10,000 marketing tools, and more every day: how do you find the right one?
So many tools - and just as many ideas, solutions and even more promises. Whatever problem you have, whatever hype you're falling for at this moment - there's a tool for it.
All of them solve a problem. At the same time, they create some.
New data sources lead to new data silos.
Instead of solving problems holistically, new side stages are opened up.
Features that are important today may be obsolete tomorrow. E.g. because a tool from your existing stack offers the same.
What's more, they cost money. A lot of money. Maybe not each one individually, but in total, companies today spend five-figure sums on their MarTech stack. Per month. This money must first be recouped in the form of acquired efficiency and sales.
Choose your tools wisely
Individual tools can solve individual problems, but they should fit into the context of your existing stack. Data is the lifeblood of marketing. It is the bridge between the offer and the customer experience. That's why (with the possible exception of pure content and project management tools) all MarTechs today generate and use customer data.
Personal data is the key to all tasks in the marketing department. Everything falls apart when customer data falls apart. And that starts with the selection of marketing tools.
MarTech tasks include
Personalization
Target group attribution
Lead qualification
Reporting
Collaboration between departments
Data protection
This is what choosing your MarTech Stack is all about
The focus on data flow begins with the selection of marketing tools. That's why it's important to identify later risks at the very beginning.
Here's how you do it:
Step 1: Data-focused mindset
At MAVENS, we are often asked for tool recommendations. We see behind the scenes of many scaling tech companies and have a comprehensive view of what works (and what doesn't). The answer is frustratingly non-committal - "it depends".
Everyone expects a clear tool recommendation. But everyone comes from a different situation and brings different preferences with them. For example, new employees often want to continue using their familiar toolset in the new environment. That's understandable. But does it make sense?
It is critical to understand the implications of the MarTech stack. The pitfalls of bad decisions are data silos, bloated costs, lengthy learning curves, and ultimately frustration with technology that hinders rather than promotes success. At the same time, we expect our tools to provide room for experience, scaling and flexibility in daily use. The round has to go into the square, as the saying goes.
Finding the right tool depends on many factors:
Goals, maturity level, financial strength and complexity of the organization
Team Skills
Existing experience and existing tech stack
Targeted funnel and customer experience
Business and data model (B2B / B2C or subscription model / single sale).
Tool comparisons are not helpful unless you know how to select the tools. Tool decisions should focus on the data that is actually needed. The goal is a lean and efficient marketing stack.
So how to start?
Depending on the size of a company, listing its current stack is a good start. Except for startups, everyone probably has a martech stack that has grown organically over the years. Some tools in this stack may no longer be used at all, others only for time-limited occasions (e.g., sweepstakes). It's helpful to have a list of all marketing tools, including who has access to them.
That is done quickly. The next step is much more complex. The central question is: What of this do we need at all, and what is missing? How big is the data delta between strategy and reality? This checklist will help:
Data inventory checklist
which data is collected in which tool?
which data is only used in the respective tool?
which data is actually synchronized with other systems?
which data ends up in the user profile?
which of these data are tracked in central dashboards and used for decision-making?
Step 2: The right data strategy
First of all, you should ask yourself what is actually to be achieved in the MarTech stack. This is about this equation:
MarTech stack = create user experiences from attributes, target groups and customer data
Actually, the stack is a loop: User experiences enable interactions that enrich customer profiles, enabling a better understanding of the target audience, which in turn should lead to better offers and interactions.
The data required for this have a fixed hierarchy:
The entity (the user) is at the center. This is where all personal data ends up.
Attributes are the sum of implicit data (behavior) and explicit data (properties).
Groups of entities are bundled via segments or target groups
Actions (such as workflows and templates) create events for these segments / target groups
How are user experiences created?
To create user experiences, appropriate workflows are created for groups: This can be a campaign, a sequence, or any other action. Profile and segment data triggers this event (e.g.: "send a welcome email upon new registration").
For each channel, your tool should be able to provide the following data hierarchy. The more complete this hierarchy is, the better the customer experience.
