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Building Intent Into Your MarTech Tool to Add Customer Value

Building Intent Into Your MarTech Tool to Add Customer Value

Why add intent data?

In 2011 there were just 150 MarTech companies. Today there are over 8,000 companies competing for our attention. So how do you stand out in an ever-growing crowd? 

You innovate. You provide more value for your clients. Here’s RevOptimal Founder, Will McCartney’s take.

“We define success as providing valuable solutions for our customers. With the LeadSift Intent API integration, we’re able to maintain a reliable and consistent intent input into our scoring models to maximize the value for our users.”

So how do you make sure you’re ahead of the curve while not only adding these benefits for the end-user but ensuring the quality matches the value you hope to provide?

This is where intent fits in. MarTech vendors are adding intent data directly into their tools to differentiate themselves from competitors. Intent data helps their customers identify, prioritize, target, and tailor messaging, directly to the prospects that are actually looking for help with a problem they solve. Here are just a few ways intent is being leveraged in MarTech tools:

  1. Intelligent personalization: sales enablement and marketing automation tools add personalization tokens based on a prospect’s specific intent triggers. If your customer knows a lead is engaging with sales OKRs, they can automatically tailor messaging with a surrounding value prop.
  2. Seamless enrichment: CRMs, conversational intelligence tools, and contact databases can enrich their customers’ prospect and customer data with their relevant intent data so a deeper understanding of the buyer’s journey and added actionability.
  3. Real-time prioritization: MarTech tools (like RevOptimal) allow customers to dynamically prioritize their leads based on their buying signals. This means prioritization alerts that adapt alongside the buyer’s journey for the most effective prioritization and targeting.

We sat down with Will to learn about the state of RevOptimal pre-intent features, how that evaluation process went, what implementation looked like, and the results customers are getting today. So you can make sure you’re in the A-class among nearly 10,000 other MarTech tools.

Before the integration

Before adding intent to their platform, the team at RevOptimal would work with each customer to identify their Ideal Customer Profile (ICP), build a target account list based on that definition, and then the customer could prioritize their outreach based on the ICP fit score. 

They needed a solution to elevate the prioritization solution. In-market buying signals provide actionable, real-time data so their customers can prioritize the accounts that matter most.

If you know who’s a fit, how do you determine who’s actually looking for you, so you can reach them ahead of your competition? This is where intent fits in.

When evaluating an intent vendor, three key factors played a role.

First, is data quality. “Compared to other providers, the coverage and quality for B2B SaaS companies is really strong. That, paired with contact-level data is a key differentiator” says Will.

Second, the API needed to be reliable, and performant. The cost of things moving too slowly or being error-ridden from the start is too high to risk an API they couldn’t trust. “This allowed us to service our clients quickly, and accurately”.

And I might be biased but saving the best for last was the team behind it. “We enjoy working with the team at LeadSift. We’ve found them to be great partners and helpful in supporting our mission to help our clients generate revenue”.

Right in the feels.

Intent implementation

Getting the ball rolling was relatively simple. Once the i’s were dotted and the t’s were crossed, RevOptimal and LeadSift’s Tech and Customer Success teams jumped in to do a technical onboarding. This helps us understand how the data will be integrated, and how we can support them along the implementation process. 

Following that comes a few weeks of API testing and ironing out parameters to make everything connect the way it needs to based on the use case. Then, connecting LeadSift intent with other analytics and data points to build the product that is now in customer hands (and pipelines), it only took 8 weeks to establish the minimum viable product (MVP).

“Implementation was easy due to the consistency and quick support. There was a time where we had trouble passing parameters, and within an hour we were on a call with the Head of Product to help us figure it out and get things running smoothly for our client”.

From a developer’s perspective, they let us know that the implementation structure was intuitive and straightforward. The ability to control pagination parameters meant they could pull intent signals with greater granularity while controlling costs. The cost structure that grows as you grow gave them the flexibility to build while saving costs, and scale as they add customers.

“That’s something that is awesome for us, being able to pull intent data as we need it, not just on an automated recurring basis”.

If you’re wondering about the endless possibilities with this data, you can see the API documentation here.

“With intent data built-in, we’re now able to inform our customers not only which prospects fit their ICP, but also which ones are likely in-market for their solutions”. 

Here’s what that workflow looks like:

The RevOptimal team works with the customer to build a training data set for their ML-based ICP scoring model. This data can include anything from CRM data, to target account lists. 

Next, they build the intent profile. This is made up of a set of signals relevant to the customer’s business solution including competitors, event attendance, hiring, and custom keywords that each play a role in the buyer’s journey.

Now that the foundation is built, RevOptimal monitors for intent signals then scores them based on fit.

