Marketing analytics: the complete guide to data-driven strategy, AI tools, and predictive Intelligence

Marketing has always been part art, part science. But in 2026, the scales have tipped decisively toward science — and those who haven’t noticed are already falling behind.

Over the past decade, marketing analytics has undergone a transformation so significant that the discipline barely resembles what it was in the early days of Google Analytics and Excel dashboards. Today, we’re talking about real-time AI inference, privacy-first measurement architectures, cookieless attribution, and predictive models that can forecast customer churn within days of a first app interaction.

This guide breaks down everything you need to know about marketing analytics in 2026 — from the foundational four-tier intelligence framework to the future of AI-powered decision-making. Whether you’re a CMO rethinking your tech stack or a marketing analyst looking to level up, there’s something here for you.

The four tiers of marketing intelligence: from hindsight to foresight

The best way to understand where your organization stands analytically is through a four-tier maturity model. Each tier answers a different question and unlocks a different level of competitive advantage.

Tier 1: Descriptive analytics — what happened?

Descriptive analytics is the starting point for every data-driven team. It summarizes historical data — website traffic, monthly sales, campaign performance — into digestible dashboards and trend reports. Think of it as the “synopsis” of your business story.

The value here is context. You can measure current performance against baseline expectations, spot seasonal patterns, and monitor KPI trajectories over time. The limitation is equally clear: descriptive analytics tells you what happened, but not why — and certainly not what to do next.

Tier 2: Diagnostic analytics — why did it happen?

This is where things get more interesting. Diagnostic analytics uses drill-down analysis, data mining, filtering, and correlation techniques to investigate the root causes behind observed outcomes.

Here’s a practical example: your descriptive dashboard flags a 15% drop in monthly sales. Diagnostic analytics can reveal that the decline was tied specifically to a product stockout that coincided with a major marketing push — a double-whammy that your surface-level metrics would never have exposed on their own. By isolating variables and testing hypotheses, diagnostic analytics moves your team from passive observation to active investigation.

Tier 3: Predictive analytics — what is likely to happen?

This is where analytics becomes genuinely powerful — and where the gap between sophisticated and unsophisticated organizations widens dramatically.

Predictive analytics uses historical data, statistical algorithms, and machine learning to forecast future outcomes. These capabilities have become a standard requirement for high-performing marketing teams. The models can identify patterns invisible to human analysts: for instance, predicting the likely churn of a new mobile app user within seven days based on their early engagement signals.

The democratization of AI has made these capabilities accessible far beyond enterprise players. Organizations of all sizes are now using predictive scoring to prioritize leads, anticipate demand surges, and allocate budgets based on forecast ROI rather than gut instinct.

Tier 4: Prescriptive analytics — what should we do?

The pinnacle of the intelligence hierarchy, prescriptive analytics doesn’t just forecast outcomes — it recommends specific actions to achieve them.

This tier combines insights from the three layers below it with optimization algorithms and AI simulations to evaluate trade-offs and constraints. Example: if your predictive models flag a 20% demand increase, prescriptive analytics might specify exact inventory levels to order, which suppliers to use, and the precise timing of those orders to minimize operational costs. It’s the difference between a weather forecast and a flight plan.

Organizations that implement all four tiers report significantly faster insight generation and measurable revenue growth compared to those stuck at the descriptive level.

The seven-step framework for campaign analytics

Having the right analytical infrastructure is only half the battle. You also need a repeatable process for actually running campaigns through that infrastructure. Here’s the seven-step framework that high-performing teams are using to transform marketing from a cost center into a predictable growth engine.

Step 1 — Define Goals and Success Metrics. Every analytical effort starts with a clear objective. Are you optimizing for acquisition, retention, or brand awareness? The answer determines which metrics matter. Acquisition-focused campaigns prioritize Customer Acquisition Cost (CAC) and pipeline velocity; retention campaigns watch Customer Lifetime Value (CLV) and churn rate closely.

Step 2 — Integrate Your Data Sources. Effective campaign analytics requires pulling together data from advertising platforms, CRM systems, and e-commerce databases into a unified model. Without this integration, analysis is fragmented and unreliable.

Step 3 — Choose Your Attribution Model. Once data is unified, applying the right attribution model is essential. Moving beyond “last-click” attribution — which dramatically undersells mid-funnel contributions — organizations are shifting to multi-touch or AI-driven models that assign credit accurately across the full journey.

