Analytics and Reporting Archives - Act-On

What is a Single Source of Truth (SSOT) and Why it Matters

Act-On — Fri, 02 Aug 2024 16:25:59 +0000

How teams collect, manage, and use data is one of the hottest topics in marketing right now. Having a single source of truth (SSOT) is crucial for protecting consumer privacy and enabling strategic decision-making. It influences your team’s ability to leverage the latest and greatest AI applications. And good customer data makes it possible to provide personalized experiences at scale.

So let’s unpack the framework that has long been considered the holy grail of marketing data: having a single source of truth for your marketing and sales data.

Everyone says you need one. And you can spend an awful lot of money trying to buy one. But what exactly is a single source of truth — and why does it matter so much?

What is a single source of truth (SSOT)?

Practically speaking, a single source of truth (SSOT) is a central hub that consolidates customer data from multiple sources. Examples: website visits, email engagement, purchase history, chat transcripts, and support tickets.

Ironically, there’s no single piece of technology or standalone tool that is a single source of truth. You can look at the tech stacks of a dozen different organizations and find a dozen different ways of approaching SSOT. But the fundamental concept is this: everyone should use the same information for decision-making, strategic planning, and executing campaigns. No data silos, no duplicative sources, no conflicting definitions.

Building a SSOT for marketing typically involves aggregating data from multiple sources in a cloud-based data warehouse or data lake. Then, you’d layer a user interface on top. While a decent amount of this data can be found in tools like your CRM, a SSOT will encompass data from marketing automation, customer support, finance, product usage, and other systems to represent a holistic view of the customer journey.

^{Finding a single source of truth in marketing is crucial for staying aligned in day-to-day operations.}

Some organizations use expensive customer data platforms (CDPs). Some rely on standalone BI tools, like Looker or Tableau. And some organizations rally around analytics within a shared platform, like the Act-On Analytics embedded in our marketing automation solution. (We’re biased, but we like this approach! It delivers insights in the tool your team is already using every day.)

The right approach to a single source of truth depends on your company size, your use case, your level of data literacy, and (let’s be real) your budget. But regardless of what a SSOT looks like at a given org, you’ll be hard-pressed to find a marketing leader who doesn’t find it incredibly valuable. Here’s why.

Why SSOT matters so much

The big-picture reason for having a SSOT is simple. You don’t want your team coming up with different answers to the same questions. That causes upstream confusion that makes it difficult to measure success for any initiative.

As consultant Greig DeSautel writes for Thoughtspot, “Data results in businesses cannot be like Google search where users must determine which answer they want from several choices (which causes concern over whether a single version of the truth exists). In business, there can only be one answer. If it isn’t the same answer for every user, data chaos and a lack of trust in data will continue to grow within an organization.”

The benefits of a single source of truth

Beyond avoiding existential data chaos, there are some tangible benefits to maintaining a single source of truth for your marketing data.

Faster, more accurate analysis

Working with an SSOT enables real-time access to the data you need, when you need it. This helps streamline campaign performance analysis and enable quicker adjustments.

Higher productivity

Wrangling and reconciling data across different systems takes up a lot of resources and mental energy. With an SSOT, your team can get the insights they need and focus more of their time and attention on strategic thinking and creative work.

More alignment across teams

When marketing shares a single source of truth with other teams (especially sales), everyone speaks the same language and shares the same understanding of what metrics mean. This helps build alignment and remove roadblocks — like that long email thread debating whose definition of “conversion rate” is correct.

Formalized data security

Most SSOT solutions come with industry-standard security certifications and data protection features like user permissions and access controls. These help protect your consumers’ data and stay in compliance with ever-changing global regulations.

Improved spend optimization

Using a SSOT, it’s much easier for marketers to compare performance across multiple factors like channels, audience segments, or content types. By zeroing in on the most and least effective spending areas, you can tweak budgets or reallocate resources to improve performance.

Email Marketing v1

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The drawbacks of siloed data

Clearly, these benefits aren’t just nice-to-haves. Leaving your data scattered across multiple systems opens up your marketing team to some risks — including compromising data integrity and wasting an awful lot of time and energy.

