Ranga Kaliyur

Best Leadfeeder Alternatives For Website Visitor Identification

April 9, 2025

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0 min read

Factors Vs. Google Analytics (GA4)

April 1, 2025

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0 min read

GA4: End Of The Road For Google Analytics?

Over the last decade, Google Analytics has dominated the marketing analytics space. This uncontested reign can be attributed to a couple of key reasons: One, GA is free. And two, GA maintains a monopoly over historical data for an enormous install base. While the former isn’t likely to change, the introduction of GA4 is set to crush 10+ years of backward compatible data for millions of websites — including yours.

By breaking history and forcing a migration from Universal Analytics to GA4, Google Analytics loses an important advantage. This, coupled with a host of issues around UI, functionality, and privacy has resulted in a dramatic turn in tides. If users are going to lose out on their historical data by next year anyway, there’s little incentive for them to stay — especially when the best Google Analytics alternatives are readily available.

“We will begin sunsetting Universal Analytics — the previous generation of GA — next year. All standard UA properties will stop processing hits new hits on July 1st, 2023” - GA Support

“GA4 feels like a huge step backwards. Tons of functionality in the previous version is missing or replaced with smart “insights” — which always goes wrong. A good time to move on to something better” - Hacker News

With this in mind, The following article compares Google Analytics (specifically, GA4) with Factors to demonstrate why, now more than ever, the latter is better suited for B2B marketing analytics, web analytics & CRO, multi-touch attribution, and more.

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Data Integration

To be of any value, B2B marketing analytics requires substantial data from a wide range of sources. This includes granular metrics across all your ad campaigns (Google, Bing, Facebook, Linkedin, Capterra, etc.), website, and CRM (HubSpot/Salesforce) events. Ideally, this body of information is unified under one platform to ensure normalized data and alignment across demand gen teams. So how does Factors compare with Google Analytics when it comes to data integration? Here’s the rundown:

Ad Campaign Metrics

Google Analytics can report basic traffic data from most commonly used social platforms — Google Ads, Facebook/Meta, Linkedin, etc. Integrating non-Google sources, however, is significantly more challenging on GA. It requires the construction of custom campaigns using URL builders (read: a time-consuming, laborious chore). Factors, on the other hand, deliver immediate no-code integrations.

The real issue with GA, however, is that it cannot pull metrics that matter from any source that isn’t Google Ads. Commonly tracked figures like impressions, engagement, click-through rates, etc cannot be reported, let alone linked with website or CRM data. This is a headache for users looking to consolidate all their marketing efforts under one roof. Factors solves for this by automatically pulling granular metrics across the board.

CRM Integration

This is another big point for Factors. Unlike GA, Factors provides robust integrations with HubSpot, Salesforce, and soon, Leadsquared. The implications of this are significant. Marketers will have the ability to connect the entire customer journey from first touch to deal won. In turn, marketers can determine exactly how their efforts across ad campaigns, web content, and offline events are contributing to larger business objectives like pipeline and revenue. This is simply not possible on Google Analytics as it does not integrate with any CRM whatsoever.

Third-party Integrations

Another reason Factors has the edge over Google Analytics is third-party integrations. Factors integrates with the likes of Clearbit Reveal (Deanonymization), Segment (CDP), Drift (Chatbot), and more to ensure an actionable web + marketing analytics experience. Again, this is impossible on Google analytics because GA4 does not integrate with third-party platforms. Consequently, this affects the quality of customer journey insights. Even with sophisticated manual analysis, the data derived from Google Analytics is likely to remain superficial at best. In later sections, we’ll explore the implications of this issue in detail.

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Implementation & Onboarding

So far, we’ve established that data integration (including third-party) makes more sense with Factors. Next, let’s examine how the two marketing analytics tools compare with regards to implementation and onboarding. For context, if you’re currently using Universal Analytics, you either have to upgrade to GA4 by next summer (June 2023) or find a GA alternative like Factors.

What does it take to upgrade to GA4?

