In July 2023, The Economist wrote a piece entitled, ‘Your employer is (probably) unprepared for artificial intelligence.’

‘AI’s economic impact will be muted unless millions of firms beyond tech centres like Silicon Valley adopt it… it would involve the full-scale reorganisation of businesses, as well as their in-house data.’

In fact, the need for the ‘full-scale re-organisation of businesses and their data’ applies not only to AI, but to all major marketing trends. The trends I’ll cover in this ebook (click the links to skip down the page) are:

• marketing personalisation
• direct to consumer (DTC)
• sustainability
• customer loyalty
• extended reality (XR) and the metaverse
• artificial intelligence.

Trends, by definition, generate a huge amount of discussion, and are subject to a lot of experimentation by businesses. But though the above trends all hold great promise, the truth is they will remain in experimental phases and/or piecemeal deployments for years to come.

For the promise to be fulfilled, brands will first need to modernise the software they use to manage data and run marketing initiatives. That, in short, means migrating towards composable technical architecture.

I’ve written this ebook with both technical and non-technical readers in mind.

For non-technical professionals

You will likely be accustomed to hearing terms such as ‘composable’, ‘MACH’, ‘API-first’ and so on, but you may not necessarily understand why they are important for achieving your strategic objectives.

I have provided a short explanation of composability below (and for greater detail you could browse the resources published by the MACH Alliance).

The purpose of this article, though, is not to get bogged down in semantics. The purpose is to explain why you can’t talk credibly about the near-future of marketing without understanding the underlying technology, and the implications this has for the usage of data.

For technical readers

If you’re battling for digital transformation, either in your own businesses or a client’s, you may appreciate having this insight to share with marketing and businesspeople in your network.

Also, I have often found that most technical professionals specialise in one area of the technology stack, or in one industry. So if you’re a customer experience or data specialist (for instance), you may still find interesting the sections on loyalty or sustainability – and so on.

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My personal goal for this ebook is that it will ease collaboration between technical and non-technical professionals, by improving mutual understanding.

As a marketer who’s spent most of his career in software (and recently co-founded a composable startup), I hope that I can help to bridge the gap between disciplines, in a way that people from both sides find helpful.

Composable architecture enables data-powered marketing

(Technical readers: click here to skip the explanation of composable architecture).

Composable architecture is an approach to assembling technology stacks out of multiple different pieces of software, from different vendors, who intentionally designed their software for this purpose.

This technology approach is an alternative to that pioneered by legacy technology vendors, who did not design their software to be easily interoperable. Instead, they built expansive software platforms which were designed to capture as much of a business’s technology budget as possible. Alongside ecommerce tools, therefore, a legacy ERP platform would typically contain a vast array of general functionality – finance, order management, PIM, and much more.

Integrations with legacy platforms are possible, and have always been necessary in order to help extend the brand’s technological capabilities beyond those of the ERP. As a result, an enterprise operating a legacy ERP will have a technology stack composed of multiple solutions – but where the integrations were hardcoded at great difficulty and expense.

By contrast, a composable technology vendor would typically serve a more precise business requirement with a more specialised piece of software. It then falls to the client to assemble its own optimal, ‘composable’ technology stack, based on its particular need.

The composable approach has various advantages, including being able to select best-in-class services for your needs, making individual upgrades without taking whole systems offline, reducing development times, etc.

These are all important problems to solve.

But what is less understood by non-technical people, yet critical to marketing, is the positive benefit that is gained from composable architecture: the clean flow of well-organised data between connected systems.

The reason this is critical is that truly effective marketing takes place when you combine data sources.

Consider personalisation, for instance.

A relationship feels personal when a business (or a person) combines multiple datasets in order to predict what will please you.

Compare the idea of a friend buying you a gift that you already have, versus one that they correctly predicted you’d love. Predicting what someone will love depends on having an incredibly detailed understanding of the individual – an understanding made up of many different data points.

Compare that with most personalisation efforts that we experience day to day: a tiny discount off the same product that we buy every week, or promotions for yellow clothes because we just bought a yellow t-shirt.

These can be nice touches, or sometimes they’re annoying. Either way, they don’t feel personal.

As this ebook explains, businesses actually already have much of the data needed to drive forward most of the marketing trends described above. The trouble is that their existing technical architectures were not designed to share data between them. This means that the necessary data sets cannot be easily combined and used to improve customer experiences, hone decision-making, and drive innovation.

Composable technology, and composable technical architectures, are the answer to that problem.

Composable solutions are ‘API first’. This means that they are designed in anticipation of the need to share data with other software components via APIs. Composable solution vendors, therefore, will extensively research the likely use-cases for their technology, and develop and maintain their APIs in order to help businesses further their marketing initiatives.

This innovation has progressed to the point where the API, today, will often be the part of the technology which receives the greatest investment in development time. A highly developed API could easily have tens of thousands of hours invested in it, since solution vendors today realise that the quality of their APIs is a competitive factor in the usefulness of their product.

For the last 10-15 years, brands have been experimenting around the marketing trends described in this article without composable, API-first technology. These experiments have been limited in scope, partly because brands’ incumbent technical architectures (known as ‘monolithic’ architectures – i.e., based on one central ERP) have withstood digital transformation, at least until recently.

Legacy technical architecture has its benefits. With just one software vendor providing the bulk of your technology, you have one relationship to manage and one monthly invoice (even if the invoice is rather large). Classic examples of such monolithic technology platforms are Salesforce, SAP and Adobe.

The trouble is, since monolithic systems are designed to be all-encompassing, these vendors did not anticipate the need to share data with any other systems.

As a result, experimenting around the marketing trends described in this article has historically required custom API integrations to be implemented in monolithic systems at great cost. Alternatively, these experiments have taken place in parallel environments, with little or no connectivity with the primary marketing stack.

This fragmentation is now viewed as intolerable by a growing proportion of enterprise brands. As their ambitions have increased, the costs of failing to adopt modern technical architecture – both direct cost, and opportunity cost – are simply becoming too high.

This is why the phrase ‘digital transformation’ is now considered a byword for migrating to composable architecture.

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It is worth mentioning that there is a lot of self-interested PR around the composable trend, and not all of it is applicable to every business.

Composable solutions exist on a spectrum – whereby some services are more tightly bundled than others, depending on the business requirement. To quote Ajay Nair, a senior engineer at AWS, “Building an evolvable architectural software system is a strategy, not a religion”.

