Jennine Rexon is a performance marketing and lead generation executive with more than 24 years of experience building customer acquisition businesses focused on compliance, transparency, and measurable results. She is the Founder and CEO of Rex Direct and President of Rex Martech, where she helps brands improve lead quality, reduce fraud, and drive growth through data-driven marketing and technology solutions. Her expertise spans lead generation, pay-per-call, affiliate marketing, and customer acquisition, with a focus on creating sustainable programs that deliver value for both buyers and consumers.

SR: How has your definition of a “high-quality lead” evolved from the early days to today?

JR:

A high-quality lead today needs to be exclusive, real-time, and validated for both accuracy and fraud. Those are now table stakes. The harder piece to measure is intent.

Over time, I’ve also learned that the buyer plays a critical role in defining what “high quality” really means. Ultimately, buyers are measuring sales, not just form fills or calls. That means lead quality is not only about how the lead was generated; it is also about how the lead is handled once it reaches the buyer.

Speed to contact is essential. Buyers need to follow up immediately, not only to work the lead properly for the best possible outcome, but also to build trust with the consumer. A lead may come in at the right time, with the right information, and from the right source, but if the follow-up process is slow or inconsistent, value is lost.

For teams trying to improve lead quality, I would focus on a few practical steps:

• Validate before delivery. Confirm contact accuracy, check for obvious fraud signals, and remove duplicate (pre-ping method is best) or low-quality submissions before they reach the buyer.
• Measure speed to contact. Track how quickly each lead is called, texted, or emailed. This should be treated as a core performance metric, not an afterthought.
• Align with buyers on the definition of quality. Do not assume every buyer values the same thing. Get clear on the metrics they use to define a successful campaign.
• Create feedback loops. Buyers should share disposition data whenever possible so sources can be optimized based on actual outcomes, not assumptions.
• Look beyond the first conversion. A strong lead process should also create future acquisition opportunities by building trust with the consumer.

The best lead programs are not just optimized for delivery but for lifetime value.. They are optimized for the full path from consumer intent to buyer outcome.

SR: Where do you see companies still cutting corners, even if they don’t realize it?

JR:

I think the industry’s move toward branded leads was a positive one. While one-to-one is not required currently, I believe leads perform best when they are sold that way.

Where I still see corners being cut is in the difference between selling true leads and selling data. This is especially common in parts of the co-registration space. A consumer may answer a qualifying question, and then that information is resold multiple times. That may technically create monetization, but in my view, it does not create the same level of consumer trust, buyer value, or long-term sustainability.

At Rex Direct, we do not believe in that approach. We sell co-registration leads once, with proper compliance and transparency.

For companies that want to tighten up their process, regardless of lead type,  I would recommend:

• Be clear about what the consumer is opting into. The consumer should understand who may contact them and why.
• Separate “data” from “leads.” A name, phone number, or email address is not automatically a lead. A true lead should include clear consumer intent and proper consent.
• Avoid over-monetizing the same consumer. Reselling the same inquiry too many times may create short-term revenue, but it can damage performance and trust.
• Review partner practices regularly. Do not assume every source is following the same standards you are. Ask how consent is captured, how traffic is sourced, and how often the lead is sold.
• Document your process. Compliance should not live only in someone’s head. Make sure consent language, source rules, suppression practices, and delivery logic are documented and reviewed.

The companies that win in the long term are the ones that treat compliance and transparency as part of the product, not just a legal requirement.

SR: When balancing cost, volume, and quality, which tends to break first?

JR:

Balancing cost, volume, and quality is a constant optimization exercise, but scaling volume is often the most difficult part.

Once a campaign is dialed in, it can be hard to find additional sources that deliver the same quality at the same price and volume. A source that works well at a smaller volume may not perform the same way when you try to scale it. That is why it is so important to constantly find, test, integrate, and maintain a healthy mix of traffic sources that, together, perform against the buyer’s KPIs.