More human error

Manual data management — like copy-pasting columns between spreadsheets or downloading a CSV from one platform and uploading it to another — inevitably leads to human error. Data can easily be duplicated, misentered, or missed entirely.

Debating whose data is accurate

In a world without an SSOT, data disputes are common. Sales, marketing, and customer success teams — or even different groups within the marketing org — all bring a different definition or understanding of data to the table. Sometimes, it feels like you spend more time debating whose data to use than actually using it.

Waiting on overloaded analysts to answer ad-hoc requests

Without an easily accessible SSOT, teams often rely on analysts who are overloaded with ad-hoc requests to deliver insights — becoming bottlenecks and slowing down important decision-making. (And that’s assuming you even have the budget for an analyst in the first place.)

Failing to comply with digital marketing regulations

In the absence of a consolidated data hub, your email team may rely on manual data wrangling for time-sensitive tasks like updating user preferences — which can lead to non-compliance with digital marketing privacy rules and even costly fines.

Losing out on the power of AI

As AI capabilities increase, marketers will need centralized and reliable data to realize its full potential.

As our own VP of Marketing Jeff Day puts it, “What audiences do you want to personalize for? How are they different? What data are you going to need to accurately drive AI to understand your customer and the business context? If you’re not collecting that information, then AI can’t do its job.”

Poor decision-making

Ultimately, the biggest cost of siloed data is poor decision-making. Without consistent, accurate, timely information, every marketer will struggle to deliver their best work — from setting strategic plans to creating content to optimizing campaign performance.

^{With a single source of truth, collaboration becomes more efficient and teams can move together as one.}

What you can achieve with a SSOT

Let’s take a look into your theoretical future and imagine a few examples of what marketing could look like with a reliable, accessible single source of marketing truth.

Enhanced personalization

A financial services marketing org’s SSOT provides a detailed view of each corporate client’s interactions, from transactions to content downloads to service inquiries. This comprehensive data allows the team to detect and quantify growing interest in investment products, and share tailored content like webinars or whitepapers through their marketing automation platform — setting the stage for a future upsell.

Cross-channel marketing optimization

With clear insights into which marketing channels perform best, a manufacturing firm’s paid ads team identifies that LinkedIn outperforms PPC — and promoting webinars generates the most qualified leads over all other content types. By increasing spend on LinkedIn webinar ads, the team sees higher ROI for their campaigns.

Identifying top-performing cohorts

Armed with aggregated analytics, a SaaS organization can conduct a cohort analysis — understanding which industries, geographic locations, and company sizes aren’t just showing interest but actively engaging and subscribing to the platform. Together, marketing and sales can use this information to refine their ideal customer profile (ICP), update lead scoring criteria, and develop targeted outreach campaigns.

Discover the difference with a SSOT

Marketing data is inherently messy, but with a single source of truth, there’s a brighter future ahead: robust integrations, consolidated reporting, and actionable insights.

Learn more in our webinar on how aggregating your data in your marketing automation platform can help you outperform the competition.

Email Marketing v1

The growth of your business hinges on your ability to attract, nurture, and convert leads. Act-On’s automated lead generation gives you everything required to nurture an unknown prospect into a happy, loyal customer.

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Calculating Confidence Level in Email A/B Testing

Act-On — Sun, 08 Oct 2023 07:00:00 +0000

A/B testing is generally used to select between two different variations of something (ex: email message), so that the winning version can be sent to the broader population. A/B testing is a comprehensive topic; we will go into it in-depth in a future blog post. In preparation for that post, we want to examine the idea of an A/B test confidence interval. It plays a big role in interpreting A/B testing results.

What is an A/B Test Confidence Interval?

An A/B test confidence interval (CI) estimates the range where the true difference in performance (e.g., conversion rates) between two groups is likely to fall, typically with 95% confidence. It helps assess the reliability of the results and determine if the observed difference is statistically significant. If the CI doesn’t include 0, the difference is likely meaningful; otherwise, it might be due to chance. Narrower CIs indicate more precise results, while wider CIs suggest greater uncertainty.

