Providing a digital experience that’s more closely aligned with what a user wants is a fairly obvious answer to why we experiment, but the way we approach it as an industry is changing.

Brands are now focused on delivering a well-timed cross-channel journey, to a wider volume of users with increasingly high expectations for personalized experiences. Isolated tests and experiments aren’t providing enough information on the entirety of a user's journey. 

So is experimentation really “dead,” as data thought leaders have been saying? Are we ready to move on to the next latest and greatest framework that generative and agentic AI have to offer?

Recently, reading Avinash Kaushik’s latest TMAI newsletter made me pause and reflect on where we’re heading as an industry. It hit the spot on many things I’ve been thinking about, specifically in how experimentation practices are evolving, and what that means for the future.

For years, A/B testing has been a cornerstone for optimization, and it’s a method that has taught the industry a lot. 

The truth is, A/B testing itself isn’t dead; it’s evolving. Or, better yet, it has to evolve. It’s not the method itself that’s to blame for the limitations we sometimes see, it’s the mindset attached to it. When A/B testing is treated as a checkbox activity, focused only on incremental changes, or siloed from other disciplines like search engine optimization (SEO), paid media, or product, its potential impact is greatly diminished.

The future of experimentation is more holistic, integrated and focused on delivering real-time, user-centric experiences. This doesn’t mean abandoning A/B testing, but expanding the lens to incorporate more sophisticated strategies that blend multiple disciplines, like SEO, conversion rate optimization (CRO), paid media and analytics.

It’s important to clarify that while experimentation as a function is typically iterative and methodical, its role extends beyond testing. Experimentation should inform and support the delivery of dynamic, real-time brand experiences, though delivering in real time itself is more closely tied to optimization, personalization and UX. 

The two complement each other: experimentation provides the precision and insights to optimize, while real-time strategies act on those learnings to adapt experiences dynamically.

This distinction matters because experimentation, while not inherently “real-time,” lays the foundation for modern, adaptable and user-centered approaches that make brands more agile. By focusing on how these disciplines work together, teams can move beyond traditional silos to deliver more innovative, more integrated strategies.

I’m not a classically trained CRO practitioner. My background is in digital marketing, product and growth for over 14 years. For me, experimentation has always been one tool in a much bigger toolbox: a way to make better, more precise decisions with proactivity and pragmatism at the forefront, always focused on ROI and accomplishing business goals.

When I approach experimentation, it’s never been in isolation. It’s always been CRO + Product, CRO + SEO, or CRO + Growth. I don’t see it as just tweaking elements or optimizing individual touchpoints. I’ve always viewed it as part of a bigger strategy to align business goals, user intent and data. This perspective has shaped how I think about the future of experimentation and how we do not need better tools, we need better thinking.

It’s important to recognize the incredible work already happening. Many teams and practitioners are pushing the boundaries of experimentation by adopting innovative approaches. They’re rethinking the entire user journey, using data in smarter ways and aligning optimization efforts with broader business goals.

And let’s give credit to the experimentation community as a whole: the content, tools and thought leadership that the community shares are invaluable for helping others grow and adapt. From blog posts to podcasts  or conferences to open discussions on social platforms, the spirit of collaboration and knowledge-sharing is what drives this industry forward.

One of the ongoing challenges with experimentation is how we define and approach it. The rapid evolution of testing has unintentionally muddied the waters with organizations vying for the latest and greatest way to provide better connected user journeys.

Experimentation, CRO, A/B testing: these terms often get used interchangeably, and sometimes the definitions create unnecessary silos. The truth is, good experimentation should already encompass the broader strategies we associate with "advanced" practices, like behavioral targeting or dynamic personalization.

The issue isn’t the name we give it, it’s how we implement it. When experimentation is reduced to tweaking surface-level elements like button colors or headlines, it misses the bigger picture. True experimentation involves connecting the dots across the customer journey, testing bold hypotheses, and focusing on business outcomes like lifetime value, retention, and revenue growth. 

While A/B testing remains a valuable tool, A/B testing’s greatest impact comes when it’s part of a larger ecosystem. 

Imagine a hypothesis born out of an SEO content strategy, validated through paid ad experiments and optimized for conversion through CRO. Creating and running tests now includes greater customer sentiment, considers how it fares against public interest and search volume, or creates the foundation for analyzing the entirety of a customer funnel. This kind of collaboration doesn’t focus on improving one metric, but aims to deliver a cohesive, end-to-end experience that aligns with both user intent and business objectives.

Moving toward this holistic approach requires more than just tools; it requires nurturing a culture of collaboration, curiosity and a strong foundation in data within your teams.

Based on my experience and the clients I have been servicing, I noticed that the teams that excel at experimentation have a few things in common:

1. They integrate data as a central pillar for decision-making, ensuring insights drive hypotheses and actions.
2. They embrace cross-functional collaboration, involving SEO, Paid Media, Product and UX teams in the process.
3. They prioritize big-picture hypotheses tied to business outcomes like customer lifetime value, retention and revenue growth.
4. They balance qualitative insights (customer voice, user research) with quantitative analysis to uncover deeper truths about their users.

This maturity doesn’t happen overnight, but the teams that focus on nurturing and growing this culture are the ones driving the future of experimentation—and there are many companies that do that very well, like Starbucks and 
Nissan to name a few.