The biggest mistake is treating scale as a simple budget increase. More spend does not always create more of the same quality. Scaling requires discipline.

A few practical steps I would recommend:

• Do not rely on one source. Even if one partner is performing well, build a balanced portfolio so performance is not dependent on a single channel.
• Scale in controlled steps. Increase volume gradually and monitor whether quality, contact rate, and CPA hold steady.
• Know which KPI matters most. Sometimes the right decision depends on whether the buyer is optimizing for CPA, close rate, contact rate, lead cost, or total customer volume.
• Watch for source fatigue. Performance can decline over time, especially if the same audience is being reached too often.
• Keep testing new sources before you need them. If you wait until volume drops to start testing, you are already behind.
• Use buyer feedback to optimize the mix. The best source on paper is not always the source producing the best sales outcomes.

In lead generation, quality and volume are both moving targets. The job is to keep optimizing the mix so the overall portfolio continues to perform.

SR: Where is AI actually delivering value today, and where is it still creating more noise than signal?

JR:

For us, AI has created the most value in operational areas like quality assurance, analysis, optimization, and sales support. It can help teams spot patterns faster, review data more efficiently, and support better decision-making.

It can also be very helpful in the content creation process, but only when it is used with a clear strategy. AI can speed up work, but it does not replace judgment.

One of the most important lessons is that AI is not a set-it-and-forget-it solution. Human involvement is still essential. You need people who understand the business, the consumer experience, the compliance requirements, and the buyer’s goals. AI can help surface insights, but people still need to decide what those insights mean and what action to take.

That is especially true in lead generation, where small decisions can affect consumer experience, buyer performance, and compliance. We have seen that AI can create value, but it can also create friction if it is not implemented carefully. For example, an AI call routing technology we tested caused consumer discomfort and abandon-rate growth.  This was a good reminder that the technology has to serve the experience, not just the workflow.

For teams using AI, I would suggest:

• Start with a specific business problem. Do not use AI just because it is available. Define what you are trying to improve first.  
• Keep humans in the loop. AI should support decisions, not make every decision without oversight.
• Measure the impact on the consumer experience. Faster is not always better if the experience feels confusing, impersonal, or frustrating.
• Audit outputs regularly. Review AI-generated recommendations, content, classifications, and routing decisions for accuracy and bias.
• Use AI where it reduces manual work without reducing accountability. Quality assurance, reporting, trend analysis, and workflow support are good examples.
• Avoid replacing strategy with automation. AI can help execute and analyze, but the business still needs clear direction.

AI is most valuable when it helps smart people make better decisions faster. Without strategy, oversight, and honest intent, it can create a lot of noise instead of meaningful value.

SR: If you were building a lead gen business from scratch today, what is one principle you would refuse to compromise on?

JR:

I would not compromise on building a small team that truly shares the same core values.

In a fast-moving business, skills matter, but alignment matters even more. You need people who can move quickly, support each other, communicate honestly, and stay deeply committed to clients and outcomes. A small team with shared values can make decisions faster and solve problems better because there is trust.

For me, integrity matters just as much as performance. You should be able to sleep at night and still wake up excited to innovate the next day.

If I were building from scratch today, I would focus on:

• Hire for values, not just experience. Experience is important, but a person’s judgment, accountability, and attitude will shape the business every day.
• Keep the team close to the customer. Everyone should understand how the work affects clients, consumers, and partners.
• Make decisions quickly, but not carelessly. Speed matters, but so does having the discipline to evaluate risk and impact.
• Protect trust. Trust with buyers, consumers, partners, and your own team is hard to build and easy to lose.
• Create room for innovation. The industry changes constantly, so the team has to be willing to test, learn, and adjust.
• Do the right thing when no one is watching. That standard matters, especially in a business where performance pressure can tempt companies to take shortcuts.

The market will always be competitive. There will always be pressure on cost, volume, and margin. But if the team is aligned around integrity, client outcomes, and continuous improvement, the business has a much stronger foundation to survive the roller coaster ride.