Furthermore, software customers value a certain degree of bundling of services, as this simplifies and reduces the work needed to achieve the necessary dataflows between martech modules. David Meakin, Head of Partnerships & Solutions Engineering at Shopline, an ecommerce platform vendor, said…

“Composability is like striking an uncut diamond; each merchant will strike it in different ways based on their own priorities. Everyone needs some composability, no one tool does everything. But if you keep decomposing you end up with a shattered mess that provides no business value and causes a lot of headaches.”

Migrating from monolithic to fully composable architecture would be difficult and time-consuming – and in many cases involve hiring and firing teams and revising business processes. In cases where brands do manage to sever ties with their legacy ERP, the cost would initially need to be funnelled into replatforming, and consequently, a new partnership with a system integrator. The payoff – though significant in the long-term – would take a couple of years to materialise.

Indeed, composable adoption is a process that will continue beyond the next decade, and most business will need to keep some component of their legacy technology for some time.

But the bottom line is that freedom and agility with data – and therefore composable adoption – will be crucial for most enterprise brands seeking to make genuine progress around the major marketing trends.

How composable martech enables personalisation

Your CRM platform, and many of your other pieces of martech, probably have ‘personalisation’ features. These mostly tailor individual actions, pieces of content, or messaging based on past behaviours or audience segmentation.

Many customers resent this kind of targeting. 63% of the 1,000 UK consumers surveyed by CM.com, a customer service vendor, said they did not consider purchase history to be “acceptable data for brands to draw on when providing a personalised customer experience”.

More significantly, though, this is only a small component of what a customer may perceive as a personalised brand relationship.

The last ‘personalisation’ effort I experienced was Sainsbury’s, a UK grocer, discounting my breakfast cereal by £0.40 because I buy the same bag every week.

This irritated me, because I’ve been a member of this brand’s loyalty program for years. It has ample data to show that I run a car (the brand also runs fuel stations) and that I occasionally buy a nice bottle of wine, so I don’t really need to save £0.40.

The brand can also easily tell that I love cooking, and that I particularly love Italian, Indian and Japanese cuisine. On that basis, the supermarket could be recommending interesting recipes, or products that I don’t already buy, but which complement my tastes, in order to stimulate additional buying and greater brand affinity.

There are three components to getting personalisation right in a digital marketing context:

1. maintain a single view of the customer, which means adopting a single enterprise CRM
2. process all your various data sources on the customer to produce rich customer profiles
3. expose that data in commercial & customer-facing systems, so that brand relationships can be tailored based on customer lifetime value – and to create a feedback loop to further enrich the customer profile.

Maintaining a single view of the customer is relatively simple since it does not require replacing any existing technology. Rather, a new, business-wide marketing CRM (such as HubSpot) would draw on all other sources of customer data without affecting the data stored in those systems, or changing the way they operate.

Most modern CRM tools offer out-of-the-box integrations with many different pieces of martech, including legacy platforms. This would mean that a marketing manager would often be able to execute this work with little or no tech support – so the real challenge would be agreeing the project with their colleagues across the various collaborating teams.

This work needs to happen because most enterprises maintain multiple CRMs. These might include the ones bundled into your automation platform (i.e. ActiveCampaign), your ecommerce platform, and your loyalty platform. In B2B contexts it would also likely include your primary industry-specific business management software, and it could even include your recruitment CRM, since your employees could also be good customers.

Once a single enterprise CRM is in place, all customer-facing teams must agree that this will become the single source of truth on the customer, used to determine what messages are sent, and when.

This solves the problem – common to many enterprises – where every team communicates with customers independently, based on their own, blinkered view of the data.

If the brand agrees to operate around a single CRM, the customer is far more likely to receive well-orchestrated messaging, instead of various conflicting messages leading to a negative brand experience.

Unlocking true personalisation by combining different data sources

More interesting work can take place when well-organised customer data – such as in the CRM, and elsewhere – is exposed to other components in the stack.

A good case study comes from Wayfair, an online furniture retailer. The brand claims to enjoy 30% larger cart sizes, thanks to personalisation achieved through the use of microservice architecture.

This presentation on Wayfair’s composable data stack is worth a watch. It’s intended for technical audiences, but from around 7:30 to 9:00, it clearly shows the sheer number of different pieces of software that exchange data with the core database in order to personalise customer experiences.

At the centre of Wayfair’s stack is a data platform called Aerospike, which processes various data sources in real-time in order to personalise customer experiences as they’re taking place.

Aerospike draws on a customer’s past purchases, and also factors in data such as browsing behaviour trends collected from across the customer base. This allows the technology to make calculations of the probability that a certain message, search result, offer, etc. will resonate well with the customer and drive further purchasing.

Of course, Wayfair has the advantage of having built its business around a composable tech stack (and a modern data platform – see the section on artificial intelligence here). Brands without this advantage will need to add individual composable solutions around their legacy architectures in order to improve personalisation. The primary technology modules which would enable this are:

• the composable customer data platform (CDP)
• and the customer experience (CX), which would usually be a content management system (CMS), or in some cases a digital experience platform (DXP).

The role of the CDP is to build up right customer profiles which can be used to inform further comms. It does this by drawing on customer information collected at various other touchpoints. Those touchpoints could include anything from a mobile app, to the media advertising solution that you use to advertise on social networks (i.e. Smartly), to extended reality (XR) experiences in a gaming or entertainment context. As such, upgrading the CDP would likely become a priority as a brand wishes to extend its personalisation efforts into omnichannel contexts.

Similarly to the CRM, a basic CDP and CMS already exist within legacy ecommerce platforms. Peter Ross, CEO of agency Cygnet Digital explains…

“Even our largest clients are not so granular as to implement a separate solution for content, customer data and customer experience. Martech solutions are varied in the scope of what they cover; whilst you can buy dedicated CDP solutions we tend to find that most companies are well-served by the CDP inbuilt in a modern CRM.”

Indeed, composable CDPs are available in various forms: dedicated solutions (i.e., Segment, Treasure Data), and they also come bundled in with other tools such as Bloomreach (a customer engagement platform).

Generally, it should be understood that composable SaaS tools which carry out comparable work are usually not like-for-like replacements, and may be acquired with other software as part of wider strategic initiatives.

Whichever module the brand decides to prioritise, as long the additions are API-first, composable solutions, they should enhance the brand’s personalisation efforts by enhancing their agility with customer data.

How composable CX enables personalised ecommerce

In the section about the direct-to-consumer trend, I go into more detail about the CX as part of a wider discussion about the composable commerce platform.

