MJ helped a global enterprise with multiple regional websites and eCommerce stores turn anonymous digital website visitor behaviour, fragmented customer data, and disconnected sales and marketing workflows into known contacts and stronger pipeline activity for sales teams.
Executive Snapshot
A global enterprise needed to improve how it captured and converted marketing demand to its ecosystem of websites, and activated sales lead and customer data across two growth models: a high-touch sales B2B CRM pipeline and a low-touch, self-serve B2C eCommerce model.
In the B2B model, marketing was driving demand through paid, owned and earned digital maketing channels and converted this demand via web forms. But sales teams had limited insight into lead quality and intent, account behaviour, and customer history.
Sales leads were being passed to inside sales with too little context, creating wasted effort, heavier lead qualification with non-standard sales SOPs, slow follow-up, missed opportunities, and weak visibility into marketing’s role in the pipeline beyond lead volume.
In the B2C eCommerce model, shoppers were browsing, adding to cart, and purchasing online, but the experience was solely self-serve.
The business had limited ability to identify accounts and contacts early-on in the shoppign expeirence, personalize recommendations, recover abandoned sessions and carts, cross-sell and upsell, or tailor experiences based on past behaviour, purchase history and more importantly, 1st party data.
MJ helped connect the dots.
The solution created a stronger foundation for anonymous visitor identification and tracking behaviors, converting the unknown or anonymous visitor to a known contact (or customer), contact enrichment, progressive profiling and data capture, lead scoring, sales intelligence, eCommerce personalization, recommendation logic, and future Customer 360 activation thru a homegrown CDP.
The result was a more connected revenue system across marketing, sales, data, and digital commerce, and enabled both new and incremental revenue wins as a result.
The Situation
The client operated across two important customer engagement models.
The first was a high-touch SLG (sales-led growth) B2B sales pipeline model, where marketing generated demand and passed leads to inside sales and sales executives. (this followed with Marketings sales funnel participation with ABM tactics executed through marketing automation).
The second was a self-serve B2C eCommerce model, where customers browsed, shopped, purchased, abandoned carts, and returned through digital various channels. The self-serve model was driven to lower OPEX or the cost-to-serve for the organization, while catering to how customers wanted to do business through self-serve channels.
Both models had digital activity. Both had customer data. Both had marketing technology. Both had sales and revenue goals.
But the customer journey was fragmented.
In B2B, marketing relied heavily on paid and content acquisition to drive people to campaign landing pages and web forms conversions. Once a form was submitted, the lead entered the marketing automation system (Oracle Eloqua) and CRM (Salesforce). From there, inside inside sales followed up by phone to qualify the lead. If the lead qualified, the next step was an appointment with a sales executive or in-store team.
The process worked, but it was fragmented and major gaps.
Every form submission was treated too similarly. Marketing had limited ability to qualify, nurture, score, or personalize the lead before handing it to sales. If a visitor abandoned the website before completing a form, that interest was mostly lost. If a lead entered the CRM, sales often lacked the context needed to know how serious the lead was, what they cared about, or what should happen next.
In B2C, the eCommerce experience was also limited. Customers could browse and buy, but the website was not using enough behavioural, transactional, or 1st party customer profile data to personalize the shopping journey.
The customer had to do most of the work.
The business needed a smarter way to recognize intent, enrich profiles, and activate personalized experiences.
The Problem
B2B: Marketing was creating demand, but too much of it was leaking
The B2B funnel was built around a traditional model: drive traffic, capture a form, pass the lead to sales.
That model missed too much value.
Visitors who came to the website but did not submit a form were treated as lost traffic. Paid media was creating interest, but the business had limited ability to recover that interest, understand it, or use it to inform future sales and marketing action.
When visitors did submit a form, the lead was passed to sales with limited information and qualification. Sales teams had to manually determine whether the person was serious, ready to speak to sales, just browsing, or not a fit.
This created common revenue problems:
- High cost per lead
- High lead abandonment
- Lost visibility into anonymous visitor behaviour
- Too many unqualified or low-context leads
- No lead scoring before sales handoff
- Limited nurture for leads not ready to speak with sales
- Manual and fragmented inside-sales qualification
- Missed calls, no callbacks or follow ups, and appointment no-shows
- Weak visibility into customer intent and product interest
- Limited sales intelligence for tailored sales conversations
- Incomplete view of marketing’s influence on pipeline and closed revenue
The business was not lacking activity.
