Turning Data into Dollars: Future-proofing CRM, Loyalty, & Retention

CRM veteran Woody Bendle (Bradley Bendle, Founder of Next Level Growth Strategies) recently joined us for a wide-ranging Q&A as part of the Marketing Insider Series. [If you missed it, you can watch the full video here.] 

A discussion on CRM quickly expanded into where marketing is really headed. 

Marketers today are navigating a storm of change: third-party cookies disappearing, AI reshaping how we engage, customers showing signs of loyalty fatigue, and leadership demanding clear ROI. But the questions at the core remain strikingly consistent: 

How do you keep customers engaged, build trust, and drive sustainable growth?

Today’s post continues the dialogue with added perspective, fresh examples, and a few provocations for senior marketers rethinking CRM, loyalty, and retention.

The data dilemma: Building  future-proof CRM strategies

The AI reality check in CRM

Loyalty strategies: Why most tank (and how to fix yours)

Staying ahead of churn with predictive analytics

Turning CRM into a revenue engine

Proving ROI in cost-conscious times

The data dilemma: Building  future-proof CRM strategies

The challenges of a cookie-less future

While marketers appreciate the need to shift away from their reliance on cookies, they face a number of challenges:

• Interoperability and integration issues: Without cookies, integrating data now requires expensive middleware, creates vendor lock-in, and needs IT and security approval. As a result, CMOs spend as much time navigating budgets and governance as they do actually building a unified customer view.
  
• Vendor uncertainty and dependence: Choosing partners is no longer about “checking features.” It’s about whether contracts, compliance, and roadmaps can withstand CFO and legal scrutiny. Google’s delays and U-turn on cookie deprecation forced marketing leaders to justify vendor pivots to boards—and many now add “vendor volatility” clauses to contracts.
  
• Loss of performance benchmarks: Marketers lose years of comparative, cookie-based performance data, complicating goals, predictions, and justifying new investments. But the harder challenge isn’t measurement, it’s managing stakeholder expectations through the transition.
  
• Operational friction for consent management: Managing user consent for first- and zero-party data collection demands new processes and continuous oversight to remain compliant.
  
• Difficulty maintaining customer trust: With more explicit permission required, there’s a greater risk of consent fatigue or opt-outs, making transparent communication even more crucial.

The future is cookie-less. Woody stressed the need to double down on zero- and first-party data, and, crucially, to align data strategy with brand strategy, not the other way around. 

While the challenges are real, they also point the way forward. With third-party cookies fading, first- and zero-party data aren’t just alternatives—they’re becoming the foundation of effective marketing and CRM strategies. 

Mastering zero- and first-party data: Best practices for marketers

Below, we outline best practices for collecting and leveraging zero- and first-party data:

• Track customer behavior with website analytics, email/SMS engagement, ecommerce transactions, offline POS systems, and registration touchpoints. Use heatmaps and session recordings. Ask directly for zero-party data through surveys, quizzes, preference centers, gamified experiences, contests, chatbots, support interactions, and post-purchase feedback.
  
• Move beyond static “once-and-done” permissions. Utilize adaptive consent systems that let customers adjust privacy and sharing preferences in real time.
  
• Pilot a “data co-op” with trusted, non-competing brands, sharing zero-party insights for cross-brand personalization, but always with explicit user permission.
  
• Replace cookie re-targeting with predictive segments from first-party purchase history, pushing contextual offers at the right lifecycle stage.
  
• Map where zero- and first-party data is collected at each channel, and design “breadcrumb” journeys that incentivize full profile completion over time.
  
• Tag each data point in your platform with consent level and expiration date. Run quarterly audits using automated tools  to flag gaps between permissions and usage—automatically archive or anonymize data past its valid use. 

Mastering zero- and first-party data is one part of the equation. The other big shift reshaping CRM is the rise of AI. While its potential is massive, many marketing leaders remain unsure how to separate hype from practical use. 

And this confusion about AI in CRM and loyalty programs is now as pressing as the cookie conversation.

The AI reality check in CRM

CMOs feeling the heat

Yes, artificial intelligence is key. But CMOs are under pressure to adopt AI, albeit the fact that confidence lags:

• Research from the Australian Centre for AI in Marketing shows only 20% feel ready to lead the change, whereas 62% say they need stronger AI skills.
  
• Still, 71% of marketers now plan to pour $10M+ into gen AI over the next three years, as per Boston Consulting Group.
  