The future of experimentation lies in creating Real-Time Brand experiences, which means dynamic, adaptive interactions that resonate with users in the moment. While this might sound similar to concepts like personalization or CRO, it’s essential to clarify their differences. Their overlap often contributes to the definitional confusion in our industry, but when understood, can unlock an exponentially optimized user experience.

Personalization focuses on tailoring content or interactions to individual users based on data signals, such as first-party data or behavioral patterns. Conversely, CRO is about optimizing specific touchpoints to drive better outcomes, often through structured experimentation. Real-Time Brands combine these elements into a cohesive, adaptive system that evolves with the user’s journey, leveraging insights from experimentation and the execution capabilities of personalization.

What makes Real-Time Brands distinct is their focus on delivering seamless interactions at scale, powered by advanced technologies like machine learning. These experiences aren’t about static “best practices” or one-size-fits-all solutions. With a dynamic, and increasingly personalization-driven audience, each individual user is constantly creating and adapting what they want. 

Instead, they are about continuously adapting to user needs in their journey’s context, whether recommending the next best product, tailoring messaging based on previous interactions or optimizing navigation flows based on intent.

This approach goes beyond personalization; it creates context-driven experiences that feel intuitive to the user while driving measurable outcomes for the business. The key is to view experimentation, personalization and real-time capabilities as complementary, not interchangeable, with each playing a critical role in building connected, user-centric strategies.

Real-time brand experiences are meant to create a sense of connection. 

Users are more online than ever before, and the trend is increasing every year. The average adult in the US spent over 11 hours a day interacting with media in 2018. 

In 2025, the same segment is estimated to spend over 12 and a half hours 

Making your brand, content, platform or campaign stand out against the collective trillions of individual hours spent seeing content requires providing the “perfect” interaction: a digital experience that matches the exact criteria a user needs to interact, engage and complete the ideal key action. 

Providing a Real-Time Brand experience shows users that the brand understands them, values their time and is committed to meeting their needs in ways that feel both seamless and meaningful.

For teams, this shift requires thinking beyond isolated experiments and embracing the complexity of modern user journeys, and necessitates the ability to orchestrate an ecosystem of touchpoints that work together to build trust, drive engagement and deliver measurable business outcomes.

At its core, A/B testing was designed to do just that: find an optimal version of a touchpoint that more-closely resonates with what users respond to. With AI, machine learning and a holistic approach to create iterative experiences from real-time data, brands can essentially create individualized A/B tests on a segmented and individual level that automatically self-adjust as interaction data comes in. 

As technology evolves, Real-Time Brand experiences will increasingly become the standard for experimentation. They represent the future of connecting with users in a way that feels personalized, proactive and perfectly aligned with their expectations.

The future of experimentation isn’t a rejection of the past but an evolution toward something bigger.

Remember, A/B testing is not dead, but the mindset around it must evolve. To unlock its full potential, teams need to move beyond isolated, surface-level tests and embrace holistic approaches that connect experimentation across SEO, paid media, product and CRO.

Modern experimentation prioritizes dynamic, real-time brand experiences that adapt to individual user needs. By blending personalization, behavioral targeting and experimentation, brands can create seamless, meaningful interactions that drive both user satisfaction and business outcomes.

The success of experimentation lies not in the tools alone, but in the mindset and culture behind it. Teams must nurture and reward collaboration, prioritize bold hypotheses tied to business goals and focus on delivering real value to both customers and the organization.

To everyone in the industry, whether you’re just starting or leading the way, thank you for pushing the boundaries and keeping this space vibrant.

Experimentation will always be a cornerstone of digital optimization. The core components of why we experiment are exactly the same: provide an opportunity for interaction that works a little bit better. Its future, though, lies in the connections we build, the problems we solve and the meaningful experiences we create together. 

Want to explore how to take your experimentation program to the next level? Let’s get in touch below.

A Legacy of Strategic Partnership

For years, Monks has partnered with Google to elevate our clients' marketing strategies to unprecedented heights.  

Driving Innovation Through Commitment

These certifications underscore our pursuit of excellence in expanding our Google Cloud knowledge base and adopting innovative solutions. Achieving the Data Analytics specialization demonstrates our ability to excel in data ingestion, preparation, storage and analysis using Google Cloud technology. Meanwhile, the Marketing Analytics specialization highlights our capability to transition clients from fragmented datasets to data-driven marketing strategies that yield measurable results.

"This enables Monks to support partners in developing both technical solutions and data strategies that drive their success" my colleague Mikey adds.

Setting the Stage for AI and Machine Learning

These specializations not only validate our expertise but also prepare us to leverage the latest advancements in AI and machine learning. By combining cutting-edge technologies with our deep understanding of data analytics and marketing analytics, we’re poised to unlock even greater value for our clients.

Expanding Capabilities & Innovating with Looker

As we celebrate these achievements, Monks is already looking ahead, leveraging tools like Looker to empower our clients with actionable insights and seamless data visualization. Mikey says, “Looker's semantic layer translates raw data into a language that both downstream users and LLMs can understand. By utilizing LookML to provide trusted business metrics, we create a central hub for data context, definitions and relationships, powering both BI and AI workflows to drive our clients’ businesses forward.”