From here, on any given day, customers can log into the platform and see ICP fit accounts that are showing intent in real-time, then pull the leads within those accounts to fuel their ad targeting, ABM campaigns, and outbound sales.

At the forefront, RevOptimal helps customers with account prioritization, intent data plays a key role in the machine learning models that determined when one prospect should be prioritized over another. LeadSift intent data is used as an important piece of the puzzle in uncovering revenue opportunities and prioritizing prospects for their clients. 

“ICP analytics and intent combined means our customers not only know who they want but also who wants them. That overlap is the key we’re able to uncover for our clients”.


If you’re looking to add actionable insights for your customers, stay a step ahead of competitors, or ideally, both, what’s holding you back?

LeadSift 360: Complete intent data. Many sources. One place.

LeadSift 360: Complete intent data. Many sources. One place.

We’ve all heard the terms “dark social” or “dark funnel” by now. Terms like this exist because so much of the buyer’s journey happens in places we can’t see. Most people aren’t waving a flag, shouting they’re in-market.

But intent data is meant to unveil those people right? 

The problem is, most intent data vendors are only providing intent signals from one source. This means you’re only getting one, small piece of the puzzle, and a few outliers could not only throw your messaging and timing for a loop, but cause you to miss the accounts that are actually in buying mode.

Introducing LeadSift 360

We’ve always had a multi-source perspective on intent, but we just took it to another level. LeadSift 360 layers more than five data sources, so you get the vastest data, scored like the stock market, so you have noise-free lead generation, directly in your sales and marketing teams’ hands.

LeadSift 360 mines the public web, including job postings, social networks, leadership changes, public forums, technographics, and any other signal on the public web, and layers it with research-based intent. This type of intent is sourced based on prospects researching and reading content on publisher websites that may signal they’re in-market.

Having more data sources means you get a 360-degree view of the buyer’s journey. The intent data essentially validates itself by being cross-referenced with multiple sources, and scored accordingly.

In practice, that means you can be confident in your timing and messaging, beat your competitors to the punch, and scale revenue.

No more black box intent data

A lot of the skepticism around intent comes (rightfully) from not knowing how it’s defined, what counts as a signal, and scoring that is only ever arbitrary secret sauce. 

So let’s break down how we’re doing things differently.

First things first, the intent data. 

Attached to every lead you get, we tell you the event that triggered the event signal. Whether that’s someone engaging with your competitor, a custom keyword, or hiring for a relevant role, you deserve to have that intel to power your messaging.

Next, scoring.

We revamped our scoring to mimic the stock market. This way, large accounts aren’t automatically, artificially inflated, and small accounts aren’t left behind if they’re truly in-market. Directly in the LeadSift platform, you can actually see why we’ve scored an account the way we have. [Note: in the platform, scores are labeled cold, warm, hot, but in the data deliveries, numbers are there for granular prioritization.]

Other research-based intent providers often send out a lot of noise. This is because the nature of that data is broad and high volume. But this can be a good thing when paired with a more narrow source. By layering research-based intent and public web signals, with trend-based scoring, you can filter out the noise and end up with clear, relevant, actionable intent leads at your fingertips. 

So, if you want to get started with noise-free intent data and scale revenue faster, you can try it free for 7 days.

IntentSity Score: Hear from our Director of Data Science and Engineering

We’re launching a new way to score accounts: Intentsity Score.

At LeadSift we track over 15 different types of intent triggers for all B2B SAS. And that gives us a volume of roughly, almost about a million daily intent data points. That translates to anywhere between hundred to thousands of daily leads for a typical LeadSift customer.

And when we have leads at this large scale, the obvious question emerges is how do we know which accounts to prioritize first? And that’s where lead scoring comes into the picture. 

One way to approach this problem of lead scoring using intent data would be to base it on the net volume of intent triggers received over a period of time. The higher, the volume of intent triggers, the higher the score. 

A net volume-based scoring system suffers from bias towards larger organizations. These organizations have bigger workforces and generate higher digital footprints. And thus, they always score high.

Another problem with a net volume-based scoring system is they treat all the intent triggers the same, but fundamentally, each trigger is unique on its own. To give an example, a funding trigger might be less frequent than an account engaging with the content. And yet the funding trigger might be more valuable in deciding the likelihood to buy than a content engagement trigger. Therefore, if you simply look at the net volume, it’s very easy to miss less frequent, but high-priority triggers.

To address these shortcomings. We designed our new scoring system and we based it on two key components. The first being a statistical model that looks at trends in intent volume rather than the net volume. And the second being a rule engine that assigns higher scores upon finding less frequent, but high-priority triggers. 