The post Jennine Rexon on modern lead gen and building sustainable growth appeared first on ActiveProspect.

Jennifer Linton is an insurance technology leader, entrepreneur, and the CEO/Founder of Fenris. She leads the development of real-time data and analytics platforms that support millions of monthly insurance quoting workflows across auto, home, and commercial lines. Her background spans 15+ years in startup growth, corporate strategy, business development, and innovation.

SR:
You’ve spent your career building data-driven businesses that challenge the status quo. How has that mindset shaped the way you think about improving the connection between insurers and consumers, especially at the very first point of their action?

JL:

Over the past two decades, whether building startups or working within large enterprises, I’ve observed how crucial data is in decision support. 

Most of the insurance industry has focused on optimizing what happens after intake with better underwriting models, better pricing, better workflows. But if the data coming in at the start is wrong or incomplete, everything downstream is working off a flawed foundation, and that was where Fenris started in 2020 by building its capability to enrich and prefill any application for any insurance product.

Today, we’re at an inflection point. Predictive insights can now be applied as early as the initial ping and continue throughout the quote, sale, and policy lifecycle. This helps insurers and consumers connect more effectively by making the first interaction more informed, more accurate, and more reflective of real risk before the process even begins.

Imagine knowing, at the first touchpoint, whether a prospect could become your best customer. That changes everything downstream. At Fenris, we’re focused on eliminating the gap between intake and insight, to align with what will ultimately yield the best outcome.

Turning insight into action

• Audit where early workflow decisions are being made with incomplete information. Most organizations focus heavily on underwriting and pricing optimization while overlooking the quality of the data entering the process. Identify where intake, routing, prioritization, or follow-up decisions are happening before enough context is available.
• Move enrichment and predictive intelligence closer to the first interaction. Apply real-time data and predictive signals before quote or underwriting so teams can make better decisions earlier, rather than correcting issues downstream after time and resources have already been spent.
• Use first-touch signals to drive segmentation and workflow orchestration. The earliest customer interactions often contain enough information to distinguish high-fit opportunities from low-fit ones. Build workflows that use those signals to influence routing, engagement strategy, and next-best actions from the start.

SR:
With Fenris focused on real-time data and predictive intelligence, how should insurers rethink the role of data enrichment in creating more meaningful and effective customer connections, and not just faster ones?

JL:

There’s been a longstanding push to reduce friction in insurance workflows, often measured by how quickly an agent or consumer can move through a process. But speed alone isn’t enough. The real value comes from validating and enriching the right information at the right time. If you rely on defaults or skip meaningful data fields, you risk undermining both your business and the customer’s experience.

The key is to use real-time predictive intelligence to identify high-potential customers early, then create an optimized journey, and enrich with the necessary data. This isn’t just about moving faster, it’s about making every interaction count. At Fenris, we deliver this through APIs and emerging use cases like agentic workflows, where bots or digital agents can dynamically request only the data that matters.

Turning insight into action

• Validate critical customer data before advancing the workflow. Identify where inaccurate or missing information is creating downstream friction in quoting, underwriting, or servicing, and prioritize real-time enrichment at those points.
• Personalize the workflow based on predicted customer value and intent. Use predictive signals to determine which prospects require additional verification, different routing, or higher-touch engagement instead of applying the same process to every submission.
• Design workflows that request only the data necessary for the next decision. Reduce unnecessary questions and leverage APIs or intelligent orchestration to dynamically enrich information as needed throughout the customer journey.

SR:
There’s a growing emphasis on reducing friction in quoting and underwriting workflows. Where do you see the biggest disconnect today between the data insurers have and the decisions they need to make in real time?

JL:

The biggest disconnect is in the distance between the data and the decisions. Often, data is applied at underwriting that, if known earlier, would have completely changed the outcome for the better. When there is no upfront segmentation, every lead is pushed through the process, regardless of fit. This is inefficient and costly.

