Now, let’s dive in!
Today, many organizations have an excessive number of technological tools, yet that feeling persists, and desired goals are not being achieved. Was the root cause of this dissatisfaction poor or low-quality solutions or was the real issue investing in tools that didn’t align with actual needs and failing to build an architecture that integrates well with existing solutions?
Let’s take a moment to recall March 2020: the time when we were suddenly locked at home for months, fulfilling all our shopping and social needs online, and the entire business world shifted to remote work. The pandemic didn’t just disrupt daily life; it fundamentally altered customer behavior and the core dynamics of the business world. This transformation triggered an irreversible wave of digitalization. In fact, during this period, we achieved e-commerce growth figures in a year that were expected to take 5-6 years to achieve.
Pandemic-infused deep changes in customer behavior not only evolved the technological needs of brands but also triggered an explosion in the number of CX and MarTech solutions available in the market. By 2023–2024, this growth peaked, reaching historic levels. In just a single year, the number of MarTech tools on the market increased by 27.8% according to “The State of MarTech Report” by @chiefmartec Mr. Scott Brinker & Mr. Frans Riemersma. This wasn’t just growth—it was a revolutionary leap for the industry. While each of these tools promised greater efficiency and ease, they also created confusion for companies trying to decide which to adopt.
Brands tried to integrate numerous tools into their architectures to better understand their customers and deliver the best experiences. However, this fast & “carpe diem approach” led to confusion about how these tools would work together, which strategies should guide their use, and ultimately how they would contribute to customer experience as well as to the company bottom-line. This resulted in “technology fatigue” for companies: too many tools, with little efficiency. Data by the CMO Survey “Managing the Challenges of Marketing Technology, Privacy and Marketplace Threats – Highlights and Insights Report Fall 2024” shows that despite companies investing heavily in tools, less than half of these tools are actively used. Even more striking, a significant 5-point drop in usage rates was observed over the short period between the spring and fall of 2024. In short, technology investments made with high expectations failed to deliver the anticipated results and slowly got abandoned, turning brand technology architecture into a graveyard.
2025 will be a critical year, not only for revisiting existing architectures but also for creating a strategic roadmap from within this chaos. Companies must move beyond just acquiring technology and start thinking about how to use these tools efficiently and ensure they work seamlessly together, not only from technology and integrations perspective but also from human resources skillsets and departmentalization perspectives. At this point, partnering with an expert consultant is essential—not just to optimize existing solutions but also to set a long-term digital strategy in this fast-evolving technological age. Companies must seize this opportunity year to view their technology investments not just as tools, but as a strategic advantage.
If you’re unsure about taking the right steps or don’t know where to begin, don’t hesitate to reach out to our expert team. Click here to ask for a quick call.
Once upon a time, “Big Data” was seen as the golden key to digital transformation. Every organization rushed to collect data, diversify it, and bring it together with various datawarehouse and/or master data management solutions. Most brands reached a certain level in this pursuit, but at this point, the data lies dormant, like a drunken sailor in the corner, perhaps used only for creating campaign ideas passively but not for any real-time activation and companies are left asking, “What do we do with the drunken sailor?”.
In other words, we’ve gathered the data, but now it’s just staring at us, and we’re staring at it. However, big data was just the starting point, and the real question was how to redefine “what really data is” and to use this “new data” in a more meaningful way. Try asking these questions and answer them honestly by yourself:
Today, the main challenge companies face isn’t just “Big Data,” but “Big Ops.” as perfectly named by @chiefmartec Mr. Scott Brinker. From our perspective, “Big Ops” is not just about storing or managing massive data, it’s about the vast scale and complexity of applications, automations, AI algorithms, processes, and human interactions working simultaneously on the data in digital businesses. This makes managing and using systems difficult for both technical teams and business units.
Several core issues lie behind these challenges:
In conclusion, solving “Big Ops” challenges is not just about storing or managing big data, but making that data usable in daily operations. To achieve this, existing systems need to be restructured for greater flexibility, and operational processes must be easier for business units to manage. AI-powered models and more flexible, dynamic workflows that business teams can use as part of their daily routines will help companies both optimize processes and unlock the true potential of their data.
Companies need to think about how to use data in a more meaningful way and make their processes more flexible, rather than just collecting and mining data. However, it’s important to remember that this transformation requires not only technological change but also a cultural shift. To “wake the drunken sailor” and unlock the potential value, it’s crucial to move forward with the right strategy and guidance. If you require assistance in navigating this process, our team of experienced consultants is here to provide the support and guidance you need. Click here to ask for a quick call.