A Commitment to Client Success

At Monks, our mission is simple yet profound: to empower clients with innovative, data-driven solutions that deliver tangible impact. For years, we’ve championed the importance of breaking down silos, enabling seamless data accessibility across organizations, and building pathways for activation (Optimizing Workflows with First-Party Data). 
These specializations are more than milestones—they embody our dedication to excellence and our pursuit of success for our clients.

“To put it bluntly, we focus on delivering tangible business value to our clients by applying both our technical and marketing expertise,” says Hayden Klei, VP Data Consulting. “We don’t focus on shiny vanity projects for the sake of industry accolades, rather, we define success by the impact we deliver to our clients’ top and bottom lines.”

As we continue to invest in Google Cloud and our talented teams, we are setting ourselves up for even greater achievements, driving value through expertise, innovation and a shared vision of success. Together with Google, Monks is ready to lead the charge into the future of marketing analytics and data-driven solutions.

For organizations seeking a trusted partner to navigate the complexities of data and marketing, Monks stands ready to deliver unparalleled expertise and transformative results. 

From cutting-edge AI solutions to enhanced cloud capabilities, Dreamforce 2024 showcased a vision where data-driven insights and intelligent automation lead the charge in boosting innovation and efficiency. So, let’s dive into the most exciting revelations from this year's event, highlighting how these advancements are set to empower businesses to reach new heights in building meaningful and enduring customer relationships.

Agentforce introduces intelligent agents across enterprise use cases.

One of the most exciting announcements at Dreamforce was the reveal of Agentforce, an autonomous agent platform that lets brands build AI agents that support their customers and employees. The idea is that when working together, humans and the AI agents who augment their abilities can drive customer success together.

These agents fulfill key, specialized roles that range from supporting marketers as they build campaigns and journeys to serving as a coach that helps employees practice their pitching. On the customer side, Agentforce can serve as a personal shopper who makes recommendations based on customer preferences, a sales development representative who engages leads on behalf of the sales team, or a service agent who can resolve cases in natural language. These agents are grounded in your data, ensuring accuracy as well as an experience that greatly surpasses standard chatbots that customers are used to.

At its core, Agentforce is a Salesforce innovation that answers the critical question of how to integrate AI into business operations. It goes beyond traditional chatbots by incorporating a higher level of intelligence, powered by your Data Cloud data, allowing it to complete repeatable tasks more effectively. Therefore, it’s essential that you have data foundations to support these intelligent actions.

Agentforce and our own Monks.Flow represent a dynamic duo of AI solutions that can significantly enhance how businesses interact with customers and manage internal processes. Together, these platforms exemplify how assistive AI technologies are crucial in helping brands scale their operations and elevate customer interactions. The synergy between Agentforce's customer-centric AI agents and Monks.Flow's operational efficiencies enables businesses to operate smarter and at a larger scale, without sacrificing personalization or effectiveness. Learn more about Agentforce in depth here.

Data Cloud puts even more insights at your fingertips with wider unstructured data support.

Speaking of Data Cloud, Salesforce announced a handful of new features to help make your data far more insightful than ever before. Perhaps the most exciting new feature is additional unstructured data support, including native processing of audio and video content.

This is transformative because it unlocks new data sources, providing a more complete understanding of customer preferences, pain points and behaviors by analyzing content like customer calls, webinars or product demos. By revealing nuanced insights into customer sentiment and intent, companies can enhance customer profiles and enable personalized, data-driven marketing strategies.

Moreover, applying AI to these sources allows for more accurate predictions of customer needs, improving customer service interactions and fostering loyalty. Once processed and searchable, this content becomes a valuable asset across business functions, enhancing operational efficiency and decision-making accuracy. For example, examining customer service chats can help support teams or sales reps identify frequent issues and improve response strategies, leading to increased customer satisfaction or higher conversion rates.

Find—and act on—information you need with enhanced search.

Despite so much data coming from so many sources, finding what you need will become a lot easier thanks to hybrid search. Hybrid search combines vector search with keyword search, bringing information together from across different media. These improved search capabilities enable faster information discovery by considering the customer's specific situation, preferences and history, thereby reducing the friction often encountered in traditional searches that rely solely on exact matches or keywords.

With personalized customer interactions, searches become more tailored to individual needs, allowing service representatives and sales teams to provide faster, more relevant responses, which enhances customer satisfaction. This leads to enhanced efficiency for teams, as quicker access to relevant customer data lets employees respond to inquiries more efficiently. As a result, customers enjoy an improved experience, receiving seamless, fast, and accurate service that effectively resolves issues or recommends products.

Additional governance and security features keep your data safe—including prevention of data leaks when using AI.

Security and governance have always been crucial to storing data, but the use of external AI applications have raised new concerns about eliminating data leaks and reducing risk. Salesforce has implemented additional data security and governance features to help keep your data safe, including the prevention of exposure to unauthorized parties when using AI. These enhancements are vital for compliance with regulations, as privacy laws like GDPR and CCPA are becoming more stringent.

Preserving customer trust is crucial, as customers expect their data to be managed securely and responsibly, and additional security measures reassure them that their data is protected. Moreover, these features mitigate risks associated with growth and innovation, providing a secure foundation for companies to expand operations and introduce new products or services without increasing vulnerabilities. You can find more details on added security, along with more info on all of the Data Cloud features discussed above, on Salesforce’s website.