• Simultaneously, almost 46% cite difficulty proving ROI as the biggest obstacle in implementing gen AI, SAS reveals.

Evidently, CMOs today are facing a challenging balancing act: they must adopt AI to stay competitive and meet CFO demands for ROI, yet fear wasting money on unproven uses. 

However, without skills and training, rushing into AI is reckless. As Woody noted, a fool with a tool is still a fool. 

Simply having access to AI doesn’t  guarantee results; without critical thinking, domain knowledge, and strategic oversight, even the most sophisticated tools can amplify errors, propagate biases, and create risks for both organizations and individuals.

Amazon, for example, learned the hard way: its AI recruitment system, trained on mostly male resumes, favored men and discriminated against women, ultimately being scrapped. 

Therefore, as Woody reminded, adoption must be guided by guardrails and real business problems. 

How to leverage AI in CRM

The pressure is real, but the path forward is fairly clear: focus on practical, skill-driven ways to integrate AI: 

• Institutionalize AI as a business capability. Identify and invite leaders from marketing, sales, data science, customer experience, and IT. Define a clear council charter focused on aligning AI initiatives with business goals.
  
• Translate AI into portfolio-level bets and KPIs that connect tightly with brand equity and market positioning. Map AI capabilities to marketing outcomes like customer acquisition cost, lifetime value, and brand loyalty index.
  
• Use AI as a market sensing amplifier. Integrate external data sources with CRM AI models. Set automated alerts for sentiment shifts or emerging needs at highly granular levels.
  
• Deploy explainable AI features that show customers why and how their data is used.
  
• Establish feedback loops to update AI models in real-time.
  
• Commission AI-generated market simulations forecasting competitor moves and customer demand shifts. Use findings to inform product launches, pricing experiments, etc. Proctor & Gamble (P&G) piloted AI simulations to forecast consumer reaction to new products and packaging (see below).
  
• Establish mentorship programs pairing data-savvy staff with creative marketers for knowledge exchange.

Source: SSRN

At Mavlers too, we approach AI adoption in the larger context of business goals. As our COO, Nital Shah, explains:

“To stay ahead of industry shifts, we attended conferences, engaged with clients, and built internal capabilities by hiring a dedicated AI team. The mandate is clear: Solve business problems faster, better, and more cost-effectively through AI and automation.

To keep this a priority, the team works directly with me and our Head of Technology, ensuring AI and automation remain central to our strategy. We’re now focused on internal testing, trials, and validations tied to business metrics.”

Nonetheless, AI is NOT a silver bullet. Ultimately, retention depends less on algorithms and more on the real value customers get from your brand. The conversation about AI in CRM and loyalty programs must therefore link directly to tangible customer outcomes.

Loyalty strategies: Why most tank (and how to fix yours)

Why loyalty doesn’t last

Many loyalty programs are stuck in discount mode, training customers to chase coupons and points rather than building genuine attachment to the brand.

“Among U.S. consumers who joined any loyalty program, they participate in 12 on average, our survey found. With that volume, it’s no wonder that many consumers find little reason to engage in some programs beyond acquiring discounts,” as Maureen Burns et al highlight in Harvard Business Review.

The result? Shrinking profit margins, predictable promos, and customers who bail for better deals.

But here are other reasons why loyalty programs tank:

• Companies launch a loyalty program and assume it will run successfully without ongoing adjustments. Loyalty strategies should be treated as ongoing experiments, not static systems.
  
• Legacy loyalty platforms make it difficult to personalize offers or integrate with new sales channels (e-commerce, mobile apps, social).
  
• Complex structures, confusing point systems, and difficult redemption processes turn customers off. 

How to fix your loyalty programs? 

Woody emphasized that loyalty programs are fundamentally about information exchange, not transactions. He also stressed that senior marketers should partner with finance.

Beyond these insights, here’s a few strategies for maintaining customer loyalty: 

• Modernize accounting and reward platforms for full visibility across the loyalty ecosystem.

• Design rewards to encourage incremental visits, rewarding existing behaviors rather than trying to incentivize entirely new ones.

• Ask members for feedback and act on it—survey regularly, implement feasible suggestions, and communicate changes to show you’re listening and build trust.

• Use scenario analysis tools to simulate changes in reward generosity and impact on margins.

• Establish thresholds to reduce reward payouts when margin dips below target levels without customer disruption.