Segments decide who is targeted. They group similar profiles ("all people on a free trial") for a specific customer experience. Most tools can group people into functional groups, such as:
E-mail lists
CRM views
Advertising target groups
To group people for the same customer experience, you use actions that qualify them for that segment - the conversion events (actions). This could be users of a free trial. The event would be "trial period has started." The subsequent events would be "trial cancelled" or "subscription started".
The event changes the status of the user. He is now a "Customer in trial period" - until the trial is canceled or the subscription is started. Status-based attributes also have advantages:
Easier to store (unlike events)
Easier to synchronize
Faster to group & document
It is important that status-based attributes are updated on every event. Status attributes become less reliable as the complexity of a customer data model and the customer journey increases. After all, you can have multiple, even conflicting statuses (e.g., "new customer who cancelled") Still, all tools should trigger all status updates in real time. For example, if a person has an "email unsubscribed" event, all tools should reflect this new status before sending the next email.
User events cannot be easily synchronized between multiple tools. The solution is to pull segments centrally from the "leading system" to manage customer experiences across all tools - especially where they are in their customer journey (i.e., their state).
With each change, the customer experience is thus reassigned, regrouped, and synchronized consistently across all tools. Instead of relying on each tool to ingest event data and update status, there needs to be exactly 1 tool for centralized status.
Checklist for segmentation functions (segments, lists, views, target groups)
Can segments be created with events?
Can segments be created with events from all data sources?
If no data sources can be connected - where do the attributes for determining the status come from?
Can leading segments be created with subordinate segments from other tools? ("Centralized segmentation")
Step 3: Dynamic templates & workflows
The next step is no longer about WHO to address, but also WHAT to say.
Many tools offer templates to define content and use it dynamically based on customer data. The simplest version of this is the first name for the address.
Hi {contact.firstName}
Outside of email, live chat, and any 1:1 channels that involve addressing logged-in customers, the tool should be able to dynamically customize templates. Website personalization, for example, is about dynamically replacing HTML elements, images, offers, or features.
Dynamic content needs fallback strategies in case information is missing or contradictory. For example, the company name could come from a demo request form, from a sales rep in their CRM, a data enrichment, or a billing tool. What if the name was spelled 4x differently or is completely wrong in the leading system? For this, data sources are prioritized and the "truest" data source for each person's attributes is finally synchronized with the leading tool.
The best tools don't limit dynamic content to text. Unless you know the full context of a person, words used generically feel unnatural ("Dear Sir/Madam") - no one would talk like that, after all.
Better templating tools therefore allow for more flexible logic to determine what variations of content users might see, and create a smoother, more natural experience from the full context of your customer data. Liquid (a templating language from Shopify) is a good example of this.
Tools such as Customer.io use liquid templating to personalize email newsletters, using words, phrases, sentences and entire paragraphs according to customer data - or completely different content if missing.
In the case of dynamic content, it also helps enormously to be able to check the various versions in a preview in order to identify pointless logic combinations or incorrect markup in good time.
Templating also helps to collect the experience about leads from sales, support and key accountants internally. Templates for internal notifications provide context that may have been overlooked by your own team and influence the way leads should be addressed (e.g., as a small but cohesive group with a shared passion).
Checklist for templates
Is content dynamically deployed based on profile data?
Is there flexible template markup for entire passages?
Is the template language easy to read and learn?
Are there previews for all event states?
Are actions triggered with "if this then that" workflows? Usually, these are visual point-and-click tools that are also easy to use.
Workflow tools are very intuitive, but they have their pitfalls. Like attributes in segments, they contain a status. As customer journeys tend to become more complex, more if-then logic is needed to define exactly what experience a customer should receive. Workflows also tend to get longer to trigger more content and experiences. On the one hand, it's easy for mistakes to creep in, on the other hand, you can't see the forest for the trees and are more concerned with marginal than core target groups.
In comparison, both segments and templates are calculated once based on all known information. This makes them easier to handle. They determine the state in a single situation definition rather than in a series of calculations.
Tip: Keep workflows simple and instead build complexity into the segments and templates.
Checklist for workflow functions:
Is there a visual workflow builder with point-and-click functionality?
