The Complete Guide to Product Qualified Leads

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Chapter 4: Gathering and Organizing Your Product Data

The goal of gathering your product data is to be able to qualify leads more effectively, and to send different messages based on different actions a user takes. 

To do this, you need to source your product usage data, then package it up into a form that can be synced to your CRMs and message tools.

This is how you can refine and personalize your messages to ensure they’re relevant to a person’s actions.

Emmanuelle Skala, previously VP of Sales at Digital Ocean (now Head of Customer Success at Toast), found they could use product usage data to personalize each sales and marketing message.

“Once you have all this rich, contextual data, your outreach is really only as good as your content. It's not okay just to say, 'We noticed X, let's have a call,' especially with the audience we're selling to.” -- Emmanuelle Skala, previously VP Sales at DigitalOcean - now VP of Customer Success at Toast.

The data around your product usage is tracked in many forms and different places -- for instance, your backend database and your analytics tools.

But the data that is tracked and stored here is NOT the same as your sales and marketing tools.

Your sales, marketing and customer-facing teams use marketing tools and CRMs to organize their data. CRMs are very different databases to your production database or analytics software. Instead, these tools store things like your customer’s lifecycle stage, company size, lead score and so on.

The result is that your data is scattered and siloed across many different tools which don’t talk to each other - a “frankenstack”.

The challenge is to package your product usage data up (from where it’s tracked and recorded) and deliver it to the tools your sales and marketing team use in a useful, reliable way for their messaging. Without this, your team will struggle to implement a product qualified leads process.

For John Sherer, Director of Sales at Appcues, he wanted his sales team to take advantage of good fit leads in their free trial. But this data wasn’t available in their CRM.

“Our CRM, which is where our sales people spend time - which is where we want them to spend time* - we don’t want them bouncing between a bunch of different tools.” -- John Sherer, Director of Sales at Appcues

Furthermore, your customer-facing team members (sales, customer success, etc) shouldn’t only look at product usage data. They need to consider other more ‘conventional’ lead qualification data, like the account size, whether the lead contacts have buying power within the organization and so forth.

To understand how this data comes together, we use the Customer Stack Framework.

The Customer Stack Framework links all your customer data together

The Customer Stack shows how data flows together between messaging potential customers, tracking their reactions, recording to a profile, deciding actions to message those potential customers again.

1. Messaging People

Sales calls. Emails. Ads. In-app chat. Whatever it is, whether they’re sent by a tool, person, or carrier pigeon - it’s all sending messages to a person.

2. Tracking Reactions

After sending a message, we want to track how they reacted. Whether this is capturing data from a from, measuring website activity, recording notes from a phone call - it’s all tracking reactions.

3. Record to a profile

With tracked reactions to messages, we want to record this to a profile. Whether it’s identified (Joe Bloggs whose email is…) or an anonymous (like a cookie) person - it’s all recording to a profile.

4. Decide actions from a profile

From your customer profiles, you have the data and context to decide what actions to take. Whether it’s enrolling in a drip email campaign, dropping them a phone call, or adding them to an ad audience with manual work, automated workflows or AI - it’s all deciding actions.

These actions then trigger more messages. You’ll notice this forms a loop - a closed loop - where all the data flows and informs the next message.

To take a few examples:

Stage Email Sales Ads
Message Send email Sales call Display ad
Tracking Opens, clicks, replies Call notes Clicks
Profile Email contact profile CRM Profile Ad cookie
Action Segmentation and drip email Sales tasks Ad audience

The point is, it’s all the same. For every customer interaction, you send messages, track reactions, record to profiles, and decide actions.

Setting up customer data flows

The theory is great, but in practice, there’s complexity in linking tools together. How do you take product usage data from your backend database, and use that to trigger activities in Salesforce?

Most companies battle with these seven techniques for managing their customer data:

1. One-click “native” integrations

For instance the Mailchimp Connect Integrations and the Salesforce AppExchange. Typically, these take a few clicks to login to each tool, connect them and start seeing the data flowing.

But, the integrations you need between the tools you use aren’t always available or do what you need them to do. For product usage data, not every tool is set up to handle an event stream or custom fields from your product database.

It’s hard to know when things go wrong, or have control over fixing it. This is a problem given the volume of product usage data most companies have.

2. Manual export and import (often via a spreadsheet)

Most tools enable you to export data via CSV or similar. This lets you manipulate it and plug it into a another tool.

But this becomes tedious and time-consuming, particularly for regular and complex tasks. It’s hard to collaborate over joint data in a way that’s reliable, up-to-date and dependable. Finally, with data processing happening in multiple tools and spreadsheets, it’s very hard to see diagnose and fix problems when the data is not right.

Since product usage data is an ongoing stream, the only feasible way to manage this manually is to batch it. This introduces a time delay, so you can’t react and reach out in near real-time.

3. Zapier and other if/then workflow tools

Simple to setup one-off automations, workflow tools can be a great way to take your first steps with automating your customer data flows.

But workflow tools struggle with complexity and large volumes. They’re best for sets of one-off data like forwarding form submissions.

Product usage data is a constant stream that brings a lot of volume and complexity. Moreover, you need to be able to combine it with other data like your account lifecycle status and related contacts to an account. Your “if this, then that” queries need so much context to make sure the right people trigger the right things.

This often results in you having to dump large volumes of data in your CRM (which is expensive) to match up the data there.