In the context of personalisation, however, these efforts are only really helpful if they demonstrably increase sales. That means that tailoring ‘content’ published in the CX – which includes product pages, product descriptions, search results, promotions, etc. – based on individual customer profiles. It also implies maintaining a feedback loop, so that information from the CX can be used to further enrich data in the CDP.

There are different approaches to composable CX.

A pureplay composable CMS, such as Contentful or Vue Storefront, will integrate with both composable and legacy ecommerce back-ends. This would be an appropriate setup for a business which wants to personalise commerce across just a website and an app, say.

The other approach is via a digital experience platform (DXP) such as Optimizely.

The DXP is a highly optimised platform for multichannel commerce – including websites, apps, extended reality (XR), in-store tech, etc. It includes a CMS, alongside other bundled services needed to sell online – including an ecommerce back-end, CRM, personalisation tools, and search and promotions engines. It therefore brings much of the ‘all-in-one’ convenience of a bundled ecommerce platform, but with the benefit of composable architecture, in making it very easy to upgrade or deselect individual modules if you wish.

The DXP is not, however, right for every company.

This is partly due to the way the DXP publishes content. In order to publish to multiple UXs, it relies on something called ‘server-side rendering’ – where content is assembled before being distributed out to the UX. This produces a net efficiency gain across multiple UXs, but if you’re only publishing to a single website or app, it could actually be slower – particularly if that website is a content-rich experience with a lot of interactive features.

In the latter scenario, client-side rendering – where native software in the UX assembles the content – is typically faster for a single website, because the UX contains software designed for this purpose.

In either case, the nature of how your content is rendered will affect your Core Web Vitals (essentially a set of metrics related to page load speed) which impact on conversion rates and SEO.

The other issue with the DXP is that its sheer extensiveness makes it difficult and expensive to adopt, as you’ll be swapping out multiple modules at once, requiring multiple pieces of incumbent technology to be disentangled.

So, a company would require a clear business case to adopt a DXP, above and beyond the desire to achieve marketing personalisation.

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An approach with a lower barrier to entry might be a composable personalisation platform, such as Ninetailed. Such a tool would be useful for companies which want to manage personalisation across omnichannel customer journeys, such as various apps and websites. It provides no-code functionality for marketing managers to personalise various aspects of the customer experience.

Regardless of the precise solutions deployed, however, truly delivering on marketing personalisation will depend on operating composable architecture. This is the only practical way for data to be fluidly exchanged between customer data managements systems, brand-side marketing management tools, and the tools responsible for maintaining and measuring the customer experience.

Direct to consumer: a trend fuelled by composability

Selling direct to consumer is a tough business.

The core technical challenge of DTC is that you are responsible for a far greater share of the product journey from production to customer. You therefore have more opportunities to optimise the customer experience and ROI – but more to think about, and far greater exposure to risk when something goes wrong.

DTC is also typically an expensive business model, since all the direct costs fall to the brand, whereas inventory management, fulfilment and customer service are otherwise carried out by retailers. Inventory management, in particular, is a major challenge, where a large proportion of retail margins are won and lost.

DTC is discussed in two primary contexts:

1. startup consumer brands – i.e., Dollar Shave Club
2. established brands investing in direct sales – i.e., Nike (which is reporting 24% growth in direct digital sales year on year).

Nike and Dollar Shave Club are two DTC success stories, but these sit amongst a large number of less-reported failures.

Customers generally prefer buying from retailers because it’s more convenient. To make buying direct worth the hassle, brands depend on composable architecture for:

• excellent control of the customer data, in order to deliver a brand experience that more than compensates for the lost convenience of the retailer
• control and visibility throughout the demand chain and supply chain, in order to ensure successful customer experiences and to keep operational costs to a minimum.

The primary composable software components needed for direct selling are outlined here, with the ecommerce platform at the end of this list – for reasons explained below.

Product Information Management (PIM) enables DTC commerce

Retailers have historically taken responsibility for conveying product information to consumers in ecommerce settings. Brands cannot sell direct, however, without getting this data organised and exposed in the customer experience, since it fundamentally determines buying decisions. This story from Joshua Hudson of Pivotree, an ecommerce consultancy, is a useful illustration of how effective management of product data unlocks DTC commerce.

Historically, product information management has been a manual process, often partly relying on flat Excel files – which brands would have used internally and also distributed to retailers. The retailer then took responsibility for getting that information organised and online. Brands and retailers alike, however, are now migrating towards composable PIM.

Bruce Wright, VP Sales at PIM vendor Pimberly, gives the example of Monsoon – a clothing brand and retailer, which already sold direct, but needed to rationalise this data in order to extend its DTC capability.

“They had multiple different spreadsheets for all the same products. The merchandising team, the procurement team, the international team, the marketing team – all these teams had their version of the product, because they all had their own KPIs. So often, the information that went out onto the website wasn’t quite right, and that would cause them problems.”

In Monsoon’s case, the adoption of a composable PIM was triggered by a need to create a single source of truth for all products. This, in turn, enabled the company to expand into the US market much more easily than would otherwise have been possible.

This was because a single SKU, sold on both sides of the Atlantic, would often require different product information in order to address the specifics of US ecommerce. This required a PIM which could maintain a single source of truth on one SKU, but with multiple additional layers of data which only applied in certain marketing contexts.

Similar challenges would also appear when selling via marketplaces such as Amazon which might, for example, stipulate a certain number of characters per product description, or require certain types of images. You may also require multi-layered product information for marketing localisation and personalisation, so that different images, descriptions or regulatory information can be exposed in the customer experience depending on the context of sale.

Legacy ERPs would contain a basic PIM, but in a far more basic form. These would also lack the benefits of API-first architecture that allow the data to be easily exchanged with different customer experiences and other connected systems. Realistically, no startup DTC brand would start out with a legacy ERP. An established brand going direct, however, would be able to harness major benefits from a composable PIM, beyond DTC marketing, including the ability to integrate with supply & demand chain systems.

Inriver, Salsify and PIMCore are other examples of composable PIM solutions. These tools typically can also automate many of the manual processes involved in managing product data, such as data entry and data validation.

Fulfilment, demand chain & supply chain: where DTC sinks or swims

Whereas ‘indirect’ brands supply a relatively small number of retailers, or retailers’ warehouses, DTC brands become responsible for order fulfilment and delivery to consumers. This means operating fulfilment centres, managing relationships with delivery companies, etc.