• Create machine learning models in order to predict churn and personalize offers automatically.

• Deliver unexpected bonuses or perks to reengage inactive customers.

• Explore new revenue streams such as co-branded cards or partner ecosystems to extend program reach.

• Partner with tech vendors to monetize anonymized loyalty data insights with strategic partners.

• Test flexible program innovations, such as blockchain-based solutions, integrating marketing, finance, and technology.

American Express, for instance, launched a blockchain-based loyalty program enabling merchants to create customized rewards for cardholders. Blockchain allows immediate, secure verification of purchases and faster rewards issuance. 

Source: The Miles Genie

Even after incorporating loyalty strategies, no loyalty program, however perfect, seems to arrest churn. So, along with data, AI, and loyalty, you need a strong customer retention program to reduce churn—if not eradicate it.

Staying ahead of churn with predictive analytics

The perennial problem of churn

While some of it is inevitable, it isn’t entirely beyond control. The hard truth is that much of it could have been prevented, leaving marketers to grapple with challenges such as:

• Struggling to identify the specific reasons why customers are leaving.
• Predicting accurately when customers are at risk.
• Accumulated “marketing debt” in the form of fragmented technology stacks and a lack of skills in areas like predictive analytics or behavioral economics.
• Dealing with the drag on growth and the hit to profitability.

How to deal with customer churn?

Woody emphasized the importance of building organizational confidence in predictive analytics and for senior marketers to partner with experienced data scientists, rather than relying solely on automated outputs.

Below, we share a few ways to start re-thinking your customer retention program:

• Stop chasing demographic patterns only. Instead, dig into controllable factors like customer satisfaction scores, usage patterns, and service interactions. Sweet Fish Media cut monthly churn from 15% to 3% by using quarterly podcast reviews to build relationships and prevent churn. 

• Your clickstream, support ticket, and usage data can often predict churn better than direct surveys. Look for patterns in micro-behaviors.

• Deploy a system of “micro-NPS” polls that trigger in-app at critical user journey moments.

• Enable self-service analytics tailored for frontline users so they can harness churn data for decision-making. 

• Plot your customer journey and identify optimal intervention windows. Too early wastes resources; too late reduces effectiveness. Test different timing to find your sweet spot for each customer segment.

• A majority of your “at-risk” customers won’t actually leave. Annoying loyal customers with desperate retention offers can backfire. So, test small segments to understand when outreach creates delight vs. irritation.

• Test non-monetary retention tactics. Many customers churn for reasons money can’t directly solve. Offering personalized guidance, creating community engagement opportunities, or simplifying product usage can address these underlying issues and help retain customers without relying solely on discounts or financial incentives.

• Identify your power users, the people who are not just using your product but are actively evangelizing it within their own organizations or networks.

• Treat churn as a temporary pause. Create a “dormant user” program that offers a highly curated, low-commitment re-engagement path, such as a “lite” version of your product, to keep them minimally engaged.

• Not every customer is worth saving. Develop clear criteria for when to shift resources from retention to acquisition in order to strengthen your customer retention program.

• Track rescue rates, long-term retention post-intervention, and profitability impact, not just whether customers stayed for the next billing cycle. 

Below is a list of predictive models worth incorporating into your customer retention program. 

Turning CRM into a revenue engine

The “tunnel-vision” approach to CRM

While many senior marketers may use a CRM system for email marketing and communication, it’s a misconception to view it as solely an email tool. When implemented holistically, a CRM can be a significant driver of measurable revenue:

• For every dollar invested in a CRM tool, businesses see an average return of $8.71. In fact, proper CRM implementation can deliver up to a 245% ROI.
  
• Strategic CRM adoption enables improved nurturing and follow-up, resulting in lead conversion rates increasing by as much as 300%.
  
• CRM systems make revenue forecasting more data-driven, improving accuracy by 32-42%. 

Most marketers use CRM primarily for contact management, organizing and segmenting lists, which overlaps with email targeting and tracking. Yet without proper training and strategic guidance, CRMs end up underutilized, serving mainly as glorified contact and email tools.

Unlocking CRM as a revenue engine

Woody recommended that CMOs must be data-savvy, with measurement at the forefront. Plus, as he rightly pointed out, marketers are “entrusted with an immense budget” and must deliver returns. To that end, here are a few strategic tips:

• Connect CRM data directly to finance and ERP systems: not just sales, but actual payment histories, contract renewal status, and account-level profitability.

