Sohan Karuna

Marketing’s transformation from a cost-centre to a revenue powerhouse — coupled with a boom in digital channels — means that marketers, now more than ever, require a granular account of their influence on pipeline and revenue.

Enter: Revenue Attribution.

B2B companies are prioritizing revenue attribution to measure their marketing performance and ROI, and track customer journeys. In fact, 76% of all marketers find that they currently have or will have in the next 12 months, the capability to employ a robust revenue attribution platform (Think with Google). Conceptually, the function of attribution is straightforward, but there are several mistakes that could easily skew your results and limit your progress when it comes to accurate, actionable revenue attribution analysis.

With that in mind, here are 8 common mistakes to avoid for your revenue attribution regime:

1. A lack of an attribution strategy

Despite the automation solutions that are embedded in most attribution tools today, it becomes easy to forget that your input plays a huge part in producing relevant results. Formulating a strategy is essential in being able to derive actionable insights from your attribution. At the end of the day, the relevance of tracking different channels and campaigns in a customer’s conversion journey is incumbent upon you.

Get organised! Start by cataloguing relevant channels to track as per your conversion goals. Label your channels and campaigns and assign budgets so that all your data across all your tools is coherent. Tracking irrelevant channels (or not tracking relevant ones) is a part of trial and error, but reliance on such incomplete data is a big red flag. One common example of this is: tracking only the performance of ad campaigns without testing its performance relative to other channels.  

Communicating with the appropriate personnel and others involved in the strategy to gain better insight on what to track and what not to is a good start.

2. Excessive reliance on preliminary revenue attribution models

The tendency to rely on preliminary attribution models — single-touch models like first and last touch or the popular last-click model — may produce quick and simple results to measure your ROI. This, however, can be an expensive mistake. Don’t get it twisted, single-touch models have their use cases — attributing PPC and short sales cycles to name a couple. But relying solely on preliminary models for all your marketing decisions will likely do more harm than good. Single-touch models are linear in nature, which is not conducive to most customer behaviour. Attribution is more effective when you strive to get as close as possible to analysing a customer’s journey across several touch-points. And having one touchpoint attributed to a customer’s conversion gives a vague, and often inaccurate, image of their journey.

3. Not testing multiple attribution models

This mistake is likely to be a consequence of the previous point — excessive reliance on preliminary models. But why is it important to test other models? When it comes to rule-based attribution and multi-touch attribution models, the general reasoning behind adopting a model is the nature of the product, the number of marketing channels, the length of the sales cycle, etc. While there’s nothing explicitly wrong with this, we cannot only rely on those factors.

There are several omitted variables around the intent of your attribution — measuring the functionality of different campaigns in conjunction with other channels, the relative probability of channel interaction, opportunity cost of campaigns, or just simply mapping out the most influential channel and ROI. Even the type of campaigns and the medium through which the customer interaction occurs could affect your decision in choosing a model. Some models are more applicable than others in producing reliable results, and the only way we’ll identify this is by testing out what works and what doesn’t.

4. Not understanding the limits of rule-based modelling

In practice, administering a combination of rule-based attribution and data-driven attribution is an effective way of producing reliable results. That being said, if you’re for the most part dependent on rule-based modelling, you’re unlikely to have transparent results. Rule-based modelling is limited, as the weights in the models would need to perfectly represent the influence of each channel in a customer’s conversion journey. This is highly unlikely as no two customers are the same. For example, a time decay attribution model will assign credits in ascending order regardless of the type of campaign or prospect’s actual behaviour. So, to help identify your most influential channels on average, data-driven attribution can be used to give credibility to different channels by assessing their KPI’s. This in turn will help you draft a custom model that makes sense to your attributing pattern.

5. Misaligning attribution data and customers/lead quality

In the pursuit of using your attribution data to aid your marketing decision making, sometimes you forget to categorize our data considering the customers involved or their lead quality. To make better sense out of your attribution data, we need to pair the interactions with customer IDs to avoid duplication of leads and accurate credit distribution across marketing channels.

Tracking our customers even helps assess the quality of their leads. What this means is some customers are likely to be more interactive and engaged with your brand than others. This even dictates if some of them become recurring customers or only ever interact with your business once. Tracking customer interactions helps you distinguish the quality of their leads. These values also contribute to calculating the LTV (Lifetime Value) of your customers.

6. Ignoring the bias

These mistakes have to do with certain biases that might compromise your decision-making pertaining to attribution. The most common ones are:

Correlation Bias

When attributing credit to different channels along your customer journey, there is a possibility for certain interactions to conceive other interactions (or at least a level of other interactions). One could over/underestimate the influence of channels with other channels simply because of the natural conversion of targeted customers. A conscious consideration of correlation vs causation must always be kept in mind.

Confirmation Bias

‍A confirmation bias is the proclivity to seek out information, and the interpretation of said information, to favour your results and personal beliefs. This type of bias is prevalent in attribution as it involves having to attribute your channels in accordance with the result that favour you. This would eliminate the organic element of attribution to favour your marketing ideals, ultimately leading to inaccurate findings and conclusions.

7. Failure to understand the channel intent

When you fail to recognize your channel’s intent, you fall short in gauging how much it facilitated a customer’s conversion. This could lead to poor decision making as a consequence. Some channels facilitate interactions with other channels more than they do sales — like a blog versus a targeted email campaign. Hence, it would be unfair to discredit the channels that did not directly contribute to sales — or other predominant goals — but probably contributed significantly to a customer’s decision to convert.

8. Attribution is not the Be-All End-All of your marketing analytics journey

As convenient and resourceful as attribution is, they will never provide a holistic, extensive picture. While attribution is valuable in showcasing a blueprint of your campaigns, channels, and marketing performance. You still require other analytics tools — Funnel analysis, Anomaly detection, SEO optimization, CRM, and other web analytics tools that help assess channels using premeditated metrics. These tools will ultimately compliment your data-driven attribution for a far more comprehensive analysis of your campaign and channel performance. In order to do this effectively, you will have to use these tools cooperatively and in real-time.

Acknowledging these limitations and making a conscious effort to mitigate them will help equip and optimize your marketing attribution journey. Don’t let the idea that there is so much that could go wrong make you apprehensive about trying out marketing attribution to begin with. Undoubtedly, it’s a steep learning curve, but the rewards far outweigh the risk involved.

And there you have it! If you’re interested in understanding how some of the most popular single-touch and multi-touch attribution models work, you might enjoy this blog piece. 

Avoiding common revenue attribution mistakes matters more than people think. It is the difference between “looks good on a dashboard” and “actually helps us make money.”

Most teams slip up early. They do not define a clear attribution strategy. They depend on single touch models even when their buyers have long, messy journeys. They never test different attribution frameworks, so they keep trusting the same model even when the data keeps changing.

Some mistakes happen later. Teams match attribution data to volume, not to customer quality. They forget that rule based models come with limits. They ignore offline touchpoints that influence deals but never show up in the CRM.

Other mistakes are cultural. Marketing and sales do not sync on what “good” looks like. Data hygiene takes a backseat. And then everyone is shocked when the numbers look off.

A multi touch attribution approach fixes most of this. It looks at the entire customer journey. It highlights which campaigns create real movement and which ones just make noise. And it helps teams measure ROI with confidence, instead of guessing their way through decisions.

