How to Buy Martech in 2024? Keys to Navigating a Changing Market
Posted: Thu Dec 05, 2024 6:47 am
The marketing technology market has never been so dynamic and complex for companies to understand. Between the proliferation of solutions, technological innovations such as generative AI, data governance issues and the growing need to orchestrate seamless customer journeys across a multitude of channels, it is difficult to navigate and make the right choices.
Deploying an effective multichannel marketing strategy has azerbaijan whatsapp number data 5 million become essential but raises many challenges. This article gives you some keys to buying martech in an informed way in 2024 and successfully transforming towards a 360° marketing approach .
Article_Martech_Hero
Summary
An increasingly fragmented martech market
The emergence of new stack architecture models
Reconciling agility and data governance
Focus on interoperability and openness
Involve business and technical teams
An increasingly fragmented martech market
The marketing technology landscape has never been larger or more diverse. According to Scott Brinker’s latest edition of “Marketing Technology Landscape,” the number of martech solutions will have surpassed 11,000 by 2023 , growing 11% year-over-year (and 7,258% since 2011!). And this trend shows no signs of slowing down.
This fragmentation is explained by the combination of several factors:
The continued emergence of new players positioned on emerging trends such as generative AI, conversational marketing, real-time personalization or data governance. These startups are challenging established players with innovative approaches.
The "long tail" of specialized solutions that address very specific business needs . We thus find tools verticalized by industry (retail, banking, media, etc.), by region (Europe, Asia, etc.) or by functionality (SEO, retargeting, AB testing, etc.). These niche players complement the large generalist platforms on the market.
A consolidation movement that sees market leaders multiplying acquisitions to enrich their offers . Salesforce, Adobe, Oracle and SAP have spent billions in recent years to build vast integrated ecosystems covering all marketing needs.
The rise of web giants like Amazon, Google or Meta who are investing massively in advertising and digital marketing. Their mastery of data and their large audience bases make them essential players.
This market fragmentation is also accentuated by the increasing overlap of functionalities between the different categories of tools . Many solutions, even if they are positioned on distinct segments, actually offer partially overlapping capabilities. This creates confusion for companies that must understand which combination of tools is best suited to their needs, while avoiding costly redundancies. In addition, these functional overlaps can also generate integration and data consistency problems when several tools are used in parallel, with customer information scattered across multiple systems. This is an additional factor of complexity for companies looking to rationalize and optimize their martech stack.
This situation makes the market particularly dynamic but also complex to understand for companies. It becomes difficult to compare offers, to evaluate the real benefits of the different solutions and to assemble them in a coherent way. The risk is to end up with a multitude of disparate tools, which do not communicate with each other and generate hidden costs.
To find their way, companies must define a martech strategy aligned with their business challenges and set up governance involving the various stakeholders (marketing, IT, data, etc.). The challenge is to build a stack adapted to their needs, by finding the right balance between generalist and best-of-breed solutions, while ensuring control of costs and complexity. A major challenge in a constantly evolving market.
Tip: To choose your marketing solution wisely, write a specification (RFP) detailing your functional and technical needs. This will allow you to compare the different offers on the market according to objective criteria. Discover our advice for writing an effective RFP .
Article_Martech_Model-Stack
The emergence of new stack architecture models
Faced with the increasing complexity of the martech landscape, companies are forced to rethink the architecture of their marketing stack. The traditional model, based on a single, centralized platform, is showing its limits. It is often too rigid to adapt to rapid market changes and the specific needs of different businesses.
To gain agility and flexibility, new architectural models are emerging:
Modular architecture that consists of assembling independent software bricks, communicating with each other via APIs. Each module can be freely chosen according to its performance and easily replaced without impacting the rest of the system. This is the "best-of-breed" model that allows you to get the best out of each solution.
The headless architecture that separates the frontend part (the user interface) from the backend part (data and process management). This allows you to connect any interface (website, mobile application, connected object, etc.) to a single base of marketing services. An approach that is particularly suited to omnichannel.
Cloud architecture , which consists of moving all or part of its marketing system to the cloud to gain scalability and speed of deployment. SaaS (Software as a Service) solutions allow simplified access to innovations while reducing infrastructure costs.