Marketing Cloud Advanced Edition helps small and medium-sized businesses do more with less.

Switching focus to Marketing Cloud, Salesforce revealed Marketing Cloud Advanced Edition, which we recently examined in depth. This edition is crafted to empower smaller marketing teams to achieve more with less, thanks to its innovative features that streamline AI-driven efficiencies, enhance user journey experimentation, and bolster cross-team collaboration. Notable additions include a reimagined campaign home, robust AI-powered tools like Einstein Engagement Scoring and Frequency, and advanced dynamic content creation. These tools are set to transform how marketers engage with customers by predicting user behaviors, optimizing messaging strategies, and personalizing interactions.

Moreover, Marketing Cloud Advanced Edition introduces event-driven messaging and two-way conversational capabilities, enabling seamless, interactive communications triggered by specific customer events. For those curious about path experimentation, the Advanced Edition supports testing various campaign paths to refine strategies further. These features are designed to address the evolving challenges marketers face today, ensuring businesses can deliver impactful, personalized marketing experiences.

From free training to product upgrades, additional goodies were shown off.

A connecting thread across the news dropped at Dreamforce is the use of AI and the crucial roles it plays across connecting with customers, understanding their needs, and ultimately building content and touchpoints that keep them engaged. Beyond new product features to support this trend, Salesforce will also offer free, hands-on AI courses and training for anyone. This is to help ensure that everyone can gain the skills they need to excel in a world transformed by AI. Learn more about the training here.

Finally, as reported earlier in September, Salesforce is offering its free Foundations upgrade to Sales Cloud and Service Cloud Enterprise Edition customers. Foundations provides a new, enhanced UI that gives brands a unified view of their customer. Read more about that update here.

Dreamforce 2024 was an incredible step forward in advancing AI-driven business growth and customer engagement.

This year’s Dreamforce left us with a wealth of insights and innovations that promise to reshape how businesses operate and interact with customers. From the transformative powers of Agentforce to the enriched capabilities of Data Cloud and Marketing Cloud Advanced Edition, the announcements this year underscore the pivotal role of AI in driving forward-looking strategies and enhancing business efficiency.

The emphasis on AI as a cornerstone for customer engagement and operational excellence reflects a broader industry shift. Salesforce's commitment to equipping businesses with the tools and knowledge to harness AI's potential is evident not only in its product offerings but also in the free educational resources made available to the community.

By fostering an ecosystem where technology and human ingenuity work hand-in-hand, Salesforce continues to empower brands to thrive in a rapidly evolving landscape. As we look forward to the future, it is clear that the advancements unveiled at this year's event will serve as catalysts for growth, enabling brands to forge deeper connections with their customers and lead with confidence into the digital age.

As a response to this issue, the most popular digital analytics tools have started to offer bot filtering features. While it is advisable to activate them, they have shown very low effectiveness against the different types of bots that abound on the web; or worse, it could filter out real, and beneficial bot traffic, skewing data in favor of inaccurate traffic. 

Bottom line, our data is at risk.

How does bot traffic impact your business?

For companies making business decisions based on data, bot traffic can have detrimental consequences on their digital strategies. It skews various metrics like conversion rates, bounce rates, total users, and sessions, leading to unexplained fluctuations. Additionally, increased traffic can raise costs for digital analysis tools, as many pricing models are based on the number of visits. AI tools and implementations that train on data impacted by traffic bots can produce inaccurate insights. Bot traffic can also bog down websites by overloading servers, resulting in slow page load times or, in severe cases, making the site inaccessible to users. In extreme situations, allowing unwanted traffic can create security vulnerabilities and lead to leaks of sensitive information.

Recently, one of our clients asked us for assistance in reviewing certain sudden increases in traffic originating from Frankfurt during the early hours of the morning, which did not align with their historical data. After analyzing the reports and cross-referencing the different available dimensions, we discovered that, during certain periods, 90% of the total users recorded in the reports exhibited behavior that was difficult to attribute to humans. This not only seriously affected the data quality but also incurred significant expenses due to the volume of visits the website was receiving.

However, it’s not just extreme situations that can affect our data quality. Even a small percentage of anomalies can lead to unreliable reports. So, how can we stop this and keep our data reliable?

Know the enemy

The first step to effectively counteract bots is to understand them. Not all bots are alike; each type demands a unique strategy. A common classification distinguishes between malicious and non-malicious bots. Let’s examine some typical examples of malicious ones.

Types of malicious traffic bots

1. Scalper bots:

These programs snap up tickets and other limited-availability goods at lightning speed, only to resell them later at higher prices.

2. Spam bots:

Designed to flood your inbox or messages with junk, often laden with malicious links. Who hasn't been on the receiving end of annoying spam?

3. Scraper bots: 

These bots automatically extract data from websites, often copying content from competitors to gain an edge.

On the other hand, non-malicious bots are the ones that can quickly handle tedious tasks. They gather large amounts of data that would otherwise take days or even months to retrieve, easing the burden on humans for repetitive tasks.

Types of beneficial traffic bots

1. Spider (web crawler): 

Google's bots are some of the most advanced. They relentlessly search the web for videos, images, text, links, and more. Without these crawlers, websites wouldn't get any organic search traffic.