What do you do when you’re stuck nurturing countless leads that drive few conversions? Lead scoring has emerged as an effective solution forthis customer conversion challenge. Studies show that B2B organizations that utilize lead scoring realize a 77% increase in lead generation ROI compared to those that don't. If this piques your interest, know that scoring your leads and determining a lead scoring model is not a cut and dry process. The following post explains what lead scoring is and explores some commonly used lead scoring models.

What Is lead scoring?

Lead scoring is the procedure of quantifying the conduciveness of a lead generated by a business. To put it simply, it is used to determine if a lead is more likely to convert or not by assigning scores to the leads. By doing so, you ensure that both your marketing and sales teams are seeding the right prospects, all while getting to understand who your ideal lead is in the process.

So far, it seems simple right? Well, scoring leads is not all black and white. Figuring out your buyer persona is a multifaceted challenge. It not only requires a boatload of data but constant revisions and maintenance over time as well.

To help with that, here is how you build your lead scoring model:

Determining lead scores

First, we need to figure out the criteria for scoring, and how many points to reward or deduct for each criterion. Here are a couple of steps to establish that:

1)     Picking your KPIs and Traits: The first step in lead scoring is selecting what you need to be judging. This involves the KPIs (key performance indicators) and common traits of leads that convert. An example of this would be that an important KPI is the number of views on the review page for a product. And a common trait could be a particular company size.

2)     Assigning the Value: It is important to understand which traits are more significant than others — like the lead’s company size over the industry. This way you can reward certain traits higher than others. You should even determine the points to be rewarded per trait — which company size converts the most and which ones convert the least, etc. You can do this by calculating the conversion rates of the leads with different levels of the same trait and comparing them to the average. The same can be done for KPIs as well.

With all these in place, you can now determine the score for each lead attribute. Remember that you must never only rely on one attribute to score your leads. The more the merrier, as the following lead scoring models deal with a wide variety of data.

Lead scoring models

A lead scoring model is nothing but the basis of evaluation for your scoring or the system on which it is predicated. With that said here are some common lead scoring models:

1) Implicit Scoring (Activity/Engagement): Implicit scoring is used to grade leads based on their level of activity and engagement with the business, its brand and its content. It utilizes a lot of tracking data across several platforms and compared to explicit scoring it is a continual process. Here are some examples of implicit scoring:

• Number of webpage visits or leads that visited the pricing page.
• Content engagement, including views, downloads, etc.
• Email engagement, email click-through rate and bounce rate.
• Social media interactions, involving likes, comments, followers, etc.
• Leads that requested for product demos and free trials.
• Leads that attended webinars.
• Form submissions, and more

2) Explicit Scoring (Suitability): Explicit scoring is used to evaluate a lead based on their business-related profile like the lead’s company size and job title. This information is used to determine the suitability of your lead’s business profile to that of a lead that converts. Explicit scoring is more commonly used in B2B interactions, given the importance of assessing the companies they deal with. Here are some examples of explicit scoring:

• Company size, which can allude to how many decision-makers are involved in the buying decision.
• Job titles that are awarded different points depending on the level of influence.
• The company’s revenue could help identify companies that are more in line with your average contract value.
• The lead’s company industry.
• The location and other demographics of the lead.

3) Matrix (Combination of Implicit and Explicit Scoring): This model is called a matrix model because it uses an incidence matrix combination of implicit and explicit scoring. This means that we evaluate a lead based on combinations of implicit and explicit traits at varying degrees. For example: A lead that is considered highly suitable based on explicit business profile traits like company size and industry can be scored poorly due to its low activity and engagement levels. The same could be said about a lead with high activity but low suitability.

The importance of both these dimensions varies based on your ideal client profile (ICP). The use of this matrix model, including models with other dimensions, are quite common in lead scoring solutions used today. Like Silverpop’s scoring system.

4) Negative Scoring: A negative scoring model implements a deduction of points to your lead scores based on unfavorable interactions and intentions. Negative scoring involves a multitude of aspects. From the low levels of activity or interest found in leads, to prospects consuming your content for all the wrong reasons. The biggest advantage of implementing this model is that it avoids inflating a lead’s score. And allows your sales team to focus more on better leads. Here are some examples of negative scoring:

• Inactive or stagnant leads that have not interacted with the business in a while.
• Leads that unsubscribe to your company newsletter.
• Rival companies researching your company.
•  Visitors that consume your content with no interest in the product, but for other reasons (academic/employment)

Regardless of which model you pick, you’re more likely to adopt a combination of these models so long as it meets your scoring requirements. And as long as you fine-tune your method in conjunction with newer customer data, you can ensure that your lead scores will always stay credible.

Lead scoring streamlines sales by identifying high-value prospects.
1. How It Works: Assign scores based on engagement levels and demographic fit.
2. Sales Enablement: Helps teams focus on leads most likely to convert.
3. Strategic Benefits: Boost efficiency, shorten sales cycles, and increase conversion rates.
Effective lead scoring aligns marketing and sales, maximizing impact across the funnel.

It’s no secret that data privacy is a macro trend that’s here to stay, and with good reason. As social interactions and business operations increasingly take place in digital spaces, users are rightfully concerned about the safety of their sensitive information.

Accordingly, government bodies and security experts have established comprehensive privacy guidelines to ensure the protection of user data. Privacy laws such as GDPR, CCPA, and PECR limit the extent to which websites and businesses can track user activity without explicit consent. While there’s no doubt that this is a win for end users, it may seem like a cause for concern to data-driven marketing teams.

In fact, 73% of GTM teams believe that data privacy regulations will negatively affect their analytical approach to marketing. This article highlights why this is not necessarily true. Let’s explore how privacy-first solutions like Factors empower data-driven marketers to flourish in 2024 and beyond.

Marketers need data. Here’s why. 

Marketers need data to understand and improve the customer experience. This, in turn, results in better conversions and revenue. With data, analytics, and testing marketers can target the right audience with the right message and persuade prospects to become customers. Ideally, it's a win-win situation: marketers spend their budgets efficiently on campaigns that work, and buyers receive relevant promotions as opposed to spammy, spray & pray advertising. In truth, this is nothing new. 

Data has been leveraged by marketers and advertisers since the days of Ogilvy, and with sweeping digital transformation, data tracking has become all the more prevalent. For example, mobile phones today constantly transmit precise gro location as a common user identifier across consumer apps. In comparison, B2B tracking has remained relatively benign — yet effective. B2B marketers have the ability to identify companies visiting their website, track their page visits, scroll depth, and other noninvasive metrics to be able to understand and improve the customer experience. 

The dawn of privacy-first analytics

So far, this sounds great. However, while the intention with which marketers collect data is rarely malicious, the tools and techniques used in this process have been, until recently, without guardrails.

Fortunately, we’ve been seeing a dramatic improvement in data privacy and security in recent years. Today, privacy-first marketing intelligence and analytics tools (Like Factors 😉) honor privacy principles to ensure that data is used only for its intended purpose — to improve the customer experience. Even widely used tools like Google Analytics are having to rework their architecture to comply with regulations.