This is a hugely competitive area in modern retail because timely and accurate delivery, in ecommerce, is the leading factor in the quality of customer experience for many customers. This is due to the exceptionally high bar set by Amazon, which has triggered a martech arms race as brands try to meet the current competitive standard.

On the demand side: DTC brands must also master the balancing act of inventory management and demand planning. The game is to hold the lowest possible stock levels, to keep costs low, whilst keeping enough stock, in enough places, to enable them to excel at fulfilment and delivery.

When demand, supply and fulfilment data are harmonised, brands can excel at things such as:

• preventing the placing of orders which can’t be fulfilled, or be fulfilled to a high standard
• being able to provide accurate delivery times, to individual customer addresses, at the point of order
• giving the customer (and customer service team) access to live, accurate order statuses
• collaborating more effectively with delivery companies, so that they have access to the information needed to better serve your business.

This harmonisation occurs by synchronising data at the source of demand (in most cases the ecommerce CMS), with your order management system (OMS) and your inventory management system.

In a legacy ERP, these modules are all part of the bundled solution – neatly synchronised in whatever limited ways the ERP vendor had been able to anticipate, and with an enterprise price-tag.

Brands without enterprise budgets, however – or brands with enterprise budgets, but with big technological ambitions, such as selling in marketplaces and social networks, are turning to more adaptable and capable technology.

Enterprise brands going direct would likely need to adopt a composable ecommerce platform (see below), in which case, they could likely benefit equally from the supply and demand chain tools bundled in with these solutions. BigCommerce, Swell and Crystallize (and also Shopify) include very effective supply & demand chain modules – and thanks to API first architecture, they would enable the brand to integrate with new marketing touchpoints fairly easily.

But brands may also choose to upgrade individual components in the stack. Composable vendors in the space include Fluent Commerce (an order management system [OMS] vendor) and Unleashed (an inventory solution).

Supply chain & fulfilment is also a key area for data analytics and AI (see below), since this technology can be deployed to identify patterns across disparate datasets, spot inefficiencies, and catch problems before they occur. A leading AI-powered inventory solution is Invent Analytics. The vendor claims to be able to generate 1.5-5% improved profitability at any enterprise currently relying on legacy demand planning software – which could be millions of dollars a month at an enterprise brand.

But even in non-AI contexts, maintaining precision control of the product journey, from supply chain through to customer, is highly restricted with legacy ecommerce systems.

Marketing personalisation technology

The promise of a more personal experience is what part of what allows consumers look past the restricted choice and (usually) higher prices that come with buying direct.

The technology needed to succeed at marketing personalisation is described in the previous section, however, the competitive standard of marketing personalisation remains fairly low.

In reality, therefore, the success factor most highly prioritised by consumers is timely and accurate delivery. The ‘personal feel’, meanwhile, may be satisfied without any special technology – i.e., by the product being sustainable or differentiated on other preferential factors, or through attractive or exciting branding.

A brand that decides to go DTC, therefore, may be sufficiently served by the personalisation tools bundled in with its CRM, ecommerce and automation platforms, whilst it develops a truly competitive demand-and-supply chain.

Composable commerce solutions

The core modules of an ecommerce platform are:

• the back end (the webstore administration platform)
• the commerce front-end (i.e. your web shop, or shopping app, and content publishing tools).

Traditional ecommerce platforms bundled up these services with all the other services needed to run a webstore. The largest and best-known bundled ecommerce vendors are Salesforce, Adobe, Magento and SAP.

In these legacy platforms, the back-end was only designed to exchange data with a single customer experience: the web shop. Yet as mentioned, modern ecommerce often entails selling via social media, apps and marketplaces – and new touchpoints are proliferating at pace.

In an attempt to keep up, brands have gradually integrated individual pieces of software with these systems, with SAP & co. charging punitive fees for each individual custom integration. Brands therefore have invested heavily in what’s known as ‘technical debt’, and development times remain glacial – whilst ecommerce evolution only accelerates.

Established brands going direct, therefore, will generally need to migrate to a composable back end. This also allows all the other martech solutions required for direct selling to be quickly and affordably integrated, tested, and replaced if they’re found unsuitable, or if the relevant campaign ends.

Interesting case studies include Audi, which adopted commercetools in order to be able to sell via its cars, and Tiffany & Co, the jewellery brand, which replatformed to BigCommerce as part of its DTC shift.

The decision to adopt a composable ecommerce system is often a major investment. Commercetools’ platform fees, for instance, can easily run to several hundred thousand dollars a year – which is usually out to the reach of a DTC startup.

Shopify, which specialises in serving SMEs with lower budgets, is also used successfully by some enterprises. It needs to be understood, though, that Shopify is not a true composable solution.

Shopify customers can choose between:

1. a bundled ecommerce platform, with both the commerce back-end and the customer experience/front end supplied as a single piece of software
2. what it describes as a ‘headless’ ecommerce back-end – for brands which want the freedom to select their own CX/CMS solution.

Its core advantage is that it’s easier to operate with less developer support, since the various other services you need to run a business, such as marketing automation etc., are offered as bolt-on features.

In truth, however, the convenience of these ‘bolt-on features’ runs contrary to the agility, of composable architecture, in being able to select best-in-class services. The ‘saving’ on development costs, meanwhile, simply gets funnelled into Shopify’s relatively high fees. Indeed, there are good reasons that Audi didn’t choose Shopify to enable ecommerce in its cars.

A recent entrant to the UK market, but one that has been used widely in Asia for some time, is an ecommerce platform called Shopline. Shopline seeks to blend the convenience of a bundled platform with the internal architecture of a composable solution. It has proved particularly valuable in social commerce – which derives from the tendency of Asian consumers to shop in super apps – and which is a growing component of direct-to-consumer marketing.

As with every software module, the brand’s ideal ecommerce platform will depend on its budgets, ambitions, and appetite for ongoing development work.

The point is that composability is a key component of being able to sell DTC, with relative freedom and a high degree of control – rather than being beholden to the channels and processes anticipated by your platform vendor.

Sustainability: only composability can save brands from greenwashing

‘Sustainability’, in a brand marketing context, breaks down into 3 core strategic initiatives:

1. being able to accurately identify your climate impact across your business – chiefly around carbon emissions, since carbon reduction is the primary factor in meeting the Paris Climate Goals
2. exposing this data in the customer experience so that customers can make informed buying decisions
3. and effective marketing segmentation, so that the sizeable minority of customers who are passionate about the environment can fund the brand’s sustainable propositions.