There are three main challenges: 

1. Data silos make it hard to connect insights from one system to another. 
2. Models and data sources require constant upkeep; what works today may be outdated tomorrow as new products, campaigns, or markets emerge.
3. Traditional workflows front-load the process with questions and only apply data at the “moment of truth”, the rate call or indicative quote. 

To truly enable real-time decisioning, insurers need to break down these barriers and bring predictive intelligence to the very start of the customer journey.

Turning insight into action

• Identify decisions that are currently happening too late in the process. Review where underwriting, routing, or qualification insights are only being applied at quote or bind, and determine how those signals could improve earlier workflow decisions.
• Break down operational silos between data, distribution, and underwriting teams. Ensure that insights generated in one system can be used across intake, routing, quoting, and servicing workflows instead of remaining isolated.
• Continuously evaluate model and data performance against changing market conditions. Establish a process for retraining models, validating data sources, and adjusting segmentation strategies as products, channels, and customer behavior evolve.

SR:
With ActiveProspect’s acquisition of VMS and Fenris already adding predictive lead scoring, what new opportunities does this partnership unlock for the industry, and where do you see it making the biggest difference?

JL:

Fenris has been a partner of both VMS and ActiveProspect, so we see clearly the potential from bringing these two capabilities together. Every partnership is about scale and synergy. 

With VMS, Fenris’s machine learning platform was enabling them to serve scores in the education and home services verticals, accelerating time to value and reducing the cost of maintaining in-house solutions.

ActiveProspect has been a leader in the lead gen space for a while, across almost every possible vertical exemplifying their strengths in consent, compliance, and lead transparency. As part of our partnership there, Fenris has been delivering its prefill data to enrich leads.

All together, we see lead buyers and publishers will benefit post Active Prospect’s acquisition of VMS, in a way that prioritizes results based on revenue potential, capacity, and predicted outcomes, transforming how leads are purchased, routed, and acted upon.

Turning insight into action

• Prioritize leads based on predicted business outcomes, not just volume. Shift from evaluating leads solely on cost or speed to using predictive intelligence that identifies which opportunities are most likely to convert or generate long-term value.
• Align lead routing with operational capacity and appetite. Use predictive scoring and consent-driven data to direct leads toward the right buyer, team, or workflow based on fit, performance potential, and real-time business constraints.
• Integrate compliance, enrichment, and predictive intelligence into a unified workflow. Reduce fragmentation between lead acquisition, validation, and decisioning systems so teams can act on more complete and trustworthy information from the start.

SR:
In an environment where AI is accelerating everything, how do you decide when sooner is better than better, and when precision still needs to win?

JL:

“Sooner is better than better,” is a reminder that in fast-moving markets, waiting for perfection can mean missing the moment. You need to deliver value quickly.

I have to give credit to my Board member, Larry, former CEO of FICO, for making me see the value in shipping products fast, even if it’s not perfect, because models and data will continue to improve over time.

At Fenris, we balance speed with our three pillars for machine learning: 

• Transparency
• Explainability
• Fairness

If a model meets these criteria and delivers value, we deploy it, knowing it will learn and adapt as more data flows in. With over 100 million outcomes informing our algorithms, we’ve seen firsthand how rapid iteration leads to better results. When the data is right, you don’t have to choose between speed and precision, you can have both. That’s the future we’re building.

Turning insight into action

• Launch models that deliver measurable value, even if they are not fully optimized. Focus on transparency, explainability, and business impact first, then improve performance over time through iteration and additional outcomes data.
• Build feedback loops that allow models to continuously learn and improve. Capture downstream outcomes such as bind, conversion, retention, or churn so predictive systems can adapt to changing customer and market behavior.
• Define governance standards before deploying AI into production workflows. Establish clear expectations around fairness, explainability, and monitoring so teams can move quickly without sacrificing trust or accountability.

The post Jennifer Linton on turning early insurance data into better customer outcomes appeared first on ActiveProspect.