With tools like Factors, there’s no risk of data being collected without consent, shared with third-parties, or sold to advertisers. Even with this secure approach, marketers can continue to access everything they need to discover new prospects and optimize their performance without intruding on privacy. 

The most important aspect for marketers is to be able to draw the line between reasonable and intrusive tracking. Collection of PII without consent or the ability to identify individual users across websites is illegal and would fall under the latter. As an important practice, marketers should vet their technology vendors keeping this in mind. 

That being said, Factors and other privacy-compliant tools are secure by design. Customer information is protected without compromise on the quality of data, analytics, or insights derived. The following sections cover the basics of what you need to know about the most important marketing data privacy regulations — each of which should be considered when investing in marketing technologies.

1. First-party cookies

First-party and third-party cookies play important roles in the collection of user information. Here’s a quick overview of what cookies are and how first-party and third-party cookies differ from each other.

Cookies or HTTP cookies are tiny pieces of data that are sent to your browser from a web server. This data is stored locally on your device so that the next time you visit a website, it can identify you as the same user. So what’s the difference between first and third party cookies? 

‍First-party cookies: FPC are set directly by the website you are browsing. Their primary purpose is to collect analytics data such as page views, button clicks, and form submissions to improve website functionality and enhance user experience. Without first-party cookies, a user would have to sign in to an account every time they visit a new page on the website or app. Even the most basic preferences like language setting would have to be reconfigured on every page without first-party cookies. In short, they’re entirely harmless and collect basic website data to help marketers eliminate areas of friction and improve website usability.

Third-party cookies: Third-party cookies are tracker cookies which are set by third-party servers (or ad servers) independent of the website a user is browsing. Third-party cookies are accessible to any website that can load the server’s script. More often than not, these cookies are used for unsolicited advertising and are set by ad networks like Google’s AdSense program.

Websites that accommodate ad spaces from servers such as Google’s “DoubleClick” also allow them to place third-party cookies. These cookies can track your browser history and identify interests to facilitate retargeting. That way, when you visit a website that also hosts a similar ad server, it will display a targeted advertisement using the same third-party cookies.

Factors.ai only uses first-party cookies to enhance your user experience with zero intention in building an interest profile or a third-party context with first-party cookies. More information on the usage of cookies here. Third party cookies are  generally considered to be questionable and in some countries, illegal. This is because there’s no certainty as to what data these cookies are collecting and how that data is being used. Accordingly, it’s best to avoid tools that use third party cookies.‍

By design, Factors only uses first-party cookies to track visitor activity and enhance user experience. Tools like Factors have no ownership rights over your user data. They do not share or monetize first-party data collected from users in any way, shape or form.

2. GDPR Compliance

GDPR (General Data Protection Regulation)

General Data Protection Regulation is a privacy regulation standard that covers data protection andp privacy in the EU and European Economic Area. Under this regulation, businesses are required to receive voluntary or opt-in consent to collect personal information of customers, which needs to be clear and unambiguous. 

Personal information is defined by the GDPR as “any information which is related to an identified or identifiable natural person”. Information like IP addresses or any other data that can be traced back to a person is required for analytical purposes will require the user’s consent under the GDPR. This is why you may have noticed several privacy-compliant websites request consent on tracking personal information when you visit.

It is important to note that the consent of collecting personal information cannot be preordained or implied like in the form of pre-ticked boxes. Instead, the user must choose to opt-in to the collection of data and provide adequate detail on the information being tracked.

When complying with the GDPR, businesses must also comply with a set of rights with regards to personal information being collected. These include:

• The right to disclose and access the information collected
• The right to request for a correction of the information
• The right to request the erasure of personal information
• The right to register a complaint on the handling of personal information
• The right to request a restriction in the processing of personal information
• The right to object to the method in which your information is being processed
• The right to retrieve personal information and transfer it to another party, and
• The right not to be subject to a decision that is based on automated processing and has an adverse legal effect on the user  

‍Factors is aligned with GDPR rules and regulations. At present, Factors stores its data in US-based cloud-company servers. Note that the GDPR does not mandate the storage of data of EU citizens and residents within the EU. Additionally, while Factors collects IP addresses for high-level enrichment such as coarse geolocation (city, state-level) and account identification, this data is purged. We do not store IP or firmographic data in our database.

CCPA (California Consumer Privacy Act)

The California Consumer Privacy Act is a state-wide data privacy law that regulates how organizations handle personal information (PI) of California residents. Under the CCPA, the collection of personal information does not require opt-in consent for adults. That being said, just like the GDPR, users under the CCPA have the right to access personal information being collected and the right to opt out of the sale of personal data to third parties. 

‍Factors is CCPA compliant. In fact, by design, we do not have the ability to share, sell, or store personal data to third parties.

PECR (Privacy and Electronic Communications Regulations)

The Privacy and Electronic Communications Regulations (PECR) represents the UK's law on how businesses are allowed to market to UK consumers using electronic technology. ‍This regulation deals with unsolicited marketing, which includes things like cold calls, fax, text and emails, etc. PECR does not apply to solicited marketing — or marketing messages that are voluntarily requested. Even if a person has opted-in for marketing from your businesses, there are still instances that are defined as unsolicited, which would have to comply with PECR. As a marketer that relies on email marketing, detailed information on the consent must be provided to the person you are emailing. Consent must be received in the form of an action, whether it is written or ticked on a box. 

The rules of PECR slightly differ for B2B, where there is an exception to retrieving consent for emails and text messages. If you intend on the processing of personal information of corporate subscribers (B2B) or/and individual subscribers (B2C), the rules of UK GDPR apply.

Surprise, surprise — Factors is also aligned with PECR regulations.

SOC2 Compliance‍

While marketing under the aforementioned regulations would advocate a fair degree of privacy assurance to your users and necessitates consent. A Service Organization Controls 2 or SOC 2 compliance raises the stakes on the safety and confidentiality of customer data. SOC 2 is a set of criteria that define how a business should go about managing customer data and the examination of relevant controls in accordance with those criteria. While it is not legislation for data privacy, an SOC2 certification is the cherry on top of your data privacy practices and the forefront of establishing security standards as a part of being a privacy-first organization. It works on 5 trust principles:

1. Security: This involves the use of tools such as application firewalls and two-factor authentication for the protection against unauthorized access of systems.
2. Availability: This refers to the software, systems, or information that is available and is being maintained at a minimum acceptable performance level.
3. Processing integrity: This ensures that a system completes its objectives in a valid, timely and authorized manner with no errors in the system processing.
4. Confidentiality: Using encryption and limited access of data to ensure its disclosure is only restricted to a few people.
5. Privacy: This refers to the personal information of the system that is being collected, retained, used, disclosed and disposed of in compliance with the organization’s privacy notice and GAPP (Generally Accepted Privacy Principles).

‍Factors.ai is also SOC2 compliant.

Playing the long game — B2B Marketing Privacy In 2024 & Beyond

As more intent and uses of personal information by businesses get discovered, postmodern norms for regulation on the safe collection of data gets more rigid. Falling short on the compliance of these regulations will lead to the obstruction of marketing efforts. Here are some reasons as to why marketers should consider becoming privacy-first:

1. Data privacy and being privacy-first is bound to become an industry standard for marketing considering that web analytics is more of a necessity than a value adding requirement.
2. The legality of data privacy regulations will severely affect the operational efficiency, and even the going concern of the business. Data privacy under legislation is an obligation.
3. The conception of regulation for data collected and processed by artificial intelligence caused by an inevitable surge in automated workload is well underway.