12-steps. That’s right. It requires 12 steps before you can upgrade from Universal Analytics to GA4. What’s more? As previously mentioned, integrating with non-Google ad platforms needs elaborate orchestration with custom campaign URL/UTM builders. Additionally, GA4 requires users to manually tag each event they want to track. In short, this means significant labor effort, engineer dependency, and time-spent waiting for incomplete datasets.

What does it take to set-up Factors.ai?

Maybe about 30-min. In fact, it’s probably closer to 10 with Google Tag Manager. Simply place the superlight SDK onto your website and integrate (no-code!) with your ads platforms, CRM, CDP, Chatbot, and more. Before you know it, all your marketing + web data will begin pouring into a single, normalized platform. Easy as pie.

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B2B Marketing Analytics

Now, let’s discuss functionality. Here’s how Factors compares to Google Analytics when it comes to core use-cases of a B2B marketing analytics tool.

Right off the top, it should be noted that GA isn’t built for B2B marketing analytics. It struggles to support tracking for journeys that involve several months, touch-points, and stakeholders. Unfortunately, these are the precise characteristics of a standard B2B sales cycle. Customers often experience countless (well, on average, 7) touchpoints — both online and offline — across ads, emails, webinars, blogs, web sessions, etc before converting. These touchpoints can occur within a window of one week, one month, or sometimes, even longer than one full year. Unlike Factors, GA4 isn't designed to analyze or attribute lengthy, sporadic interactions for B2B marketers.

Additionally, and as previously mentioned, GA is unable to track and measure granular campaign or event metrics from non-Google platforms. Since B2B marketers target (and retarget) marketing efforts to their audience across several channels, it’s nearly impossible to consolidate these figures on GA alone.

Factors eliminates this data silos issue for marketers and demand gen folk with the help of aforementioned third-party integrations. Once data has been collected, Factors delivers an end-to-end marketing analytics suite that’s tailor-made for B2B teams.
This includes web analytics, funnels, custom events and KPIs, multi-touch attribution and more. Let’s explore why Factors has the edge over GA across these use-cases.

Data Accuracy, Marketing Funnels,
Revenue Attribution, & More

At its core, Google Analytics is a website analytics service. And to be fair, GA does a half decent job at it, especially for a free tool. That being said, there are significant limitations with GA that Factors solves for:

Data Accuracy

‍Certain web metrics are not precise on GA. Let’s say a lead lands on a blog on your website. Before they can start to read the article, their doorbell rings and they leave to answer with the blog page open on their laptop. The lead returns in about 8 minutes, clicks out of the blog to schedule a demo. Google Analytics would inaccurately assume that the blog played a massive role in this conversion because of the significant (8 min) time spent on page. In reality, however, the lead did not scroll even a little to read the rest of the piece. Factors solves for such issues by tracking granular details like cursor activity and scroll depth percent to ensure your data, and the insights derived from that data, is as accurate as can be.

Marketing Funnels

‍On GA4, you’re limited to page-to-page funnels. That is, GA4 only considers funnels wherein each webpage is a separate step. As such, GA4 struggles to track multi-session engagement. Let alone a funnel across ads, web, and CRM. Funnels on GA may only be used to reliably measure drop-offs and conversions that occur in the length of a single web session (Eg: Blog -> Pricing -> Schedule Demo in one web session). Additionally, event funnels are not supported on GA4. Hence, if you’d like to track how specific website content is contributing to MQLs, leads, & revenue, GA4 won't suffice.

Customer Journey + Revenue Attribution

As previously mentioned, GA does not integrate with CRMs like HubSpot, SalesForce, or Leadsquared. This severely impedes cogent customer journey analysis and revenue attribution. Without laying the entire map from ads, offline efforts, web sessions, and CRM events, you are left with an incomplete picture of what’s driving revenue and pipeline. Consequently, this affects data-driven decision making, which ultimately results in suboptimal marketing strategy and ROI. It is not feasible to derive end-to-end marketing insights into what’s working and what’s not with Google Analytics. While GA might be able to track the source of traffic, it cannot determine the cause.