As you develop your customer data flows with more tools, more triggers and more data types, workflow tools becomes quickly overwhelming. Besides struggling with the volume and complexity, it becomes very difficult to diagnose and fix problems across many workflows when the data is not right.

4. “All-in-one” platforms

As your business matures, managing customer data becomes a more expensive, time-consuming problem. Platforms like HubSpot and Intercom have a lot of functionality to manage multiple jobs-to-be-done in your customer operation - combining sales, marketing, customer success, and so on under one tool. This helps simplify your stack and lower the mental overhead for your team managing multiple tools.

But not every tool does everything. You still need to integrate closely with other tools. Intercom doesn’t have a sales CRM. HubSpot is not designed for  support and post-sale lifecycle stages that customer success and support work from.

The reality is you’ll have a few “all-in-one” tools for each main team in your organization. Yes, you’ve the hygiene of having less tools to manage, but you still need to be able to sync them together with each other and all your other key tools.

5. Code custom integrations

Developing your own integrations gives you complete flexibility and control over your customer data flows.

But this often comes at the cost of diverting development resources from product and engineering to work on integrations - where it’s not their primary objective. The scope of integrations often gets scoped down, integrations may get built but not maintained, and it can be slow to get fixes. Even with the most technical teams, custom coded integrations are usually a low priority.

6. Data warehousing

Instead of investing time, money and calories into connecting tools together, you can aggregate and dump all your data into a data warehouse like Amazon Redshift.

The problem with this is it’s only half of the problem. The data needs to flow back into each of the tools you’re in a reliable and up-to-date way. You end up needing to use the other five techniques (especially manual export and import) to get the data where you need it.

7. Customer data platforms

“CDPs” are custom built for combining, transforming, segmenting, and syncing customer profiles and customer data between a variety of different tools. With the clicks instead of code, you don’t need a developer, but you have the complete flexibility to control your data.

They can inject large volumes of complex data, match it to one account, and combine their data into one customer profile. Advanced customer data platforms can then transform data (into formats for each tool you use), segment those profiles, and sync them in real-time to all tools you’re using. This ensures every tool you’re using has the full context of each customer ready to take action.

Customer data platforms come in different forms - some for enterprise companies, B2C, and mobile. Hull is a B2B customer data platform to sync data between tools like Salesforce, Intercom, Segment, HubSpot, SQL databases, Slack and more.

Instead of managing multiple tools individuals, teams across a startup are able to manage all their customer data in one place, and sync to all their tools.

“Aligning customer audiences across multiple tools is a pain that all marketing & growth teams face. Because Hull centralizes the data store & segmentation engine we've reduced campaign overhead dramatically.” -- Guillaume Cabane, VP Growth at Drift, previously VP Growth at Segment

Remember, the goal is to be able to use your product usage data for different messaging to target the right people at each stage.

The seven different data integration methods have different advantages and disadvantages. The core capability needed for product qualified leads is to ingest large volumes of product usage data AND transform it into a form that your CRMs and messaging tools can use. This may require you to look beyond your existing data integration methods.

How to gather and organize your product usage data

     1. Identify the type of data you need for product qualified leads

          a. Product usage data

          b. Lifecycle stages

          c. Sales activity

          d. Firmographics

     2. Identify: where is each type of your data stored?

          a. Product database

          b. Analytics tools

          c. CRM

          d. Marketing automation tools

          e. Data enrichment providers

     3. Where are your marketing messages sent from?

          a. Marketing automation platform

          b. Email newsletter tool

          c. Live chat

          d. Ads

          e. Website & CMS

     4. Where is your sales team using?

          a. CRM

          b. Sales cadence tools

          c. Research and prospecting tools

     5. How do you plan on integrating your product usage data with your other tools?

          a. One-click “native” integrations

          b. Manual export and import (in batches)

          c. If/then workflow tools (like Zapier)

          d. “All-in-one” platforms (like Intercom)

          e. Custom coded integrations

          f. Data warehousing (like Amazon Redshift, in batches)

          g. Customer data platforms (like Hull)

Takeaway Points:

- The data your sales team needs is not in the tool they use (usually your CRM)

- Your goal is to combine all data you’re tracking into one profile. Then, use all that data together to decide actions and messages. See the Customer Stack model.

- Different teams use a mix of the seven data integration methods to get the data sales needs into the tools they need to use. You need to assess each option and figure out what what works best for you.

Authored By

Claire Suellentrop

Founder, LoveYourCustomers

Claire Suellentrop helps high-growth SaaS companies get inside their customers’ heads. Previously the Director of Marketing and #2 employee at Calendly, she’s seen firsthand that truly effective marketing stems from a deep understanding of existing users’ needs.   

Now, she works with companies like Wistia, FullStory, and MeetEdgar to uncover their best customers’ needs and desires, then uses those juicy details to create more relevant, high-converting marketing and onboarding campaigns. 

Ed Fry

Head of Growth,

Ed Fry is passionate about helping marketers grow their organizations and directly contribute to revenue. He was the first employee at and worked with thought-leaders like Rand Fishkin, Co-Founder of Moz, and Dharmesh Shah, Co-Founder of HubSpot. During his tenure, membership grew from 5,000 to 165,000+ members between 2012 and 2016.

Ed currently oversees Growth at Hull - a customer data management platform that eliminates data problems for marketing and sales teams alike. 

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