Let’s take a look at each of them in more detail. The statistical model analyzes 90 days of intent data volume to quantify trends in it. It generates a baseline of usual intent volume and compares the most recent volume against the baseline to compute the intensity of the trend.

How you’d score is similar to Z-score that calculates a values relationship to the mean of a group of values. The score generated by this model is essentially a measure of the upward or downward trend in intent volume. Through this model, we can also generate insights like, “An account received X percent or higher or lower intent volume in the last 30 days than the usual 30 days volume.”

The second component of the new scoring technique is a rule-based component. In this, we look at the last 90 days data to find the occurrence of high-importance triggers. A few examples of high importance triggers are an account receiving funding, or an account seeing leadership changes, or an account entering a new partnership.

Upon finding these triggers, the scoring model factors them in to assign high scores to these accounts. This helps the scoring model to generate insights like, “An account received a high score because it received funding in the last 90 days”.

To sum up our new Intentsity Score, at its core, uses a statistical model and rule-based engines to analyze trends in intent data volume and score accounts.

A higher Intentsity Score would generally mean a higher chance of the account being in its buying journey. From a BANT sales, lead qualification perspective, the Intentsity Score addresses the need and the timing piece. 

We are very excited to launch this and would love to have you try it.

Direct Dials: Reaching the Right People, Right Away

Direct Dials: Reaching the Right People, Right Away

What if you could know who was hunting for your solution? Maybe even researching your competitors?

How much would that help fuel your marketing and outbound team?

Now you can find those people and reach them on all channels. Right away.

With contact-level intent data plus direct dials, you can receive phone numbers, emails, and LinkedIn profiles of the 7% of your market that are actively in the buyer’s journey.

So you can call them before your competitors.

How to make a great cold call

  • Timing. Depending on your average sales cycle, nailing time is key to getting ahead of competitors. Use every tool you can to prioritize who’s looking now, and reach out to them first. This could be based on web traffic, content downloads, or signals of buyer intent. Timing means you can get to them first, and with relevant messaging.

  • Targeting. You have to know who you should be talking to. Go deeper than just knowing thier job title. What’s their role? What type of tasks are they working on? Are they at a large enterprise or a small startup? Are they working in a specific industry? Figure out the type of persona that sees the most success with your solution, then make sure those people are where you focus your efforts.

  • Messaging. Relevant messaging means everything. Once you’ve nailed down who to target, take time to understand them. What does their day-to-day look like? What are their pain points? What would make their life easier? This helps you make your calls conversations instead of pitches, and means you can pinpoint how you can help right away.

  • Touchpoints. It typically takes more than one call or email to get the ball rolling. Get in touch on multiple channels, multiple times. Stats used to recommend a 5 email sequence, but when it comes to a multi-channel approach, 20 touchpoints isn’t off base. Of course, test what works for you, iterate, and optimize.

Why does intent matter?

There are literally thousands of people you could be calling every day. But only 7% of them are actually looking to buy.

When you know who those people are, and which signals of intent they’re showing, your team reaps the benefits.

  • A morale boost – confidently reaching out to more people who want to hear from you feels good and builds momentum.
  • Higher conversion rates – isn’t of dumping time into leads that don’t even have your solution on the radar, focus on the ones who do.
  • An edge on competitors – know when people are looking into your competitors and talk to those leads before they do.

What does our direct dials coverage look like?

🌎  Coverage in 33 countries

🏢 Data on 3.3 million companies

👤  Numbers for 8 million managers and above

About LeadSift

LeadSift identifies B2B prospects based on intent signals, qualifies them, and delivers them to your inbox. Whenever a prospect engages with your competitor’s content, or content published within your industry, LeadSift will discover and deliver them directly to you – complete with verified and accurate company and contact info. LeadSift data can now be enriched with direct dials so you can reach the right people, right away.

How to Build a Multi-Channel Intent Data Strategy

How to Build a Multi-Channel Intent Data Strategy

How Siemplify Generates 60% of Revenue with LeadSift

As intent data becomes a staple in B2B marketer’s stacks, actionability becomes more and more challenging. It’s reported that 67% of marketers struggle to activate their intent data (The B2B Marketer’s State of Intent Data). And what good is data without a plan to use it? How do you build a multi-channel intent data strategy?

We sat down with the team at Siemplify to cover how they integrate intent data throughout their sales and marketing processes, for a multi-channel approach. They’ve built intent throughout the funnel so the data is prioritized and activated continuously, and they have the results to back it.

Rather than looking at it as a challenge, some marketers are using intent data as the glue to connect multi-channel marketing strategies and let them feed their sales efforts.

Multi-channel strategies are all about understanding where your prospects are, and meeting them there.