Step 4 — Build Your Dashboard Infrastructure. Connect marketing spend to real-time business outcomes in a single source of truth. Leadership shouldn’t need to call three people to get a clear picture of campaign performance.

Step 5 — Interpret and Act. Numbers without interpretation are just noise. If a campaign’s CAC payback period is too long, that’s a signal to reallocate budget — not a footnote for next quarter’s review.

Step 6 — Automate Your Data Pipelines. ETL (Extract, Transform, Load) automation ensures decision-making is based on fresh data rather than last week’s spreadsheet export.

Step 7 — Iterate. Marketing analytics is not a set-and-forget function. Regular testing, model retraining, and benchmark reviews are what separate teams that improve from teams that plateau.

The technology stack: AI takes center stage

The analytics platform landscape is defined by three characteristics: real-time processing, AI-driven insight generation, and seamless cross-channel measurement.

AI-Powered Analytics and Marketing Agents

Artificial intelligence has moved from a peripheral feature to a core component of the analytical stack. Advanced platforms now analyze massive datasets in real time to detect campaign inefficiencies, automate reporting, and surface optimization recommendations before a human analyst would even notice the signal.

Nearly 91% of companies are expected to work with marketing automation tools, with AI-enabled analytics growing significantly year on year. More strikingly, purpose-built AI “agents” are now being deployed to execute end-to-end marketing workflows — embedding brand logic and contextual intelligence directly into execution decisions, not just reporting surfaces.

This shift enables truly personalized marketing at scale: customer experiences shaped by predictive insight, delivered in real time, without requiring manual intervention at every touchpoint.

The Platform Landscape

Different tools serve different needs. Here’s a practical overview of the major categories:

Google Analytics 4 remains the dominant tool for behavioral tracking through its event-based model and AI-powered forecasting. But the landscape has diversified considerably — Adobe Analytics leads in machine learning-based segmentation, while Mixpanel dominates for deep product and event analytics.

The three big challenges: silos, privacy, and data quality

No discussion of marketing analytics is complete without addressing the systemic obstacles that prevent even well-resourced teams from achieving their analytical potential.

Breaking Down Data Silos

When marketing, sales, and finance each maintain separate data stores that don’t talk to each other, the result is a fragmented view of the customer journey — missed opportunities, inaccurate reporting, and duplicated effort. It also results in a particularly frustrating dynamic where every team has its own “version of the truth.”

The solution requires more than technology. It demands strategic leadership, a cultural shift toward data transparency, and the implementation of centralized repositories like cloud data warehouses where all teams draw from the same source.

Navigating Privacy Regulations

GDPR, CCPA, and an expanding web of regional privacy laws have fundamentally altered what marketers can collect, store, and act on. The phase-out of third-party cookies has further complicated cross-site tracking and attribution.

The answer isn’t to collect less — it’s to collect differently. A first-party data strategy, built on direct customer interactions and explicit consent, has become the foundational requirement for maintaining measurement capabilities in a privacy-first environment. Brands that get this right gain a durable competitive advantage; those that don’t will find their analytics increasingly blind.

The Nine Dimensions of Data Quality

Analytics is only as good as the data it operates on. The industry recognizes nine dimensions of data quality that must be maintained for reliable insights:

1. Accessibility — Data is available and integrable into business processes
2. Accuracy — Values correctly reflect real-world events
3. Completeness — No critical fields (email addresses, revenue figures) are missing
4. Consistency — Data is structured uniformly across systems
5. Precision — Information is recorded at the level of detail the business requires
6. Relevancy — Data is applicable to the specific decision at hand
7. Timeliness — Data is updated frequently enough for operational use
8. Uniqueness — Each record is distinct, with no duplicates skewing analysis
9. Validity — Data conforms to defined business rules and verifiable sources

Weak data quality doesn’t just produce bad reports. It erodes trust in analytics broadly — and once teams stop trusting the data, they revert to gut instinct, which defeats the entire purpose of building the infrastructure in the first place.

Key performance indicators by channel

KPIs should connect marketing activities to business outcomes — not just confirm that activity is happening. Here’s how leading teams are measuring performance by channel.