Usability - UI and UX

What’s surprising is that even with these significant drawbacks, users complain about GA4’s useability. It can be overkill — unintuitive, excessive, and far too technical — for marketers looking for basic reports. And completely irrelevant to marketers looking to dig deeper into their data. Let’s let twitter expand upon this…

Website owners, is it just me or is the new GA4 just HORRIBLE? It's like it's designed only for retail sites or something, very hard to get the basic info that I used to rely on... Think I'll switch back! Awful!

Trevor Long (@trevorlong)

I usually can find my way round any piece of software quickly. But Google Analytics 4 is making me cry...I've never seen a tool upgrade that made simple things sooo complicated :face_palm: Non-tech business owners were already struggling to use it. But now they have NO chance.

Gill Andrews (@StoriesWithGill)

1. The UX of Google products in general suck
2. GA4 is a new level of suckiness
3. I get the feeling Google doesn't have a UX team and/or never tests the usability of their products
4. If you are not a monopoly you would never be able to get away with this

Tom Kasperski (@TomKaz)

Factors is simple by design. Users go from onboarding to creating powerful (and basic) marketing reports in a matter of minutes.

Now Is The Best Time to Switch Over From Google Analytics To Factors.ai

And there you have it. A non-exhaustive set of reasons as to why Factors.ai makes far more sense for B2B marketers over Google Analytics. With the imminent arrival of GA4 (and the consequent break in years of historical data), now is the best time to make the move to Factors.

Google Search Marketing in 2022: Keyword Matching

April 1, 2025

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0 min read

Search marketing with Google Ads is kinda cool. It helps users who are looking for specific information, products, or services connect with businesses looking to sell specific information, products or services — all through a wonderfully powerful, complex search engine. But how does search marketing work? More specifically, how does keyword matching work in the latest iteration of Google Ads? Let’s find out…

How does keyword matching work on Google Ads?

There are 7 steps involved in Google Search Ads to connect the right audience with the right message using keyword matching. Here’s how it works:

1. First, a user types a search query into Google. Google then processes this text against spell-checks, synonyms, and related terms to form what’s called the “retrieval query”. This retrieval query wrangles all relevant search ad keywords that could be served into a set.

2. From this set of keywords from the retrieval query, Google verifies eligibility based on keyword match type, campaign, ad group, etc. This is performed using advanced machine learning and natural language tech to understand and optimize matching for intent and relevance. Other factors considered by Google are budget, geo, negative keywords, creatives, landing page, time of day, etc.

3. When choosing from multiple eligible keywords from the same account (For example, if company X bids on both “B2B marketing analytics tools” and “B2B marketing analytics software”), Google will prioritize those keywords that are closer to being an exact match to the search term. So if a user searches “marketing analytics software”, they will receive the former search ad. Once filtered down, Google has its set of ad groups with eligible, relevant keywords.

4. With this set of ad groups containing eligible keywords, Google’s responsive search ads creative system will automatically rally the “best performing creative — including headline and description” for the user based relevance.

5. Next, we arrive at the stage wherein bids are calculated using Ad Rank. Ad Rank is a scoring system that assigns value to ads to determine if or not your ad will be presented to the audience. Of course, your bid amount is an important factor in determining Ad Rank as well.

6. Here, Google Ads chooses the optimal combination of ad relevance and ad rank. Once again, Google’s algorithm is looking for landing page quality and keywords in an ad group. The latter implies it’s highly important to group keywords by theme, to ensure favorability.

7. The final step is straightforward. Once Google Ads processes all the aforementioned information, each advertiser enters into auction and those advertisements with the highest Ad Rank (including and especially bid amounts) are displayed for your audience to see.

Keyword match types on Google Ads

As the name suggests, keyword matching matches words and phrases from the search ads you bid on to terms that people actually use when searching. Hence, it’s crucial to bid on the relevant keywords to ensure your ads align with what your audience is looking for. Google Ads offers three match types. The accuracy with which the keyword needs to match a user’s search query will be determined based on match type you choose:

1. [Exact match]

‍As you may have guessed, [Exact match] types require an exact match between the keyword and the search query. For example, if the keyword is “B2B marketing analytics”, only search queries that mean the same, like: “B2B marketing analytics software” or “B2B marketing analytics tools” will trigger the search ad.