Composable architecture is needed to achieve these objectives because of the sheer quantity and complexity of data that needs to be recorded, processed and published, in order to move past generic ‘greenwashing’ statements.

Consider the hypothetical example of an FMCG company with a relatively low carbon footprint, which puts a generic ‘eco-label’ on the packaging of all its 2,000 products. That includes, however, the 5-10% of products which are actually more harmful than its competitors’ – causing customers to make less-sustainable buying decisions.

Consider also the example of two comparable manufacturers, which make competing products, but one of which is based in a remote location – causing all its employees and suppliers to rely heavily on fossil-fuel powered transport.

Transport emissions and employee commuting are categorised as ‘scope 3’ emissions, considered to be outside of the company’s direct control – but which often account for the majority of a business’ emissions. How or whether the end-customer recycles their product waste also comes under this category.

No business will ever be able to measure all its scope 3 emissions with total precision. Nonetheless, significant progress is being made towards providing usefully accurate readings of a company’s carbon footprint, and of individual products. This is taking place with the help of composable tech.

How composability enables corporate emissions reporting

Many software vendors now offer specialised solutions for measuring and monitoring carbon emissions as they fluctuate.

Climatiq, an emissions management software vendor, provides access to a database of known emissions factors to help the business calculate carbon emissions in their business, and maintain real-time flow of information.

Of course, this information can only ever be as reliable as the data inputs. But as this diagram shows, maintaining such a composable platform allows the business to incorporate its carbon reporting within the rest of its data stack.

This diagram doesn’t quite convey all the data inputs and outputs that would be needed for sustainable marketing.

The data sources would also include your PIM – so that accurate emissions data of individual products can be made accessible throughout the business. The outputs would include the CMS/CX so that this data can be exposed to consumers as they shop (see below), and also data visualization tools, to make the data useful to businesspeople, investors, regulators etc.

At product level, sustainability hinges on accurate product information

Conveying sustainability information at product level is really just one iteration of accessing and publishing accurate product information at the point of sale. It therefore overlaps significantly with the software needed to enable brand to go DTC.

The brand would ideally maintain a record of the carbon impact for each product or service, that stays up-to-date as that metric fluctuates – which happens frequently with consumer products. For instance, the brand switches to an avocado supplier that’s 4,000 miles closer than the old one, or poor weather causes farms to rely more heavily on greenhouses or fertiliser.

Practically speaking, maintaining such an up-to-date record means integrating your supply chain management solution with the PIM so that this data can then be published in the CX.

Whilst the CX would normally be a digital touchpoint, it should be noted that this could apply equally to offline settings such as product packaging and in-store displays.

Clearly, printed packaging cannot show ‘live’ information – but given that most packaging only really matters at the point of purchase, that would rarely be a problem. You could therefore integrate your packaging supplier’s print management system with your PIM and/or supply chain systems. This information could then be kept up-to-date without having to make design changes to the printed assets.

For B2C marketing campaigns

Contrary to popular belief, most customers will not prioritise sustainability in their buying decisions (Navigate B2B has written an expanded piece on this topic which you can read here).

Whereas a majority of consumers say they would rather pay more for an eco-friendly product, multiple pieces of research have shown that price, quality and personal taste usually take preference.

This is known as the ‘say-do gap’. It’s a major headache for brands, their boardrooms and their investors, because sustainable alternatives to everyday products typically cost brands a lot more to produce.

There are 3 key lessons here for your marketing strategy:

• without the right marketing, sustainability could easily prove financially self-defeating
• if you were to promote highly detailed sustainability information to all customers, you’d be wasting marketing resources (and probably racking up carbon costs on junked emails)
• there is a major commercial opportunity in promoting sustainability to the 20-25% of customers who are genuinely passionate – since you can earn greater profits on more expensive products.

The solution here is for sustainability data to be shared in real-time with the marketing team, so that they can show accurate and useful information to the right customers.

The right technology will likely depend on your business category and campaign needs. In ecommerce, though, the most effective solution would likely be for your marketing automation system to draw on information from PIM and the CDP. That would allow the CX, and comms issued via the CRM, to be personalised based on the customer’s full range of preferences – and show sustainability messaging to those who are really interested.

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It should be recognised that companies are incredibly early on in the journey of measuring their carbon footprints accurately.

The ‘dream’ is for companies to be able to automate their carbon footprint – but the chief obstacle here is that corporate supply chains are exceptionally complex. iPhones comprise components from 200 different manufacturers; whether or not those manufacturers record and share sustainability information, and how they do so, will be down to them.

Wresting control of their own data, therefore, is the first hurdle that most businesses will have to overcome in order to provide truly useful and accurate sustainability information. That’s already in progress – and as we discuss in the section below on AI, the data stack is increasingly trending towards composability.

The crucial point here, though, is that with the sheer amount of data involved in marketing around sustainability, there is very little meaningful progress that brands can make with legacy monolithic systems.

XR: the metaverse is primarily composed of data

‘The metaverse’ – much like ‘the internet’ – implies that people will be able to explore virtual worlds for entertainment, business or shopping, all whilst being tracked, measured and advertised to.

Of course, this does not yet exist, nor will it for some time.

Andrew Elia, founder of Arishi, a digital agency specialising in the customer experience and extended reality, explains:

“There are a lot of platforms that claim the title of being “Metaverse” such as Decentraland and The Sandbox, but there is no interoperability between them, so they do not meet the aspirations of a completely decentralised system like the web.”

Companies, including multiple UAE banks, explains Andrew, which have “proudly paid out loads of money for ‘real estate’ for their metaverse presences”, have in fact…

“…just paid private companies for something that is only visible to certain parties and only those who use a certain platform.”

What is now becoming possible, however, is the ability to carry out fairly sophisticated marketing campaigns in virtual words. Pursuing this will require brands:

• to have the freedom to create and publish to virtual environments relatively independently and at scale
• …and to be able to integrate all the rest of their martech with the relevant XR software. Customers would then be able to carry out transactions, consume personalised content, earn and burn loyalty points in the virtual world, etc.

This is becoming possible thanks to the development of composable commerce platforms. Before this technology emerged, XR deployments held little promise beyond being expensive experiments (albeit often entertaining). Examples include Walmart’s Jurassic World AR game, and Ikea and Currys’ ‘Point and Place’ AR tech, enabling customers to visualise products in their homes.

These experiments currently depend mostly on working with a digital agency which specialises in XR. But a parallel for how this will evolve comes from the early days of the internet.

