SaaS Customer Data Integration: The Complete Guide

Customer data is the lifeblood of every customer-facing team. As your team scales and you add tools to your martech stack, it's inevitable your data becomes siloed & outdated, your tools don't all talk to each other, and your teams become misaligned.

The cure is customer data integration. Here's a quick primer on how to get started with customer data integration.

But first, it's important to approach customer data as a system. It's not just data - it's tools, teams AND data.


Teams own tools; tools own data. This is the power dynamic you will always bump into.

Customer data integration is about making all your tools, teams and data work seamlessly together so personalization, attribution, reporting, GDPR, and all the fun stuff becomes a possibility reality.

Let's tackle these one by one.

First tools...

Tools: Understand your martech stack.

You need to have a solid understanding of all your tools, tracking & databases which hold any customer data.

(You probably already have this documented under "Data Processors" as part of your GDPR compliance efforts - check with your data protection officer on your team.)

  • Tools (sales, marketing, but also product, finance & operations)
  • Tracking (website, product, elsewhere)
  • Databases (product databases, data warehouses)

From this list, you need to be able to understand how these tools "talk" to each other (or what integrations between tools are missing).

One of the first things we do with any new potential customer at Hull is draw a data flow diagram like this one.

From here, we'll ask questions to better understand how your data flows. (What data objects are used? Are the integrations one-way or two-way? How does data flow across multiple tools?) But the high-level data flow diagram is an important first step.

You might also notice a lot of duplicate capability in your stack like many tools with email marketing features.

Having lots of tools complicates any customer data integration project. Most martech tools are sold on annual subscriptions - we recommend being ruthless in "hiring" tools for jobs-to-be-done (just as you would with an employee) and reviewing your jobs-to-be-done to making your tool "hiring" & "firing" decisions.

Team: Customer data needs an owner

Though customer data is used by many customer-facing teams (sales, marketing, support, product), it's much easier to manage if there are dedicated owners. Just like with any other specialized task, customer data management should not be everyone's problem.

Remember, teams own tools; tools own data.

Early stage companies might have a sales or marketing manager line manage a team as well as manage tools & data.

As teams grow, usually there's specialization. Sales & marketing managers take care of the team, and sales & marketing operations take care of the tools & data.

As teams continue to grow, and the demands of tools & data become more complex, we often see customer data management be centralized under business operations with dedicated technical resources (like data engineers) to manage customer data integration, data warehousing, business intelligence, and similar projects.

We have a dedicated guide to customer data management & operations for different stages of your company.

Data: You need usable data in many sources

Last but not least, you need to understand your customer. In marketing, this is loaded language - who doesn't claim to be "customer centric" :)

Here's what we mean by customer data with respect to customer centricity. As soon as your business & customer base scales beyond your founder's brains, you need to rely on tools & data to give you that complete context over every lead & customer. Customer data is what enables you to understand & react to anyone who interacts with your brand & product.

That context needs to flow across your system of tools & teams, so every interaction is focused on who the customer is, what they've done in the past, and what you would like them to do next.

With this in mind, here's how you should approach assembling the data you need.

Firstly, you need a clear idea of who you're selling to - your ideal customer profile. This should be a clear, common, objective definition that you can easily tie to data. Your ideal customer profile is what defines rules like lead qualification.

This is not a "persona". Personas are used to describe how you talk to & sell to someone, often with a name for easy reference. Ideal customer profiles define who you sell to.

Second, you need to have the context of what a person (or company) has done, including their most recent actions. The most effective method we've found for understanding this is customer journey mapping.

Draw your customer lifecycle or marketing funnel on a big whiteboard or sheet of paper. Make it a collaborative effort with someone from every team who "owns" customer data (sales, marketing, product etc.). From a customer journey map, it becomes very simple to identify the key moments & conversions to track.

Third, instead of mapping all your different (perhaps competing) data sources to each other, you need a way to combine all your data into one clean & unified customer profile that can act as your "single source of truth" for all your other tools & teams.

With customer data, it's important your source of truth needs to be able to update & sync data out in real-time. This isn't about triggering real-time actions (like web personalization or live chat, though that's nice) but making sure that whenever you do take action, your data is the most accurate, most up-to-date version possible - your "golden customer record".

If there's lag in syncing data between your tools, you can't rely on the data in any one tool to be up-to-date, so your teams won't trust it. (Find a sales rep who hasn't been embarrassed on a call saying the wrong thing or a marketer who hasn't botched an email with a funky {merge} tag).

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Now, think of using data to design experiences

Your customer journey is made up of a series of experiences, like your free trial, onboarding, enterprise upset, and so on. Each of these experiences is owned by a team (and their tools).

You need to think about how to orchestrate 1:1 personalized experiences at scale by matching content with your customer data.

For instance, how do you decide WHO your sales reps talk to, WHAT they say, and WHEN they say it?

Customer data is tied to people (and company) profiles in the form of attributes (properties, traits) and events (actions). This data informs three types of rules which drive your customer experience:

  1. Segments - WHO to talk to
  2. Templates - WHAT to say
  3. Workflows - WHEN to say it

With those three rules, you can orchestrate a customer experience by triggering actions & matching content from your tools.

Pick a customer data integration strategy for your scale

When you're a smaller, earlier stage company, you probably don't have many tools or complex data setups. Native integrations, Zapier, and some manual exports & imports will work well.

But as you scale, your teams across your company will introduce more tools and types of data. If everything is supposed to point to everything else, it becomes incredibly complex & opaque, and difficult to integrate. Look at this...


You'll also find tools don't easily talk to each other, like your backend database or analytics tools with your sales CRM & marketing automation tools. Ready-to-go integrations don't exist.

Where integrations do exist, you will find many integrations are only one way, or have limited capabilities (like with Zapier or Segment). Integrations maintained by companies are often logos to say "yes, we have that" at a tool level, but not on the data flow level.

Without a centralized customer data integration strategy, it becomes hard to keep data complete & up-to-date in all your end tools. You need something to go in the middle of your martech stack to unify, transform & sync any customer data between all your tools, tracking & databases.


Orchestrate all your tools, teams & data with Hull

Hull is a real-time customer data platform. Combine data from all your tools, tracking & databases into a unified customer profile. Then cleanse, enrich, segment, and sync across all your tools.

Explore Hull's customer data platform

Move fast by implementing brick-by-brick, not a BIG BANG release

We have never seen a successful customer data integration project where everything was implemented all in one go because customer data integration projects have so many moving parts; tools, teams & data.

Once you have mapped your customer journey and then drawn a data flow diagram of the key elements of your system - tools, teams & data - we strongly recommend breaking this down into individual projects and expanding incrementally.

You might be able to work through each use case quickly (great!) but this is far faster (we've seen) than tackling everything all at once.

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The Complete Guide to Customer Data

Ed Fry

Prev 'Ed of Growth at Hull, working on all things content, acquisition & conversion. Conference speaker, flight hacker, prev. employee #1 at (acq. HubSpot). Now at Behind The Growth

If you've questions or ideas, I'd love to geek out together on Twitter or LinkedIn. 👇