Today, Google Analytics is illegal in Austria, Italy, Sweden, Denmark, and other European countries because the CLOUD Act allows US authorities to demand personal data from Google, Facebook, Amazon, and other US providers — even when they’re operating in external locations (like the EU). Regulation will only get more stringent — like the new revisions of the CCPA under the CPRA which goes into more detail on the sharing or disclosure of personal information. Being compliant early will help you stay ahead of the game.

More businesses will need to prioritize being privacy-first by building a decision framework around the management of personal information. This means making data privacy, its regulation, and the control of user data for the long haul the cornerstone of your business and marketing efforts.

With stricter regulations, privacy is now central to effective B2B marketing strategies.
1. Regulatory Landscape: Laws like GDPR and CCPA demand transparency in data usage.
2. Marketing Response: Shift toward privacy-first strategies that respect user consent.
3. Strategic Benefits: Ensure compliance while preserving targeting precision and personalization.
Adopting privacy-conscious practices builds trust, protects brands, and sustains long-term marketing effectiveness.

According to Hubspot, marketers spend nearly 210 minutes a week analyzing data from different sources. What’s interesting, though, is that marketing professionals often struggle to determine the channels that facilitate customer journeys to fuel pipeline and revenue.

Coincidence? No.  

With a gamut of channels, touchpoints, platforms, and campaigns running simultaneously, it becomes difficult to determine which marketing strategy brings value to the table. 

Especially in the case of B2B marketing, multiple online & offline channels are involved. For instance, online channels involve social media, content, email marketing, etc., whereas offline channels include ebooks, webinars, workshops, meetings, etc.  

Thankfully, marketing attribution reporting can effectively solve this problem and assist businesses in shifting from intuition-driven strategies to customer-centric and data-driven strategies. 

Attribution reporting allows marketers to do an in-depth analysis at a granular level and give a clear picture of the direct impact of marketing strategies and tactics. 

Read our blog to understand exactly what attribution reporting is and what you can learn from marketing attribution reports to put your revenue growth on the fast lane . 

Let’s get started! 

Table Of Contents

• What Is Attribution Reporting? 
• Why Use Attribution Reporting And When To Use It? 
• What You Can Learn From Marketing Attribution Reports?
• How Can Organizations Leverage Attribution Reports To Skyrocket Their Conversions?
• Bonus Information: What Is The Attribution Window 
• Wrapping Up
• FAQs

What Is Attribution Reporting? 

Attribution reporting gives you a bird's eye view of the path your customer took before converting. Moreover, it also gives an in-depth insight into how different marketing efforts have cohesively worked to fuel conversions. 

Attribution reporting will help you to determine the following. 

• From which channels are the customers first becoming aware of your brand?
• Which campaign is driving the maximum demo form submissions or signups?
• Which piece of content/ad are they interacting with between opportunity creation and closed-won?
• Provide an actionable view of the buyer’s journey across multiple stakeholders who interact with multiple touchpoints over many months. 
• A transparent overview of the channels to generate leads, nurture them and finally convert.
  

You can leverage many attribution models to create a comprehensive report, such as first interaction, last interaction, linear attribution, etc. Attribution reporting gives crystal clear insights into the specific parts of your strategy and helps you highlight the areas that need improvement. 

All in all, marketing attribution reports summarize your customer journey data by building a timeline of touchpoints at a user and account level, combine that with vital channel metrics such as impressions, clicks, and spending and visualize the insights into a cohesive and effective report

Why Use Attribution Reporting, And When To Use It? 

One of the most rewarding aspects for a marketer is to see the successful result of their efforts. Once you start noticing the number of conversions from a strategy you have implemented or a piece of content you have posted, you know you have done your job right. 

But getting conversions is just one part of the job! The most gratifying part is to be able to measure and correlate the amount spent with the business ROI. 

This is where attribution reporting comes into play. 

An attribution report is nothing but a presentable outcome of your customer journey and campaign data. Therefore, an attribution report is only as valuable as the underlying data itself. Within your Marketing Strategy, attribution fulfills the need to optimize your marketing spending, allocate resources better, scale the right initiatives, and track channel performance.

That being said, you wouldn’t want to rely on a false source of optimization or, worse, vanity metrics to determine your marketing strategy. Attribution reporting provides you a credible foundation to build a data driven marketing execution engine.

Unlike a marketing team’s requirements for tracking KPIs, which tend to be an everyday ordeal, the frequency of usage of attribution reports is determined by the following factors

• How frequently are the campaigns optimized?
• What is the conversion cycle length from first touch to revenue
• What is the cadence of executive reporting for the CMO
• How frequently are budget re-allocation decisions made at your company
  

What You Can Learn From Marketing Attribution Reports?

Here are the learnings you can expect from marketing attribution reports. 

1. Better Comparison With  Model-Based Information

Companies increasingly use a multi-channel approach to educate and inform their target audience based on their preferences.  However, when too many channels are in action, it becomes challenging to determine which channel contributed the most to pipeline and revenue. 

Attribution reporting allows marketers to determine the contribution of each channel based on the chosen model and compare the results of different types of attribution models to make your investment decisions. For example, an Influence attribution model shows the amount of pipeline and revenue influenced by each campaign or content, whereas a First Touch Attribution report only credits the campaign or content for the revenue where it was the very first touchpoint.

Further, the conversion goals in attribution can be set as Top of the Funnel KPIs such as Leads, Demos or Mid Funnel Metrics such as MQLs, SQLs or Bottom of the Funnel metrics such as Pipeline and Revenue, helping Marketers understand the influence of each channel at various stages of the funnel. 

If you are a Saas company with both a PLG flow (SignUp and then Product Milestones) as well as a sales-led flow (Demo and then Opportunity Creation), you can use attribution analysis to understand which channels are most effective for each of these go to market models.

2.  Get An Overview Of Baseline Metrics

Baseline Metrics within Attribution provide a channel-level overview of investment metrics such as Impressions, Clicks, and Spending, along with platform-specific metrics such as Keyword Match Type, Search Impression Share for Google Ads. 

Attribution reporting tools aggregate these investment metrics across channels, enabling a Marketer to understand how much are they spending by a campaign, Ad group, creative, and keyword. Using these insights, Marketers can get a complete view of the performance metrics for each Campaign.

3. Analyze Conversion Metrics

A good attribution report combines the baseline investment metrics along with conversion metrics across the funnel such as leads, demos, SQLS, pipeline, and revenue. This helps Marketing teams move beyond measuring marketing efforts on metrics such as leads and get an accurate understanding of the impact on pipeline and revenue.  

Based on this information, you can assess the following:

• How many leads does each channel or campaign generate?
• How many of these leads are then converted to demos and sales-qualified leads by the campaign?
• How much pipeline and revenue were influenced by each of these channels or campaigns?