It was lacking a connected system to turn activity into intelligence and intelligence into action.
B2C: The eCommerce journey was not personal enough
The B2C model had a different version of the same problem.
Customers were browsing products, adding items to cart, buying online, abandoning sessions, and coming back later. But the experience was not using enough of that behaviour to guide what happened next.
The eCommerce journey was mostly self-serve.
There was no personalization based on product interest, prior purchase behaviour, cart activity, customer segment, lookalike patterns or 1st party account data.
Marketing automation existed, but it was not yet powered by a deep enough customer intelligence layer.
This created missed opportunities to:
- Re-engage abandoned shoppers
- Recover abandoned shopping carts
- Recover high-intent browse behaviour or inform sales of customer account activity
- Recommend relevant products
- Cross-sell and upsell
- Increase average order value
- Improve repeat purchase
- Build stronger lifecycle journeys
- Create more relevant email, SMS, and ad experiences
- Turn first-party customer data into revenue action
The business did not need more generic campaigns.
It needed more relevant customer experiences, it needed new account intelligence to arm sales, and it needed new data to enable personalized account-based marketing (ABM) lifecycle programs.
The Opportunity
The opportunity was to turn fragmented digital behaviour into a connected revenue system.
For B2B, that meant moving beyond a basic form-fill model.
The business needed to identify and capture more digital intent, convert more unknown visitors into known contacts, enrich lead and account profiles, score interest, and give sales teams better intelligence before they made contact.
For B2C, the opportunity was to use browsing behaviour, cart activity, transaction history, and 1st party customer account data to create more personalized and tailored shopping journeys.
The larger opportunity was bigger than one funnel or one platform.
It was to build a foundation for a future Customer 360 model: a connected customer intelligence layer that could support marketing activation, sales prioritization, eCommerce personalization, and better business reporting.
This was not a dashboard project.
It was a revenue architecture project.
The Solution
MJ helped design and deliver a connected customer intelligence and activation model across both B2B and B2C.
The strategy was simple:
Turn anonymous visitors and their behaviour into known intelligence. Then turn that intelligence into better sales action, better marketing activation, and better customer experiences.
The solution focused on five core capabilities.
1. Anonymous-to-known identification
The first step was to create a better way to understand anonymous website behaviour.
Anonymous identifiers were used to capture digital behaviour before a visitor became a known contact. Once that person browsed and clicked, their behavior and high-value events were tracked to build an anonomyous profile of the visitor.
Once the visitor submitted a form, clicked from a known email, or matched through approved first-party data signals, their historical activity could be stitched to their known profile.
This gave the business a much stronger and enriched view before the conversion, and a deep understanding what happened before the visitor raised their hand to contact the business.
Instead of seeing only a form submission with a few form fields completed, marketing and sales could begin to understand the journey that led to it and a much more enriched story of their browsing and shopping experience.
They could see stronger signals such as:
Content viewed
Product or service interest
Custom event interactions (ex. click to get a quote, or an online finance calculator interaction and its details).
Marketing campaign source and details
Landing page activity
Returning visitor behaviour
Email engagement
Form activity
Cart or browse activity
Prior customer or sales history
Quote, appointment, or service-related signals
This helped the business move from isolated touchpoints to a more useful customer timeline.
2. Data enrichment and customer profile intelligence
MJ helped define how customer and lead data should be enriched across marketing, sales, CRM, eCommerce, and analytics sources.
For B2B, this included combining digital engagement with marketing automation and CRM data, campaign history, sales activity, quote history, appointment activity, lost lead status, and customer account context.
This included AI capabilities that analyzed and summarized the contacts online behavior, campaign engagement, historical transactions, etc. to distill it into a plain-english narrative with recommendations the Sales and Support teams could adopt before contacting or meeting the contact.
For B2C, this included combining eStore shopping behaviour, cart activity, product interest, custom events (ex. finance calculator and promo interest), purchase history (if customer authenticated)and lookalike patterns.
The goal was to create more useful profiles for both Sales and Marketing activation, but also enriched the CDP strategy foundation for a true Customer 360.
Not just more data.
Better data.
Data that could both be commercialized and help teams answer practical questions:
- Who is showing interest?
- What are they interested in?
- Are they new, returning, inactive, or already known?
- Are they showing high buying intent?
- Who should we contact now, next and later?
- Have they purchased before?
- Did they request a quote?
- Did they miss an appointment?
- What product or service is most relevant?