2. “Phrase match”

Phrase matching is marginally less rigid than [Exact match] types. It essentially considers all searches wherein the primary keyword is part of a larger string of search text (i.e. a phrase). For example: “Best software for B2B marketing analytics”

3. Broad match

Broad match provides the most loose matching out of the three match types. It considers the exact keyword, phrases around the keyword and all related terms around the keyword. For example, Google may trigger an ad for the search term “B2B marketing attribution” because it's somewhat related to “marketing analytics” as well.

Note: In short, Exact match keywords are a subset of Phrase match keywords. And Phrase match keywords are a subset of Broad match keywords.

Broad Keyword Matching on Google Ads

Google Ads have increasingly been pushing Broad match types as their AI-algorithms continue to improve their understanding of language, intent, relevance, etc. In recent year, keyword matching on Google Ads has evolved from a pure syntax-matching system (wherein a user’s search query text simply matches an advertisers search ad keyword) to a semantics based system (wherein broadly related themes and topics are recognized as relevant enough inquiries to warrant the display of an indirectly relevant search ad). Here are some signals that broad match takes into consideration (in addition to exact keyword and phrases):

1. Other keywords in the ad group: Arguably the most important signal is relevance of other keywords within a specific ad group. For example, if the search term is “salmon sweaters” and your ad group consists of the keywords “orange sweaters”, “red sweaters” and “blue sweaters”, Google Ads will understand that in this case, salmon refers to the colour and not the fish.

2. Previous searches: Google Ads also takes into account a user's previous search when deciding what ad to present. For example, let’s say a user previously searches for “manchester city vs liverpool football score”. Google uses this historical data in the future so that simply searching “man city vs liverpool” will retrieve the football score without mention of either word.

3. User location: This one is straightforward. Google analyses user location to personalize search results. Eg: B2B SaaS marketing agencies based in New York vs B2B SaaS marketing agencies near me. This may or may not be as relevant to your marketing efforts depending on the type of product you’re selling. Still quite handy to be aware of.

4. Landing page: Last but most definitely not least is an ads landing page. Does the landing page contain relevant keywords? Does it contain quality content — including images and creatives, to ensure a valuable experience for the visitor? These are questions to keep in mind when constructing and improving upon your landing pages.

And there you have it! An overview into how keyword matching works on Google Ads.

Curious to learn how Google Analytics compares to Factors.ai? Read on here

Pipeline Velocity: Definition, Formula & Strategies

February 11, 2025

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0 min read

There’s no doubt that B2B sales is increasingly being conducted in a methodical, scientific manner. Using a wide range of metrics and KPIs, this data-driven sales process ensures minimal revenue leakage and optimized pipeline performance. You may have heard of a few common sales metrics: customer acquisition cost, customer lifetime value, average revenue per user, etc.

This article focuses on a lesser known, yet enormously important metric to monitor & improve the overall health of sales: pipeline velocity. Let’s explore everything you need to know about pipeline velocity; what it is, how to calculate it, and most importantly, how to improve it.

What is pipeline velocity?

In short, pipeline velocity is the speed at which qualified opportunities move through the sales pipeline.

In other words, pipeline velocity is used to measure how quickly leads are being converted into paying customers. This helps understand the efficiency of the sales process and identify areas of improvement.

Think of a literal pipeline: if it’s chock-full of debris and leaks, the flow of water will be limited and inefficient. On the other hand, if it’s squeaky clean, a large volume of water can flow uninterrupted at maximum speed.

Similarly, a high-velocity sales pipeline results in a consistent, voluminous flow of leads and ultimately, revenue. You can see why it’s so important to keep track of this metric.

How to calculate pipeline velocity?

Pipeline velocity is calculated using 4 other metrics:

Opportunities - how many qualified opportunities are in your pipeline?
Deal size - what is the average contract value of deals in your pipeline?
Win rate - what percentage of opportunities will likely convert successfully?
Length of sales cycle - on average, how many days does it take to close a deal?