2. Backlink checkers: 

These tools help you find all the links a website or page receives from other sites. They’re crucial for SEO.

3. Website monitoring bots: 

These bots watch over websites and can alert the owner if, for instance, the site is under attack by hackers or goes offline.

My goal isn’t to exhaustively detail every type of bot out there, as they are constantly evolving. Instead, I want to highlight the various behaviors that influence our filtering and removal strategies, as well as the complexity involved. In the end, whether they are good or bad, all bots are unwanted in our reports, and we need to minimize their impact on our data.

Countering bot attacks with the right tools

Nowadays, you can find both automated and manual strategies to tackle this challenge. In the case of automated solutions, bot filtering programs stand out, either integrated into analytical tools or specialized software for AI-driven bot detection. However, as mentioned earlier, their effectiveness tends to be low, and in many cases, they come with associated costs.

On the other hand, we have non-automated solutions that provide better results, and we can categorize them based on the filtering approach they adopt:

Reactive Approach: Apply custom filters at the report level. This method is simple and flexible, requiring no development-level changes. It's an effective first step for early detection. Utilizing tools available in analytics platforms—like GA4 segments, Looker Studio filters and data warehouse queries—makes it easy to implement, though it’s less robust.

Preventive Approach: Implement filters before collecting data. Although this can be challenging and resource-intensive, it effectively prevents the impact on reporting and restricts bots from accessing the website and its servers.

Establishing a data quality review cycle

To keep our data free from bot traffic and ensure optimal results, it’s best to use a comprehensive strategy combining both preventive and reactive measures. This is known as the data quality review cycle, a model of continuous monitoring designed to constantly detect anomalies. It involves collaborative efforts from analysts, developers, and product owners to find efficient solutions that safeguard the integrity and reliability of the data.

Although we can’t entirely eliminate bot traffic from our reports, proactively implementing data quality review strategies offers us practical and effective ways to address this issue.

In summary

• Bots can serve both harmful and benign purposes; in both cases, it's crucial to keep them out of reports.
• Bot traffic has negative consequences for both digital and commercial strategies.
• While analytics platforms have features that automatically block some bot traffic, their effectiveness is limited.
• Constantly monitoring anomalies in the reports is essential for identifying bot traffic.
• To avoid unwanted traffic influence and ensure that the data is not biased or contaminated, it’s necessary to implement both preventive and reactive measures.
• Including a data quality review cycle in the workflow is crucial for keeping the reports free from bot traffic.

For those who already migrated to GA4, the retrieval of historical UA data might still be a challenge, but one that needs to be addressed as soon as possible. The end of UA brings the irrevocable loss of priceless historical data, which is key to understanding performance over time. This loss prevents you from identifying trends or addressing questions about past purchases or campaigns, creating gaps that can impact your bottom line. 

That said, If exporting historical data from UA properties was easy, I wouldn’t be writing about it. To tackle these challenges, Media.Monks has developed our own Universal Analytics Data Export & Archive Tool, a custom tool that helps clients efficiently extract and store their historical data.

A tailored solution designed to solve common challenges.

Unlike off-the-shelf tools, the UA Data Export & Archive Tool offers tailored data extraction, ensuring data is accurately captured and organized according to your specific business needs. On top of that, standard UA users had very limited data export capabilities. But even with an upgraded 360 version, achieving a seamless and comprehensive export is difficult due to backfill limitations.

With a focus on frictionless delivery, the UA Data Export & Archive Tool addresses these limitations to guarantee a smooth transition. “Our data scientists have created a custom script to solve our clients UA export issues, whether your property was upgraded to 360 and linked to BigQuery or not.” says Brianna Mersey, Senior Director, Data. “The tool uses the Google Analytics Reporting API (v4) to export data into BigQuery or any designated data warehouse.”

In other words, UA Data Export & Archive Tool lets you export and own your UA historical data in a first-party environment—even if you don’t have the 360 version. 

Seven steps for a straightforward process.

The UA Data Export & Archive Tool is designed to make the data extraction and archiving process seamless and efficient. Here’s a breakdown of how it works:

• Initial Assessment: We begin with a thorough assessment of your current UA setup and data needs. This helps us understand the scope of data to be exported and any specific requirements you may have.
• Custom Python Scripts: Using Python code in Google Colab, our data scientists have developed scripts that automate the data export process. These scripts are customized to create aggregated reporting tables aligned with your desired dimensions and metrics.
• Data Aggregation and Structuring: The exported data is aggregated and organized into structured tables. 
• Data Storage: Once the data is exported and structured, it is securely stored in the data warehouse of your choice, such as BigQuery or any other designated storage solution. This ensures you maintain control over your historical data.
• Custom Reporting: Our solution offers the ability to build up to five custom tables or reports based on your specific requirements, enabling you to access the most relevant insights for your business.
• Expert Support: Throughout the process, our team provides expert support to ensure your data is accurately captured and properly aligned with your new analytics system. This includes assistance in setting up a secure data warehousing solution if desired.
• Privacy and Compliance: The tool adheres to industry best practices for privacy and data security, ensuring that your data remains confidential and compliant with all relevant regulations.

Efficiently organized data turns into capitalized opportunities.