Today, contact centers are much more complex than before. Inquiries from various channels, processes managed by diverse tools, and representatives striving to understand different customer needs create a dynamic structure that’s like managing an orchestra. However, now, AI is stepping onto the stage, not only as the conductor but also as the composer of the symphony itself.
Generative AI and natural language processing (NLP) technologies are revolutionizing self-service. Now, regardless of the channel, the first point of contact for customers will be an AI solution. But this isn’t just a chatbot; it’s a solution that understands the customer’s emotions, responds to questions in context, and represents the brand’s voice in every interaction. GenAI-powered IVR systems, first response automations, and chatbots provide not only faster responses but also eliminate wait times and offer a more human-like, effective experience.
This transformation isn’t limited to self-service tools. In representative-assisted processes, AI acts as an invisible assistant to contact center agents. Sentiment analysis can now be done in real-time during voice calls, CSAT predictions can be generated, and the most appropriate response suggestions can be offered. What is even more impressive is that past customer interactions are analyzed, and a quick, concise summary is provided to the representative. This way, representatives can focus on the customer instead of getting lost in the details, offering more effective and empathetic service.
At this point, the power of AI is not only limited to operational improvements. Reporting and quality management have been taken to a whole new level. Traditional analysis has been replaced by deep AI analysis covering entire calls and conversations. These analyses don’t just build a more transparent and measurable contact center operation; they also provide valuable insights for product development, marketing, and sales strategies. AI processes data from customer interactions to reveal which products are in higher demand, which messages resonate most, and which channels are more effective. At the same time, more targeted training programs aimed at improving the performance of the agents can also be organized based on these analyses.
AI also takes proactive customer care to the next level, transforming companies into not only problem solvers but also problem preventers. By analyzing customer behavior and historical data, AI can identify potential dissatisfaction and act before issues arise. This creates an experience where customers feel the brand is “always one step ahead.”
However, this transformation is not just about technology. The impact of AI and ML in contact centers is breathing new life into business culture. Data-driven decision-making processes are not only improving operational efficiency but also enabling companies to better understand their customers and build more meaningful connections.
In conclusion, contact centers are no longer just service points—they have become the epicenter of strategic growth. With the power of AI, customers are offered a “faster,” “more personal,” and “more effective” experience. The future will be shaped by brands that work with AI to build meaningful connections with their customers. If you want to be part of this transformation and bring your contact center into the future, don’t hesitate to get in touch with our expert team. Click here to ask for a quick call.
So, the ultimate goal of a Modern Marketer today, which is online & offline customer journey orchestration needs a Conductor! Long-live “the 1st party- data layer & Brand ID 2.0”
This year, we doubled down on our last year’s Trend “BrandID 2.0” and enriched it with the “1st party data layer”. You may read last year’s note on BrandID 2.0 by clicking here.
Although the panic caused by the “cookie apocalypse” seems to be eased down lately, diminishing returns on third-party based solutions showed that having BrandID 2.0 in place (i.e. a brand ID resolution) and a unified 1st party data layer for brands have become a necessity, regardless of this crisis.
According to eMarketer, today, approximately 40% of browser traffic does not support 3rd party cookies! Addressability concerns escalate year-over-year due to signal loss caused by increasing market share of cookieless browsers, decreasing rates of consumer cookie acceptance, together with already high-impact ATT and similar frameworks…
On the other hand, connecting offline experiences into online and vice versa is much more crucial than ever, hence the only way to achieve this is to own and control your own ID mechanism. Understanding, recognizing, and meeting customer expectations at every touchpoint of the (digital & non-digital) customer experience is the key to success in today’s competitive environment. Traditional data warehouses (DWH) and analytics tools are falling short in increasingly complex digital ecosystems to collect, put meaning and activate data/signals.
The “customer 360” structure we talked about five years ago no longer meets today’s needs. We now are in search of systems that not only define the customer but also track every footprint in the journey from an anonymous user to a known customer, consolidate their behavior at every touchpoint, understand it, interpret past interactions, and predict future needs. And, besides, do this whether these are online or offline signals, transactions or footprints.