Here’s the most commonly accepted pipeline velocity formula:

Pipeline velocity = (Opportunities x average deal size x average win rate) ÷ length of average sales cycle (in days)

Let’s take an example. Say we have 60 qualified opportunities at various stages along the pipeline. The average deal size of these opportunities is $5000. Historically, we’ve observed a win rate of 20% and sales cycles of around 30 days. Accordingly, our pipeline velocity may be calculated as follows:

Extrapolating this, we arrive at a figure of $2000/day x 30 days for $60,000 per month.

You may notice from the pipeline velocity formula that there are a few ways to improve pipeline velocity:

Increase number of opportunities
Increase average deal size
Increase win rate
Decrease length of sales cycle

Each variable is a lever that may be pulled to ramp up pipeline velocity. Of course, the most obvious way is to increase the number of opportunities/leads and deal size (easier said than done!). That being said, improving the buyer experience is a low-hanging fruit that results in dramatic improvements in win rates and quicker sales cycles.

But what makes improving the pipeline velocity so important anyway? Here are a few benefits of tracking and optimizing pipeline velocity:

Why is pipeline velocity important?

As HubSpot’s director of sales, Dan Tyre, puts it:

“Sales managers live in fear that their pipeline is a bunch of fluff. In today’s world of instant gratification, uncovering a sense of urgency and establishing sales pipeline velocity is important because it uncovers a slow-moving, or worse, stagnant pipeline”.

1. Understand the overall health of the sales pipeline

Understanding your pipeline velocity helps keep tabs on the overall health of your sales pipeline. By knowing what works and what needs improvement, you can bring iterative, targeted changes to the sales engine. More revenue, less costs — win, win!

2. Ensure accurate sales forecasting

Measuring your pipeline velocity on a regular basis helps with accurate sales forecasting. For instance, taking the previous example, we have a pipeline velocity of $2000 per day, which can be expanded to $60,000 per month or $180,000 for the quarter. Using pipeline velocity is accurate as it’s based on real-time sales data, not estimates.

3. Improve attribution & ROI

A powerful use-case is realized when pipeline velocity is used in tandem with attribution modeling. Picture this: each of your pipeline sources, broken down by qualified opportunities, deal size, win rate, and of course, pipeline velocity:

Source	Opportunities	Avg Deal Size	Win Rate	Pipeline Velocity
Paid Search	20	$6000	30%	1200
Paid Social	30	$4000	10%	400
Cold Outreach	6	$5000	10%	100

In combination with attribution, pipeline velocity can provide valuable insight into the most effective channels — which in turn can help guide marketing decisions and resource allocation. In this case, we see that even though paid social brings in more opportunities, it’s paid search that results in the most ROI given its larger deal size and better win rate.

Sales cycle benchmarks for SaaS

Pipeline velocity itself varies significantly based on the nature and size of the company in question. Instead, here’s a breakdown of the benchmark of length of sales cycles in SaaS

Length of sales cycle:

Deals < $2000 ACV: 14 days
Deals < $5000 ACV: 30 days
Deals < $25,000 ACV: 90 days
Deals < $100,000 ACV < 90-180 days
Deals > $100,000 3 - 9 months

Depending on the nature of your business, your win rate should be anywhere from 5-20%. Of course, the number of opportunities and deal size is specific to your product, marketing & sales efforts. It wouldn’t make sense to maintain or refer to benchmarks in this case.

How to improve pipeline velocity?

In short, improving pipeline velocity involves eliminating points of friction along the customer journey and aligning workflows and stakeholders to ensure smooth sailing. Here are a few tactics and strategies to do so:

1. Make the most of existing traffic

Your website is a goldmine of hidden opportunities in the form of yet-to-be-converted accounts. Use an IP-based account intelligence tool (like Factors) to reveal anonymous accounts already engaging with your website, review pages, and ad campaigns.

Given that these accounts are already familiar with your brand, they’re far more likely to convert: thereby increasing your “number of opportunities” and “win-rate”.