Are there workflows and triggers for each event, attribute or segment?
Are there powerful segmentations and templates?
The availability of data determines the quality of the customer experience
The world is full of attractive marketing tools. Quadrants, comparison portals, and test reports provide the information you need. At the same time, it is becoming increasingly difficult to differentiate between tools.
As described, data drives the quality of the customer experience. It should be the key differentiator between tools. For example, most email tools support workflows, while advanced templating tools do not.
Even popular tools have significant limitations. Mailchimp, for example, can only synchronize a limited number of attributes. Although there are segmentations, merge tags and templates, and automated workflows, a lack of attributes limits the context that can be established about an email subscriber. This limits the usability of the entire tool.
For each channel, there should be a leading tool that profiles every contact, as well as every interaction. This only works if everything is trackable and the tool doesn't miss any context.
Checklist for selecting workflow tools for each channel:
Can all required channels be defined and addressed?
Does it meet the criteria for segmentation, templating, and workflows?
Are all interactions tracked and delivered?
Can content for this channel be triggered and personalized by external triggers?
Step 4: Data integration
Tools are rarely category leaders of several channels at the same time. This means that in the medium term, several tools will be in use. This in turn means that the same users are created in multiple tools. To create full context, all changes and updates should be propagated to all other tools.
Now it's time for a data integration strategy
The tool that hosts all the profiles is the data-carrying system for all the others. Here lies the "golden customer record" that is synchronized with all attributes and with all other tools.
How to create profiles and link them together
Use the complete customer data model as an attribute
Include all user events
Include all segments
Public documentation shows how well a tool integrates. The most common are RESTful APIs, which cover both read APIs (send data from the tool) and write APIs (send data to the tool).
Checklist for the assessment of data portability
Is there a documented API?
Is there a read API for retrieving data?
Is there a write API to create and update data in the tool?
Is there a DELETE write API for deleting data? (e.g. for GDPR compliance).
Does the API display all data objects. Entities (people and companies), associated data (events and attributes), and segments (or equivalents)?
Data Enrichment Tools
Data enrichment incorporates relevant data from both internal and external sources. Reasons in marketing for data enrichment can be:
Complete customer data model
Identify ideal customer profiles
Target individuals and companies in campaigns or
Personalize messages
A use case could be the value determination of a customer contact. For this purpose, the product database and the payment history are integrated. This data has nothing to do with marketing on the surface, but it helps to gain a holistic picture of the customer and thus gain important insights that definitely have an influence on the marketing strategy.
Data enrichment usually requires a new data structure, since the existing form does not always fit the new application. This transformation makes data enrichment quite complex.
Data enrichment also occurs in a loop. The tool requests data from an identifier, the newly obtained attributes are returned and stored centrally and are thus available to all tools.
Checklist for data enrichment tools
Which identifiers are used for enrichment?
Which data can be used and how?
How is this data forwarded and weighted?
Is it defined which version of the same data (e.g. job title) is used?
Tracking Tools
Tracking tools capture user activity outside your own communication channels
Website analytics
Landing page tools (form and pop-over capture)
Forms and surveys
Payments and billing
Meetings and video calls
Third-party lead sources (such as G2Crowd or Facebook Ads).
But be careful! Tracking tools are passive tools that grab data, group it and play it back. They do not necessarily use the identifier from the data-carrying system. This leads to duplicate profiles or data, which in turn almost certainly leads to duplicate profiles in the data-carrying tools. A vicious circle .
For this purpose, additional IDs are created that support the identifier. So the e-mail also receives a tracking ID. The tracking data, including the identifiers, flows into the leading database for high-volume data such as web analytics as well as for low-volume subscriptions - and everything in between.
Checklist for Tracking Tools
Which channels, platforms, websites and actions are not captured?
Which identifiers can be captured?
Can it capture multiple identifiers at once and link data together?
How can this data and multiple identifiers be tracked in the data-carrying system?
Step 5: Start-up, Scale-up or Grown-up? Tools for each level of maturity
Channels, tools, and data requirements change as a company matures. Many of the requirements necessary later are completely uninteresting for start-ups and scale-ups. This includes, for example, rights management or automation routines.