4. To Get Clarity On ROAS 

ROAS (return on ad spend) is a crucial metric that is used to measure the total revenue generated on every dollar spent on marketing. By bringing together the investment and conversion metrics, Attribution Reports highlights the profit margin and ROAS at a campaign, ad group, creative, or keyword level.

Companies may define different ROAS thresholds based on the type of campaigns - such as Product Feature Promotion, Competitive Takeout, and Brand Building. Also, depending on whether the campaign is more experimental (entry into a new product category or new geographic territory) or a well-established one, the ROAS thresholds may be different. Granular ROAS data allows marketers to make data-informed bidding decisions resulting in cost savings and improvement in return metrics.

5. Non-Paid Channels vs Paid Channels

It has always been a struggle for Marketers to determine whether paid or non-paid channels help accelerate your sales. However, attribution reporting gives you an extensive overview of different channels (such as Paid Search, Social, Referrals, Review Sites, and Organic Content) and their contribution to pipeline and revenue.

For instance, Let’s assume your business is active on LinkedIn and drives traffic from the platform through posts and ad campaigns. But when a lead is converted through LinkedIn, you will need to know which tactic contributed to the result - Was it the organic posts or ad campaigns? 

With attribution reporting, you can determine whether the lead got converted organically from the posts you shared or the ads campaign you are running or whether both tactics played a part in the conversion. 

A distinction between direct and non-direct sources of traffic helps identify your PPC leads and your organic ones. This, in turn, helps both the paid marketing teams and the content marketing teams optimize their execution strategies.

6. Attributing Sales Funnel

Attribution reports also enable Marketers to go beyond a single conversion goal and visualize the entire marketing and sales funnel (Leads, Demos, SQLs, Pipeline, and Revenue) at a channel, campaign, or ad group level. 

Armed with this data, Marketers can get a sense of the conversion rates by channel for each stage and focus their efforts accordingly.

7. Get A Clear Picture With Data Visualization

Lastly, because the Attribution Reports and underlying data are exhaustive and cover the entire customer journey and channel mix, it may feel a bit daunting to analyze this data solely in tabular form. 

The report can include dimensions such as keywords (and associated metadata such as keyword match type), ad groups, campaigns, campaign themes, and channels, as well as metrics such as spend, impressions, clicks, CTR, and conversion metrics as well. 

Phew.. - quite a handful to analyze this table of 15+ columns and 100+ rows to unearth actionable insights. This is where intuitive visualizations play a role in facilitating a better understanding of the data through formats such as scatter plots, bar charts, and line vs bar visualizations.

Further, an AI-powered attribution tool like factors.ai  is capable of offering augmented features in a report, such as recommendations on campaign bidding, trends in cost per MQL and SQL, and much more) 

How Can Organizations Leverage Attribution Reports To Skyrocket Their Conversions?

Now that you know what you can learn from attribution reports, we will take you to the next step. After doing an in-depth attribution analysis, now is the time to take some steps to accelerate the momentum of the conversions. 

Following are some ways organizations can leverage attribution reports:  

1. To Create A Result-Driven Content Strategy 

A crucial part of online marketing is creating a content strategy to ensure that the content created will be focused on the customer journey stage they are in. 

With attribution data, marketers can get an overview of the entire customer journey and leverage it to build a result-driven content strategy. 

2. Where Should You Expend Your Marketing Efforts? 

We all know attribution reporting gives deep insights into which channels drive conversions and users. Therefore, we can focus on those specific channels and generate maximum leads.  

3. To Fully Understand The Customer’s Journey 

You may know which channel drove the conversions, but you should also know about the touchpoints your customer interacted with before converting. 

Attribution reporting has the capability to do so, and therefore, it allows you to fully understand the customer’s journey right from the start till the end. 

Understanding this will allow you to create more effective strategies and journey paths that are aligned with buyer preferences.

Bonus Information: What Is The Attribution Window? 

An attribution window, also known as a conversion window, is the timeframe within which conversion will be attributed to a touchpoint. In layperson’s words, it can be defined as a time frame between which a potential lead viewed/clicked on your ad/piece of content and later performed your desired conversion action

 For example, suppose your attribution window is 20 days. In that case, any touchpoints (like users interacting with your landing page) incurred by prospects will only be linked to a conversion (actions like a demo request) if it occurred within 20 days of the touchpoint. Attribution windows also help distinguish your fresh leads from your re-engaged ones and hence remove the impact of interactions that happened a while ago.

The total number of conversions can be skewed if you don’t set the right attribution window. If you look it up, they’re different recommendations on setting an attribution window. Some recommend as little as 7 days, while others suggest 90 or 180 days. 

Setting the attribution window is largely dependent on the expected conversion cycle from first visit to revenue as well as the internal understanding among Go to Market teams (Sales and Marketing) on what would be the appropriate conversion window. Our recommendation would be to compute your average conversion cycle based on historical data and set double that value, post aligning with the sales team.

Wrapping Up

Without a doubt, we can say that attribution reporting is the most effective way to understand and measure the impact of Marketing Efforts on business outcomes. Insights generated from marketing attribution can become your most valuable asset to drive maximum ROI. 

When picking a solution to power your Attribution reporting, you want the best of the best. So keep your eyes peeled for solutions that offer capabilities such as:

• Bring in touchpoints from across data sources - such as website events (digital marketing)and offline touchpoints (webinars, events, e-books, sales calls, and meetings)
  

• Attributing your entire marketing and sales funnel stages and rather than focusing on a single conversion point such as Leads.
• Present both baseline investment metrics and conversion metrics, with the computation of ROI at a channel, campaign, ad group, keyword, page URL, or Theme level.
• Has advanced features to distinguish between new business vs expansions or new leads vs reactivated ones.
  

Opting for a solution that has these capabilities and more can take your attribution reporting to the next level.

Get started with attribution reporting with Factors.ai 

Factors.ai is an AI-empowered attribution reporting tool that helps you to fuel your marketing efforts by effectively comparing and customizing attribution models to generate a clearer picture based on metrics. 

Factors.ai has the capability to create attribution reports at both company and user levels, can track both website and non-website events, and has a customized dashboard that collects and visualizes all crucial data in one place.

If you’re interested in taking your business to next level by analyzing your marketing efforts with robust multi-touch attribution modeling and deep data-driven insights to make an informed decision then, schedule a demo and start for FREE at factors.ai.

Attribution reporting tracks how different marketing channels influence conversions and revenue, offering a complete picture of customer journeys.

1. Core Functionality: Maps multi-touch interactions across campaigns and platforms.
2. Key Insights: Identifies high-performing channels, highlights underutilized ones, and reveals ROI by touchpoint.
3. Strategic Benefits: Enhances resource allocation, replaces guesswork with data, and boosts campaign efficiency.
Leveraging attribution reporting enables marketers to make smarter decisions, maximize returns, and refine strategies based on real performance data.

FAQs ‍

1. What does attribution mean in marketing?

In marketing, attribution refers to the process of identifying and assigning credit to the various marketing channels and touchpoints that contribute to a conversion [or any desired action]. 

By understanding the effectiveness of these different marketing channels, businesses can optimize their marketing budget and resources to maximize their ROI.

2. Why is attribution reporting important for marketers in 2023? 

Attribution reporting provides a holistic view of how different marketing channels work together to drive conversions and revenue. It enables marketers to see which channels drive the most conversions and revenue and which are driving the most users to their website or mobile app. 