2. Let visitors experience your work

As companies increasingly move towards product-led growth, it’s becoming all the more important to show, not tell. While not all products (especially those at early stages) can adopt PLG models, it’s really quite simple and effective to put up an interactive product tour on your website. This gives visitors a chance to know a little more about your work before choosing to book a demo, rather than having to go in blind.

Again, this likely increases the number of opportunities, reduces average sales cycle length, and improves your win rate.

3. Document. Everything. Always.

There’s no doubt that sales demos and discovery calls are important. But more often than not, buyers don’t have the time to sit through another 30-min. Make life easier for your sales reps, CS team, and of course, the customers themselves by introducing comprehensive documentation on everything they might need to know.

Use-cases, How-tos, Implementation, etc, etc, etc should be easily accessible to anyone interested in your work — to mitigate the risk of unnecessary back-and-forth friction. This will certainly help reduce the length of the sales cycle.

4. Align relevant stakeholders

A vital, yet often overlooked step is ensuring alignment across marketing, sales, CS, and the customers. This involves timely handoffs, relevant communication, straightforward pricing and product details, and clear PoCs across every stage of the customer journey. This helps both the customer and internal departments streamline the sales process end-to-end.

5. Stay on top of data & metrics

The accuracy of your pipeline velocity metrics (and any other metric, really) relies heavily on the quality of your data. Ensure you’re regularly maintaining numbers on qualified opportunities, deal size, and length of sales cycle in your CRM so the same may be leverage for pipeline velocity measurement.

How Factors help monitor & improve pipeline velocity

As important as it is, it can be a tedious, unintuitive chore to measure pipeline velocity — unless you have the right analytics solution, of course :)

Factors is an AI-fuelled intelligence & analytics platform that helps teams identify, score, and track accounts across the customer journey. We’re talking about automated sales velocity calculations, flexible conversion funnels, IP-based account identification, multi-touch attribution, and more — everything you need to kickstart and refine your ABM process and…pipeline velocity!

Accelerate B2B Sales with Pipeline Velocity Optimization

Pipeline velocity is a crucial metric that measures how quickly qualified leads convert into customers, enabling businesses to refine their sales process.

It’s calculated using four key factors:

1. Opportunities: The number of deals in your pipeline.

2. Deal Size: The average value of each deal.

3. Win Rate: The percentage of deals successfully closed.

4. Sales Cycle Length: The time it takes to close a deal.

Improving pipeline velocity enhances sales forecasting, boosts ROI, and ensures a healthy pipeline. Strategies include optimizing existing traffic, effectively showcasing products, and aligning stakeholders for smoother deal progression. AI-driven tools like Factors streamline tracking and analysis, making it easier to refine your sales process and drive faster conversions.

Clearbit + Factors: Partnership Announcement

October 25, 2024

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0 min read

Linkedin Ads For Early-Stage Teams: Framework & Priorities

October 25, 2024

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0 min read

With over 750 million users, LinkedIn is by far the largest professional network in the world.

What started off as a simple platform for like-minded business people to connect, has transformed into a social media behemoth. Today, LinkedIn offers everything from algorithmic news feeds, LinkedIn groups, live streaming, and of course, a wide range of advertising mechanisms.

What does this mean for us B2B marketers? Opportunity.

LinkedIn’s massive database of professionals, companies, and industries may be leveraged by marketers to reach out to the right audience with the right message and drive high-quality opportunities.

‍But there’s no hiding behind the fact that paid marketing on Linkedin can be expensive and competitive — especially for Seed/Series A companies looking to make limited budgets go a long way.

As a result, early-stage teams generally prefer spending on Search Ads over LinkedIn. The former is believed to drive more high-intent leads and in turn, better return on ad spend. Conversely, LinkedIn is thought to be better suited to bigger companies for expensive, top of the funnel branding campaigns.

‍This is not necessarily true.

When executed well, LinkedIn ads can be an effective channel to generate high-quality leads and bottom-of-the-funnel pipeline — even for smaller teams. This chapter of our no-nonsense guide explores the Linkedin ads framework we’ve crafted over months of wins, mistakes & learnings as an early-stage start-up.

