Have you ever experienced this? A disconnect between sales and marketing, despite having an abundance of data at your disposal.

It doesn’t add up. Qualifying leads should be easy with an abundance of data, but few companies appear to use all their data effectively.

Leads can be defined by more than a score, an industry, or a form submit. This is simple, “shallow” lead qualification that blurs the context that sales need to qualify, communicate with, and close deals.

Instead sales has to put the pieces together themselves - trawling Google, LinkedIn, and engaging in back-and-forth “discovery calls” to get basic answers.

Familiar?

What if we took it to another extreme? “Deep lead qualification”. Combining multiple sources of data to build a rich profile on each lead - then decide whether they’re a fit and have intent to buy or not.

We’ve found deep lead qualification to dramatically increase sales efficiency. Doubling qualified leads, and doubling the speed to close, it’s transformed sales operations.

Here’s how and why it works.

Sales and Marketing Don't Speak The Same Language

Picture the scenario - you’re in a French restaurant. You order steak, ‘frite’ and a sauce on the side. A few minutes pass. Your plate arrives. It’s a raw steak, fries, and no sauce. What you received isn’t what you ordered.

This is the biggest problem with lead qualification - sales and marketing don’t speak the same language.

Sales isn’t getting the qualified leads they expect and need (so they’re wasting time and effort digging into each lead to figure out the full story), but marketing feel they are handing off what sales has ordered and then blaming sales for not following the right process to close them. More "sacré bleu!" than "bon appetit".

The problem starts with how leads are qualified. Whether it’s a simple form submit, lead score, product usage, or something else, lead qualification boils down to data. The more types of data you’re using to target your ideal customers, the more refined you can get.

Deep Lead Qualification is evaluating leads across multiple dimensions and data points to establish fit and intent. By using the depth and breadth of data you have in all your tools and databases, you can qualify leads before they hit your SDRs and account executives - before they need a human to review them.

For instance, deep sales qualification could look at firmographics, contacts, and their engagement:

Sales Needs Context to Start Conversations.

The reason for all this data is to direct and empower sales to close more deals. The CRM should not just specify who to speak to; the CRM needs to also specify why.

Sales are hungry for that clue or trigger as to a lead's underlying need. It doesn’t make sense to treat different leads in the same way.

Hey, I noticed you just attended our webinar on Widgeting. How can I help you with Widgeting at your company?


Hey, I noticed you just viewed our pricing page. How can I help find the best deal for you?


Hey, I noticed you just hit your trial limits. How can I help you get the best value out of Widget Inc.?

Without these clues as to why someone is interested in buying, it’s much harder for sales to be specific and communicate the specific value that matters to the lead - without bouncing between tools, search engines and other diversions outside the core sales workflow.

Today, Sales Don’t Have Enough Context

Sales teams have to establish fit with brute force - back-and-forth “discovery calls”, trawling Google, LinkedIn, and other tools outside their workflow. It becomes their job to try and put the pieces together before they can accept it as a qualified lead themselves.

Every business has unique logic which should be reflected in their CRM for their sales team to take advantage of. Like the difference between serving steak raw and cooked, there’s needs to be a common language to serve up those leads. The common language comes from common data.

The Answer is Not “Dump Everything” in the CRM

Too much data is just as harmful as too little data.

Firstly, it’s expensive to host all your data in your CRM (Salesforce charges $150/month per 500MB). It doesn’t take long for the cost to add up significantly when you start sending and updating lots of data.

Second, it’s hard to keep a lot more data up-to-date, clean, and high quality. Without usable data, sales won’t trust or depend on the CRM for insights. This means they don’t use the data you’ve worked hard to insert into their workflows.

Third, by sending everything you lose control over what data guides the sales conversations. “Hey, I noticed you use Google Fonts. Do you need our {Complex Technology}?”. Cringe. You don’t want to send sales people digging for random insights - you want them to see the insights so they can communicate precisely and effectively.

By putting all your data in your CRM, you flood your sales team with irrelevant data, pay an inflated CRM bill, lose control over sales conversations and have more data to wrangle with than before - everybody loses.

Yes, all things should be in Salesforce but you want you want to give sales a finished dish to eat, not a kitchen to make something they can eat. What matters is the right data at the right time that’s reliably showing up in your CRM. With this, you can guide sales conversations with the data points that matter most whilst saving time and money.

"Predictive" Matches and Lead Scores Miss ALL the Valuable Context

The opposite extreme is to compile all these signals into scores. By computing a score, or even running predictive analysis over a large dataset, you can find good fit leads. This is especially useful for finding opportunities amongst large or complex datasets which would take lots of time to design and compute.