- What should happen next?
This is where customer data becomes commercially useful.
3. Progressive profiling and smarter lead capture
For the B2B journey, MJ helped establish the web form strategy.
The existing form model had too many isolated forms (over 85 forms), too much duplication, and too little flexibility. Many forms asked for similar information, but they did not adapt based on what was already known about the visitor, let alone capture valuable meta-data to support Marketing campaign attribution, CASL and GDPR compliance, existing customer or new, and more.
The new approach introduced a more scalable and intelligence form strategy with:
- Reusable form templates
- Flexible field customization
- Multi-step and singular progressive profiling
- Soft-touch capture
- Campaign attribution capture
- Consent and compliance metadata (CASL + GDPR)
- Hidden source and behaviour data
- Self-serve customer data confirmation
- Paywall engagement
This made the capture process smarter.
Instead of asking every visitor for the same information every time, forms became adaptive and personalized to the individual based on what was already known, what still needed to be captured, and what would help qualify the lead.
This reduced friction and improved data quality.
It also helped the business keep customer information more current by giving known contacts opportunities to confirm or update key details, and greater flexibility for Marketing to create, customize and launch new campaign forms with the necessary no-code flexibility and speed.
4. Lead scoring, sentiment signals, and sales intelligence
For the B2B sales model, MJ helped create a stronger lead intelligence layer.
The solution used behavioural (ex. content and custom event engagement), campaign, profile, and CRM signals to support lead scoring and sales prioritization.
Sales teams could receive a clearer snapshot of the lead or account, including a contextual AI profile summary of the lead including:
- Lead score + grade
- Product interest
- Topic interest
- Intent signals
- Campaign engagement
- Email opens and clicks
- Website activity pre and post campaign engagement
- Account activity (ex. quotes)
- Missed appointment or no-show history
- Lost lead or inactive lead status
- Customer profile updates
- Recommended next best action
This helped inside sales and sales executives prioritize their time.
Instead of treating every lead the same way, teams could better understand who was ready for outreach now and the context on the contacts intent, who needed more nurturing, and who required a different conversation.
The value was not just better scoring.
The value was better sales conversations.
Sales teams could approach customers with more context, more relevance, and more confidence.
5. Personalized eCommerce journeys and recommendations
For B2C, MJ helped create the foundation for more personalized eCommerce activation.
The solution used customer behaviour and transaction data to inform recommendation logic and personalized journeys across the website, email, SMS, and digital advertising.
Signals included:
- Products browsed
- Products added to cart
- Products purchased
- High-intent product views
- Prior purchase categories
- Abandoned cart activity
- Customer segment
- Lookalike behaviour
- Repeat purchase potential
- Cross-sell and upsell opportunities
This allowed the business to move beyond generic product promotion.
Customers could receive more relevant experiences based on what they had actually shown interest in or previously purchased or recommended from a look-a-like model, while shoppign in-the-moment, or pushed from a timely campaign.
Examples included:
- Browse abandonment journeys
- Cart recovery journeys
- Product recommendation modules
- Post-purchase cross-sell
- Personalized email and SMS campaigns
- Digital ad audiences based on product interest
- Win-back journeys for inactive and churned customers
- Repeat-purchase prompts based on purchase history
The result was a more useful shopping experience for customers and a stronger commercial engine for the business.
The Operting Model
The work was not only technical.
MJ helped connect strategy, data, workflows, platforms, teams, and reporting.
The delivery included:
- Cross-functional stakeholder discovery
- Voice-of-business research
- Marketing and website performance analysis
- B2B funnel performance analysis
- Lead-to-sale journey review
- Inside-sales qualification process review
- Sales executive workflow review
- CRM and pipeline data analysis
- Closed-won and closed-lost pattern review
- eCommerce path-to-purchase analysis
- Customer segment and cohort analysis
- Form strategy redesign
- Anonymous-to-known data model
- CDP data architecture and identity resolution solution
- Customer and lead enrichment model
- Lead scoring requirements
- Sales intelligence report design
- Progressive profiling logic
- eCommerce personalization requirements
- Recommendation engine input design
- Campaign activation program flow and logic
- Measurement framework
- Dashboard requirements
- Backlog Customer 360 / CDP roadmap
This helped the business move from fragmented activity to a more connected operating model.
Marketing could better participate in pipeline. Sales could act with better intelligence. eCommerce could personalize the customer journey. Digital and data teams had a stronger foundation for future Customer 360 activation.
