CRM is crucial to any first-party data strategy because it sits at the center of every customer interaction: through behavioral and environmental triggers, your customer is feeding inputs that influence future experiences, like product recommendations and personalized messaging. Essentially, data makes personalized experiences possible—and when done right, those experiences in turn generate more data that brands can act on. With the death of the cookie on the horizon, these insights will become even more critical to your marketing strategy.

There’s no better time than now to unify data within a CRM ecosystem to improve the efficiency of teams, inform future business strategies and, of course, enhance customer experiences overall. These efforts involve building data pipelines that help them better learn about their customers and engage with the right message at the right time. With the help of automation, a powerful collaborator that helps teams pull off outcomes that only eluded them before, the sales team can focus on only the most qualified leads. 

Not sure where to get started? No worries; I’ve gathered a couple brands who have successfully transformed their CRM ecosystems to fuel personalized experiences at scale.

Translate behavioral cues to key business insights.

Beyond driving conversion, one of the most impactful results of a strong CRM strategy is being able to leverage behavior data to guide better consumer experiences—of which Australian Community Media (ACM) makes a prime example. ACM is a large media organization that operates over 140 local news mastheads across Australia, serving both free visitors and paying subscribers. That’s a lot of relationships to manage and readers to serve. To those ends, the brand relies on email marketing and onsite personalization via Salesforce Marketing Cloud to reach and continually engage with readers.

ACM wanted to better understand subscriber behavior to create more personalized, relevant experiences in the form of automated content recommendations. Previously, this content was manually selected by editors or determined by publish date. Using Marketing Cloud Personalization, we were able to pull from subscribers’ engagement and platform behavioral data (like affinities toward news categories) to build personalized recommendations—boosting not only relevance but also employee efficiency.

This data did more than simply help serve personalized content to email subscribers. Armed with insights into which topics readers enjoy the most, editors can now easily plan out future content and focus on the kinds of stories their readers care about the most. More broadly, these same insights allow ACM editors to better predict engagement across the user journey—showing how CRM data can extend beyond marketing to unlock critical business insights that ultimately serve audiences. The best part: automated content recommendations free the editors to focus more on these strategic concerns of how to build better impact.

Elicit engagement to personalize at scale.

If you struggle to glean insights from audience behavior, here’s a tip: make it as easy as possible for customers and prospects to tell you more. This simple step was the cornerstone of Woodlea’s CRM refresh. Woodlea is a master-planned community of 7,000 lots located 30km west of Melbourne. With a need to focus on buyers at the right time, their sales representatives wanted to be able to give special attention to novice buyers. But this posed a challenge: how could they personalize communication and experiences at scale?

We began by helping the brand insert forms into email sent to buyers, a move that increased engagement while generating significant user data in the process. The newly interactive emails included simple questions and a prompt for recipients to build out their profile in Woodlea’s customer portal (powered by Salesforce Experience Cloud). The fact that these forms were embedded into the actual email content made it a seamless user experience and increased the percentage of leads who engaged. This first-party data then fed back into Woodlea’s Salesforce CRM, allowing for automated lead nurturing and qualification. These efficiencies freed the sales team to focus on two key buyer personas: those ready to make a purchase and first-time buyers who needed more attention throughout the buyer’s journey.

Enhance CRM to start building your first-party data foundation now.

The best time to transform your customer experience was yesterday, but there’s still time before cookie deprecation to experiment with new ways of generating first-party data—and CRM is at the heart of the process. From eliciting user engagement to gain key insights, to building efficiencies through automation and automation, linking data and inputs across a connected CRM ecosystem goes a long way in serving stronger, more personalized customer experiences and key business goals—so don’t wait.

Need help or don’t know where to start? Reach out to learn more.

With insights included from our Global EVP Tyler Pietz, the report stresses an important takeaway: brand, marketing, finance and data managers have a lot to gain from working closely together and building cross-functional integration systems. To that regard, we believe that while companies may invest in different tools for each department, the way each one manages data should be a more general decision that takes into account the broader business context. Seeing the full picture is pivotal to success, and we can only do that with complete, well-organized, integrated data systems.

Keep the enterprise goals in sight.