• Connect your CRM to product telemetry. Go beyond sales data—track feature adoption, usage frequency, along with key workflows to predict churn early.

• Build a robust measurement framework to track CRM-driven contribution across marketing-sourced pipeline, sales velocity, retention, and expansion revenue.

• Don’t rely only on CRM’s built-in dashboards. Instead, export your CRM data to specialized analytics tools for granular modeling (e.g., customer lifetime value, cohort analysis, and predictive pipeline modeling).

• Deploy enterprise-grade sales acceleration platforms with embedded real-time AI coaching, pipeline visualization dashboards, and deal inspection analytics integrated with your CRM platform. 

• Build AI agents in your CRM to automate high-touch tasks like hyper-personalized social selling, sentiment analysis, and adaptive sales playbook recommendations. 

Finnair, for example, employs agentic AI in CRM to analyze customer behavior and preferences for personalized service offerings. Their proactive AI-driven approach has increased customer loyalty by 25% and lowered complaints significantly.

Source: MKSE

Now, once revenue starts flowing, attribution becomes critical—not just knowing where sales came from, but being able to tie every dollar spent to measurable business impact.

Proving ROI in cost-conscious times

The CFO mandate for CMOs

Attribution remains one of marketing’s toughest challenges. CMOs are under constant pressure from CFOs to justify every dollar of spend. The difficulty lies in connecting marketing activity directly to revenue impact—and most leaders face a familiar set of hurdles when it comes to proving ROI: 

• Limited visibility into which channels, campaigns, or tactics within the marketing mix are truly driving results.
  
• Gaps in technical and data literacy among CMOs and their teams, making it harder to extract value from tools.
  
• Ongoing difficulty in trusting, cleaning, and operationalizing data that is often fragmented or inaccurate.
  
• Internal cultural resistance where decisions are still made on “gut feelings” rather than on data.
  
• The difficulty in balancing near-term performance goals with the long-term investments for building brand trust.

Source: McKinsey & Company

Since late 2023, reported pressure has surged—up 20% from CEOs, 52% from CFOs, and 21% from boards. B2C product marketers feel this most acutely, with 65.6% citing pressure from CEOs, 67.9% from boards, and 77% from CFOs. 

How CMOs can demonstrate ROI to CFOs

Woody emphasized that marketers must act as true partners to their CFOs—operating with full transparency and keeping the bigger picture in view. Attribution may be a difficult path, but here are a few practical ways you can approach it:

• You don’t need expensive, complex tools to begin. You can perform basic attribution by using data you’re likely already collecting from Google Analytics, your CRM, and individual marketing channels like Facebook and Google.
  
• The data from your own existing tools is often free. Use it to understand where channels fit in the funnel, deduplicate conversions (so you’re not double-counting sales), and test hypotheses about customer behavior.
  
• Your focus should be on your peer team and colleagues, not just your marketing team. Learn to speak their language and frame your marketing impact in terms that resonate with them, such as revenue, customer experience, and business growth.
  
• Position your marketing department as an integral part of the business that drives top- and bottom-line growth. This shift in perception is crucial for securing budget and buy-in—which is central to any effective customer loyalty and retention strategy.
  
• Consider dropping simplistic single-touch models like First or Last Click. Instead, adopt a multi-touch attribution model that gives a 360° view of the customer journey, crediting all touchpoints that contributed to a conversion.
  
• Leverage dynamic weighting and real-time adjustments in attribution instead of relying on static models.
  
• Break your initiatives into short “proof experiments” (weekly or biweekly) to keep stakeholders engaged and aligned.
  
• Recognize when “good is good enough” to avoid the trap of chasing a perfect solution. The goal is actionable insights, not a flawless model.

Wrapping up

Woody’s insights push us to challenge old assumptions and rethink the future of data, AI, loyalty, churn, and CRM—not as separate challenges, but as interconnected levers of growth. At the core, they all point to one unifying theme: building trust while delivering measurable value.

If you haven’t seen the full conversation with Woody, watch it here for even more examples and strategies.

At Mavlers, we partner with marketers who want to make this shift—from discounts and dashboards to systems that drive ROI and long-term growth. With our future-proof CRM strategies and AI-driven customer retention at the center, you can build a customer retention program that delivers. 

If that’s the future you’re building toward, connect with us for a 30-min no-obligation call today!