With this information, marketers can make more informed decisions about where to allocate their marketing budget and resources.

Factors vs. Dreamdata: Pricing, features & reviews

Marketing analytics. Revenue attribution. Customer Journey mapping. Three invaluable use-cases, two all-encompassing alternatives, and one right choice for B2B marketers. 

The following blog comments on two powerful off-the-shelf B2B attribution and analytics platforms, Factors and Dreamdata, to determine why the former may be a better fit. We elaborate on key benefits and exclusives that Factors provide, which ultimately lends itself to being the preferred B2B marketing attribution and analytics solution.

‍Dreamdata Vs. Factors.ai: Common features and use-cases‍

When it comes down to it, both Factors and Dreamdata are proficient B2B attribution and marketing analytics tools. Before we pick apart some differences, let’s highlight some similarities between the two solutions:

Multi-touch Attribution

For a marketer, multi-touch attribution forms the bridge between prospects’ marketing touchpoints and deal won revenue. The purpose of marketing attribution is to help teams optimize their resource allocation and eliminate poor marketing spend. But for an attribution solution to be truly effective, it ought to be functional, adaptable and intuitive. Both Factors and Dreamdata are equipped with powerful multi-touch attribution modeling that’s capable of assigning value to touchpoint data across the funnel based on revenue impact.

Marketing analytics 

Factors and Dreamdata are versatile solutions built upon strong analytical foundations. Both B2B marketing solutions make use of performance analytics and real time event reporting to fulfill your business's tracking and analytical requirements . This would include tracking and reporting essential site metrics, paid campaigns, organic metrics and more.

Customer success

Customer success management is a crucial component of onboarding, troubleshooting, and deriving the most value from a tool. Both Factors and Dreamdata have both received high praise for their customer service in the onboarding processes and dedicated support for a pleasant experience. What must be noted is that unlike Dreamdata, Factors.ai provides dedicated support across all its plans — not only high tier ones.

Dreamdata vs Factors.ai: Pricing & Plans

Let's get down to brass tacks. First, we discuss the difference in pricing between Factors and Dreamdata. Your martech investment decision should revolve around one thing: ROI. Ideally, you’re looking for a cost-effective solution that doesn’t compromise on its service value.

Here are 4 reasons why Factors is the most cost-effective Dreamdata alternative:

1. Economical plans with Factors: As of today, Dreamdata’s paid plans start at $999/month. This is more expensive than even Factors’s higher tier growth plans ($799/month). Accordingly, Factors is generally better-suited to SME start-ups looking for a wider range of features.  
2. User seat limitation: Dreamdata has restricted user seats per tier. Starting with 5 seats, all the way up to 10 for their highest tier business plan. Factors have no user seat limitation for all tiers.
3. Limited stage models: Dreamdata offers limited user stage models, which is a filter that allows users to segment their customers or leads. Alternatively, Factors can create endless custom user stage models.
4. Dedicated support: Dreamdata’s dedicated support and onboarding is only available in their highest business tier plan. Factors.ai has no such restrictions. It offers the following benefits across all pricing plans and tiers:

1. End-to-end onboarding support
2. Dedicated customer success manager
3. Connected slack channel

In conclusion, we believe that Factors’ pricing plans are more cost-effective and accessible to that of Dreamdata’s. Despite this, Dreamdata does offer basic website analytics for free. While we don’t do this, we do offer a free 14-day trial of our entire platform.

Why B2B Marketers Love Factors.ai

This section highlights a catalog of features that cannot be found using Dreamdata and are exclusive to Factors’ users:

#1. AI-powered “Explain”

Explain is a one of it's kind AI tool that empowers marketers with automated insights into what's helping and hurting conversions. And the best part? The conversion goal is 100% flexible: demos, MQLs, newsletter downloads, web sessions, or any other touchpoint you're interested in optimizing.

Factors will then run thousands of funnel queries and rank the insights using artificial intelligence before neatly presenting them to you. These insights will be separated into columns that show positive (above average conversion rate) and negative (below average conversion rate) ratings. Each column lists different combinations of conversion paths that can be further broken down into sub insights that show even more conversion paths. 

#2. Account Intelligence

Factors partners with industry-leading data partners to provide IP-based account deanonymization. In short, this means that users can discover high-intent companies engaging with the website, product reviews, or ads — but are yet to convert. This in turn empowers efficient marketing and intent-based sales outreach.

#3. Slack and Email alerts

This nifty feature allows users to configure automated alerts that keep users up to date with their KPIs’ progress or regression over a predefined interval. Users can pick from several default KPI or use their custom built ones and apply filters for specific insights. Once this is done, users can configure a criteria for triggering the alert which can be for example, an increase or decrease by 10%. Factors will then send alerts to your slack and/or email once every interval that you select, with the last interval period serving as the comparison for your new insights.

#4. Path analysis

The path analysis feature in Factors presents a complete overview of the entire event journey and the number of prospects in the form of something akin to a tree data structure. This feature breaks down specified events into a number of steps, both of which are configurable, and will display the number of prospects within each unique event path.

#5. Auto button click tracking

Another feature exclusive to Factors in this comparison is the automatic button click tracking. As the name implies, this feature and as complicated as it is code-wise, requires no developer dependency to set up. And can ironically start tracking button clicks with the click of a button. It also displays the total number of clicks right next to each button click.

And this concludes our comparison. If you want to substantiate these claims and are interested in learning more about Factors.ai. Feel free to schedule a personalized demo.

When comparing Factors.ai and Dreamdata, Factors.ai stands out as the more cost-effective and flexible solution.

1. Pricing: Factors.ai starts at just $399/month, significantly more affordable than Dreamdata's $999/month starting price.
2. User Seats: Unlike Dreamdata, which limits user seats per tier, Factors.ai offers unlimited user seats across all plans.
3. Customization: Factors.ai allows for customizable user stage models, whereas Dreamdata has more limited segmentation options.
4. Support: Factors.ai provides dedicated support and onboarding for all pricing tiers, while Dreamdata reserves these services for its highest-tier plan.

Both platforms offer multi-touch attribution and marketing analytics, but Factors.ai’s flexible features and affordable pricing make it a preferred choice for B2B marketers looking for comprehensive, scalable analytics solutions.

Several analytical solutions are available for B2B marketers to track and analyze data for their businesses. Two of the most popular choices are Heap and Mixpanel. Though both tools share significant similarities, they also distinguish themselves with unique features and capabilities. 

In this article, we introduce both tools, explain how they are set up, and share key features and limitations to help you make an informed purchase decision. 

Without any delay, let’s delve into it. 

What is Heap?

Heap is a unique digital analytics platform that automatically captures customers’ interactions, analyzes them, and generates actionable insights to improve customer experience, retention, and conversions. 

Heap can track user interactions on websites and mobile apps, allowing businesses to analyze their customer's behaviors and generate data-driven insights.

The tool has a wide range of features, including automatic event tracking, retroactive data capture, and real-time reporting. Heap also allows businesses to segment their data by users, sessions, and events, making it easier to identify trends and patterns. 