For example, the result of an A/B test might say “Variation B wins, with a 96% Confidence Level.” What does that mean? And how it is estimated?

Let’s look at an A/B test example above. Say there are two variations of email creative that we want to test. Suppose our desired outcome is more clickthroughs. We want to identify the email variation that generates better clickthrough rates using a small list. This way we can use the winner for a bigger campaign down the line.

Accordingly, we do the A/B testing with the two email variations, and we get the following results:

Table 1: A/B Testing Results

At first glance, Variation B appears to have done better. However, could this be due to random chance? This is always a possibility, considering there is a much a larger number of email contacts in this database, and the A/B test was done only on a small sample of it.

We look at the confidence level statistic to address this concern. Deriving it requires a few calculations, as follows.

Calculating Conversion for Confidence Level

In our example, conversion (represented by P) is calculated as:

P = Number of Clickthroughs / Number of Emails Sent

As shown in Table 1,

P _{(Variation-A)} = 2.0% = 0.02

P _{(Variation-B) =}2.5% = 0.025

A/B Test Standard Error

The A/B test standard error (SE) represents the statistical accuracy of an estimate. In this case, the conversion rates we calculated.

The conversion calculation of each variation described above has a standard error associated with it. This is calculated using the following formula:

The A/B test standard error equals the square root of the conversion rate, multiplied by (1-conversion rate), divided by the sample size.

Expressed as a mathematical formula, it looks like this (SQRT stands for “square root of”):

SE = SQRT {P*(1-P) / Sample Size}

In our case, when we apply the formula to both our A and B variations, it looks like this:

SE _{(Variation-A)} = SQRT {0.02(0.98) / 5000} = 0.00198

SE _{(Variation-B)} = SQRT {0.025(0.975) / 6000} = 0.0020

(Read this article for more information on A/B standard error and related concepts.)

Now, we will use those two formulas in yet another formula to get an important number.

Significance of ZScore in Confidence Interval

A statistic, usually referred to as the “ZScore,” helps us to determine whether the conversions in the two variations are really different because there is a true difference, and not because of random chance. It is calculated using the following formula:

The ZScore equals ( the Conversion in Variation B minus the Conversion in Variation A), divided by the square root of (Standard Error of Variation A, squared, plus the Standard Error of Variation B, squared).

ZScore = { P _{(Variation B) –} P _{(Variation A)} } / SQRT { SE² _{(Variation A)} + SE² _{(Variation B)} }

Here’s how that looks using our example numbers to calculate the ZScore in confidence interval:

ZScore = {0.025- 0.02} / SQRT {0.002^{^2} + 0.00198^{^2}} = 1.77

Estimation of Confidence Interval

The ZScore derived above can be roughly explained as the number of standard deviations between the Variation-A and Variation-B conversions. The greater the ZScore is, the more confident we are that the conversions we calculated in the two variations are actually different from each other.

We derive the confidence interval from a normal distribution curve (which can be used in cases with larger sample sizes of 1,000 or more.) Refer to a normal distribution probability table and you can derive the probability corresponding to ZScore of 1.77 is 0.96. The ZScore of 96 confidence interval means that we are 96% confident that conversion derived in Variation B is truly higher than conversion in Variation A. Yet another way of saying this is: There is only a 4% chance that conversions derived in the two variations are actually the same, as an after-effect of random chance.

Generally in the case of email A/B testing, a confidence level of 95% or above is recommended. Therefore, in our example, we can be very confident that using Variation B is superior for producing the intended outcome (clickthroughs), and so we can confidently use it for our broader campaign.

Hopefully, this overview has shed some light on the concept of a A/B test confidence level, standard error and ZScore.

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8 Marketing Analytics Use Cases for Better Insights

Act-On — Wed, 04 Oct 2023 08:00:00 +0000

Your marketing platform houses crucial marketing data that drives your business. But what good is all that data if you can’t view and parse it in the formats and reports that make sense to you and your team? That’s where marketing analytics software comes in and with it, some great marketing analytics use cases.