Email Marketing

Email remains one of the highest-ROI channels, but its measurement has evolved significantly:

• Open Rate: Benchmark for bulk emails sits around 20%. Well-segmented or behavior-triggered sends can achieve 40–70%.
• Hard Bounce Rate: Keep below 2%. Exceeding this damages sender reputation and risks blacklisting.
• Active Audience Trend: The cohort that engages with most communications typically generates the majority of email (not forgetting to perform a free email validation) revenue — track its growth closely.
• Customer Lifetime Value (CLV): Total expected revenue from a customer, used to calibrate investment in retention vs. acquisition.

Social Media

The shift away from vanity metrics is complete for sophisticated teams. What matters now:

• Engagement Rate — depth of interaction (comments, shares) over surface signals (likes)
• Conversion Rate — social activity tied to form submissions, downloads, or purchases
• Social Share of Voice (SSoV) — your brand’s ownership of the broader conversation relative to competitors
• Cost Per Result (CPR) — ad budget efficiency relative to specific campaign objectives

Read more: How to market your business on social media

SEO

Technical health and user intent alignment are the defining factors:

• Search Impressions — an early indicator of SEO momentum, often improving before rankings shift
• Keyword Rankings — positions 1–10 are the only positions that meaningfully drive traffic
• Core Web Vitals — Largest Contentful Paint and First Input Delay as user experience signals with direct ranking implications
• Session Duration — a proxy for content-to-intent alignment post-click

Paid Advertising

Financial efficiency is the north star for paid channels:

• Cost Per Acquisition (CPA) — unsustainable the moment it exceeds the customer’s profit margin
• Return on Ad Spend (ROAS) — revenue per advertising dollar, expressed as a ratio
• Customer Acquisition Cost (CAC) — total marketing spend divided by new customers; filter out returning buyers to avoid inflating the number
• Quality Score — platform-specific metric affecting both ad frequency and click cost

Why not measure everything at once

After all, we can reduce all the complex measures to simple metrics:

• clicks;
• users’ moves;
• brand mentions in reputable sources;
• actions at different stages of the sales funnel.

The key problem is we can’t know everything about our users at once. Arguments:

1. We can’t track potential clients’ actions on different devices if there is no synchronization between social networks and email etc. After all, the buyer can see the beauty salon advertising on his iPad, then drive a brand request (the name of the salon) in Google and forget about it. And then, a month later, having a 2 hours gap between classes, come and get a haircut.
2. We can’t track real-time brand awareness (e.g. publishing useful content for you guys and mentioning Proofy.io and email verification inside). Yes, it improves conversion, forms loyalty, increases the average check, and even can create demand for new services. But this we can only understand in the future, after a careful marketing campaign.
3. We can’t predict the results of users’ cognitive biases. E.g. when we set a specific goal and create a binding effect. Sometimes these bindings are positive and sometimes they are not. For example, you say “we have a promotion when ordering a haircut — super-styling for the price you set yourself.” And add: “for example, $10, $15, $20”. Despite its real cost can be only $6, people are more likely to pay at least $10. The same way with promotions and discounts: “I don’t need that suitcase, I came for the iron. But damn, it’s only worth $200 now, not $600, I’m saving $400.”
4. We can’t know everything about our users, and we don’t need to. Most often it’s enough to understand the results of “before” and “after” to assess the actions’ correctness. The main thing is to remember the goals and specific problems that we solve. Therefore, in most cases using simple and accessible tools is enough.

Attribution modeling in a cookieless world

Attribution — determining which touchpoints deserve credit for a conversion — has always been complex. The deprecation of third-party cookies has made it significantly harder, and significantly more important.

Comparing Multi-Touch Attribution Models

MMM vs. MTA: Choosing the Right Tool

As cookies disappear, Marketing Mix Modeling (MMM) has returned to prominence. Unlike Multi-Touch Attribution (MTA), which tracks individual users across sessions, MMM uses aggregated statistical modeling — making it immune to individual privacy restrictions.

The practical guidance: use MTA for near-real-time tactical optimization of creative and bidding; use MMM for long-term strategic budget allocation and incrementality analysis. The most analytically mature organizations are running both in parallel.

Building for a Cookieless Future

Server-side tracking, probabilistic modeling, and identity resolution tools are now standard components of the privacy-first measurement stack. Platforms like Northbeam and Adobe are using identity resolution to stitch together customer journeys that would otherwise appear as separate, untraceable sessions across devices and browsers.