As Pietz says in the report, “The CMO’s role is to take business insights and bring them to market so value can be realized.” That means turning the data strategy into something actionable. CMOs are responsible for identifying the tactics that will help the business achieve its goals—and things like profitability, revenue drivers and risk management are important factors to weigh as they carry out strategies to attract leads, move into new markets and beyond.

To get there, however, CMOs need access to a comprehensive view of the brand’s consumer data—and the more departments and data points it encompasses, the better. But here’s where it gets tricky. Forrester’s findings show that 37% of B2C marketing decision-makers say driving decision-making with customer insights is one of their organization’s biggest marketing execution challenges. Rather than a skills issue, we believe this is due to structural problems that leave marketing leaders drowning in disorganized data, as well as a disconnect between departments.

Before collecting more data, learn to control it.

With changing privacy laws and platform restrictions, CMOs are hungry for new first-party data collection methods and alternative data sources. This is great, as first-party data is at the heart of any digital maturity and transformation journey. However, it’s important to remember that building a strong enterprise data strategy is not about acquiring as much data as possible; rather, it’s about extracting the most value from it. 

To do so, we need to start by establishing solid data foundations that ensure the information we have is accessible, timely, trustworthy and fit for purpose. While hiring new services like analytics tools and buying platforms is easy, the real challenge is to bridge together all of those data sources and systems in ways that allow us to see the full picture.

In other words, accumulating data is a misuse of our resources unless we can access its benefits—and as companies continue to explore new spaces where they can connect with consumers, the need for solid data foundations will become more pressing. Yes, a lack of control can lead to redundancies and misinformed decisions, but data hoarding presents even bigger risks in terms of user privacy. As the report illustrates, the consequences of a potential breach can be catastrophic if we don’t establish rigorous governance practices.

To deliver a great customer experience, create cross-functional data systems.

In addition to establishing solid data foundations, leveraging your data to its full potential also demands getting rid of departmental silos. While teams often consider only the data that is available to them, it’s possible to unlock additional expertise if we bank on stronger integration capabilities across channels, data sources and technologies.

Marketing teams, for example, may look at lead generation and engagement, while product teams focus on retention and acquisition. However, you need all metrics to get a clear view of the customer, and so you’ll achieve better results if they aren’t tracked separately.

As Forrester says in their report, determining a customer’s next-best experience requires “an orchestra of IT, data science, risk and compliance, marketing, sales and service, and CX.” To get high-quality customer insights, CMOs must look beyond their marketing-specific perspectives and collaborate with their counterparts across departments. Complaints and inventory may not be part of marketing’s responsibilities, but they surely impact the customer experience. Ads for out-of-stock products are a major turn-off for consumers, and nobody likes a brand that continues to do out-of-touch marketing while ignoring the countless complaints in their inboxes and social media comments.

The bottom line is, everyone can benefit from being aligned on what type of data will help the business thrive and how to track it, but CMOs play one of the most important roles in squeezing the juice out of it. While marketing leaders can’t design a fully-fledged data strategy on their own, they are the ones who take these consumer insights and turn them into marketing programs that bring the desired results for the business. Instead of focusing on collecting huge volumes of data, work on unifying these sets in an organized, secure environment, and you’ll find yourself instinctively making data-driven decisions.

“It’s so much more powerful being a part of a team that’s full of mixed backgrounds and experiences,” says Doug Hall, VP of Data Services and Technology. “Tech isn’t just for computer science graduates. If we didn’t have a rich tapestry of skills and experiences woven into the team fabric, we’d have a homogeneous glom of great skills, but we’d be more likely to do the same things this week as we did last, and in the same way.”

My move from non-tech to tech-first taught me that many things surrounding data are isolated from business needs and outcomes, even though you don’t want this to happen. When teams operate in silos and data works in isolation, so does experimentation. This inevitably leads to random acts of marketing and chaotic reporting. Rather than siloing teams, data should unify them—even if they have totally different KPIs. For instance, marketing teams look at lead generation, engagement and visibility, while product teams focus on retention and acquisition. In short, if everyone has a separate way of tracking and collecting data, this also means that everyone is looking at different things. 

Viewing the full picture is pivotal to success. 

The bottom line is that all of this information is data, and everyone should be aligned on what type of data will actually help the company move forward. Companies may invest in tools that serve one or more departments—GA4, for one, can support marketing and product teams—but the way each team or department collects data should be a company-wide decision. In turn, this means that a company’s data collection mechanism needs to be strong and reliable to be able to support every team and department in a business and help spur progress. The goal is to unite, not separate. That’s why it’s critical to align what matters in terms of data collection and measurement with the company’s business needs. 