Even before reaching out to our ultimate goal of connecting online and offline customer experience seamlessly, fragmentation in the digital customer experience needs to be addressed to overcome addressability concerns and omnichannel communications needs. In the complex world of digital customer experience, the Foundational & Unified 1st Party Data Layer and BrandID2.0 (brand ID Resolution) are like the unseen conductors of an orchestra. By ensuring independent systems work in harmony, they allow brands to understand their customers and provide them with a seamless experience. But what are these concepts, and why are they so valuable?
Data Layer: The Backbone of Customer Experience
The Data Layer is a central nervous system, a.k.a. information layer that brings together all the customer data an organization collects in a meaningful and organized manner. Data from different systems, such as websites, mobile apps, CRM, analytics tools, marketing automation tools, etc. is collected here in a common language, processed, and activated.
Enhancing your data layer with a non-marketing cloud datawarehouse will also boost up the power of marketing campaigns, offers and customer experience as they’ll be enriched with data from finance, sales, customer service, etc., bringing in more customer insights to real-time decision-making process. This way, online and offline data can easily be unified, AI & ML systems may use rich sources of signals, and hence more value will be created when digital footprints & behaviors are combined with enterprise data.
Why is it Important?
1. Centralized Data Management: Marketing and CX teams can access data from various systems from a single point, reducing complexity. All applications and tools may benefit from a centralized repository rather than trying to manage everything all by themselves vertically.
2. Personalization and Analytics: The Data Layer is essential for marketing and CX teams to provide personalized experiences and make data-driven decisions, hence “share data” across applications.
3. Quick Activation of New Channels: When a new channel or tool needs to be added, the integration becomes easier and faster thanks to the data layer.
4. Ease of Maintenance: A well-organized and regularly maintained data layer allows for quick identification and resolution of issues.
In other words, the Data Layer is a fundamental building block that enables brands to effectively utilize the data they have.
BrandID 2.0 (Brand ID Resolution): The Hidden Hero of Customer 360
BrandID 2.0 (brand ID Resolution) is a technology that brings together customer data from different channels (whether be it advertising channels or contact center or a mobile app or an analytics/mining infrastructure), databases, and devices through various identifiers, creating a consistent and unified profile for each customer, without a need for a costly “master data management” project.
How Does It Work?
1. Data Merging: All traces, from cookies left on a website to logins on a mobile app, actions on a loyalty platform to a purchase in the shop floor or over a call center are merged like a chain and become accessible for all connected channels and sources.
2. Identification: These traces allow the creation of either an anonymous or identifiable customer profile. All traced identifiers, from AdTech identifiers like ID5 & UID2.0-like consortium identifiers or CTV identifiers to CRM IDs or MarTech tool IDs would be unified under a customer profile in time.
3. 360-Degree View: It offers broader insights into each customer, helping brands understand their needs better.
Why is it Important?
1. Solution to the Cookie Apocalypse: As third-party cookies and tools/services based on them turn out to be unreliable and inconsistent with diminishing ROI, if not are becoming obsolete, ID resolution allows brands to recognize and engage customers using their own data.
2. Simplifying Complexity: Data from different online and offline systems and identifiers are merged to create a single customer profile, but not a record, hence easing the way to manage data without a “master data management” approach.
3. Key to Customer 360: Without ID resolution, obtaining a holistic view of the customer is impossible.
4. Addressability: Diminishing addressability due to the last couple of years’ ID fragmentation & chaos in the AdTech/MarTech world could only be overcome by a systematic ID pool management that would elevate reach to customers.
Data Layer and ID Resolution: Stronger Together
The success of an organization depends on the harmonious operation of these two technologies. The Data Layer collects and organizes data, while BrandID 2.0 (ID Resolution) unifies and makes sense of it as well as makes it available to all sources and channels.
What Are Their Values?
1. Reliable and Clean Data: This structure can be used not only for marketing but also for customer service, loyalty programs, and advertising, which introduces the possibility of a unified customer experience for the first time.
2. Competitive Advantage: Brands that can effectively use this new “data” gain a significant advantage over their competitors by providing meaningful offers for all potential and existing customers, making real-time hyper-personalization in all channels a reality and streamlining today’s disconnected offline and online experiences.
3. Long-Term Success: Without BrandID 2.0 (ID resolution) and data layers, modern customer experience solutions cannot be sustainable.
Data layers form the backbone of today’s marketing and customer experience ecosystem. These infrastructures make it possible to not only understand the new definition of “data” but also collect, manage and use it effectively.
