Sales teams need people to talk to, and it is marketing's job to deliver that. Cue endless discussions about how marketing can generate leads.
At Hull, we work with data-driven marketers to implement playbooks that drive business growth. All of them are looking to generate more leads, but none of them approach lead gen in the same way advocated by many 'listicles' in the space.
We're going to break down the lead geneneration strategies we've spotted:
- The three most common sources of qualified leads, and how to set them up
- How teams generate new leads and opportunities from their existing marketing effort
- Which three tactics are most effective at increasing the number of sales-ready leads.
This is Spotted.
In our Spotted series, we break down and share the trends, tactics & techniques we see scaling SaaS teams do (not just what they say) on Hull and beyond. Subscribe below for the next article in the series.
The problem with lead generation
This will not be a post outlining 101 ways to capture people's email addresses.
The problem with just depending on email capture to drive lead generation is it doesn't solve your business problem.
The business problem isn't generating leads. It's generating sales opportunities. Pipeline. Deal flow.
Not every lead will generate sales opportunities. This is often where sales and marketing do not align - marketing sends more leads (not better leads) and sales become frustrated since they can't close them. Some teams call this the "lead pile up".
You need a different strategy.
This is why data-driven marketers care about qualifying the leads they get and maximizing sales opportunities - not just top of funnel lead generation
For data-driven marketers, this means shifting your focus from the top of the funnel to the mid-funnel. How do you build a better qualified lead?
The top of the funnel might have lots of volume, but lacks much data on each lead.
The bottom of the funnel might have lots of data, but it lacks volume.
Only the middle of the funnel has both volume and plenty of data. This is the biggest source of leverage for automation:
- Trial signups
- Webinar registrants
- eBook downloaders
- Event attendees
The Right Mental Model for
Lead Opportunity Generation
There are three dimensions that data-driven marketers use to identify leads that are likely to create sales opportunities - fit, intent & engagement.
- Fit: Do they match your ideal customer profile?
- Intent: Are they in the market to buy?
- Engagement: Have they interacted with your brand or product?
To identify your highest value leads, you need to "join the dots" with your data around each of these areas and distinguish your different types of leads and subscribers.
Here are the most common ways we see data-driven marketers generate leads that generate opportunities.
Tactical Takeaway #1: Find your best marketing qualified leads without endless forms
Marketing teams will generate marketing qualified leads to pass to sales when there is a signal of intent like a demo request. They're ready (they're asking!) to talk to sales.
But whilst signals like demo requests are great for signalling intent, and they imply engagement (they've probably evaluated your product on your website before), they don't often indicate fit.
Demo requests could fit anywhere on this chart, but best-fit companies (that match your ideal customer profile) should be prioritized by sales since they're more likely to create new sales opportunities.
To fill-in-the-blanks in their profiles, most data-driven marketers use data enrichment providers. By sending a "key" like
domain, data enrichment tools can return over 100 data points like job titles, employee count, country, industry, and more.
This means you can shorten your lead forms, focusing your questions on contact details and asking the one simple question your sales team can use to qualify, segment, or start a conversation with.
But you can also still get the data you need to see if the lead matches your ideal customer profile.
Using data enrichment, you can then segment and prioritize your leads based on the data
Here's an example of how we might combine data from Clearbit and Datanyze to enrich demo requests from Intercom, Typeform, and HubSpot.
Data-driven marketers use data enrichment to identify their best marketing qualified leads.
Bonus: Use enrichment to identify the best leads to nurture
You don't just have to limit your data enrichment to high-intent leads (like demo requests). Some teams enrich all their leads to identify best-fit leads before they show intent.
By segmenting out these companies and creating hyper-personalized lead nurturing, you can increase their engagement and intent to buy over time.
If you already have a high volume of leads, this is one of the most effective methods we've spotted for driving an increase in sales opportunities - some companies have seen a 20X increase in marketing qualified leads with this form of nurturing strategy.
Voila! You can now identify fit, intent & engagement amongst your marketing qualified leads (MQLs)
Tactical Takeaway #2: Find your best conversation qualified leads (CQLs) without endless chatbots.