The operative word is context—whatever we do, we must keep this in mind. Getting your company or clients to believe in the data at hand starts with analysts and measurement marketers understanding where the business is right now and where it can go. By actively participating in the inner workings of a business—with a focus on resource allocation and the processes that generate money—and analyzing relevant and purposefully collected data, you can help steer your company or clients towards profit. 

As such, I recommend every digital analyst to get familiar with a business’ internal processes. You can use this knowledge to implement tracking and analytics systems that align with the company’s procedures. A good example of this is how we helped the multinational alcoholic beverage company Diageo deploy GA4 across its 150 brand websites. As Hall explains, “Due to alcohol regulations around the world, most countries require an age verification gateway, which is a major conversion blocker that goes above and beyond consent management. This means that measurement and optimization are crucial for Diageo—and that’s how we knew that deploying consistent measurement across all brand sites was the best solution.”

In short, every digital analyst should come to understand the business context and goals to make sure the tag management and analytics tools are both implemented effectively and in line with the needs of the business. The secret sauce here is to closely collaborate with business-focused team members like marketers, consultants and account managers, who can provide guidance on what data is needed and how it will be used. Sure, we can rely on our experience and heuristics, but that doesn't mean any of our assumptions can be valued as truth. Once you’ve actually combed through a specific business context, you can start to define the right strategy for your business—and even then, it’s a matter of seeing how things play out before you can confirm or reject your hypothesis. Experiment, experiment, experiment!  

Become data mature to make your cash flow. 

Ultimately, this all feeds into a company’s data maturity, which Forbes defines as “a measure of an organization's ability to use data, along with how well the organization leverages those capabilities.” It’s not just about making data-driven decisions, but also about making sure data resources are accessible across an organization. The more data mature you become, the more you can scale—a topic that Coquet will discuss in more detail during the upcoming SUPERWEEK conference.  

With scale comes growth, which, in turn, can lead to new opportunities—and let’s be honest, this is an outcome that every business is after in their search for better tools, better consultants, and better digital marketing partners. When it comes to collecting data and tracking user behaviors (with consent, but this goes without saying), businesses do not want to miss out on any opportunity to get new customers, while staying relevant to their existing ones so that they continue to trust and purchase from them. More happy customers equals more cash flow. In the end, profit is the ultimate validation of growth (and that you’re doing a good job), both from a product and a customer experience perspective. 

Three takeaways to make your data take off. 

While it may take some time to find the most advanced tech stack or the best digital marketing partner—one that truly understands your business and all its needs—there are some changes you can make today. Trust me when I say that these actions will pay off in the end and help your cash flow grow. 

First of all, start by defining the problems you are aiming to solve and the questions you are seeking to answer with your data before you implement anything. This will help fine-tune your efforts and ensure that you are using the right tools and approaches to address the specific challenges you face. 

Second, consider (and research) the possibility of teaming up with a data consultant or specialist, who is able to provide expert advice and guidance on what tools and approaches are best for your specific problems and questions. This is particularly helpful if you are working on a complex or unique challenge that requires specialized knowledge and skills.

Third, teamwork always makes the data dream work. It’s crucial to collaborate with your team members and exchange your knowledge and experience—as Doug said, the more mixed the expertise, the better. By closely working together and sharing what you know, you can pool your collective knowledge and experience in setting up your measurement strategy. Keep in mind that within a business context, every team has its own problems and questions. As a leader, it's important to begin by having them define these, which, in turn, will reveal how aligned your team is around the company’s needs.

The main lesson that you should learn from this article is that context is key. At the end of the day, understanding human behavior, the business landscape, and how a company and its clients bring in money is what makes a successful digital analyst. I didn’t know this ten years ago, but I do now and I’m very happy to share these insights with you—find Julien, Doug, me and many other Data.Monks at SUPERWEEK 2023 and learn more about what really matters in managing your data.

To help brands in their transition, our data experts have written a guide that explores how brands should approach personalization going forward—including how to assess new technology providers, frameworks and methodologies to structure your planning—and long-term goals to strive for. Access your copy by filling out the form immediately below.