Elia helpfully described this in a 2022 podcast by The Rosenstein Group (disclosure: this podcast is produced by Navigate B2B). In the early 2000s, says Andrew…

“…someone would build a website in flat HTML, the client would come along and request changes, you’d go back to the developers, they’d tweak it, and content management evolved to make that a less technical skill.

We’re currently seeing that same sort of curve with XR.”

Elia says that Arishi is now talking to brands about building XR applications on top of commercetools and Magnolia, which “can operate seamlessly in this area”.

Magnolia is a composable digital experience platform (DXP). The DXP could be usefully described as an ecommerce platform optimised for omnichannel marketing, and which includes an advanced CMS tool for publishing to multiple customer experiences. Crafter is another composable CMS which claims to be suitable for XR applications.

With a fully composable back-end and customer experience, it becomes practically achievable for brands to publish and commercialise VR experiences efficiently, with their full range of marketing tools. In the coming decade, this will happen with little or no developer/agency support.

With XR being at the cutting-edge of marketing, there isn’t any realistic alternative to composable architecture for brands which want to manage this themselves. This is simply because most of the technological innovation around virtual environments is now taking place based on MACH principles.

Brands can certainly keep playing with XR by paying a relatively high cost for experimental campaigns. But they will be excluded from commercialising virtual worlds in a way that has a significant impact on revenues, unless they undergo wholesale digital transformation – the priority being adopting composable commerce.

Brands which do make these changes, however, will find themselves in an advantageous position, as and when the metaverse finally becomes a reality. That point is at least a decade away, but it’s something no brand would wish to be locked out of when it comes.

Customer loyalty: composability extends the reach of your program

Every businessperson is attracted to the idea of loyal customers, and generic, high-level articles about loyalty – i.e., “loyalty is all about customer experience” – are popular with the marketing press.

Customer loyalty, therefore, is kind of an ongoing marketing trend. Interest certainly peaked in the last few years, as an adverse economy forced businesses to look at more capital-efficient marketing approaches.

Loyalty marketing is relatively poorly understood by non-practitioners. But if you are a member of any loyalty programs, you can begin to understand the technical challenges facing this industry by considering the experiences you’ve had with these brands.

When you realise how restrictive many of these programs are, it becomes clear why loyalty marketing is an excellent case for adopting composable architecture.

For instance, you might wonder…

• why you have to log into different environments to access your loyalty account, and to shop the brand’s primary ecommerce channel. Think British Airways/Avios, Nectar/Sainsbury’s, and many more
• why it can take 3 business days or more to transfer loyalty points from one program to another (i.e., send your American Express points into your Marriott Bonvoy account)
• and most of all: why it’s so difficult to spend your points on anything you actually want, despite having collected thousands of points with the brand, over many years.

The short answer to all of these questions is that traditional loyalty platforms were designed as silos – whereas resolving these problems would depend on connectivity between difference pieces of martech.

Chuck Ehredt, Founder & CEO of Currency Alliance, a loyalty solution vendor (with whom Navigate B2B collaborates on content marketing), notes that…

“Loyalty marketing has often been an afterthought for most brands, and therefore the loyalty program and underlying technology are often appended to the company’s technical architecture – as opposed to integrated as a core functional area to enable efficient customer acquisition and retention.”

If you really want to understand digital transformation in loyalty, the best recommendation I can make is to follow Currency Alliance’s blog.

But to summarise the key points here: a traditional loyalty platform would comprise the two core loyalty modules:

1. the loyalty points bank – which records transactions and takes responsibility for accounting and fraud prevention
2. and a loyalty rules engine, which implements ‘what-ifs’, i.e., if the airline seat may go unsold, or if the customer appears extremely valuable, triple points may be offered to close the sale.

…alongside technology modules which will be familiar to every marketer:

3. a CRM
4. a campaign management system.
5. …and a third-party ecommerce environment known as a ‘redemption catalogue’ where customers can redeem offers.

Crucially, the only loyalty-specific modules required are the points bank and rules engine. The most rational setup, with modern technology, would be to integrate these modules with a brand’s CRM, campaign management system and ecommerce environment.

In practice, however, brands have mostly acquired these modules as part of the loyalty platform, and run them alongside the parallel systems in their primary ecommerce platform. This is due to both the loyalty system and the ERP being based on legacy internal architecture, making them difficult or impossible to integrate.

That, in turn,  greatly limits the scale and effectiveness of the brand’s loyalty marketing initiatives. Ehredt explains:

Those brands that build loyalty as a core pillar into their business model – with the customer at the centre – will require composable elements such as the points bank and rules engine to be ‘open’ by nature.

That requires an API-first, microservices architecture. This would allow customer insights, which are captured across most touchpoints, to be fed into a central data store, so that modern marketing technology can deliver highly personalised information/offers, based on rich customer profiles.

Making such a move would allow loyalty teams to be able to answer all of the problems posed above – and more. Loyalty professionals would like to achieve things such as:

• displaying points pricing in the brand’s primary ecommerce channel
• allowing you to ‘pay with points’ at any point of sale
• issuing incentives for non-transactional engagements that are known to lead towards purchases, such as engaging with content, or interacting with influencers or your customer service team
• enabling you to ‘earn’ and ‘burn’ their loyalty points in third-party environments – such as social media channels, and the commerce environments of partnered brands in your loyalty program.

There is not space to cover all of these things here, so instead, I will focus on one strategic initiative that many brands are working on, and indicate the technical challenges that will need to be overcome.

Enabling loyalty transactions in the everyday commerce

The technical difficulty of allowing customers to spend points, in your primary web store, depends on the architecture of your commerce stack, and the generation of loyalty technology that you are using.

The enterprises running the largest and most popular loyalty programs mostly have the worst-case scenario:

• a legacy loyalty platform, with all 5 loyalty modules described above – which runs in parallel to…
• a legacy commerce stack, based on a legacy ERP.

This presents the challenge of how to enable the necessary exchange of data between the two systems, in order for customers to choose to spend or earn loyalty points without logging into multiple systems.

In theory, this could be achieved via a hardcoded integration. In practice, however, this would be a prohibitively expensive and difficult exercise due to the number of different connections that would be required.

Some of these connections are relatively simple. For instance, the points bank and ecommerce platform would need to be integrated so that the customer can see their points balance in the customer experience. Ideally, this points balance would then automatically update at the point of transaction.