The data-centric architecture that places data at the heart of the system. All applications are interconnected via a data management platform that creates a single customer repository. The Data Warehouse is the cornerstone of this approach by allowing the collection, unification and activation of customer data.
The common point of these new architectures is to emphasize interoperability and the fluid circulation of data. The challenge is to break down the silos between the different tools in the stack to create a coherent and agile ecosystem. This involves defining exchange standards, setting up API governance and ensuring data quality throughout the chain.
The choice of the architecture model will depend on the maturity of the company, its business challenges and its IT constraints. But one thing is certain: the ability to evolve its stack quickly and integrate new solutions will be a key differentiator. Companies that will be able to take advantage of these new architectures to activate their customer data will be best equipped to meet the growing expectations for personalization and engagement.
Reconciling agility and data governance
Agility has become a must for companies that want to remain competitive in a constantly changing environment. This translates into the need to quickly adapt marketing campaigns, test new approaches, and integrate new tools into the stack. But this quest for agility must not come at the expense of data governance.
Indeed, data is at the heart of modern marketing strategies. It allows us to understand customer behaviors and expectations, personalize messages and optimize journeys. But to be usable, data must be reliable, consistent and secure. This is the challenge of data governance, which aims to define the rules and processes for managing data throughout its life cycle.
However, the proliferation of data sources (CRM, website, mobile applications, social networks, etc.) and marketing solutions makes this governance increasingly complex. Each tool has its own formats and data models, which creates silos and inconsistencies. Not to mention the regulatory issues related to the protection of personal data (GDPR, CCPA, etc.) which require increased traceability and control.
To reconcile agility and data governance, companies must adopt a global and unified approach to their marketing stack. This involves:
The implementation of a customer data management platform that collects, cleans and reconciles data from different sources. The RCU becomes the single repository of customer truth and feeds all the tools in the stack. The CDP makes it possible to identify unified customer profiles, segment audiences and orchestrate cross-channel campaigns.
The definition of a common data model that applies to all solutions in the stack. This model must be designed to meet the needs of different businesses (marketing, sales, customer service, etc.) while respecting market standards. It must be scalable to easily integrate new data sources and new use cases.
Implementing API governance to facilitate the integration and interoperability of different tools. APIs allow systems to communicate with each other and automate data flows. But they must be documented, secured and monitored to guarantee quality of service and data protection.
Adopting a privacy by design approach that integrates the protection of personal data from the design of solutions and processing. This involves mapping data flows, defining retention periods, collecting consents and allowing individuals to exercise their rights (access, rectification, portability, oblivion, etc.).
Raising awareness and training teams on the challenges of data governance. Because beyond tools and processes, it is a whole data culture that must be established in the company. Each employee must understand the importance of data quality and protection and adopt the right reflexes on a daily basis.
As data becomes an increasingly strategic asset for businesses, data governance is becoming an essential element in maintaining consumer trust. With customers increasingly attentive to how their personal information is used, rigorous governance, focused on quality, security and regulatory compliance, is essential. It reassures consumers that the company is serious about treating their data. Conversely, poor governance can quickly erode trust and reputation.
But beyond this issue of trust, well-thought-out data governance can also become a real lever for performance for the company. By adopting an agile and unified approach, companies can fully exploit the potential of their customer data while respecting regulatory and ethical constraints. They thus gain in operational efficiency, in the ability to personalize journeys and in customer engagement. Far from being a hindrance, data governance proves to be a powerful lever for marketing activation and business performance when it is implemented intelligently.
Focus on interoperability and openness
In this context, interoperability and openness have become key issues for companies. It is no longer enough to stack best-of-breed solutions; they must also be able to communicate with each other smoothly and securely.
Interoperability refers to the ability of systems and applications to exchange data and work together without any special effort from users. It is based on the use of standardized formats, protocols, and APIs that allow the different components of the stack to understand each other and collaborate.
Openness, on the other hand, refers to the ability to easily integrate new tools and data sources into one's information system. It assumes a modular and extensible architecture, based on pre-integrated APIs and connectors.