Need answers at a quick glance? Continue reading on for a quick FAQ that will help you get started.

Will I still be able to use Google Optimize after September 2023?

No, Google plans to sunset the product entirely. This can be taken to mean that the product will no longer be accessible past this date, and experiments running at this date will turn off.

Does this apply to Optimize 360 as well as the free product?

Yes. Google intends to sunset the product entirely, across both the free and 360 tiers. Note that by the sunset date, all organizations should have migrated their GA360 contracts to GA4, meaning that Optimize 360 is provided free of charge.

Should I use Optimize with UA or GA4 up until the sunset date?

This is entirely dependent on your existing UA and GA4 setups. UA 360 will continue to be available until Optimize’s sunset date, so you can continue to use it if you are more comfortable with that dataset. Otherwise, you can use GA4 data to power reporting and audiences. Linking Optimize with UA is available even after renewing GA360 contracts with GA4.

What should I do if I want to continue testing and personalizing my website after Optimize is sunset?

You will need to procure an alternative testing and personalization platform. Our report details the factors that should go into making that decision, and you should note that alternative platforms will have different commercial models than Optimize.

Will I still be able to use Google Analytics with a new third-party Experience Optimization tool?

Google has announced that they are investing in integrations between GA4 and third-party tools, with the intent being that GA4 will act as a centralized measurement hub that can be used to analyze and report on experiments that are delivered via a different platform. Media.Monks can provide more details on these integrations as they are made available by Google.

What will happen to my historical data?

Optimize uses Google Analytics data for reporting, meaning the raw data from past experiments will still be available in GA (and BigQuery if using GA360). Regardless, we recommend our clients collate test results in a central register to build an insights and learnings repository to fuel future decision-making.  Media.Monks can support the creation of a learning repository before the sunset if required.

Key watch-outs:

• An Optimize container can only be linked to UA or GA4 one at a time, not to both. Media.Monks do not recommend running experiments out of dual containers, so you should choose whichever dataset has the most actionable data.
• There are many factors that go into selecting an alternate vendor, and a proper assessment takes time. Organizations should bring this process well ahead of September 30 to ensure the continuity of capabilities.
• Deploying, validating and ramping up a new testing/personalization platform could take a number of months, meaning organizations should start the selection process now.

To get detailed steps on how to prepare for the Optimize sunset and plan your post-Optimize goals, simply fill out the form above to download our report.

What are the core capabilities of this technology? First up, CDPs support data aggregation, giving you a better and more unified view of your (prospective) customers. Second, they help you unify multiple data sources through a single ID manager, thereby facilitating ID resolution and management. Third, CDPs help you understand how customers act on different channels and thus enable you to predict consumer behavior. Finally, CDPs support customer activation. They’re first-party data tools that focus on making sense of different data sources, while executing effortless activation. 

Essentially, CDPs can help you diversify your brand’s targeting strategies and reach audiences at scale, all by leveraging your first-party data. If you ask our Associate Director of Customer Data Elia Niboldi, first-party data is your most valuable asset, not only because it’s durable and exclusive to your company, but also because it will be central to any future targeting strategy—and Customer Data Platforms are here to help you leverage this data. Niboldi sat down with Ian Curd, Global Consumer Data Director at Diageo, Martin Kihn, SVP Strategy, Marketing Cloud at Salesforce, Jackie Rousseau-Anderson, Chief Customer Officer at BlueConic, and Chris Thomson, Account Director, Strategic Finance Accounts at Treasure Data, to talk all things CDPs and why now is the time to dive into this complex technology.

Bidding to value isn’t new. In fact, a lot of advertisers have been doing it for many years. Where an advertiser is supplying revenue data directly to the platform, such as revenue from a tag or linked ecommerce data from Google Analytics, value bidding is already taking place. However, for businesses with more complex or longer sales cycles, or driving multiple channels of interaction with customers, understanding value can be an arduous and complex task.

Use value-based bidding to maximize ROI.

In a nutshell, when you use bid strategies in your media buying platforms, the main difference between a Target CPA (cost per acquisition) and a Target ROAS (return on ad spend) bidding strategy is that while Target CPA adjusts your campaign bids to help you meet a predefined cost per conversion goal, Target ROAS adjusts bids to help you maximize the value of conversions you’re receiving as a result of your advertising, and thus focuses on ROI. 