More teams using live chat and chat bots to engage leads on their website. It's great as a "second net", but it can still leave a lot of opportunity on the table.
The challenge with live chat is you don't always have the full context on a person.
- Who are they?
- Are they a good fit lead?
- Are they an existing customer (looking for support, not sales?)
- ... or a partner? Competitor? Existing opportunity?
To start to narrow down these options, live chat tools often offer a chat bot to ask the basic questions. What is your
But this doesn't create a seamless, delightful experience for the person interacting, particularly if they've given the details previously. And long chat bots can lack the charm of interacting with a real-life human or the quicker, pragmatic nature of a form for capturing more data.
To solve this, live chat tools are also starting to use data enrichment to reveal the companies (and data about them) who are chatting about them - this uses reverse IP lookup technology.
The challenge is that your data needs to be associated with all the rest of your data about a lead and account so you have the full context to take action. You might be able to determine fit, but not if they're a customer/competitor/partner/existing opportunity, or what they've done previously.
To resolve this issue, you need to have a way to "join the dots" with the rest of your data, so when a company is identified as talking to you, you can bring all the context you have on them together.
You need to be able to sync your chat tool instantly with your systems of record (product, CRM, marketing automation).
Voila! You can now identify fit, intent & engagement amongst your conversation qualified leads (CQLs)
Tactical Takeaway #3: Find your best product qualified leads (PQLs) without getting lost with data analysis
For many SaaS companies, free trials are at the heart of the buying experience. The challenge many sales teams have is who to reach out to and when - beyond guesswork or cherry picking recognizable names.
Like with marketing qualified leads (like demos), it can be challenging to identify the best-fit companies.
Even amongst best-fit companies, some will engage with their trial and continue to buy your software whilst others don't. The challenge here is connecting your product to your sales and marketing tools so you can trigger outreach, automation, and campaigns with your own data.
The best practice we see with free trial conversion - data-driven marketers segment their trial sign ups by fit (using data enrichment instead of long forms) and assign their best-fit trial users to sales reps.
All the trial signups will then get nurtured based on the actions they take within the product - particularly the key activation events which indicate likelihood to buy. This is what signals buyer intent.
Using your product actions to trigger real-time outreach, either from a sales rep or from automated emails, chat, ads, and more, we see data-driven marketers dramatically increase their free trial conversion rates - some as much as 70% on their mid-to-low fit leads.
The same product-triggered tactics can be applied to customer success to nurture account health and expansion - not just to free trial conversion.
Voila! You can now identify fit, intent & engagement amongst your product qualified leads (CQLs)
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Tactical Takeaway #4: Find your best marketing qualified accounts (MQAs) who are ready for sales outreach
The three most common forms of lead generation - MQLs, CQLs & PQLs - all focus on one individual within a company. But this doesn't represent how most B2B SaaS purchases happen (with many stakeholders) and can mean you miss out on opportunity creation.
All your lead generation efforts will create engagement with your brand & product. Some of this will tip over into signals of intent to buy (demo requests, trial signups etc.).
But this can leave a substantial amount of potential sales opportunities on the table - accounts (not just individual leads) that are engaged but have not:
- "MQLed" by requesting a demo
- "CQLed" by talking to you or your bots
- "PQLed" by using your product (and having budget & authority to buy)
But if you took all these signals in aggregate at the account level, they'd definitely be worth reaching out to. The "almost there" accounts.
Some people may call this outbound. The danger here is most teams treat inbound and outbound as separate funnels & channels, but your buyers don't.
The best data-driven marketers understand their buyers journey at the individual (website visitors, leads, prospects) and account level. The identify "warm" accounts that are just a few steps away from becoming a true sales opportunity and continue to engage them.
By tracking how companies are engaging with you (across the actions of many associated website visitors, leads, prospects & trial users) you can identify which accounts to target and reach out to as they progress through the modern buying journey.
The best data-driven marketers leverage all the context they have (from joining the dots in their data) to make their outreach highly personal.