First-party data isn’t just a legal compliance strategy — it’s a measurement strategy. The brands investing in it now are building infrastructure that will continue to function as cookie deprecation accelerates.

B2B vs. B2C: analytics strategies are not interchangeable

The frameworks above apply broadly, but how you implement them depends heavily on whether you’re selling to businesses or consumers.

B2B: Precision Over Reach

B2B marketing is defined by long sales cycles, multi-stakeholder procurement, and rational decision-making frameworks centered on ROI and risk reduction.

• Primary KPIs: Pipeline velocity, lead quality scores, account engagement rates
• Attribution preference: W-Shaped or full-path models that recognize awareness, lead creation, and conversion
• Personalization axis: Industry, role, and account-level targeting
• Top channels: LinkedIn, email, direct sales

Account-Based Marketing (ABM) has become the dominant approach — precision targeting of high-value accounts rather than broad funnel generation.

B2C: Speed and Emotional Connection

B2C marketing operates at much higher volume, with faster purchase cycles and decisions more heavily influenced by price, convenience, and brand perception.

• Primary KPIs: Transaction volume, ROAS, basket size, purchase frequency
• Attribution preference: Last-touch or time-decay models that reflect rapid conversion paths
• Personalization axis: Product recommendations and behavioral signals
• Top channels: Instagram, TikTok, retail and e-commerce platforms

The distinction between B2B and B2C analytics is narrowing as shared digital tooling improves and buyer expectations converge — but the underlying measurement strategies remain meaningfully different.

Building a data-driven culture: the human side of analytics

Technology alone doesn’t produce analytical maturity. The organizations that extract the most value from their data infrastructure are those that have built cultures where fact-based decision-making is the norm — not the exception.

Overcoming Decision Biases

Human intuition, when left unchecked, produces predictable and costly distortions. Senior leaders often favor strategies aligned with past successes, even when current data points elsewhere. Several structural techniques help counteract this:

• The Devil’s Advocate — assign someone to actively argue against the proposed strategy, surfacing blind spots and alternative scenarios
• Red Team / Blue Team Exercises — for major investments, prepare rigorous arguments for both favorable and unfavorable outcomes
• The Premortem — before launching, imagine the initiative has already failed and work backward to identify what went wrong

None of these require additional technology. They require intellectual honesty and organizational commitment.

The Five Levels of Analytics Maturity

Organizations evolve along a predictable spectrum:

1. Chaos — decisions made on instinct; data is hard to find and harder to trust
2. Reactive — basic reporting exists, but it’s backward-looking and siloed
3. Proactive — standardized dashboards, some cross-channel visibility
4. Strategic — predictive models in active use; analytics informs investment decisions
5. Embedded — analytics is a core part of every workflow; predictive insight shapes every business outcome

Moving up this hierarchy requires something more fundamental than new tools: it requires increasing data literacy across the entire organization, not just in technical teams.

The road ahead: intelligence, action, and the automated future

Marketing analytics is at an inflection point. The infrastructure for unified, AI-driven intelligence exists. The regulatory pressure to build privacy-first measurement architectures is intensifying. The competitive penalty for analytical immaturity is growing measurable and large.

The most significant trend on the horizon is the closing of the loop between insight and action. AI-powered systems are already beginning to automate spend reallocation and creative adjustments in real time — moving beyond historical reporting toward active performance orchestration. The human marketer’s role shifts from data interpreter to system designer, exception handler, and strategic director.

The role of SEO is also evolving from satisfying algorithmic checklists to optimizing for AI-driven discovery and voice search intent — a shift that will require tighter alignment between content strategy and predictive analytics than most organizations currently have.

What all of this points to is a future where the brands that thrive are those that have done three things well: unified their data across every channel and touchpoint, dismantled internal silos that distort the customer picture, and built a culture where the data earns genuine trust and informs genuine decisions.

Marketing analytics has become the essential guidance system for brand strategy. In the AI-fueled, cookie-free environment that defines the mid-2020s, mastery of these frameworks isn’t a technical advantage — it’s the primary competitive differentiator. The organizations that get this right won’t just react to the market. They’ll shape it.

Industry experts about marketing analytics

Andrew Tsionas, co-founder and managing partner of Kaizenzo Inc

Marketing analytics is a data-driven approach to measuring and optimizing marketing activities for your business.