But, it only solves part of the problem. Scoring blurs the context that sales needs to start a conversation. It blindfolds your sales team.

John Sherer, Director of Sales at Appcues put it this way:

Sales people are emotional. They have the impression that there’s a lot of context that is not a part of the score. And then the score just becomes a part of the whole story. And it’s about as valuable as the raw inputs.

A score becomes part of the whole story needed for qualification, and still leaves salespeople searching for context to qualify and start a conversation. There’s all sorts of ways a lead could get a score of “70% fit”, but each 70% could have different needs.

In conversation, “Hey, I noticed your company has a lead score of 70%” is useless compared to “Hey, I noticed you just viewed our enterprise pricing page.”

Scores on their own are mysterious - it’s the ingredients that make each score that give the valuable context, like Submitted a demo request Ideal company size Viewed the enterprise pricing page. A score can give guidance, but ultimately it’s up to the sales person to uncover whether a lead is a fit or not and find the right path to engage a lead in a sales conversation.

Deep Lead Qualification = Combine All That Data About Each Lead Into One Profile, Then Qualify

Hull Combine into one profile diagram

To qualify leads using multiple dimensions of data, and to give your sales team the context they need start a conversation, you need to combine all your data about each lead into one “master profile”.

This needs to happen before it hits your CRM and your sales team. You want to show sales a finished dish, not a kitchen.

With all your data in one place, you can then apply all the logic you need to identify and mark those leads - this is deep lead qualification. Sync just your qualified leads with every element of their profile that’s useful (and nothing more) to your CRM.

This means your sales team can spend their time with the right people, have the full context of the deal so they can speak directly to each leads’ needs to move the deal forward, and close best fit customers faster thus delivering higher revenues and lower churn. Winning!

Implementing Deep Lead Qualification at Your Company

The reason you don’t see many sales teams using many combinations of data, then bubble up the signals that matter to their CRM to maximize their sales efficiency is that it’s a really hard problem to solve.

First, there are combinations of data that are hard to bring together. For instance, website sessions Viewed pricing page and product usage data Logged in with firmographics >30 employees, healthcare industry. This data is all tracked and stored elsewhere in forms that aren't native to each other.

Second, integrating tools takes a lot of technical effort. Where native integrations don't exist or don't provide the functionality that your business logic needs, you have to wire together webhooks and APIs with your own technical team that is intended for product and engineering (cue internal politics)

Third, even if you can wire the tools together, you need to be able to run computation, transformation, segmentation, and syncing so that you have the right data in the form that's useful (and yet native to each tool) for sales to work with.

Finally, you need all of this to work seamlessly so that the CRM is constantly up-to-date with the right data. Without this, sales won't trust the data there. Sales won't use it. All your data wrangling will fall on deaf ears.

CRMs weren’t built for this purpose. They're a window into a world of data, not a brain. They're a table at a restaurant to consume food, not a kitchen to prepare food. Where can this data management happen? Where is the brain?

The emerging solution is to "dump" all data in a data warehouse like Amazon Redshift. Here, you can cheaply store and query large volumes of data. But it doesn't enable rapid computation, transformation, and "thinking" that makes the right data accessible in a CRM. It's half a solution. The “logic” as to what data goes where and in what format is missing. It is pushed instead to your sales team to manage manually in their CRM and other tools.

What's needed is a near-realtime sync engine to connect data and logic across all your tools in whatever format, without needing a developer. This sync engine takes responsibility for keeping your CRM up-to-date all the time with the right data. This is what it takes to implement deep lead qualification.

Hull Star Diagram

Deep Lead Qualification with Hull

Hull is a customer data platform. which connects to all your tools and databases. It pulls all that data, and gathers everything and every action about each person into one "master profile", and connects those profiles around the same accounts. You can then sort, segment, and sync updated profiles and accounts to your Salesforce, marketing automation, and all your other tools in real time so they’re all on the same page.

Deep lead qualification - using multiple data sources to profile and qualify your leads precisely - is possible with Hull.

Hull customers like Mention, Lengow and Appcues are doubling the accuracy and number of qualified leads, and closing them in half the time.

Appcues eliminated 30% of their trial users from their sales team's list of leads and creating intelligent triggers to cue and clue in their sales team on the right accounts.

Lengow fixed a 50% drop off in responses to demo requests using Hull as their customer data platform.

If your sales team is struggling to cope with poor fit leads, incomplete, or inconsistent data, and slow sales cycles, we'd love to talk (Chat to us with Intercom on the right here, or request a live demo below).

Not sure how it all works? Learn more about customer data platforms right here.


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