Let’s dive into how Siemplify does multi-channel powered by intent, how they optimize, and the numbers proving it works (hint: 60% of revenue identified early by intent).

Where intent fits in Siemplify’s stack

For marketing, Siemplify uses Marketo for almost all of their marketing operations including email nurturing, automation, and connecting their content syndication programs. For ads platforms, Google AdWords is the go-to. 

On the sales side, Siemplify generates reports through Salesforce CRM to get a visual on the top accounts, top leads, and any intent-driven leads that may not have made the cut but are still worth contacting since they’re been engaging. All of their outreach is done through Outreach.io. 

Bizible for tracking.

LeadSift is their only intent data source. Since the data is provided at the contact level, they use it across sales and marketing in a number of ways such as lead generation, scoring indication, ABM lists, and more (we’ll get to that).

“LeadSift has been incredibly easy to work with. They’ve been truly like a partner in that anytime we have questions, need help, or even suggestions on how to optimize intent streams they have been very flexible and eager to help and that has gone a very long way.”

– Christopher Mitchell, Demand Generation Manager, Siemplify

How to set up intent streams

Siemplify started with foundational keywords related to their solution then evaluated results weekly. They would check which leads didn’t fit their ICP and optimized for this by making changes or removing keywords that didn’t bring in relevant leads.

They’ve built two intent streams. One is keyword-focused and the other is competitor-focused. The intent campaign focuses on the keywords that will most likely signal positive buying intent, like ‘SOAR Vendors’. This is the ‘main’ campaign and the one that helps identify in-market buyers.

The leads that LeadSift sends over are scored according to the type of trigger that caused them to be sent in the first place, leads that have engaged with keywords directly (engaged with industry news) are given a higher score, than similar leads in the account that aren’t showing intent at all. 

Siemplify applies a relatively small score based on each intent signal, roughly about the same as a web page view. But they aggregate them at the account level and apply some relatively simple score modifiers to try and differentiate between organizations that are considering SOAR vs 1 person that is very interested. 

Here’s an example:

They use a roll-up within Salesforce to pull all of the LeadSift scores from all contacts to the account level, and then add a multiplier based on the number of individual people who are showing intent. So 10 people with 1 or 2 intent signals each will score higher than 1 person with 10-20 intent signals.

How Siemplify does multi-channel

“We’re able to layer intent data on top of everything else we’re doing top of funnel”

– Christopher Mitchell, Demand Generation Manager, Siemplify

Starting with ABM campaigns, Siemplify gives content syndication vendors a list pulled directly from LeadSift intent data to target accounts that are actively showing intent to buy. This allows them to better allocate budget and gain a better ROI from content syndication programs since they are focusing efforts on more relevant accounts.

When it comes to leveraging intent with ads, Siemplify has set up automation so that leads that signal positive intent are automatically synchronized over to Google AdWords with the intention of getting Siemplify’s offer in front of the prospect with some top of funnel messaging. Where they can find a match on their ad platforms they adjust the bids for AdWords slightly higher to help get better placement.  

LeadSift is built into Siemplify’s scoring. Leads get extra points if their account is showing intent. From a marketing perspective, they also provide this data to the SDR team so that if an account shows intent but isn’t on their target account list (TAL) or a lead is showing intent but hasn’t reached the MQL threshold, they still know those are relevant leads and accounts to prioritize, prospect into and follow up on. 

Siemplify dives into the granularity and visibility of knowing specifically which keyword or competitor each lead is engaging with for purposes. They are able to put together strong value propositions based on specific keywords leads are actively engaging with so they can be sure their messaging hits the nail on the head every time.

“LeadSift helps us identify better opportunities for strategic marketing and in most cases are informing us of a buyer’s intent before anyone else”

– Mike Hardwicke Brown, VP Demand Gen, Siemplify

The next step will be implementing intent data into their marketing nurture program so leads showing intent towards certain custom keywords are put into an expedited track or sent specific emails based on topic engagements.

The Results

“We started using LeadSift 3 years ago when we were a younger company, trying to figure out how to strategically use intent data,” says Mike Hardwicke Brown, VP of Demand Gen at Siemplify, “LeadSift came forward as a potential partner and delivered on that promise. They have implemented multiple feature requests and in several cases done some pretty significant product development to help us solve our problems. It’s what most vendors promise and few deliver.”  

LeadSift lives across Siemplify’s sales and marketing funnel, tying the two departments together with intent-driven leads and the power to nurture them through the pipeline with relevance and value.

Today, with LeadSift identifying intent signals in over 60% of Siemplify’s opportunities before they are created, LeadSift aids in driving over 60% of their pipeline and revenue. 

Want a custom report of leads showing intent towards your solution right now?