It’s about measuring what works and what doesn’t, so you can make decisions about how you market your product or service in the future. This is done by collecting data about who is doing what, when, where, and why.

We use marketing analytics to make better marketing decisions. It helps us understand which channels are most effective at driving sales and leads, as well as which content gets the best response from customers. We can make changes to our site or social media campaigns to reach specific audiences more effectively.

This allows us to target specific customers with our marketing efforts better and avoid wasting time and money on advertising that won’t convert into sales

Gerrid Smith, Director of E-commerce of Joy Organics

Marketing decision-making. To my knowledge, one of the most effective uses of marketing analytics is to guide marketers in making better-informed decisions. As you can see, marketing analytics is mostly used to direct marketing efforts. This method can help you determine the strengths and weaknesses of your approach so that you can keep moving forward successfully. With time and practice, you may anticipate audience reaction to future advertisements with more accuracy. By revealing the patterns that underlie customer behavior, marketing analytics make developing a strategy a lot less of a guessing game. You’ll have a much easier time getting to your marketing goals if you can get the proper strategies down on the first go.

Robert Warner, Official Member of Forbes Agency Council and Head of Marketing at VirtualValley

Avoid blunders. In my perspective, marketing analytics are effective because they help businesses avoid making huge mistakes. Using marketing analytics can help you not only make better decisions but also prevent making worse ones. Investing in regular performance reviews can help you spot problems before they have a significant effect on your return on investment. Essentially, marketing analytics inspire your team to be more proactive in their approach to problem-solving. Additionally, marketing analytics can document your achievements and setbacks. This can help you not only stay away from catastrophic blunders but also from repeating the same ones. In the future, your team will be able to better allocate marketing resources because they will know exactly what factors contributed to a campaign’s underperformance.

Shawn Malkou, Managing Broker at X2Mortgage

Marketing Research. Tracking data is the first step in marketing analytics. There is an abundance of data available. However, only data created by your marketing channels will be tracked for marketing analysis. Digital marketing usually focuses on channels like websites, social networks, apps, and paid media, among other things.. So, you need to keep an eye on how customers use these channels to see how they react to your efforts. Through tracking, you get access to data regarding the effectiveness of your marketing strategies. The data that needs to be analyzed can then be gathered either automatically or by hand using a spreadsheet. After the data has been tracked and collected, it needs to be easy to get to so that an analysis can be done. Ultimately, raw data is incomprehensible, but graphs and tables make it understandable. Data visualization is a part of marketing analytics that deals with how data is shown visually. Most of the time, this is done with the help of graphs, tables, maps, and other pieces of information that are put together into performance panels or dashboards.

Ashley Chambers, marketing director and partner of ASAP Cash Offer

Marketing analytics is the strategic process of measuring, managing, and analyzing data to drive marketing decisions. How marketing analytics works in your company can make all the difference in your success. 

The marketing analytics process begins with data collection. Data is collected from a variety of sources, including customer behavior data, customer demographic data, market trend data and competitor data. This data is then cleaned and organized so that it can be analyzed. 

Next is to establish KPIs, or key performance indicators. KPIs are measures of performance that help marketing teams track their progress and identify areas of improvement. Once KPIs have been established, marketing teams can begin to analyze their data. 

Data analysis helps marketing teams understand what is working and what is not working. It also helps teams identify opportunities for improvement. After analyzing their data, marketing teams can use marketing intelligence tools to make better decisions about where to allocate their resources. 

Krittin Kalra, Founder at Writecream

Marketing analytics is the process of taking all the data that a company has collected and turning it into actionable insights. This is done through the use of dashboards, analytics software, and data visualization. The process can be broken down into three main steps: gathering data, analyzing data, and taking action. Gathering data is the process of collecting data through various sources, such as social media, SEO, and CRM. Analyzing data is the process of turning the collected data into insights and understanding what it means for the business. Taking action is the process of analyzing the insights to determine what the next steps are for the business.

We use Google Analytics to track our website traffic and the conversion rates of our marketing campaigns. We use Adwords and Facebook Ads to drive traffic to our website and to generate leads. We use Google Analytics to track our website traffic and the conversion rates of our marketing campaigns. We use Adwords and Facebook Ads to drive traffic to our website and to generate leads.