Heap’s key features include

1. Customer 360 analytics captures all user interactions and actions on your website and/or mobile app and sends data to your data warehouse to get a comprehensive understanding of the customers' journeys.
2. Funnel optimization helps remove friction and optimize the customer journey to increase conversion rates.
3. Intuitive dashboards help track your critical business metrics and coordinate insights-driven action across the organization.
4. Integrates with 50+ sources/websites ranging from CRM and attribution platforms to marketing automation and user onboarding & adoption platforms. 
5. Usage Tracking/Analytics, allows detailed tracking and analysis of users' interactions within your website and/or mobile app.

What is Mixpanel?

Mixpanel is a product analytics platform that helps businesses improve user experience by tracking and analyzing user interactions with their website or mobile app

Mixpanel equips businesses with insightful reports surrounding user interactions, monitoring the growth of key user cohorts and even comparing current trends with earlier ones. 

The tool also illustrates the user flow within your website/mobile app, discovering the paths taken by users before they make a purchase. Moreover, the flow helps businesses locate friction in the user journey. 

Some of the key features of Mixpanels are

• KPI monitoring, keeping track of the status of previously identified performance measurements. 
• Data integration, integrating or connecting with different sources/apps for analysis and dashboard preparation
• Customer journey mapping, visualizing every interaction made by the user with the business 
• Funnel analysis, mapping the user flow to a set of funnel steps that results in conversion [achieve desired results in general]
• Customer segmentation, segmenting users based on attributes, cohorts or actions to uncover engagement drivers.

Getting started with Heap

Heap is easy to implement as it does not require extensive coding, removing the dependency on software developers. 

To set Heap up, you need to sign up for an account, add a tracking code to your website, and adjust your settings. The time taken to complete the set-up process may vary depending on the complexity of your website and the type of data you want to track. 

Also, after the installation, Heap will automatically track data retroactively.

Setting up Mixpanel

As for the time needed for implementation, the Mixpanel team assures that the entire process will take less than 15 minutes, but it depends on the complexity of the website and the tech stack used. They are also providing codeless implementation by partnering with Freshpaint, in which case, they say the time taken will stretch to 30 minutes.

The tool's main downside is its lack of codeless automation for retroactive tracking, but recently, they have partnered with Freshpaint to allow users to choose between codeless or coded tracking. 

Heap vs Mixpanel: Deep Dive

Heap and Mixpanel are both popular digital analytics tools with unique features and capabilities. So it's essential to understand their key differences before deciding which is the best fit for your business needs.

1. How Event Tracking Works in Heap vs. Mixpanel

The capability to monitor events, such as individual user actions like button clicks, form submissions, page views, etc., and is an essential feature for all marketing analytics tool. Luckily both Heap and Mixpanel  provides this functionality. The only difference is that Heap automatically tracks them, while Mixpanel requires you to instrument custom code on the website to start tracking events. 

Even though Heap tracks events automatically throughout the website, they also deliver a custom option for users to manually track and enrich their dataset with flexible APIs that capture clients and server-side events.

As for Mixpanel, like Heap, a tracking code must be installed on the website or mobile app. Once that's done, businesses can set up events to track. This can be done using the Mixpanel JavaScript library, which provides a simple way to track events and send data to Mixpanel.

Mixpanel's wide-range of APIs offers businesses an alternate approach to tracking events across servers, mobile apps, and other sources. 

But the real problem with event tracking is when it comes to tracking non-website events.

Marketing is not just about the things that happen on the website. In a typical B2B customer journey, touchpoints such as webinars, gated content, meetings, sales calls, field events, and so on play a crucial role in moving the customer down the funnel. This function is absent in both of the tools, which limits their applicability in a B2B context.

This is where marketing analytics tools like Factors come into play. The tool can help businesses track both website and non-website events and overcome the said limitation. 

2. Custom Dashboards and Reporting 

A good dashboard requires the ability to customize dashboards with the data you want to track. This helps to provide an overall insight without jumping tabs.

Dashboards in Mixpanel and Heap are customizable, and users can create multiple ones for the KPIs you want to track. They provide options for filtering charts, categorizing dashboards, and so on. 

Following are examples of how each of their dashboards looks like. 

Heap:

Mixpanel: 

But even though both tools make these arguments, the most common issues related to them are their slow customer support, the requirement for basic technical knowledge for usage, their limited documentation, etc. 

According to reviews on platforms like G2, TrustRadius, and Capterra, when it comes to dashboarding and reporting, the users have found 

• The dashboard’s functionality to be limiting as they can’t add certain types of reports,
• The intuitive UI is found to be confusing and not so easy to use.
• Creation of dashboards to be time-consuming. 
• There is a lack of more explicit filters in the dashboard

The above-mentioned are a few of the many suggestions and cons in their users’ reviews. 

While both Heap and Mixpanel are working to address their limitations, there are other options available in the market that may also meet the needs of users. Factors is one such example.

Factors provide visualization of every bit of information, data-driven insights, and emphasizes any fluctuations or changes from the ordinary, and all are available within a single dashboard. 

3. User Segmentation

Both Heap and Mixpanel offer user segmentation features that allow businesses to group users based on their characteristics and behavior. However, this feature's functionality and ease of use differs between the tools.

Based on Functionality

Heap's Segments feature allows businesses to create custom segments based on events, properties, and time. On top of that, it also allows businesses to track user behavior retroactively.

On the other hand, Mixpanel's segmentation feature allows businesses to segment data by user, session, and event; and create custom reports and dashboards. And based on the segments, the tool allows businesses to conduct A/B testing and in-app messaging as well.

Based on Ease of Use

Heap's Segments feature is comparatively simple, with a dedicated "Segments" option that makes creating and managing segments easy. 

Whereas in Mixpanel, its user segmentation feature is also easy to use though it may take some time to learn how to navigate through the different options to undertake cohort analysis and custom segmentation.

The crucial aspect to keep in mind is that the ability to segment users relies on the data that the analytics tool has about them. Unfortunately, Heap and Mixpanel lack robust integrations with CRM systems to bring in CRM data for segmentation. 

Though both platforms have integration with HubSpot (or, equivalently, Salesforce) for the purpose, Heap only pulls in two data sets from Hubspot, Email Interactions and Contact Properties. And, Mixpanel's integration with Hubspot is even more limiting and only syncs user properties with none of the CRM event objects. 

But both platforms do not provide options for other valuable data sets such as Company Properties, Deal Properties, and Deal Progression, as well as Events recorded in CRM such as Form Submissions, List Additions, Sales Calls, and Meetings, which are critical for B2B companies. 

However, Factors excels in this regard as it supports CRM integration with HubSpot and Salesforce and can help B2B businesses in tracking contact, account, and opportunity properties as well as all events, campaigns, and activities recorded in the CRM.

4. Insights

This is a unique feature Mixpanel has over Heap. The main focus of this feature is to visualize trends and compositions in the acquired data. It allows users to analyze events and user profiles, compare current data with previous data, create custom events, and more. 

Using the insight feature, a business can track and analyze the performance of different UTM sources and identify which source generates more conversions or any desired results. This would further help businesses optimize their marketing strategy and drive more conversions.

In insights, the metrics calculated across the entire time period will be visualized in simple bar graphs, stacked bar graphs, or pie charts. Following is an example of the pie chart. 