By focusing on interoperability and openness, companies can:
Avoid data silos and inconsistencies : by making their different marketing tools (CRM, marketing automation, CDP, BI, etc.) communicate, they ensure that customer data is synchronized and up to date throughout the system. They can thus build a 360° view of their customers and deliver consistent messages across all channels.
Gain agility and time-to-market : thanks to standardized APIs and connectors, they can quickly integrate new solutions and new functionalities into their stack. They can thus adapt to market developments and customer expectations, without being slowed down by compatibility or data migration issues.
Reduce costs and complexity : by opting for interoperable and open solutions, they limit the specific developments and resources required to make their different tools communicate. They can thus focus on their core business and on optimizing their campaigns, without worrying about the underlying technical infrastructure.
Foster innovation and differentiation : by having access to a large ecosystem of partners and third-party solutions, they can enrich their marketing stack with innovative and differentiating features. They can thus test new acquisition, conversion and loyalty levers, and stand out from the competition.
To take full advantage of interoperability and openness, however, companies must address several challenges:
Define a platform strategy that articulates all the building blocks of the stack around a common base of data and processes. This platform must be designed to evolve over time and integrate new channels and new points of contact.
Implement API governance to secure data exchanges and ensure quality of service. This involves documenting APIs, managing versions, controlling access and monitoring performance.
Develop a culture of open innovation internally and externally. Internally, this involves training teams in new tools and best practices for interoperability. Externally, this involves establishing strategic partnerships with publishers, integrators and innovative start-ups.
Ensuring the protection of personal data in a multi-stakeholder environment. With the GDPR and other privacy regulations, companies must be able to trace data flows, collect consents and guarantee the security and confidentiality of the information exchanged.
By addressing these challenges, companies are empowered to build an agile, scalable, and customer-centric marketing stack. They can thus gain operational efficiency, responsiveness, and personalized journeys. Interoperability and openness are not an end in themselves, but a means to deliver more value to customers and strengthen their engagement over time.
This is the challenge of a platform like Actito, which allows you to operate all channels and customer data in a single intuitive and user-friendly interface. Thanks to its APIs and pre-integrated connectors, Actito easily interfaces with other solutions on the market (CRM, e-commerce, advertising servers, etc.) to orchestrate personalized cross-channel journeys. It thus allows companies to gain agility and performance, while maintaining control of their data.
Deploying an effective multichannel marketing strategy has azerbaijan whatsapp number data 5 million become essential but raises many challenges. This article gives you some keys to buying martech in an informed way in 2024 and successfully transforming towards a 360° marketing approach .
Article_Martech_Hero
Summary
An increasingly fragmented martech market
The emergence of new stack architecture models
Reconciling agility and data governance
Focus on interoperability and openness
Involve business and technical teams
An increasingly fragmented martech market
The marketing technology landscape has never been larger or more diverse. According to Scott Brinker’s latest edition of “Marketing Technology Landscape,” the number of martech solutions will have surpassed 11,000 by 2023 , growing 11% year-over-year (and 7,258% since 2011!). And this trend shows no signs of slowing down.
This fragmentation is explained by the combination of several factors:
The continued emergence of new players positioned on emerging trends such as generative AI, conversational marketing, real-time personalization or data governance. These startups are challenging established players with innovative approaches.
The "long tail" of specialized solutions that address very specific business needs . We thus find tools verticalized by industry (retail, banking, media, etc.), by region (Europe, Asia, etc.) or by functionality (SEO, retargeting, AB testing, etc.). These niche players complement the large generalist platforms on the market.
A consolidation movement that sees market leaders multiplying acquisitions to enrich their offers . Salesforce, Adobe, Oracle and SAP have spent billions in recent years to build vast integrated ecosystems covering all marketing needs.
The rise of web giants like Amazon, Google or Meta who are investing massively in advertising and digital marketing. Their mastery of data and their large audience bases make them essential players.
This market fragmentation is also accentuated by the increasing overlap of functionalities between the different categories of tools . Many solutions, even if they are positioned on distinct segments, actually offer partially overlapping capabilities. This creates confusion for companies that must understand which combination of tools is best suited to their needs, while avoiding costly redundancies. In addition, these functional overlaps can also generate integration and data consistency problems when several tools are used in parallel, with customer information scattered across multiple systems. This is an additional factor of complexity for companies looking to rationalize and optimize their martech stack.