Nowadays, due to different data patterns, personalization, audience management, activation, and analytics processes cannot be managed effectively without a unified, well-structured and maintained data layer. Today, simply using channel-based CX and marketing tools or supporting them with AI vertically is no longer enough. The new competition is between those who can collect and use data correctly across online and offline channels to create unified customer experiences and those who cannot.
Today, brands are talking not just about omni-channel solutions, but about integrated DCX & martech ecosystems where CDPs, CRMs, DXPs, and analytics systems work together. What’s more, unifying the data created through these tools with the offline world (i.e. data) brings in abundant competitive advantage to brands. Hence, the data strategy at the center of these systems goes beyond marketing tools and creates a strong foundation across all customer touchpoints, such as call centers, advertising, and loyalty programs. However, achieving success without this foundation is not possible.
A significant portion of brands still do not have a centralized data layer, which leads to missed insights, missed opportunities, and inefficient processes.
A high-quality, reliable, and centralized data layer is a key that not only strengthens marketing performance but also empowers all customer experience processes. Taking the right steps to create a foundational & unified first-party data layer will not only help you stand out in today’s competition but also secure future success. If you’re wondering how to begin this transformation, Labrys consultants are ready to guide you. Click here to ask for a quick call.
Therefore, it should be sourced, modeled, stored, manipulated and distributed like it! The fastest roadmap to incorporate AI in marketing efforts is to focus on AI Automation and get help on building up content, either creative or functional.
This would not only remove the “copywriter-bias” of likes and dislikes of the creator person himself/herself, but also automate content creation process to be more hassle-free, compliant with content creation guidelines and optimize headcount where more content is needed to be created for ever increasing number of customer touchpoints and channels.
Labrys Modern Marketing Framework 2.0 positions “content” (production & distribution) as the glue and a horizontal process underlying true-omnichannel experience across online & offline channels. So, with this approach, some important questions to ask today would be:
– Do you have a detailed digital brand guideline as you have for offline, for not only how your logo will be used but also for all digital assets and how they’ll represent your brand?
– What is the best (and fastest) way to source and produce content for micro-moments, from a simple push message to a hyper-personalized customer offer?
– How would you test, optimize, orchestrate and analyze content?
– How long does it take to produce each content on the omnichannel experience you create and, if possible, would you model and automate content creation, at least parts of it?
– How many pieces of content do you produce and where do you use it? Are there content pieces that you underestimate to be “content” (i.e. from product catalogues, media & digital assets (like push messages, emails, banners, kiosks & ATMs, etc.), to locations, error messages, user manuals & guides, etc.)
– What types of content do you produce? Videos, texts, sound/voice, etc.
– Do these content “talk” to each other, are they in sync?
– Do you know who saw what in your audience? Do you have an established mechanism to orchestrate content over the customer journey?
Now, Generative AI (GenAI) and AI Automation unlock the power to help you answer these questions and design brand specific solutions to these problems.
In today’s extremely crowded customer touchpoints environment, more and more pieces of content are required to grab the attention of any audience in each of those. Giving an audience a reason to get engaged with that same content is another burden. Achieving this in an omnichannel way is nearly choking! But, knowing what these audiences have been exposed to on which channel and acting accordingly in other channels is nearly impossible!
Or is it?
What is really required to achieve this mystical goal from the content perspective is to be able to have a clear content strategy for all touchpoints, produce multiple numbers of content and versions & adaptations of it in a fast and coordinated way. A clear tracking & measurement infrastructure must also be associated with it. In order to achieve such a scale, we need to take each single part of content creation as “a production” by itself, with a process associated with it.
AI Automation would be the most valued approach in this arena. With AI Automation, as in a production line, a clear process needs to be defined with various AI agents producing the content at each stage, whether it is a text, video or sound and delivering it to the next in the desired format. Once everything is ready, distribution of content can also happen as part of the automation flow.
This new approach would never undermine agencies and brand teams’ efforts, but an upskilling would definitely be needed to adapt to the changing environment and tools. Human value add would be inevitable in this process, especially for below:
– Prompt engineering for creating the content desired
– Choosing the right AI agents
– Budgeting the production efforts with respective to selected AI agents
– Proof-read all produced material and recycle if necessary
– Ensure the content produced is distributed from the right channel to the right audience at the right time.
– Track and analyze the results for better and optimized prompts to use next time with respect to customer responses in every touchpoint.
All-in-all, in today’s world, “branding lies in experience” that you create through various modes of content! If you’re wondering how to begin this transformation, Labrys consultants are ready to guide you. Click here to ask for a quick call.
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