Nevertheless, all companies in an industry have comparable data problems and user lifecycles, no matter how large they are.
We therefore distinguish three principal market segments for MarTech tools
Pre-Product/Market-Fit (Start-up)
Post-Product/Market-Fit (Scale-up)
Scale-up (Grown-up)
Best MarTech Stack for Start-ups
Pre-product market fit companies don't need to prioritize acquisition, but they do need to understand their customer data.
The resources to do this may be minimal. At least one member of the founding team will likely take the lead on customer development and use what little budget they have for low-cost tools.
At this stage, registrations come from one-off promotions like sneak previews, closed user groups, or micro-influencer collaborations. There is a lot of manual contacting, but no stable funnel yet. Nevertheless, profiling people and companies at this stage is already incredibly important to identify the ideal customer profile as quickly as possible.
So even start-ups need a tool that lets you capture customer data
One identifier (like e-mail or phone number)
All data about the customer united in one place
Qualitative data & analysis in one central place
The data should be such that it can be transferred to other tools at any time, at least as a CSV file. They should have clean and valid identifiers, and all other data should be such that people and companies can be classified together - an embryonic CRM.
Importantly for startups, customer data is easily comparable at this stage to identify nuanced trends in data.
The crucial thing is that data actually exists as data – not just in the heads of the founders :)
With all these requirements, it's hard to argue against the value of a spreadsheet program like Google Sheets as a "source of truth" and a simple email contact program to manage the tools. Perhaps with a simple Sheets add-on to keep it all organized.
Best MarTech Stack for Scale-ups
Start-ups need to figure out who to sell to. For scale-ups, it's about figuring out how to sell to those customers while refining your "Ideal Customer Profile." The priority is acquisition. It is refined by analyzing churn, or freshly quitting customers.
At this stage, scale-ups are likely to hire their first sales and marketing managers to manage both the initial team and the tools. There is a budget for the basics for the website, email tool, accounting, etc.
As a scale-up, it all feels like a missed opportunity.
DThe most common mistake at this stage is trying to compensate for the lack of budget and resources with a variety of different tools to take advantage of all channels and opportunities, rather than solving the initial problem of how to make each channel used work:
Appear in the top 10 on Google for a relevant term with an appropriate volume.
Run performance ads that bring in more than they cost
Build referrals and collaborations that lead to conversions
Build email lists and social media reach to the point where they lead to noticeable traffic
A hodgepodge of seemingly simple, free tools likes to create chaos when tracking, organizing, and personalizing the customer experience. This can significantly slow down the team. There are two important tools that need to be set up for each functional role.
Sales needs a reliable tool to work productively, close deals, and leverage customer data (segments, templates, and workflows). At this stage, advanced data integration and reporting capabilities are secondary to productivity.
As a scale-up, avoid overhead, that's all.
Marketing needs a tool to capture and target leads. This tool needs to feed into (or be identical to) the sales tool and ensure that both teams are working with the same data and context from the start. Scale-ups should not use multiple tools and channels while resources (team, budget, time) are tight. The way your team uses the tools (and whether they can spend time and focus to use them properly) has much more impact than adding additional tools.
Fewer channels lead to better data flow and understanding about leads and customers.
It might look like this:
Marketing leads
Ads
Website (CMS & Analysis)
Mailings
Sales leads
Sales CRM
Live chat
At this stage, tools can still be completely synchronous (everything is connected to everything else), although this is known to lead to a problem later on, which only "a single source of truth" solves again. But as the saying goes: "Don't solve problems until they are problems".
Best MarTech Stack for Grown-ups
Once a company can truly scale, more tools can be deployed.
Responsibility for people, tools and data are mostly separated at this stage. The MarTech stack is then managed at the operational level. Some teams leave the tool selection to their operational units (Sales, Marketing, but also Service and Human Resources).
The fastest growing grow-ups are centralizing tools and data operations across the organization.