‍We won’t be discussing the basics of Linkedin Ads given that there’s loads of resources available on this as is. Instead, you can expect to find practical guidelines to pick off low-hanging fruit and drive RoAS.

‍Linkedin Ads For Series A: Framework & Priorities‍‍

Quick results with limited spend and minimal effort is at the core of our LinkedIn ads framework. With that in mind, we suggest using LinkedIn ads to target the following audiences:

Retarget prospects that are already engaging with your company
Target customers of your competitors
Target top of the funnel ICP audience with ABM

Given that not all accounts are equally likely to convert, It’s important to prioritize the right set of audiences. Here’s an order of priority we’ve been seeing growing success with:

Priority	Audience Set	Problem/Solution	Brand
1	Retargeting to website visitors	Audience is aware	Audience is aware
2	Targeted to competitor customers	Audience is aware	Audience may or may not be aware
3	Targeted to general ICP (ABM)	Audience may or may not be aware	Audience unaware

1. Your first priority should be to retarget accounts that are already interacting with your brand — visiting high-intent pages, engaging with G2 reviews, or viewing previous LinkedIn ads. Given that these accounts already know about your product/company in some capacity, we can safely assume that they’re problem, solution AND brand aware.

This audience is at a stage where they’re researching solutions (including yours!) to solve a challenge that they’re actively facing. This set can also include lost and churned accounts that have returned to engage with your brand.

In short, this audience is relatively further along the sales funnel and accordingly, will require the least effort (and spend 😉) to convert.

‍2. Next, look to target customers of your competitors. While this set of audience may not be aware of your brand, they’re certainly aware of the problem and are in fact already using an alternate solution. This implies that they’re ready to buy and may consider switching to your solution if it’s a better fit. In terms of ideal customer profile, it doesn't get much better than this. Use sales intelligence tools like Builtwith or Slintel to generate competitor customer lists.

‍3. Finally, target your general ICP audience with account-based marketing (ABM). This consists of the set of accounts that fit your ideal customer profile criteria (based on size, industry, revenue range, technographics, etc). Although this set of audience would make great customers, they’re unaware of your brand as well as the problem your product is solving for. Accordingly, these accounts will require the most effort (and spend) to convert.

With this priority framework established, let’s explore how to build these audience lists, run ads that convert, and optimize paid LI ads.

I. Build Audience Lists

‍For Retargeting…

Here’s a 3 step process on creating an audience list for LinkedIn retargeting

‍Step 1. Identify accounts from your website, reviews, and ad impressions

Use LinkedIn’s website tracking pixel in tandem with IP-based account identification tools to discover anonymous companies engaging with your website, G2 reviews, and previous LinkedIn ads.

Tactical Tips: LinkedIn’s website tracking pixel is limited to the number of visitors who actually accept cookies upon landing. This may be an issue for smaller teams with limited traffic because visitors accept cookies only 11% of the time. This may dramatically shrink your audience list. Luckily, there’s a quick fix:

‍Use an “opt-out” cookie policy instead of an “opt-in” policy everywhere outside the EU to have cookies accepted by default. Both policies are privacy compliant outside the EU, but an “opt-out” policy will result in far more accounts identified by the LinkedIn pixel.

Step 2. Filter down your targets

Depending on the size of your website, you may identify hundreds or thousands of unique accounts every week. It’s probably unrealistic to go after each and every one of them. Instead, refine your list by only targeting accounts that visit high-intent pages (Pricing, Landing pages, Comparison blogs, G2 reviews etc) and fit your ideal client profile based on geography, industry, technographic, revenue range, etc. Once complete, you’ll be left with a list of high-fit, high-intent ICP accounts.

Tactical tips: In order to launch a campaign on LinkedIn, you must target at least 1,000 members. (Or 300 members, with Matched Audience — but we strongly discourage the use of MA). Given that you’re likely targeting multiple people from the same company, a final list of 500 accounts is a good starting point.