Some of the connections, however, are hugely complicated. For instance, the loyalty team would require the brand’s entire inventory (in the ecommerce back-end) to be exposed in the loyalty management interface. That would allow them to select or deselect products for points transactions, and adjust reward pricing based on predicted ROI.

That would be far more effective, however, if the inventory analytics system were also integrated, so that loyalty pricing was adjusted appropriately based on supply and demand. Of course, this work is partly or mostly automated at most brands, and so these automation elements would need to be applied to loyalty pricing as well.

This is only a few examples of the kinds of connections that would need to be achieved in an optimal loyalty-ecommerce integration. Pulling off such an integration to its full potential, with legacy tools, would be a years-long development project, possibly to the tune of millions of dollars.

Instead, these problems can be solved at far lower cost by adopting composable tools – which could imply a few different things.

1. Apply composable loyalty solutions to a legacy ecommerce stack

To set up an entirely new loyalty program: a composable points bank and a rules engine (as provided by Currency Alliance) would be sufficient for a brand to run a loyalty program in conjunction with its primary marketing stack.

This would greatly simplify the task of making the various connections described above – whether you’re working with a legacy or composable commerce platform – since the composable tools are optimised to integrate with other solutions.

Or, to improve an existing loyalty program: the composable points bank and rules engine could be used in conjunction with the existing loyalty technology. The composable solution would then exchange data with both the legacy loyalty platform, and the brand’s primary ecommerce environment. Using the composable loyalty solution as a kind of ‘bridge’ in this way would be a lot simpler than trying to hardcode the two separate legacy platforms together.

The alternative would be to select a bundled loyalty platform, built on modern design principles. Antavo is one such vendor describing its solution as an API-first, cloud-based loyalty platform.

Antavo is a bundled solution, so you would not get the benefit of selecting your preferred points bank or rules engine. For that reason, it wouldn’t work for an enterprise seeking to enhance its existing loyalty platform. But it would be an effective solution for a business seeking a lot of packaged functionality that it could stand up quickly.

Of course, Antavo includes a native CRM and campaign management system. If you wanted to select your preferred tools for those modules, therefore, you would need to carry out further integrations in order to synchronise them with Antavo’s native modules.

2. Replatforming to composable commerce, to unlock the legacy loyalty platform

In a reverse of the above: the API-first nature of composable commerce software would greatly simplify integration with legacy loyalty solution(s).

A good recent example was the adoption of Contentstack – a DXP solution – by Air France-KLM. This occurred in order “to create seamless and personalised customer journeys” and gain “the agility required to support growth channels and adapt to changing customer needs.”

Of course, as a DXP, Contentstack is not a loyalty solution, but it contains the ecommerce platform, CRM and campaign management system. This simplifies the work of introducing loyalty mechanics to your desired touchpoints and channels.

This, in turn, could unlock some highly innovative loyalty use-cases – such as in a brand’s social media channels.

Composable commerce architecture has been instrumental in enabling social selling. Illustratively, Brave Bison, a social commerce agency, builds branded ecommerce environments on back-ends such as BigCommerce, to enable customers to shop in YouTube and other social networks.

I have not yet read a case study of a brand adding loyalty mechanics to its social and influencer marketing at any meaningful scale. But with these kinds of commerce now becoming mainstream, it is inevitable that these channels will become new competitive environments in loyalty marketing.

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Without straying too far into the future, the crucial thing to understand is that loyalty teams which do not gain the advantage of composable architecture will be left behind. In Chuck Ehredt’s words…

“Most brands who remain dependent on legacy technology are probably 5+ years away from differentiating themselves based on personalisation. But that also opens the door for more nimble enterprises to capture significant market share if they use loyalty marketing and modern technology as a weapon in the short-term.”

The best-known brands, in loyalty, typically only have around 25% of customers enrolled in their programs. This greatly hampers the ability to cultivate lifetime value – and this is in large part due to outdated technology.

Getting more customers involved in the loyalty program will require understanding those customers in fine detail, and rewarding desirable actions across all your marketing touchpoints.

Crucially, it will also mean being able to form loyalty partnerships more easily – since that also implies connecting different pieces of technology at different companies. Extending the partner network in this way is a leading, ongoing strategic objective for most brands, since it makes their loyalty points more useful and valuable to customers.

This will depend entirely on being able to connect various marketing systems, including those operated by other brands, and being able to extend this ongoing. This can only practically be achieved with composable architecture.

AI becomes scalable with composable architecture

The trend towards composability is just the ‘full-scale reorganisation’ needed, as The Economist put it, for AI to be of significant practical use in marketing.

AI, essentially, is the combination of:

• data processing
• with software that makes decisions or takes actions based on that data.

Since composable architectures evolved in order to help share data between connected systems, it’s easy to understand how AI is of more practical use at companies at an advanced phase of composable adoption.

To give an example:

• a traditional CDP without AI functionality would be able to combine data insights from various systems in order to put customers in certain segments, and build up a rich and highly useful understanding of the customer base
• with AI, you could do things such as:
  • ‘fill in the gaps’ about that customer, where there is limited data, but where the data you do have is recognisable from customers that you know more about
  • predict the customer’s next move(s) with a range of probability, and allocate marketing budget based on the likelihood of them taking certain actions, and the predicted ROI.

Of course, AI can only ever be as useful as the data which supplies it – just as with data processing in any other context. The good news is that the same improvement in brands’ data architectures, which allows them to take advantage of composability, is also highly useful for preparing to take advantage of AI.

A widely discussed concept in the data sphere is that of the ‘modern data stack’ – as opposed to the ‘traditional data stack’.

In a traditional data stack, the data storage is likely centralised in a single database in the monolithic ERP. That centralised database will be tightly coupled with software that allows a professional to query the database in order to extract reports. Those reports, known as ‘business intelligence’ (BI), are then used to inform decision-making.

A key problem with the traditional data stack, however, is that processing times in such a setup are slow, compared to the processing speeds that are now possible with more modern tools.

Even at Shopify, a relatively modern enterprise, their legacy BI solution left analysts, “starting reports and actually walking away from their desks to let them finish.”

Denis Zgonjanin, then of Shopify, said:

“Because it took a while to load, our reporting got in the way of hundreds of people trying to do their jobs.”

The modern data stack, by contrast, is intended to enable greater speed and efficiency. Instead of a single, centralised data module, the modern data stack is assembled of various components, whereby data is better organised, and processed into more useful chunks.

With more software components working alongside each other, each highly specialised for certain purposes, the entire platform becomes faster and more responsive. Processing times also fall, because the modern data stack is cloud based – spreading the load across many different vendors’ cloud-based servers, as opposed to one company’s servers.