For Google Ads and the new Search Ads 360 in particular, Google has been clear about the fact that CPA bidding or bidding for conversions is limiting the ability of bidding algorithms to eke out performance, as you are assuming that all customers that interact with ads are bringing in the same business value. 

However, we all know that this is not the case. Customers come in all shapes and sizes; some will take longer to make decisions to purchase or interact with your business, some are going to be customers interested in smaller purchases, while others still will be looking at larger purchases or longer sales cycles. This can also become even more complex when customer touchpoints move from online to offline, such as an outbound call center. 

It wouldn’t make much sense to bid for all of these customers with the same value logic. By focusing on segments of customers based on the value they would bring to us, we can maximize our return on our ad spend. This is especially true for B2B or subscription businesses, where not all prospective clients are equal. 

The complexity of value-based bidding only needs to be as complex as you need it to be for your business, but the level and complexity of the data you are sending to your performance platform will provide you with much more robust reporting metrics, and more data for bidding algorithms to get things done.

As we can see, as we move up the complexity of our bidding goal, moving away from clicks/conversions to value and then profit, we need to supply the platform with less proxy metrics, and more revenue and value data. At the most mature stage, the ultimate goal for businesses is to send customer lifetime value data to the platforms to enable automated bidding and to predict future customer buying behavior based on their previous purchasing patterns.

Test and set up value-based bidding using proxy metrics.

For direct sales and subscription businesses, value-based bidding would of course involve simply passing back the value of the sale or rolling subscription back to the platform as an offline conversion, for example in Campaign Manager or Google Ads. However, if your marketing is targeted towards lead generation and longer sales cycles, bidding for value becomes slightly more complex, requiring the use of proxy value metrics. 

For example, let’s say that you have four stages within a typical sales journey, all trackable via conversion tags or Google Analytics, or perhaps via integration with CRM as an offline conversion. It could look like this:

Lead Submitted (25%) → Marketing Qualified Lead (20%) → Sales Qualified Lead (15%) → Closed Deal 

We need to work backwards from the Closed Deal value, to assign a value to a Lead submission:

Closed Deal $1000 → SQL $150 → MQL $30→  Lead Submitted $7.50

Given that a Closed Deal is worth $1000 in this example, we divide each subsequent stage by the prior stage conversion rate.

We can now understand the value of the first conversion point in the customer sales cycle and assign a value to the lead submission, then perhaps do the same for other conversion points on your site (for example, phone calls or “contact us” forms). These values can then be assigned to our bid strategies to assign the real value of customers to your business. Remember, machine learning is only as useful as the information that is being supplied to it!

Once you have values assigned to conversion points, you can use features such as Custom Columns in Search Ads 360 or Google Ads to add these values for your automated ROAS bid strategies, then let the platform algorithm do all the hard work with this new information. 

Look ahead to predicted lifetime value.

Of course, the ultimate goal we should seek with bidding in performance media is to add more of a predictive value to our target, so that the bid strategy is able to bid on keywords that are likely to drive longer lifetime value, rather than one-off purchases, short-term subscribers or low value B2B customers. This can be done by adding predictive intelligence to our bidding platform, and involves integration of CRM with a data platform and machine learning tool, such as Google BigQuery and BQML. 

You can then export these predicted values to your platform of choice as offline conversion data, and point the bid strategy at this particular goal to maximize, which in this case predicts lifetime value. This is where we think all marketers should aspire to be and plan towards, and it’s something we bring up often with clients as an important horizon goal to have with the future of their first-party data. 

Customer value-based bidding, combined with media platforms bidding algorithms, will help you monitor the real impact of advertising on your business and make the right decisions to develop growth strategies, ultimately allowing you to capture the customers that generate the most value, and those that matter most. Again, the data you share with platform algorithms is a crucial factor in competitive success, and unlocking insights related to value will prove crucial to brands looking to improve performance within an intensely competitive digital landscape.

When it comes to the privacy arena, what trends are you seeing agnostically?

Privacy is one of the fastest changing and most complex realms in the digital space, even more so than Web3. On top of that, privacy is an ever-present undercurrent—ongoing in everything that we do. With a plethora of global and regional players involved—the tech sector, regulatory bodies, public opinion—we can safely say there’s a complex interaction at play, which makes coming up with any long-term prediction or silver bullet solution practically impossible. As a consequence, our waterways can quickly go from clear to muddy. What follows is a sense of fear, uncertainty and doubt among many companies. 

