell banner for a new credit card ten seconds later?" |
| Batch processing | "Why does it take 24 to 48 hours for our systems to recognize that a customer just had a major life event (like a massive withdrawal) and trigger a retention response?" |
| Static journeys | "If we put a customer on a 30-day onboarding journey, what happens to that journey if they do something completely unexpected on Day 3?" |
| Product-centricity | "Are we pushing this specific loan offer because the data shows the customer actually needs it today, or just because we have a monthly product quota to hit?" |
The so what? test
Once you think you've found a root cause, apply the Solution Designer's critical thinking lens to the engagement request:
Is this a channel problem or a brain problem?
- If the goal is to make the email look prettier, that is a channel or UI problem. If the goal is to calculate exactly who gets what message at what exact millisecond across any channel, it is a Pega-shaped Brain problem.
Are we pushing products, or are we showing empathy?
- If the system cannot automatically suppress a sales offer to prioritize a critical service interaction, the architecture is broken.
Pega Customer Decision Hub solutions for root causes
After the Solution Designer has drilled down to the structural root causes - siloed channels, static journeys, batch processing, and product-centric logic - the transition to the Pega solution becomes a natural architectural shift.
Pega Customer Decision Hub is not just another marketing tool; it is a centralized intelligence layer that fundamentally changes how the enterprise makes decisions.
Here is how a Solution Designer maps root causes to Customer Decision Hub features:
1. The silo problem versus the centralized brain
- Root cause: Channels (web, mobile, email, call center) have their own isolated rules, resulting in disjointed and inconsistent customer experiences.
- Solution: Centralized decisioning. Customer Decision Hub acts as the single brain sitting behind all channels. It decouples the decision-making logic from the individual touchpoints.
- Outcome: When Customer Decision Hub makes a decision, it updates the customer's state instantly across the enterprise. If a customer declines an offer on the web, the call center agent sees that immediately and won't offer it again.
2. The static journey problem versus Next Best Action
- Root cause: Customer journeys are hard-coded. If a customer deviates from the predefined path, the journey breaks and the experience degrades.
- Solution: Always-on Next Best Action. Customer Decision Hub destroys the concept of static campaigns. Instead, it recalculates the absolute best action to take for an individual at the exact moment of interaction, based on their real-time context.
- Outcome: Journeys become dynamic and fluid. The system adapts instantly to unpredictable customer behavior, always calculating the next logical step.
3. The tone-deaf problem versus customer empathy
- Root cause: Engagement is driven by internal business targets (for example, "sell 1,000 mortgages this month") rather than the customer's actual situation, leading to tone-deaf offers (for example, trying to sell a mortgage to someone actively reporting fraud).
- Solution: Arbitration (balancing value and context). Customer Decision Hub uses AI propensity models to calculate what the customer needs, and balances that against what is valuable to the business. It categorizes actions into Sales, Service, or Retention.
- Outcome: Customer Decision Hub proactively suppresses a cross-sell offer if it detects a high-priority service issue, prioritizing empathy and long-term retention over a short-term, tone-deaf sale.
4. The batch processing problem versus real-time event triggers
- Root cause: Enterprises rely on nightly data loads and batch segmentation, meaning they are always reacting to what the customer did yesterday.
- Solution: Streaming data and event processing. Customer Decision Hub listens to real-time signals (a dropped call, a high-value withdrawal, a prolonged web page visit) and acts on them instantly.
- Outcome: Enterprises move from retroactive analysis to proactive, in-the-moment engagement, capitalizing on fleeting moments of customer intent.
By mapping problems this way, a Solution Designer proves that fixing the customer experience isn't about buying a better email platform or redesigning a website. It's about ripping out fragmented logic and replacing it with a centralized, AI-driven decisioning hub.
Wells Fargo improves engagement with real-time decisions
See how Wells Fargo transformed customer engagement by moving from batch campaigns to real-time decisioning:
- Business issue: With over 70 million customers, the bank struggled to personalize engagement at scale. While they had massive amounts of data, they couldn't operationalize it fast enough to have relevant conversations while the customer was actually in the channel.
- Solution: They implemented Customer Decision Hub as a real-time learning engine to calculate the Next Best Conversation in milliseconds. The system analyzes billions of interactions to dynamically adjust to a customer's behavior the moment it happens.
- Results: Wells Fargo delivered over 1,000 decisions per second, resulting in a 3x to 10x increase in customer engagement rates across their digital touchpoints.
For more information, see Wells Fargo personalizes real-time conversation.
Boundary conditions for Customer Decision Hub
A key skill for a Solution Designer is to quickly qualify the problem, ensuring that you invest time and resources where you can deliver transformational value. These boundary conditions are based on years of successful (and unsuccessful) Customer Decision Hub implementations.
The six core boundary conditions
To qualify as a problem suited for Customer Decision Hub, it should meet these six conditions:
| Boundary condition | Threshold | Why it matters |
|---|---|---|
| 1. Company type | B2C (Business to Consumer) or select B2B | Pega's AI is most powerful when fueled by high interaction volumes and rich customer history, which are characteristic of B2C models. Our product templates are designed for this scale. |
| 2. Company revenue | > USD 3 billion annually | Organizations of this size have sufficient complexity (multiple products, channels, and business units) to generate a significant return on investment from a centralized decisioning engine. |
| 3. Number of customers | > 1 million customers | A customer base of this size is required to generate the millions of daily interactions needed to train our adaptive AI models and prove our key differentiators. |
| 4. Average customer value | > USD 100 per year (ARPU) | To justify the investment in a sophisticated decisioning platform, the value of retaining or growing each customer relationship must be substantial. |
| 5. Business issue | 1:1 customer engagement | The opportunity must focus on enhancing the value of the customer relationship (for example, Grow, Nurture, Service, Retain). These are the core issues Customer Decision Hub is built to solve. |
| 6. Number of actions | Between 50 and 2,500 | Fewer than 50 actions suggest a lack of complexity, limiting the value of arbitration. More than 2,500 actions can create challenges for real-time decisioning and management. |
What to do if a use case does not meet the boundary conditions
If the problem does not meet these conditions, it is probably better suited to a Pega Customer Service or Pega Platform solution.
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