And for the metrics calculated for segmented time, the feature uses a line chart or stacked line chart for visualization. The following is a stacked line graph for reference. 

On the other hand, Heap has the Illuminate feature. It utilizes a data science layer to analyze a dataset and automatically identifies insights that lead to significant business results, even for events that weren't tracked previously. Also, the tool can uncover insights that would be missed by other tools, leading to better business results. 

Though this feature is limited compared to Mixpanel's Insights, it helps businesses to find hidden opportunities and frictions and understand whether the user behavior is hurting or helping with conversions. 

5. Integrations

Heap and Mixpanel offer integration capabilities with over 50 tools, allowing businesses to combine data from different sources.

The Heap Connect in Heap allows businesses to bring user data into data warehouses such as Snowflake and Redshift. 

Mixpanel allows integration with tools like AWS, Google Cloud, and Microsoft Azure. 

Though they both provide integration with common sources like Zendesk, HubSpot, Salesforce, and Segment, the tools also provide some unique integrations as well.

Some unique integrations with Heap are

1. Shopify
2. FullStory
3. Clearbit
4. RedShift
5. Eloqua

Some unique integrations with Mixpanel are

1. Slack
2. Adapty
3. Apptimize
4. Elevar
5. Microsoft Azure

6. User Timeline

A user timeline refers to the visual representation of a specific user’s actions and behavior over time within and outside a website. It shows each user’s visits to a website, downloads, engagement with emails, and other activities in touchpoints. It is essential to gain insights into the user’s interests, preferences, and behavior, which further helps marketers customize their campaigns.  

The Group Analytics feature is the closest thing Mixpanel has to a user timeline. Though it doesn’t necessarily provide a timeline, the feature enables marketers to track each target account’s engagement within the website. It also allows marketers to identify upsell opportunities and churn risks. 

Heap, however, has a User & Session View feature that enables marketers to see granular, user-level data on how each user interacts with the website/app. It also presents a list of users in reverse chronological order based on their most recent activities. 

The timeline of this activity can be adjusted from the last 7 days to the date the user first interacted with the website/app. 

Both Heap’s and Mixpanel’s features do not account for offline touchpoints of the users/accounts or provide an extensive view of the user timeline.

So if you are looking for a tool that can empower businesses with a detailed account of each user’s timeline and engagement with the website, then Factors would be a good choice. 

With Factors.ai, a business can get an account-level timeline as well as user-level timelines through deanonymization. Also, the tool brings in all key touchpoints, including meetings, calls, web data, app data, etc., whereas the others [Heap and Mixpanel] focus only on the web and app data.

Following is a view of the account-level timeline in Factors.

Following is a view of the user-level timeline in Factors.

Heap vs Mixpanel: Pricing 

The pricing plans of these tools differ based on the features that come with each plan. 

Given below is the pricing plan of Heap. And from it, you can see that there is a lack of transparency in the plans, and you have to contact them to get an idea of the overall expenditure. 

On top of the free plan, they also allow a “7-day free trial”.

Mixpanel provides a free option with limited features and a pricing of $25 per month for the basic plan. But like Heap, Mixpanel also requires prospects to contact the team to upgrade the plan and get estimated pricing for their enterprise plan. 

Recap

Heap and Mixpanel are both great analytics platforms with their own pros and cons. To give you an overview of both tools’ strengths and weaknesses, please take a look at the following tables.

Heap: Pros and Cons

Pros

• Automatic retroactive tracking
• Customizable dashboard
• Rapid implementation [codeless]
• Provide necessary integrations
• Can create custom segments based on different metrics

Cons

• Does not provide deeper insights into trends compared to Mixpanel’s Insight feature.
• Though the dashboard is customizable, it is more product analytics oriented. 

Mixpanel: Pros and Cons

Pros

• Can track both event-level, and user-level data
• The insights feature helps visualize trends and compositions in data
• Provide necessary integrations 
• Allows customization of dashboards
• Can compute retroactive tracking

Cons

• Requires dedicated developers to implement.
• Even though they allow codeless implementation with Freshpaint, the implementation can consume more time. 
• Has intuitive dashboards but is complex from a marketing point of view.
• Doesn’t have automatic retroactive tracking.
• Since it requires codes, continuous tag maintenance is required.

Limitations of Heap and Mixpanel

Both these tools provide options for both product and marketing analytics. But at the present day, these tools are best used and known for product analytics. And so, the features they both provide tends to focus more on the product. It means a marketing professional trying to use these tools will need help getting around the tool. 

Also, non-website event tracking is absent in both tools, which limits the data acquired surrounding a business’s users. Considering that every insight made by these tools is based on these data, it’s safe to say that they are not the perfect fit for marketing analytics. 

Top Digital Analytics Platforms

Digital analytics platforms help businesses track user behavior, optimize customer experiences, and drive data-driven decisions.

1. Top Platforms: Heap, Mixpanel, and Factors.ai.
2. Key Features:
- Heap: Automatic event tracking, intuitive dashboards, over 50 integrations.
- Mixpanel: Manual event tracking, granular user behavior analysis, trend visualization.
- Factors.ai: Combines automatic event tracking and in-depth analysis, seamless integrations.
3. Strategic Benefits:
- Heap: Quick implementation, retroactive data analysis.
- Mixpanel: Detailed user insights and performance monitoring.
- Factors.ai: Comprehensive solution, easy-to-use, robust analytics without complexities.

Implementing these platforms provides powerful insights, enhances user behavior tracking, and supports smarter business decisions.

Still on the Fence? Complete Your Analytics Stack with Factors

In conclusion, Heap and Mixpanel are both popular analytics tools that provide businesses with powerful features for tracking and measuring user interactions and behavior. They both offer a wide range of features, helping businesses with event tracking, event reports, identifying UTM sources, and more. 

Heap and Mixpanel are known for focusing on products rather than marketing analytics. So, it's worth considering other options that may better align with your business needs and goals, particularly when it comes to marketing analytics.

Factors is a marketing analytics tool built for B2B and SaaS marketers with a focus on account-based analytics and robust  CRM integrations. The tool is purpose-built for marketers and has an advanced multi-touch attribution feature at an account level covering website and offline touchpoints. 

Its UI is simple and easy to use, and it takes about 15-20 minutes to set it up. You can sign up for FREE and learn how Factors can transform your marketing operations.

FAQs 

1. What data does Heap collect?

Heap collects user interactions and user behavior data on a website or mobile app. It includes data on events such as button clicks, form submissions, page views, and user properties such as device type and location.

2. Where does Heap io store data?

The collected data is stored on secure cloud-based servers. Heap also uses Heap connect to connect with data warehouses like Redshift and snowflake for the purpose too. 

3. What is the use of Mixpanel?

The primary use of Mixpanel is to track and measure user interactions and user behaviors on a website or mobile app. It allows businesses to understand how customers engage with their products/services and generate actionable insights to improve their UX. 

4. Does Mixpanel require coding?

Yes, it does require coding, which is, in fact, a downside of using Mixpanel. You will need tech support to create/write codes that can help track events. However, recently, Mixpanel has partnered with Freshpaint to make the codeless implementation available for their users.