3. Build a target member list:

At this stage, we have a brand-aware and possibly in-market set of ICP accounts ready for targeting. Use a sales intelligence tool like Apollo, Zoominfo, or LinkedIn Sales Navigator to create a list of at least 1,000 relevant members to target based on their roles, seniority, etc.

‍Tactical tips: We find that it’s valuable to create awareness across the entire company you’re targeting. Accordingly, we strongly recommend targeting at least 2-3 employees from every account: final users, their managers, and the final purchase decision makers.

‍For Competitor Customers & ABM

The process for creating audience lists in these cases is straightforward. Skip straight to building target member lists using sales intelligence tools like Builtwith, Zoominfo, Slintel, etc. Construct lists of competitor customers and ICP accounts by apply the right filters (technographics, firmographics, roles) so you’re left with the right contacts from the right companies.

Now, we’re all set to run highly targeted ads that drive conversions.

‍II. Run ad campaigns

At this stage, we have a primed list of high-fit, high-intent audience fit for targeting. It's safe to assume that every member we’re targeting would find the product/service we’re marketing to be, at the very least relevant, if not of explicit interest to them.

So now, we run great ads! Here are a few point to keep in mind:

‍Define objectives

The objective and approach of your LinkedIn ads should differ based on the audience you’re targeting. For instance, retargeting ads should look to convert brand-aware accounts and accordingly can be far more direct as compared to ABM ads targeted towards brand-unaware accounts. Here’s how to think about it:

Audience	Objective	Ad Funnel
1. Retargeting	Sign-up	Stage 1: Direct ads with Leadgen form
2. Competitors customers	Sign-up	Stage 1: Comparison ads Stage 2: Leadgen form
3. ABM	Branding	Stage 1: Brand ads Stage 2: Leadgen form

With retargeting ads, ads you’re targeting members that have already visited your website, interacted with your review pages, or viewed a previous ad. We needn’t create brand awareness from scratch. Instead, we should aim for these ads to generate sign-ups. Accordingly, use straightforward lead-gen forms instead of content assets or website redirects here. In this case, leads generated and conversion rate will be the two key objectives. You may also track cost per conversion and cost per lead. Targets for these will vary based on your budget and ACV.

Tactical tips: Keep the number of form fields to a minimum. Work mail and phone number are plenty.

With ads targeted towards competitors' customers and ICP audience in general, it’s better to use a 2-stage funnel: the first stage involves running comparison ads or branding ads to create awareness about your solution. The second stage involves converting target accounts with standard lead gen forms. While this is a more elaborate process than a simple lead gen form, it’s sure to drive better conversions as the target audience will be aware of your work, and thus more likely to submit a form.

‍Make a mark with messaging

You do not want to run pesky ads and have people mute your campaigns. It’s vital to incorporate customer research into your ad copy and designs to capture positive attention. Even little things like line breaks and emojis can make or break your campaign.

Remember to sell on every element of the ad: the intro text,headline, in-image text, description, etc. Most users won’t consume every part of the ad in its entirety — so ensure that each element is persuasive in its own right:

Depending on the target audience, you’ll want to use different messaging. The two examples shared above are relatively more direct with a clear objective — “let us give you a free trial”. This will probably be better suited to retargeting campaigns.

For ads targeted towards competitors, however, comparative ad creatives are likely to perform better. That being said, it’s also important to stay on the right side of the law and respect copyright and trademark policies. Here are a few competitor ad creatives we’ve found success with:

Experiment. Experiment. Experiment.

Continue to experiment with different ad formats, messaging, and creatives until you identify what clicks. Here are a few examples of ads we’ve found success with:

‍1. Testimonial ads:

2. Before/After ads:

3. Ads with a hook or questions:

And there you have it! Advertising on LinkedIn, when done right, can be a highly effective channel to drive pipeline and revenue. To conclude, here are a few common mistakes to avoid while running LinkedIn ads:

When using LinkedIn targeting, ensure that job titles are set in inverted commas so LinkedIn only targets users with those specific titles as opposed to related ones. Eg: ‘CMO’, ‘PMM’, etc.
Do not use the audience network on LinkedIn as it generally targets irrelevant members resulting in wasted spends.