Results & Impact
The strategy spun off into multiple projects over its tenor, prioritized across quick wins to long-game move the mountain opportunities.
In the end, it evolved the entire Marketing organization and modernized its stratgegy, capabilities, operations and how teams engaged cross-functionally.
It created a stronger foundation for revenue ops. and its growth, sales productivity, customer personalization, and marketing accountability.
B2B impact
The B2B model improved how marketing-supported demand moved into sales action and Marketings participation in the sales pipeline.
The business gained a stronger ability to identify, track, enrich, score, and prioritize leads and customers. Sales teams received more useful context before outreach, helping them tailor conversations and focus on the right opportunities.
The solution supported:
- Higher-quality lead intelligence
- Better lead prioritization
- Stronger inside-sales qualification
- Improved appointment booking results
- More relevant sales conversations and more confident inside sales teams
- Better visibility into customer and account intent
- Stronger nurture opportunities for slow pipeline, inactive or lost leads
- More scalable account coverage through digital engagement
- Better alignment between marketing, inside sales, and sales executives
- Clearer marketing participation in pipeline and revenue attribution
Most importantly, marketing was no longer limited to generating a form fill.
Marketing could play a larger role in identifying demand, enriching the customer view, nurturing interest, supporting pipeline movement and truly shift the Marketing organization from a cost-centre to a hybrid revenue centre for the business.
B2C impact
The B2C model improved how customer behaviour was used to personalize the shopping experience.
The business gained a stronger ability to use shopper behaviour, cart activity, purchase history, and customer segments to inform product recommendations and shopping journeys.
The solution supported:
- Better shopping abandonment recovery
- More relevant and personlized product recommendations
- Stronger cart recovery opportunities
- Better browse abandonment activation
- Improved cross-sell and upsell potential
- More personalized marketing automation and ad experiences
- Better use of first-party customer data
- Stronger repeat-purchase opportunities
- A more meaningful online shopping experience
Instead of making every customer self-serve through the same journey, the business could begin adapting the experience based on what the customer cared about.
Why this matters
Most brands are sitting on a mountain of data and useful customer signals.
The issue is that data is not opertionalized against valued use-cases, and those buyer signals are often fragmented across customer channels and tools; websites, CRMs, marketing automation platforms, analytics tools, eCommerce systems, sales activity, and customer databases.
When those signals stay disconnected, businesses lose value.
Sales teams miss context. Marketing loses attribution. eCommerce journeys stay generic. Customer data becomes a reporting asset instead of a revenue asset.
This project helped show what happens when those signals (and teams) are connected.
Anonymous behaviour became known intelligence. Known intelligence became better segmentation and enabled better conversations. Better segmentation became stronger sales and marketing activation. And stronger activation created a foundation for better revenue performance.
That is the shift modern businesses need to make.
Not more dashboards.
Not more disconnected campaigns or tools.
Not more siloed operating models or teams.
They need a connected revenue architecture that turns data, technology, marketing, sales, and customer experience into measurable business action.
Final thoughts
The future of growth is not about chasing more traffic.
It is about recognizing demand earlier, understanding customers better, and acting with more relevance.
As more and more products and services become commoditized, one of the only means to win the customer will be through a better experience (CX) and relationship between the customer and the brand.
For B2B companies, that means giving sales teams richer lead and account intelligence so they know who to call, when to call, and what conversation to have.
For B2C companies, that means using customer behaviour and 1st party data to create more personalized and relevant shopping experiences that accelerate and improvve conversion, retention, and customer lifetime value.
For marketing leaders, it means proving that marketing is not just creating activity. It is helping build pipeline, influence revenue, improve customer experience, and reduce wasted spend at a speed and scale never seen before.
For digital and data leaders, it means commercializing customer data beyond dashboards and reports. Yes, data engineers and data science teams can participate in revenue growth opportunities.
This is where I help.
I connect strategy, data, MarTech, website and eCommerce, automation and AI-enabled workflows, and managed execution into a practical operating mode and growth system.
Because strategy without execution is theatre.
And execution without strategy is waste.
Most businesses already have the data or signals they need to improve sales, marketing, and customer experience.
They just need to connect them to activate them.
I help businesses of all shapes and sizes and high-growth teams turn fragmented customer data into a connected revenue architecture that identifies demand earlier, personalizes engagement, and moves faster from signal to revenue.
Start with a conversation and explore what’s possible.