This can have multiple moving parts. For instance, at Hull we want to target accounts who have recently:
- Almost MQLed (started booking a demo, but didn't pick a time)
- Almost CQLed (started a conversation, but didn't share a use case)
- Almost PQLed (started signing up for the product, but didn't share their email address)
Bonus: This is where other intent data like topic surging, reading reviews on 3rd party sites, tracking technologies they're trying can also be incorporated at as a trigger.
For all of these, we don't have an
For prospecting across an account, it can help to use a data provider with APIs so this can be automated. Together with research by your sales team via tools like LinkedIn Sales Navigator, you can piece together the right people you need to target.
For other engaged leads (where we know their email. Trial signups, newsletter subscribers, chat conversations etc.), we can tie more data to their profile as well as the account. But it is still important to build the picture of the account too.
By thinking in terms of accounts (and prospecting within that), you can increase the number of sales opportunities from your existing marketing effort (they're already engaged!) instead of just thinking of generating new leads.
Voila! You can now identify fit, intent & engagement amongst your engaged accounts
Tactical Takeaway #5: Maximize engagement with leads to accelerate their buying journey
If you think about the challenge of lead generation as opportunity generation, then you can focus on maximizing engagement (towards intent to buy) amongst known contacts.
This gives two paths to grow:
- Increasing the number of contacts (hard to move quickly)
- Increasing engagement amongst the contact you have
The challenge most marketing teams have with increasing engagement is hammering the same method they've always used - send more email.
But your leads burn out if you hammer them with email, and the only metrics that increase substantially are your unsubscribes.
Data-driven marketers use three different strategies to increase engagement.
- Opt-in engagement cycles
- Omnichannel marketing
Personalization strategy is about controlling WHO you talk to (segmentation), WHAT you say (templating), and WHEN you say it (workflows). The best practice is to combine all the context they have on their customers to send only hyper-personalized messages to their customers.
Not just email campaign & automated messages messages, but newsletters, chat on the website, transactional email, in-app notices, and so on. Aim to personalize every touchpoint in the buyer's journey - it can help to do customer journey mapping to lay out the opportunities for improvement here.
Opt-in engagement cycles is about asking leads to take a specific action which leads to a sequence of activity and engagement. For example:
- Registering for an event or webinar means a series of reminder emails, attending the event, and follow up posts.
- An email course that's triggered by course completion based on completing sequential elements.
- Live chat triggered by UTM parameters from email
How can you structure offers for people to opt into that trigger a sequence of engagement?
Omnichannel marketing is about synchronising tools that can deliver messaging across multiple channels, so instead of burning out leads with email you can supplement messages via other channels. Email and ads are both scalable and easy to turn on, but to also consider live chat, web personalization, targeted sales outreach, and offline gifts.
To make this work, you a centralized tracking & segmentation engine to update all your tools in real-time when leads do (or do not) take action. This needs to be real-time to ensure your different tools are not displaying different, conflicting messages over different channels to the same lead.
Used separately (or together) personalization, opt-in engagement cycles, and omnichannel marketing allow you to maximize engagement and create more sales opportunities - all without burning out your list.
Voila! You can now maximize engagement amongst your best-fit accounts
Best-fit criteria for data-driven lead generation
You have working lead generation process (however small)
You already have a funnel in place to capture email addresses through sales-ready offers like demo requests and trials, or simpler offers like a newsletter subscribe or live chat capture.
Every marketer wants more leads :) But the challenge here is making sure you reliably have some new leads coming in already.
You have a clear ideal customer profile.
Your ideal customer profile (ICP) is about identifying a clear, common, objective definition of your best-fit customers.
This is important since your ICP defines precisely what data you need to qualify leads (see our guide).
Without this, you won't have a way to measure "fit" - one of the three key dimensions of data you need.
You have a sales engagement strategy (however basic)
If you sent sales 100 best-fit leads tonight who were all ready to buy, what would they do with it?
In the same way you need to build a structured, data-driven process around lead generation, you need to make sure you have process around how you enable sales to engage leads.
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