James Angel, Co-Founder of DYL

Design your own dashboards for monitoring key metrics. Creating personalized dashboards in your chosen analytics software is a great way to monitor and analyze your marketing data in real-time. You can quickly zero in on the specifics you need and ignore the fluff, saving valuable time. The most effective analytics dashboards consider their target audience throughout development. Key stakeholders outside of marketing at most, if not all, firms weigh in on important business and marketing decisions.. Marketers, in my opinion, will increasingly use reporting and data visualization tools to better communicate their findings and suggestions to decision-makers.

Jamie Penney, CEO of ShoppingFoodie

Establish the key performance indicators. Metrics are a vital part of marketing analytics, therefore it’s important to choose the right ones to track the progress of your whole marketing effort. The measures you use to compare the success of different platforms or approaches should be clearly defined before you begin your analysis. If you’re collecting and analyzing data that doesn’t pertain to your company, you’re wasting your time. Further, you shouldn’t let yourself get overwhelmed by data. Set some objectives to guide your metric selection. To what end are you running these marketing campaigns? Once you know what you want to accomplish with each campaign, you can decide which metrics to track. Choosing more effective marketing strategies As you can see, marketing analytics is most useful when it serves as a roadmap for future campaigns. In doing so, you may learn not just where your plan is succeeding but also where it can be strengthened to ensure continued success. With time and practice, you’ll be able to gauge the likely response of your target audience to each next campaign with more accuracy. By revealing the patterns that underlie customer behavior, marketing analytics make developing a plan a lot less of a guessing game. That way, you can get your marketing strategies off the ground quickly and successfully.

Laurice Constantine, Digital Managing Editor @ Forbes Middle East, ex-executive producer @ CBNC Dubai and founder of www.casadar.com       

Marketing analytics assists businesses in understanding the big picture. It also allows them to delve deeper into their industries’ more specific, micro-marketing trends. The data can help businesses increase the number and quality of leads by advising them on how to optimize their advertising and target the most desirable clients.

Every marketing project is a procedure. Marketing analytics can help you determine where your attention should be focused during the process as well as the best marketing mix for your company. Remember to follow best practices in analytics by specifying the questions that need to be answered, gathering high-quality data, and selecting the information that is important to you.

Sometimes you end up having more questions, leading you to repeat the analysis process to learn more about your marketing efforts.

Dan Thomas, the Sales Director at AIQuoter

Building Customer Loyalty. Marketing analytics provides information about how customers interact with our brand, social media, and what they search for. Such information takes getting to know our customers to a new level, making specific marketing targeting possible. Having these insights helps in converting more leads and setting up personalized campaigns, which increase customer loyalty, so every buyer keeps on returning. In addition, it helps in mapping customer behavior and preferences, from which we can tailor our marketing initiatives to meet the needs of individual customers.

Julianne Stone, Co-founder of Cicinia.fr

Marketing analytics is often used by companies to gain a better understanding of how customers think, what they’re interested in, and what they’re doing. It can help marketers understand who their customers are and how they’re engaging with their business. This can help them better tailor their campaigns to different audiences and create more effective ads.

A common use of analytics is to measure the effectiveness of a campaign. Through analytics, you can see if your ads were seen by the right people, as well as how many people clicked through to your website or app. This can give you valuable insight into how to improve your campaign.

Another function of marketing analytics is to predict future trends based on past data. For example, you might use it to determine which products are likely to be most popular in the coming months or years. You can also use it to understand what consumers want or need, and how that differs from current offerings.

Jonathan Merry, the Vice President & Co-Founder at Bankless Times

Market analytics is a study purposed with determining and evaluating data connected to the performances of marketing activities. It entails applying technology and analytical processes to marketing-related data. These activities assist businesses to understand what drives consumer actions and also refine their marketing campaigns and optimize their ROI. To understand how marketing analytics works, one has to grasp more than just the technological tools used in the analysis.

Identifying what you want to measure is the first step in trying to understand the marketing processes. Some of the metrics to be included in this approach include ROI, conversion rate, click rate, or brand recognition. Assessing your analytical capabilities, and filling in the gaps marks your second step. This is where you scrutinize and choose the marketing technology that is on offer in the market and utilizes them in your cause. Acting on the data collected is the final process. Marketing analytics allows for more successful marketing campaigns and a revamped customer experience, – all of which lead to greater profitability.