This situation makes the market particularly dynamic but also complex to understand for companies. It becomes difficult to compare offers, to evaluate the real benefits of the different solutions and to assemble them in a coherent way. The risk is to end up with a multitude of disparate tools, which do not communicate with each other and generate hidden costs.
To find their way, companies must define a martech strategy aligned with their business challenges and set up governance involving the various stakeholders (marketing, IT, data, etc.). The challenge is to build a stack adapted to their needs, by finding the right balance between generalist and best-of-breed solutions, while ensuring control of costs and complexity. A major challenge in a constantly evolving market.
Tip: To choose your marketing solution wisely, write a specification (RFP) detailing your functional and technical needs. This will allow you to compare the different offers on the market according to objective criteria. Discover our advice for writing an effective RFP .
Article_Martech_Model-Stack
The emergence of new stack architecture models
Faced with the increasing complexity of the martech landscape, companies are forced to rethink the architecture of their marketing stack. The traditional model, based on a single, centralized platform, is showing its limits. It is often too rigid to adapt to rapid market changes and the specific needs of different businesses.
To gain agility and flexibility, new architectural models are emerging:
Modular architecture that consists of assembling independent software bricks, communicating with each other via APIs. Each module can be freely chosen according to its performance and easily replaced without impacting the rest of the system. This is the "best-of-breed" model that allows you to get the best out of each solution.
The headless architecture that separates the frontend part (the user interface) from the backend part (data and process management). This allows you to connect any interface (website, mobile application, connected object, etc.) to a single base of marketing services. An approach that is particularly suited to omnichannel.
Cloud architecture , which consists of moving all or part of its marketing system to the cloud to gain scalability and speed of deployment. SaaS (Software as a Service) solutions allow simplified access to innovations while reducing infrastructure costs.
The data-centric architecture that places data at the heart of the system. All applications are interconnected via a data management platform that creates a single customer repository. The Data Warehouse is the cornerstone of this approach by allowing the collection, unification and activation of customer data.
The common point of these new architectures is to emphasize interoperability and the fluid circulation of data. The challenge is to break down the silos between the different tools in the stack to create a coherent and agile ecosystem. This involves defining exchange standards, setting up API governance and ensuring data quality throughout the chain.
The choice of the architecture model will depend on the maturity of the company, its business challenges and its IT constraints. But one thing is certain: the ability to evolve its stack quickly and integrate new solutions will be a key differentiator. Companies that will be able to take advantage of these new architectures to activate their customer data will be best equipped to meet the growing expectations for personalization and engagement.
Reconciling agility and data governance
Agility has become a must for companies that want to remain competitive in a constantly changing environment. This translates into the need to quickly adapt marketing campaigns, test new approaches, and integrate new tools into the stack. But this quest for agility must not come at the expense of data governance.
Indeed, data is at the heart of modern marketing strategies. It allows us to understand customer behaviors and expectations, personalize messages and optimize journeys. But to be usable, data must be reliable, consistent and secure. This is the challenge of data governance, which aims to define the rules and processes for managing data throughout its life cycle.
However, the proliferation of data sources (CRM, website, mobile applications, social networks, etc.) and marketing solutions makes this governance increasingly complex. Each tool has its own formats and data models, which creates silos and inconsistencies. Not to mention the regulatory issues related to the protection of personal data (GDPR, CCPA, etc.) which require increased traceability and control.
To reconcile agility and data governance, companies must adopt a global and unified approach to their marketing stack. This involves:
The implementation of a customer data management platform that collects, cleans and reconciles data from different sources. The RCU becomes the single repository of customer truth and feeds all the tools in the stack. The CDP makes it possible to identify unified customer profiles, segment audiences and orchestrate cross-channel campaigns.
The definition of a common data model that applies to all solutions in the stack. This model must be designed to meet the needs of different businesses (marketing, sales, customer service, etc.) while respecting market standards. It must be scalable to easily integrate new data sources and new use cases.