To grow faster, grow-ups are constantly testing new channels and trying to get everything out of them. This requires new tools, which makes data synchronization difficult. The volume and variants of customer events are increasing dramatically, and the logic to understand the "state" of leads, customers, and accounts is made more difficult - if not completely thwarted - without a leading database and the resulting "golden customer record".
To understand what each channel contributes to the revenue goal, challenges such as multi-touch attribution - the synthesis of all event data from multiple different sources - become common. To get the most out of each channel, advanced segmentation, dynamic content, and personalization also become necessary. All of this changes the data concept requirements.
For grow-ups, sales-friendly CRM or a simple marketing automation tool lead to data silos that don't properly map the complexity of customer journeys.
Questions arise about attribution, reporting, personalization of messages, or team alignment.
Where there are data silos, there are silos between teams.
The big mistake at this stage is for individual departments to take responsibility for the entire company's data problem instead of just their own needs. It then looks like this:
Sales chooses an enterprise CRM
Marketing uses a "larger" marketing automation platform
IT launches a data warehousing project
External service providers are brought in to create synchronization of each team's tools
This approach will not solve the problem of data silos. New tools are always needed for new channels. The sheer number of tools thwarts their synchronization.
Your team should identify and address this problem early. Each team needs the data in their tools, but the data sovereignty of all these tools is centralized at the enterprise level. This becomes the key operational task of the MarTech stack.
We now know four common types of tools that qualify as the "single source of truth" for your customer data:
CRMs
Marketing automation platforms
Customer data platforms
Data warehouses
Scaling companies need to think carefully about which database leads all others. This then synchronizes data from all the tools, tracking systems and databases used in real time.
CRMs, marketing automation platforms, and data warehouses by themselves always provide only an incomplete view of the data. They suffer from a lack of control in data connectivity, one-way data flow, lack of real-time calculations, and poor usability.
Without a clear, focused strategy to create a truly unified profile, no tool will be able to capture the full context of a customer. Some teams remedy this by aggregating and abstracting data into attributes to synchronize between tools. However, this results in unstructured data that is difficult to read and organize. The tool that holds all the attributes is missing.
Customer Data Platforms (CDPs) solve this problem. A CDP is software that captures and unifies customer data from multiple sources to provide a single, coherent, complete view of each customer. Digital sources include:
Behavioral data, such as actions captured on a website, in an app, or through other channels such as live chat or digital assistants, as well as the number and length of interactions and the frequency of those interactions
Transactional data, such as customer purchases and returns from e-commerce or POS systems
Demographic data, such as customer name, date and month of birth, and address
Summary: Find your MarTech stack
Long lists of comparable tools are not helpful if no one can really tell them apart. In the end, data usability and portability decide the best tool for your purpose.
The question of how tool-specific data harmonizes with other sources will sooner or later become the decisive quality criterion of a MarTech application.
So what should your MarTech stack be able to do?
Segment target groups
Create templates
Define workflows
Create customer profiles
Ensure data portability and API quality
Integrate and control third-party data
Use tracking data (inbound data)
Share data with tracking tools (outbound data)
You should be able to answer these five questions before making any MarTech decision:
What customer experience do I want to create?
Which channels do I want to use?
How valuable is the data acquired for the entire customer journey?
How portable is the data (inbound & outbound)?
Who will install, maintain and use the software?
MAVENS supports you in choosing the right MarTech stack with the following services:
Functionality: We make sure that the software provides all the functions you need today. And tomorrow, too.
Ease of use: It's important that your team can use the software intuitively and without errors - preferably without seminars and separate licenses.
Integration: Data needs to flow, which is why no software stands alone anymore. Your software should integrate perfectly with your existing IT infrastructure and be extensible via API interfaces.
Costs: Licenses, customizing, onboarding, maintenance and scaling: The cost structure is often complex. We take into account not only the set-up but also ongoing costs such as maintenance fees, support and extensions.
Support and service: We make sure your software provides good support and documentation.
Security: We make sure the software has robust security features to protect your data and systems. Now and in the future.
Sustainability: We look at whether the software will meet your needs in the future and whether the vendor will provide regular updates and new features.
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