In times of economic uncertainty and in a data era where everything is moving faster, having historical data is essential for adjusting marketing strategies and making predictive and informed business decisions. That’s why the urgency to properly export your UA data cannot be overstated. “If you have even the slightest concern about losing your data, for benchmarking and year on year comparisons, now is the time to act,” says Mersey.

With only weeks left before UA shuts down, every day counts. Don’t risk losing valuable historical data and keep the insights crucial for your business’s success.

If you need to retrieve and store your historical UA data, we're here to help. Fill out the form below to get in touch with one of our data experts. 

But a key theme was the need to unify disparate data sources, orchestrating teams around unified workflows to maximize data impact—in one word, the event focused on integration. This strongly resonated with me, because it’s exactly what my team is built to help brands achieve; as a unitary partner and systems integrator, we specialize in creating platform solutions that seamlessly integrate AI and customer data to drive growth. From new product announcements to sales stories, let’s look at the growing need for an integrated approach to customer relationship management (CRM) and the role that a unitary partner can play in helping brands maximize its impact.

Here's what Salesforce announced this year.

One of the most exciting announcements was the introduction of Einstein Copilot for Marketers, set to release in June. This tool translates customer data into actionable campaign briefs, offering generative AI features like copy creation and automated communications. Salesforce is also now orchestrating seamless handoffs between multiple Copilots to enhance team collaboration. These innovations bridge the gap between customer data insights and content creation to drive impact across the business. For example, you can pair Einstein Copilot for Marketers with Einstein Copilot for Merchandisers to uncover up-selling opportunities.

Salesforce also announced enhancements to Data Cloud for Commerce, providing a unified view of customer data from numerous commerce data points. This empowers marketers to create hyper-personalized experiences—but when paired with Einstein Copilot products, these efforts become even more impactful.

Another major announcement was the Zero Copy Data Partner Network, connecting technology, system integration, and data ecosystem partners. This network allows marketers to draw data from a broader array of sources (without that data needing to be housed on their Salesforce platform), amplifying their AI-driven efforts.

What’s interesting about these announcements is the emerging, overarching theme of integration and collaborative workflows to help marketing teams work better together. This is the bread and butter of a unitary partner who can ensure that data accessibility, unified customer views and team collaboration are optimized across the business. By bridging together expertise across disciplines like data, media, content, and technology, such a partner is best suited to deliver the full potential of these solutions as they work in harmony with one another.

Peek inside the success stories that were shaped by seamless integration.

While at Salesforce Connections, I had the chance to speak with brands—learning their needs, pain points and the opportunities they most look forward to—and got to watch the different speaker sessions that we hosted or participated in. These conversations presented a range of success stories demonstrating how integrated solutions helped brands unlock new possibilities in their marketing. Here are three goals that my team has been able to help brands achieve.

Data integration and unified customer views. In a talk that is available on demand, Alex Furth, Marketing Manager, Digital Innovation from Gatorade shared how Einstein AI and Data Cloud unified fragmented consumer data, enabling effective and tailored marketing strategies. Theresa McCombs Marketing Director, Brokerage Services and Julia Homier Digital Marketing Consultant, from Holmes Murphy told a similar story in the talk “Drive Financial Services Marketing ROI with AI-Powered Data,” where they detailed their journey with Salesforce's Marketing Cloud to enhance customer engagement and data management through centralized automation tools and Einstein AI. Both brands’ successes show how unified data solutions significantly enhance engagement and performance metrics, exemplifying the need for centralized, strategic data management.

Personalized engagement and customer loyalty. Our session with PepsiCo, “Data-Driven Engagement for PepsiCo Tasty Rewards,” focused on their Tasty Rewards loyalty program, which uses Salesforce Marketing Cloud and Einstein to drive loyalty and increase long-term value. They achieved a 100% increase in open rates and a 170% increase in click rates. If you missed the talk, you can still learn more about PepsiCo’s approach to scaled personalization from a different angle in a previously recorded webinar.

Meanwhile, Broadway Across America showcased how Salesforce’s solutions personalized customer experiences, significantly increasing month-over-month (MoM) subscriptions. “My favorite part of Connections was talking about some of the innovations we’re helping Broadway Across America with, mainly the SMS texting strategy for them in 25 different markets, and they’ve seen awesome results,” my colleague Amy Downs, VP of Commercial at Media.Monks, noted. “Their MoM subscription increase was 7%, compared to a 0.14% increase before we implemented that strategy.”

The lesson: data-driven engagement strategies drive significant increases in subscriptions and long-term customer loyalty—an important consideration for marketers who are embracing product-led growth strategies.

Marketing automation and strategic alignment. Another significant consideration that Theresa and Julia at Holmes Murphy emphasized was the importance of consolidating varied automation tools. By implementing Einstein AI and aligning marketing strategies with business objectives, together we were able to streamline operations and surpassed industry engagement benchmarks. Centralizing operations through consolidated automation tools not only boosts engagement but also enhances overall marketing efficiency, demonstrating the critical role of integrated, strategic automation in achieving business goals.

That’s a wrap on an event that’s all about connections.