Implementing API governance to facilitate the integration and interoperability of different tools. APIs allow systems to communicate with each other and automate data flows. But they must be documented, secured and monitored to guarantee quality of service and data protection.
Adopting a privacy by design approach that integrates the protection of personal data from the design of solutions and processing. This involves mapping data flows, defining retention periods, collecting consents and allowing individuals to exercise their rights (access, rectification, portability, oblivion, etc.).
Raising awareness and training teams on the challenges of data governance. Because beyond tools and processes, it is a whole data culture that must be established in the company. Each employee must understand the importance of data quality and protection and adopt the right reflexes on a daily basis.
As data becomes an increasingly strategic asset for businesses, data governance is becoming an essential element in maintaining consumer trust. With customers increasingly attentive to how their personal information is used, rigorous governance, focused on quality, security and regulatory compliance, is essential. It reassures consumers that the company is serious about treating their data. Conversely, poor governance can quickly erode trust and reputation.
But beyond this issue of trust, well-thought-out data governance can also become a real lever for performance for the company. By adopting an agile and unified approach, companies can fully exploit the potential of their customer data while respecting regulatory and ethical constraints. They thus gain in operational efficiency, in the ability to personalize journeys and in customer engagement. Far from being a hindrance, data governance proves to be a powerful lever for marketing activation and business performance when it is implemented intelligently.
Focus on interoperability and openness
In this context, interoperability and openness have become key issues for companies. It is no longer enough to stack best-of-breed solutions; they must also be able to communicate with each other smoothly and securely.
Interoperability refers to the ability of systems and applications to exchange data and work together without any special effort from users. It is based on the use of standardized formats, protocols, and APIs that allow the different components of the stack to understand each other and collaborate.
Openness, on the other hand, refers to the ability to easily integrate new tools and data sources into one's information system. It assumes a modular and extensible architecture, based on pre-integrated APIs and connectors.
By focusing on interoperability and openness, companies can:
Avoid data silos and inconsistencies : by making their different marketing tools (CRM, marketing automation, CDP, BI, etc.) communicate, they ensure that customer data is synchronized and up to date throughout the system. They can thus build a 360° view of their customers and deliver consistent messages across all channels.
Gain agility and time-to-market : thanks to standardized APIs and connectors, they can quickly integrate new solutions and new functionalities into their stack. They can thus adapt to market developments and customer expectations, without being slowed down by compatibility or data migration issues.
Reduce costs and complexity : by opting for interoperable and open solutions, they limit the specific developments and resources required to make their different tools communicate. They can thus focus on their core business and on optimizing their campaigns, without worrying about the underlying technical infrastructure.
Foster innovation and differentiation : by having access to a large ecosystem of partners and third-party solutions, they can enrich their marketing stack with innovative and differentiating features. They can thus test new acquisition, conversion and loyalty levers, and stand out from the competition.
To take full advantage of interoperability and openness, however, companies must address several challenges:
Define a platform strategy that articulates all the building blocks of the stack around a common base of data and processes. This platform must be designed to evolve over time and integrate new channels and new points of contact.
Implement API governance to secure data exchanges and ensure quality of service. This involves documenting APIs, managing versions, controlling access and monitoring performance.
Develop a culture of open innovation internally and externally. Internally, this involves training teams in new tools and best practices for interoperability. Externally, this involves establishing strategic partnerships with publishers, integrators and innovative start-ups.
Ensuring the protection of personal data in a multi-stakeholder environment. With the GDPR and other privacy regulations, companies must be able to trace data flows, collect consents and guarantee the security and confidentiality of the information exchanged.
By addressing these challenges, companies are empowered to build an agile, scalable, and customer-centric marketing stack. They can thus gain operational efficiency, responsiveness, and personalized journeys. Interoperability and openness are not an end in themselves, but a means to deliver more value to customers and strengthen their engagement over time.
This is the challenge of a platform like Actito, which allows you to operate all channels and customer data in a single intuitive and user-friendly interface. Thanks to its APIs and pre-integrated connectors, Actito easily interfaces with other solutions on the market (CRM, e-commerce, advertising servers, etc.) to orchestrate personalized cross-channel journeys. It thus allows companies to gain agility and performance, while maintaining control of their data.