The below diagram by Altexsoft, a digital agency, is a useful representation of the various components in the modern data stack.

Not everyone needs to understand the data stack in detail. Many non-technical professionals, however, will recognise some of the example vendor logos in this diagram – such as Salesforce on the left, and on the right, Tableau (a very widely-used data analytics & visualisation tool).

The crucial things to understand from the marketing perspective are:

• the data sources (left) – which could be the technology that runs your webstore/website, your app, your platform, any business software used by your teams, or any other digital touchpoint
• the data uses (right) – the software which is used to turn the data you’re storing and collecting into actionable insights

…since these are the data inputs and outputs that marketers are likely to use or rely on for campaigning purposes.

The majority of businesses today probably have a hybrid version of this model, with some modern-cloud-based data management tools alongside a centralised database in their monolithic ERP. But the further businesses move towards the modern data stack, the more well-organised data can be supplied for data processing, at faster speeds.

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It should be noted that the traditional data stack does yield certain advantages, the value of which stand apart from the company’s investment in AI.

Companies’ monolithic ERPs, by nature, unite a very large proportion of their tools and systems – whether thanks to the original design of the monolith, or hardcoded integrations over the years. That includes systems which exist outside of the ecommerce platform, but which are still highly relevant to marketing – such as your in-store tech.

Cygnet Digital’s Peter Ross explains…

“To take a hypothetical scenario: say you wanted to analyse the effect of your email marketing on in-store transactions.

These are two entirely different systems, but at some point over the years, the company has probably found a need to manually integrate its POS software with its monolith – so all the data is exposed in the centralised data lake. That would enable you to run your queries without any technical development.

The data processing may be slow, and you might need a degree in data science to extract the intelligence – but ultimately the centralised database gets you the business intelligence you need.

Companies transitioning to a modern data stack are often not prioritising moving the POS as part of their digital transformation. In such cases, pulling this same report would require either a series of complex queries, or a large data export and mangling or exporting everything to a BD platform – which in itself might not be able to query by this without setup.”

The value of incumbent systems is a hurdle not only to AI adoption, but to wider digital transformation. Ultimately, though, companies which want to make use of AI at industrial scale will eventually need to break these legacy dependencies, and unite the majority of touchpoints and tools under a modern data stack.

This should be a major priority, since making optimal use of increasingly large quantities of data (however well-organised) is already beyond the human capabilities of even the largest organisations.

The process of becoming highly useful and influential, in marketing contexts, will play out over decades, rather than years. The same Economist article, referenced at the start of this ebook, said…

“Gathering data is tiresome and running the best [AI] models fearsomely expensive — a single complex query on the latest version of ChatGPT can cost $1-2…

These costs will fall, but it could take years for the technology to become sufficiently cheap for mass deployment. Bosses, worried about privacy and security, regularly tell The Economist that they are unwilling to send their data to modify models that live elsewhere.”

As obstacles to AI fall, however,  brands will gradually find that they are able to make better use of new composable solutions that they adopt.

Certainly that includes solutions in the data stack – Snowflake, Amazon Redshift, BigQuery and Databricks being some of the better-known cloud data warehouse vendors.

But whether businesess are innovationg around CX, sustainability, DTC, XR, or any other strategic initiative, they will inevitably acquire new, composable solutions which are purpose-built to exchange data via APIs.

That will accelerate the flywheel of AI adoption – allowing businesses to make better use of the AI features within other software, and of specialised AI tools for producing and acting on business intelligence.

Enterprises’ existing technical architecture are simply too heavily dependent on legacy platforms, and overly restrictive of data sharing, for AI to fulfill its potential in marketing, or any other area of the business. That will only change through ongoing composable adoption.

Consensus-building around composability, between brands and technology companies

Similar to all the marketing trends discussed in this ebook, composability is itself a marketing trend –subject to the same hype as any other trend.

That hype takes a wide variety of forms, ranging from uninformed commentators venturing opinions, to vendors attempting to reposition their value proposition in order to catch the wave. Indeed, vendors such as Salesforce and SAP are claiming to have launched composable propositions – i.e., by moving their data storage into cloud – when in fact their software architecture remains monolithic in nature.

In this context, brands should be curious and pragmatic, in equal balance, about the potential of composability, and technology companies should assist in understanding from as neutral a position as possible.

Composable adoption is certainly not about buying the latest tech, or only selecting systems carrying a MACH Alliance badge. That will only cause you more problems.

Some helpful guidance comes from Chris Lemmer, Cofounder of digital agency SwiftCom:

“If you’re thinking of using a composable or microservices architecture, ask yourself, “Why?” and try to answer these questions:

1. Why do you think this is needed?
2. Why does this architecture exist?
3. Where did it come from initially?
4. What problem(s) does it solve?
5. Do I have any of these problems?

Many people jump on the composable wagon because it sounds cool and innovative, but they end up buying themselves a burden. Millions of dollars are wasted on premature optimisation, instead of spending on marketing or growing the business to a level where optimisation is needed.”

Specifically related to marketing: the “why” is an increasingly competitive environment where brands are making rapid progress around the trends described in this article.

The right approach will depend partly on your scale and your business’s marketing maturity.

For B2C startups, or larger companies with little or no ecommerce at present, it may be too early to start thinking critically about technical architecture – composable or otherwise. The capabilities of out-of-the-box tools such as Shopline (and even Shopify, WooCommerce or Wix) will allow you to get close to the current competitive standards in personalisation, supply chain management, loyalty, etc., relatively easily.

Customers of such businesses may even perceive a better experience compared to enterprise brands – for the time being, at least – because they’re not suffering poor experiences related to technical debt.

Where composability becomes crucial is in optimising enterprises’ technical architectures so that they can be the ones setting the competitive standard in the coming decades.

You can argue about specific architectural approaches, but you cannot argue about the importance of data in unlocking the major marketing trends. Nor can you question the importance of well-organised data stacks, and highly-capable APIs, in making that data readily available and useful for whatever business plan to achieve.

“Composable adoption” implies selecting tools, and implementing strategies that put your business on this path. This will enable you to work towards cutting-edge marketing methodologies in increments, without gutting your architecture or gambling on any particular setup, or any particular tools.

This allows you to be led by your most urgent strategic priorities and goals, all while future-proofing your technical architecture based on competitiveness, connectivity, scalability, and unceasing change.

About Navigate B2B: SaaS marketing specialists

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