Customer experience through customer architecture

Turn Your Tech Stack into an Ecosystem: A Technologist’s Guide, Part 2

by | Sep 16, 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 bigger role than merely enabling execution. There are other elements in the “smart marketing” continuum that must be accounted for. 

This won’t come as a surprise to anyone with even a passing familiarity with the current martech solution market, but 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 2 of a two-part series on essential areas for consideration when assessing technology solutions. For Part 1, click here

 

Modeling & Metadata
Definition
Describes the internal structure of data after it has been ingested; this is how one stores data in the system for downstream use.

Why is this important?
Choose your data models carefully — they must serve a purpose, a desired downstream outcome. The model needs to serve you for ingesting data, segmentation, targeting, content, reporting, et al. And the data items must be consistently available to you across the stack. 

Of course, there will be times when you need to assist the solution in understanding the semantics behind your data. Providing this information in the form of metadata annotating your underlying data model allows general-purpose marketing tools to be brought to bear on your bespoke data model.

Questions to ask of the technology solution:

  1. How are you enabling business users to contextualize data for better reporting, insights, and core marketing utilization (e.g., tagging of sources as paid or organic)?
  2. How are these insights reflected in downstream reporting that shows conversion rates broken down by each segment?

 

Customer targeting & segmentation ownership
Definition
Building audiences for targeting with marketing campaigns

Why is this important?
The exploration and understanding of target audiences cannot be a function of your data engineering teams. It has to be put in the hands of the marketers who will ultimately define and execute marketing campaigns over the segments.

The key is to enable data engineers to get data into the system quickly and easily while empowering marketers to quickly build intuition and to experiment with various ways of slicing and dicing audiences. If marketers are waiting 2 weeks for the data team to come back with a new segment they’re interested in, only to find it contains 5 users, you are not effectively unlocking the potential of your data.

Questions to ask of the technology solution:

  1. At the core of marketing is audience selection, for which proper data underpinnings are critical, so how do segmentation tools integrate into core data and systems? 
  2. To what extent can marketing, product, or growth teams own and maintain these tools?

 

Content & personalization
Definition
The use of personalized attributes of contacts, and potentially joining in additional data, like that from a product catalog, to produce content and copy for marketing messaging

Why is this important?
The days of spray and pray are over. Every time one company improves upon its personalization abilities, the bar is raised for every company. There are many things that fall under the umbrella of “personalization.” The first thing that pops into your mind is probably product recommendations. Personalization can also include triggered content flows, automatically adjusting email cadence when the system notices exceptionally high or low open rates, geo-located promotional offers.  

Questions to ask of the technology solution:

  1. How do your content management tools integrate with marketing systems? 
  2. How does personalization tech (recommender systems, etc) integrate? 

 

Experimentation
Definition
Running controlled tests that can measure the lift for marketing variants across dimensions including timing, channels, content, and more.

Why is this important?
Fundamentally, marketing is about changing behaviors, and experimentation is the cleanest way of measuring attribution and ultimately, causality. To holistically test the full customer experience, experimentation solutions must work across all channels of communication.

Questions to ask of the technology solution:

  1. How can you run coordinated, multi-channel experiments across all of your customer-facing systems? This should include systems that control on-site personalization, email, direct mail, ads, and more.
  2. What do workflows look like to configure, deploy, and then measure experimental results? How much can be done in a self-serve capacity, and what needs custom or engineering support?
  3. How are you going to educate and promote good experimentation culture? How is experimentation part of your company strategy? How do you plan on educating the business around when to run an experiment?

 

Insights & reporting
Definition
Data analysis in the form of standing reports that indicate marketing campaign operations and performance as well as exploratory tools for understanding factors driving those outcomes.

Why is this important?
Best-in-class marketing is iterative and requires a tremendous amount of hypothesis testing. But you can’t formulate hypotheses in a vacuum. You need to be able to find patterns and trends in data to inform your hypotheses, making insights and reporting a fundamental cornerstone in both the front and back end of any experimentation approach.

Questions to ask of the technology solution:

  1. How are you building analytical tools and support? 
  2. How much are you leaning on full-service models (with analysts/BI resources who assist with requests) vs investing in self-serve tooling? 
  3. To what extent are reporting and insights integrated into workflows? 

If you have questions about these lines of thinking or would like to go deeper into understanding how to address these issues, don’t hesitate to contact us here.

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