Attending Salesforce Connections was an exhilarating experience, showcasing the transformative potential of integrating AI and data to drive marketing innovation. The success stories from brands like PepsiCo, Gatorade, Broadway Across America and Holmes Murphy highlighted how unifying data with Salesforce's powerful tools opens up new possibilities. These brands have achieved remarkable success by leveraging coordinated workflows and seamless data integration, and I’m excited to continue supporting brands in their journey to do the same by unlocking the full potential of their CRM and the technologies like AI that rely on it.

But here, I want to focus on a specific trend that I’ve noticed has grown over the years. CMOs have long felt the pressure to tie their marketing efforts to tangible business impact, but this need has intensified due to the increasingly elevated roles of C-suite peers like the chief product officer (CPO). This is especially true among technology-focused CMOs, who are at the forefront of adopting a product-led growth strategy to effectively address these evolving challenges.

What is product-led growth?

Product-led growth marks a significant redirection of priorities. It places the product itself at the center of the growth strategy, leveraging it as the primary driver of customer acquisition, activation and retention. In this model, funds that were traditionally allocated for upper-funnel marketing activities are increasingly being diverted to support enhanced product development. For CMOs, this shift means embracing new competencies and rethinking how to best support not just initial sales, but the entire customer lifecycle.

This approach not only ensures more sustainable growth but also aligns marketing efforts more closely with the broader business outcomes. And while it signals a major change in strategy, it doesn’t need to be treated with existential dread about your relevance as CMO. Here’s how to get ahead of the shifting expectations that CMOs face.

As C-suite dynamics shift, closer collaboration is key.

Moving away from traditional "brand" thinking, CMOs must now adopt a data-driven approach that aligns closely with product development and customer experience enhancements. This expanded role ensures that marketing strategies are deeply integrated with the product to enhance adoption from the outset, and often means looking beyond front-end acquisition metrics to also focus on deeper, more substantial metrics like customer lifetime value.

At the same time, CMOs also face a shift in power. As the product itself becomes the central tool for growth, the role of the CPO becomes more prominent. In some organizations, this shift can lead to CMOs losing some traditional powers as CPOs take a leading role in driving user acquisition and retention through product innovations—making closer collaboration across the C-suite vital, ensuring that marketing not only attracts but also materially contributes to the financial success of the company. Effective collaboration relies on understanding customer motives and mindsets across the full funnel, which we’ll touch on below.

Embrace a wide-lens view across the customer journey.

In a product-led growth strategy, the modern CMO’s duties extend beyond the initial point of sale. Successfully delivering on this strategy requires a holistic view of the customer lifecycle, ensuring that marketing strategies not only support initial product adoption but are also tightly aligned with ongoing customer retention and expansion.

Achieving this full-funnel view relies on the deployment of sophisticated CRM technologies that are capable of not only reaching new customers but also re-engaging existing ones at various stages of their journey. The effective use of CRM tools allows CMOs to maintain a continuous dialogue with customers, personalize marketing messages, and optimize the timing of outreach efforts. You can learn the ins and outs of this strategy in our Generation AI report, which goes deeper into building interconnected touchpoints that are reactive to customer needs and engagement across the full funnel.

Automated product recommendations can empower sales teams by predicting which products a customer is most likely to purchase next, thereby enhancing the effectiveness of sales conversations and increasing the chances of upselling.

CRM data is crucial to tying marketing activities to business objectives.

So, you’re facing the pressure to expand your horizon beyond the upper funnel and the initial point of sale, and you’re aware of the role that automation and CRM tools will play to help you get there. But how do you begin building a robust data infrastructure to make the most of your CRM data and the latent insights within?

Everything starts with your data foundations, particularly in areas such as marketing and media data warehousing. A well-structured data warehousing strategy not only facilitates better data analysis and insight generation but also helps in breaking down the silos between marketing and product teams.

Next, refine the key performance indicators (KPIs) that signal impact. In the context of product-led growth, CMOs need to look beyond traditional metrics such as cost per acquisition (CPA). Instead, shift your focus towards KPIs that offer deeper insights into customer behavior and loyalty, such as customer lifetime value (CLV) and average number of purchases per customer. These metrics provide a more nuanced understanding of how effectively the product and associated marketing efforts are retaining and engaging customers over time.

In the spirit of collaboration with your sales team, also consider how to accelerate deal cycles and enhance the sophistication of lead scoring mechanisms. These efforts help in identifying the most promising leads faster and more accurately, thereby improving the efficiency and effectiveness of the sales process.

Thrive—don’t just survive—in the evolution of the CMO’s role.

The key to successful product-led growth lies in the ability to integrate marketing, sales and product development into a cohesive strategy—and with the groundwork laid for powering relevant customer experiences, you’re ready to build new alignment with the CPO and CRO. A good first step here is to build a roadmap that aligns product release schedules with the overall marketing plan.

This enables you to communicate product innovations in the market, maximizing both product adoption and the impact of your marketing. For example, analyzing sales performance to decrease time to close can reveal insights into how marketing can better support sales. This approach resembles lead scoring but goes deeper by enabling sales teams to focus on prospects who are most ready to buy, based on predictive analytics and behavioral data provided by marketing.

When planned successfully, a product-led growth mindset ensures the entire organization is geared towards leveraging its products as the primary growth driver. By fostering this level of collaboration, CMOs, CPOs and CROs can ensure their strategies are not just aligned but are also mutually reinforcing, leading to sustained growth and competitive advantage.