In working with companies across the board, we still see a lot of confusion around technical terminology, with partners raising questions such as, “What is personal data compared to Personally Identifiable Information (PII)?” To be frank, we believe this is in part driven by clickbait. Headlines propagating that “GA is illegal” cause unnecessary confusion and concern, when the fact of the matter is that Google’s GA4 as a product has gone through a massive rebuild from the ground up to address and tackle the issues in question. As a baseline trend, we’re receiving more and more questions about privacy matters with regards to all products in the digital marketing ecosystem—and we welcome them with open arms, because we’re here to help solve the riddle. 

How are you helping clients navigate this new, data-focused advertising landscape?

Our objective is to help our partners take proper control of and ownership over their data collection and activation. Therefore, we first focus on basic data hygiene, conducting health checks and audits. It’s very important to know what your company has in store, so we ask questions like what data is collected, which cookies are set, how is the collected data used, and who else is getting the data of your users? Creating a graph of 3P consumers and beyond is complex and thus requires high levels of scrutiny. 

Though the third-party cookie deprecation has been pushed back until at least late 2025, we don’t like to wait around and carry on in the same way we have always done. Rather, we’re embracing a first-class, first-party and privacy-first strategy, for instance by helping companies migrate to GA4—because we see no reasons for taking a reactive approach. We make sure our partners get on the front foot as fast and efficiently as possible, with a strong emphasis on automation. When you’re working with large data volumes, you can’t rely on human-centric processes to manage compliance. For instance, we have implemented automated machine learning as part of the data pipelines in order to prevent PII ingestion. There’s no way that a company can afford to manage a breach retrospectively or be proactive without automation—simply put, this is the most efficient way to scale. 

What are the main lessons that you have learned on this journey?

First of all, we clearly communicate to every brand we partner with to always aim for transparency, make a plan, and move beyond the minimum. Let’s be honest, the economic headwinds that we are all currently facing mean that every dollar, euro and pound spent needs to deliver a return more than ever before. As such, preserving data quality is our top priority. To give you the full scoop: everything we do to be more transparent, protect users’ privacy, and apply rigor and governance to data collection and activation is, in fact, enhancing the data quality, too. So long as you go about your data the right way, you can’t go wrong. 

Speaking of data quality, another key lesson that we have learned is to use time to the fullest. Yes, industry leaders like Google may unexpectedly push back plans, but rather than seeing this as an issue, we believe it can work to our advantage. Setting up the privacy tech for this tool is quick and easy—the hard part is changing the people and processes, which we know can take a while to get completely right. Though we expect that many brands will interpret this extension of GA360 as extra time to look around and perhaps jump ship to another technology, we believe that this is a risky strategy. Instead, we recommend our partners to take this change of plans as an opportunity to perfect, rather than a chance to pivot. There are no excuses to delay GA4 migration. It's imperative to use this time to manage change, translate data workloads, dashboards and data pipelines, and ensure all those GA360 assets become high quality GA4 assets.

Do you see gaps in performance between brands that invest in privacy and those who don’t?

There's a clear and definite advantage to taking a strong privacy-first approach to data—and companies are catching on. People are realizing (or, at least, starting to realize) that we’re not playing a zero-sum game and the exchange of data in return for personalization and better ad targeting is the data privacy transaction we all engage in—with reciprocity being the key word. Those who are best able to complete this transaction at scale will be rewarded with the best results, whereas those who continue to walk the third-party cookie path will fall behind. For some time now, we’ve been helping many of our partners run on a healthy diet of deterministic and probabilistic data and not trip over the mix of consented first-party and modeled data, and we can tell you: they are in good shape. 

Want to discuss next steps? Get in touch. 

Everything you do for privacy feeds back into your data quality, hence the opportunity to sharpen and perfect your process of migration to GA4 is one to take with both hands. It’s essential for brands to look beyond the obstacles of GA4 and work to get the best first-party data off the back of the migration.

We’re here to help make sense of changes in the privacy landscape, how to make use of GA4 data, and how such technologies can support all your marketing needs. If you have any questions with regards to GA4 migration, please reach out to growth@mediamonks.com. We’ll schedule some time to discuss any questions you may have and see how we can support your analytics needs.

