technologists guide to marketing stacks ecosystems

Turn Your Tech Stack into an Ecosystem: A Technologist’s Guide, Part 1

by | Sep 8, 2020

As a technologist, your job is to build systems and infrastructure to support the business and drive value. This value varies in form, from more efficient processes to improved and/or automated workflows, all to optimize outcomes. 

For marketing and customer experience technologies, this means supporting the deployment of targeted messages, campaigns, and other experiences. But technology plays a much more significant role than merely enabling execution. You must consider other elements in the “smart marketing” continuum. 

It will come as no surprise to anyone with even a passing familiarity with the current martech solution market that martech stacks — especially for legacy brands — can be miles high and made only taller by the massive gaps between systems and solutions. Each piece must not only solve its central problem; it must also connect to, enable, and amplify every other function within the stack. 

You build technology and buy technology, but you don’t want a stack — you want an ecosystem. 

What follows is Part 1 of a two-part series on essential areas for consideration when assessing technology solutions. 

 

Integration
Definition
The process of establishing an account and the data necessary to execute marketing campaigns.

Why is this important?
Here’s a pretty well-kept secret: technology is moving fast these days. After a one-year contract comes up for renewal, you might have found that the world’s best point solution simply doesn’t fit your use cases. Flexible iteration of your stack is essential to remaining not only competitive but relevant. Easy, seamless integration is critical to keeping pace. 

Questions to ask of the technology solution:

  1. How much effort is initial integration, and what are ongoing integration requirements & support? 
  2. How rigid are data schemas, and does your current data infrastructure match downstream requirements? 
  3. What happens if input data doesn’t conform? How much effort will it take for requisite transformations?

 

Migrations
Definition
Changing the data in an account, whether by adding, removing, or updating data.

Why is this important?
Change is constant for any company. If you’re undertaking a digital transformation, change is even more central. But when the business changes, so too must the data, so choosing technology that enables seamless, continuous migration is crucial.

Questions to ask of the technology solution:

  1. How do you plan on accommodating data migrations with downstream marketing systems? 
  2. Who owns the migration, and how will you provide service continuity?
  3. Is the solution built for continuous change, or do migrations mean downtime and interrupted service for your end-users?

 

Data Transformations
Definition
Manipulating and combining data on the way in to produce enriched, derived, or aggregated data for downstream use.

Why is this important?
Last-mile data transformations are a fact of life for any downstream application that relies on data. Simple operations like joining datasets or computing averages or percentiles should ideally be handled in a self-service capacity downstream.

Great customer experiences require detailed personalization — which ultimately comes from fine-grained data. Take, for example, an ecommerce campaign that segments first purchases by category. If the orders table doesn’t include this information, it must be appended from the product catalog.

Operations like these are standard, and workflows should support them.

Questions to ask of the technology solution:

  1. What data transformations do you support, and where are they applied in the system?
  2. Who can define these transformations?  Can my team self-serve, or does each require a change request to an account manager?
  3. Are there any scale limitations to these transforms?  Will some work when applied to new data but buckle when applied to the full historical dataset?

 

Operability
Definition
A property of the overall system, how often it is functioning correctly and available versus malfunctioning or offline

Why is this important?
Your marketing operations depend on your martech solutions’ uptime. Suppose you pick a vendor with poor operations. You’ll quickly witness service unavailability’s ripple effect to broken data to internal inefficiencies to missed opportunities.

Closely related is how a solution acquits themselves during peak traffic periods, like Black Friday or Cyber Monday. Exceptional organizations know when traffic spikes are coming and preemptively scale their systems accordingly.

Questions to ask of the technology solution:

  1. What happens when data breaks or has other inaccuracies that impact the application downstream? 
  2. How are business users alerted to issues? 
  3. What workflows are in place to allow technical & non-technical resources to identify, fix, and remediate data issues with downstream messaging/marketing impacts?

 

Quality Assurance
Definition
Ensuring you use the most authoritative and accurate data available and that you’ve set up safeguards for detection and remediation for when data isn’t accurate.

Why is this important?
Just as data migration is a continuous process, so must be the quality assurance of that data. Martech that “gets” data has multi-layered data interaction built-in so marketers can understand what’s going on with their data, build intuition around patterns and cadence of change, and dig in to fix issues when they inevitably arise.

Questions to ask of the technology solution:

  1. What are your mechanisms to QA data for end-business applications? 
  2. What feedback loops are in place such that business stakeholders can identify and flag data issues? 
  3. What tooling investments are you making to allow end-business stakeholders to make short-term inline fixes to data issues?

Click here for the second installment of this two-part series, where we dive into the critical questions to ask around modeling & metadata, segmentation, experimentation, and more.

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