As we look to the future, the evolving role of the CMO in driving product-led growth underscores a clear message: adaptability, collaboration and a deep understanding of data are paramount. CMOs are not just participants but are strategic architects in reshaping the marketing landscape to harness the full potential of product-centric strategies. By fostering robust partnerships with CPOs and CROs, and by embracing advanced technological tools, CMOs can ensure their initiatives not only align with but propel the broader business objectives. Let this be a call to action for all digital marketing leaders to embrace these changes boldly and creatively to drive sustained growth and create enduring value in their organizations.

Google's Meridian is built upon the foundation of the previously released RBA/LMMM materials. The developments include geo experiments to ingest into the modeling, as well as detail on reach for YouTube. The emphasis on triangulation via A/B testing to enhance MMM accuracy is a strategy we are well-versed in ourselves and offers a good base to start from. However, it is crucial to note that while Meridian provides a step forward in measurement, it remains just a tool—a sophisticated one that requires expert hands to wield effectively. 

At Media.Monks, we pride ourselves on our robust internal platform that is industry-leading in terms of speed and functionality. Meridian gives a step up for brands who are just starting off in their MMM journey, helping them move away from last click to better quantify media uplifts.

From an experienced modeler’s perspective, these are some of the key points to consider with Meridian:

• The methodology behind Meridian is solid and makes sense around the emphasis on triangulation, which enhances the accuracy of the results.
• However, experienced econometricians will be essential for operating Meridian effectively in-house. Brands must ensure their teams possess the expertise to source the right data, build the models to reflect the real world, and translate data insights into actionable ROIs and response curves, or they risk making flawed decisions from the outputs.
• As with all MMM initiatives, data quality remains a critical factor in whether or not you’re adding value or making accurate decisions. Having accurate and full data across all drivers of sales (media, price, promotions, seasonality, climate, etc.) is critical for MMM. Strong data foundations also gives a significant advantage, whether brands are utilizing Meridian or any other technology.
• Effective communication within organizations is key to driving traction and implementation of MMM strategies, and explaining models clearly and effectively is key for any MMMs success.
• The launch of Meridian represents a shift away from outdated attribution models towards a more accurate, incremental media valuation approach. Even if it isn’t the best-fit tool for all brands, it is another step in the industry’s maturation, especially in the wake of cookie deprecation and changing privacy legislation.
• Smaller clients with simpler data structures, such as ecommerce clients spending less than $2 million USD on digital media, will benefit from this tool as an entry point to the world of MMM.
• Some clients may question running their media measurement on a platform from a media owner

In conclusion, Google's Meridian offers a solid starting point for less complex brands looking to enhance their measurement capabilities via a framework. Increasing the usage of MMM can only be good for the industry as a trusted tool to measure and optimize media. That being said, hard work is still needed in attracting econometric talent into the marketing world to maintain model accuracy and increase adoption of these methodologies. At the end of the day, a model is only as good as its modeler: you can have the best model in the world, but if it's not fed with accurate, high-quality data or delivered clearly to key stakeholders, it's not going to be trusted (and therefore, adopted) in an organization.

A good step forward, but still more to do on the talent front. See our post on apprenticeships to learn what we are doing to address this.

For more information on how we can help with your marketing effectiveness measurement or Market Mix Modelling, visit our Measurement page or contact us.

Firstly, the importance of data in driving decision-making has become paramount. Businesses recognize the need for robust data-driven insights to navigate the complex marketing landscape. MMM provides a solution by quantifying the impact of various marketing activities, enabling businesses to make informed decisions based on solid evidence.

Secondly, the fragmentation of marketing channels has made decision-making increasingly challenging. With a multitude of platforms and channels available, businesses are seeking ways to measure the impact of their marketing investments accurately. MMM offers a holistic approach, allowing businesses to understand the effectiveness of each channel and optimize their investments accordingly.

Moreover, the rapidly changing economy poses unique challenges for businesses. Factors such as inflation, consumer confidence, political stability, global conflicts, and oil prices can greatly impact business forecasting. In this dynamic environment, clients are eager to utilize the latest intelligence to make intelligent decisions. MMM provides the means to analyze and adapt to these changing circumstances, enabling businesses to stay ahead of the curve.

Structurally, the attribution landscape has been undergoing significant changes. The deprecation of cookies, the rise of walled gardens, and the increasing digital investments have necessitated a more comprehensive and incremental approach to measurement. MMM has evolved to meet these demands, offering agility and granularity that align with the needs of today's market.

Gone are the days when MMM was a slow cruise liner, calmly sailing through the seas of marketing. It has transformed into an agile and adaptable tool, capable of navigating the challenges posed by channel fragmentation and rapid economic changes. MMM allows businesses to quantify what is working, accounting for the latest circumstances and driving data-informed decision-making.

As the phoenix rises from the ashes, MMM has risen to the occasion, demonstrating its resilience and ability to deliver meaningful insights. In a world where marketing strategies must constantly adapt, MMM stands as a powerful tool, guiding businesses towards success in an ever-changing landscape. Embracing MMM means embracing the future of marketing, where data and insights reign supreme, and agile decision-making holds the key to unlocking growth and profitability.

For more information on how we can help with your Marketing Effectiveness measurement or Market Mix Modelling,visit our Measurement page or contact us.