Insights for this piece were contributed by Doug Hall, VP Data Services and Technology, EMEA; Julien Coquet, Director of Analytics, EMEA; Suzanne Jansen, Head of Data Strategy, EMEA; Véronique Franzen, Senior Director Business Consulting, EMEA; Jakub Otrząsek, VP Data, APAC; Sayf Sharif, VP Data, NAMER; Michael Neveu, Director of Data, NAMER; and Wenting Wang, Senior Director of Data & Analytics, UK.

In the latest episode of In a Monk’s Opinion, our SVP, Global Performance and Enterprise eCommerce Kashif Zafar sits down with special guest Holly Elliot, VP Growth Marketing at Hatch, and fellow Monks Robbie Wiedie, VP Creative Services, and Jackie Andreetta, Associate Director of eCommerce, to discuss the smart-sleeping giant’s approach to marketing and advertising and their most recent strategic decisions. Furthermore, they talk about Hatch’s partnership with Media.Monks, the value of starting a creative mission with data, the importance of keeping track of trends and meeting customers where they are, and why advertisers shouldn’t miss out on Amazon. The golden thread? Whatever you do, always test and learn. 

In case you missed it—you can watch the full conversation below.

Hatch has had creative consistency and an unquestionable brand ethos since day one, which was instantly clear to Holly when she joined the team in 2021. As any growth marketer knows, starting a new role shortly before Q4 can be quite intense, but luckily she was given a warm and well-branded welcome. However, in preparing the marketing and advertising for the upcoming holiday season, Holly discovered two gaps: channels were optimized in silos and creative work was conducted based on campaigns rather than evergreen performance—in short, nothing that Media.Monks can’t solve.  

Acting as an extension of Hatch’s creative team, our main task was to make memorable content that could also perform, keeping in mind the company’s paid efforts. “We like to think of performance creative as fuel for the media engine,” says Robbie. The secret to success is starting with the data. So, our team first looked at the performance data on all the creative work to date, and then took existing assets, learned to speak the brand language, and created iterations of the top performers. Through a post-production-only methodology, our team repurposed existing creative assets into channel-specific iterations in a fast and efficient way—with the first social ads going live within a couple of weeks, before moving on to user-generated content and larger campaign stories. “We often say data is our creative director and this really holds true,” says Robbie, noting the role that insights can play in refining creative ideas.

While Hatch’s internal creative team primarily works on brand marketing, Media.Monks focuses on the performance part. “The speed at which we work with Media.Monks is so essential,” says Holly. “We’re able to test content on new platforms very quickly, which we couldn’t have done on our own.” On top of that, media learnings make the process seamless. This is crucial, as the health and wellness brand plans to focus on media diversification even more in the future. “It’s all about making sure you find new places to reach people,” says Holly, to which Robbie adds: “And that it’s based on the learnings, because you can’t argue with data.” Curious to hear what else the future of Hatch will hold, and how the brand readies itself for the upcoming holiday season? Take a look at the video above to find out more.

In the latest episode of IMO, Brianna Mersey, Media.Monks Associate Director of Data, sat down with Marc Laurent-Atthalin, VP Data & Digital Media at Reitmans, and Zamira Khamidova, Director of Data North America at Media.Monks, to discuss what it took to shift the traditional retail business into a modern data stack capable of enabling marketing use cases. Throughout the conversation, they shared insight into how to set your data foundation up for success, the importance of aligning stakeholders across the business, and the need for a “test and learn” mindset. If you missed the episode, we’ve got you covered—watch it in full below.

Reitmans was able to adopt such advanced analytic methods because the team had already established a strong foundation of data, making it easy to implement models that were more reactive to customer behavior and transaction histories. Marc and Zamira offered advice on how to select which data sources to start with (opt for rich, actionable data sets that give you lots to work with) and how to ensure that data is clean and reliable.

Marc also shared the importance of aligning with internal stakeholders to gain buy-in and chart the path forward. “It’s important to build a clear vision and strategy initially and communicate it properly, and then use data to build your case,” Marc said. By tying data objectives with business outcomes, his team was able to prioritize use cases and deliver results quickly, then test and iterate from there. For example, building a 360-degree view of the customer provided insights to drive conversion, fulfilling the key business need to propel growth.

When it comes to initiatives like these, treating your partner as an extension of your team is key. “Collaboration is hugely important,” Zamira said. “When it comes to data, there are lots of questions on the context—especially with historical data, so it was important for us to have two-way communication and hear back on any questions we had.” This level of regular communication also helps with further collaboration as the engagement evolves. 

So, where is Reitmans headed next? You’ll have to watch above to find out—including the brand’s plans around Web3, which is set to transform the brand-customer relationship even further. And if you’re eager for more insights from Media.Monks subject matter experts and our partners, mark your calendar for the next episode of IMO later this month.

What is A/B testing?

In a digital environment where everything can be tracked and measured, testing your strategies is a no brainer. Content strategy plays a key role in driving organic traffic and converting sales, so by leveraging and optimizing your content, you can increase the chances of shoppers purchasing your product over your competitors. A/B testing is one of a handful of the best practices you can use to enhance content on the platform.

Also known as split testing, A/B testing on Amazon is a method of determining the best-performing product listing variation. By comparing two versions of the same content, you can identify the exact element that is driving purchase-ready shoppers to act, taking into account metrics such as conversion rate, sessions, and total sales. That being said, you should have a strong understanding of your current metrics, performance and challenges before building an A/B testing plan. This will allow you to have a solid base foundation before making any changes.

Start by running an A/B test experiment on Amazon.

Amazon launched its own A/B testing tool in 2019 called Manage Your Experiments, which allows US brand owners to test two variations of one product listing element. As a brand owner wanting to test different A+ content elements, you must have eligible ASINs, otherwise they will not be displayed in the A/B test experience. To be eligible, ASINs must belong to your brand, and they must have high enough traffic in their respective categories to determine content winners with confidence. 

Keep best practices for A/B testing in mind.

By following a few tips, you can begin your test experiment with confidence. Here are some recommendations:

• Strategically create a hypothesis to learn something from the experiment regardless of the outcomes.
• To get a larger sample size, experiment on high-traffic ASINs.
• Stick to making one change at a time to avoid confusion on which variables influenced the experiment outcomes. 
• Don’t stop early – you can run experiments for four, six, eight or ten weeks to ensure accurate results.

Get to know the elements of A/B testing.

Nearly any element of your A+ content can be A/B tested. However, you should use your own judgment to determine what to test based on your current performance. Consider working with an Amazon Ads Partner to strategically outline the testing method. Some variation ideas for your A/B experiment: 

• Utilize a comparison chart.
• Rearrange and update your modules and images.
• Present the same images and text using a different module layout.
• Include lifestyle images.
• Highlight one set of product features versus a different set.
• Add your brand name to the product title.
• Use different headlines to engage and motivate shoppers to learn more about your product.

Keep in mind that your experimental content should differ from the existing content, otherwise it is less likely to affect consumer behavior and you may not be able to confidently determine a winning variation. The key to a unified branding strategy is consistency, which applies to your color scheme, icons, layout, and even text fonts.

Wait for optimal data.

Patience is key here as most tests need several weeks to gather enough data to determine a winning variation. While Amazon recommends testing for eight to ten weeks, you can adjust the schedule or even turn off the test while it is running. You will start to see data within one or two weeks, although this preliminary data is not representative of the true impact of your experiment. Be sure to let your experiment run its full course before interpreting the results and making a decision. 

Gauge the effectiveness of your experiment.

While launching new marketing initiatives on Amazon can lead to an increase in traffic, you should know how to gauge the effectiveness of each initiative to dial in on what works best. 

Once your A/B test ends, you will get the following from Amazon: 

• Recommendations on which content variation is more effective
• A confidence level of the recommendations
• A confidence interval of likely outcomes from that content
• Estimated 12-month impact on sales

These insights will highlight the most effective content for your product detail pages to boost total sales and conversions for your Amazon products. By learning from these experiments, you can optimize and improve your other product listings. You might find it beneficial to run experiments during different seasonal periods as this will provide you with a better understanding of your consumers and their expectations. 

When analyzing your results, be sure to keep your audience in mind to make the most informed decision. Although it takes time and patience to run A/B tests, the optimization is worth it if you want to dominate your market. Happy testing and selling!