Most of the major marketing platforms have some form of analytics built into the user experience. Act-On, a leading marketing automation platform is excited to announce that we’ve just made a major update of our marketing analytics functionality, including a new AI search that lets you ask data questions in natural language.

Whatever marketing software you use, gathering the right insights is key to improving performance. But often those key insights lie deeper than what’s provided by standard performance reports (e.g., email send reports, landing page visitor reports). That’s where a true analytics engine is required, to enable you to dive deep into your data, segment according to your business needs, and bring in custom data.

Let’s go through some marketing analytics use cases. What reports should you be looking at regularly to get the most out of your automation stack?

TL;DR: Your marketing automation platform holds powerful data — if you know how to use it. These eight analytics use cases help you uncover trends, diagnose deliverability issues, track form behavior, evaluate CTA performance, and measure campaign impact across audiences. With features like AI-powered insights, smart filters, and custom dashboards, you can turn raw data into actionable strategy and collaborate more effectively as a team. If your current tools fall short, it might be time to upgrade to a marketing automation platform like Act-On.

^{You’d be all smiles too if your marketing analytics software gave you the right data in easy-to-read formats.}

View side-by-side email results to spot trends and anomalies

Results for one email are handy. Results for multiple emails are a force multiplier for marketing strategy and one of our favorite marketing analytics use cases. Visualizing metrics like click and open rates across a range of emails helps you spot trends and potential outliers in your marketing programs.

Perhaps a certain category of email always seems to get higher engagement. Or maybe a certain subject line format is having outsized impact compared to others. These are the types of observations that can lead to new standards and processes across your team that you wouldn’t have come up with otherwise.

Every audience is unique. Now analyze email, content, and subject line performance per unique audiences. Then segment audiences by company size, geo, or industry. Applying this more advanced analysis across your email results is how you get to know your audience better than anyone else.

Track daily form activity to draw conclusions about audience behavior

Monitoring daily form activity is like having a pulse on your audience’s behavior. By tracking how your audience interacts with your forms on a day-to-day basis, you gain valuable insights that can shape your marketing strategy.

Are certain days of the week more popular for form submissions? Do certain forms consistently perform better than others? Are there trends in the timing of form submissions that coincide with specific marketing campaigns or events? These are the types of questions you can answer with reports on daily form activity

Understanding these patterns can help you optimize your marketing efforts. For instance, if you notice that your forms receive more submissions on Fridays, you might consider launching targeted weekend campaigns. Or if you find that certain forms have a higher abandonment rate, you can fine-tune them to improve conversion rates.

Easily spot deliverability failures in customizable charts

Deliverability is the lifeblood of your email marketing campaigns. With customizable charts, you can easily keep a close eye on deliverability and take swift action when issues arise. Drill into key email deliverability metrics such as bounce rates, spam complaints, and email opens in real-time.

Keeping your finger on the pulse of deliverability means you can spot failures and warning signs as soon as they develop, rather than waiting until your sender reputation takes a dive. Are you working with outdated email lists or technical issues? What’s causing that spike in bounce rates?

By monitoring email deliverability in real-time with customizable charts that suit your specific needs, you can maintain a healthy sender reputation and ensure that your emails consistently reach the inboxes of your audience.

^{Your team relies on marketing analytics software to get the job done. Does your automation platform have what you need?}

Understand suppression practices behind your recent email sends

Nearly every marketing automation program uses suppression filtering to prevent emails from going to customers, active sales, or any group that is not appropriate for the given email program. Maybe it’s an issue with the recipient’s domain, or too many hard or soft bounces at the address. Maybe someone on your team has added certain emails to a manual suppression list. Whatever the reason, this advanced marketing analytics use can be very handy. Understanding these trends with suppression can clue you into deliverability issues before they become major problems.

Did a certain email or specific nurture program have more bounces or failed deliveries than others? Maybe it’s time to audit that list for deliverability and make sure there’s not a deeper issue.

Maybe you’ve been beating up a list with too many repeated sends, triggering suppression rules for that nurture program. That could be a sign that you need to stop sending so aggressively to that list, or risk being reported as SPAM by recipients who could otherwise be prospective customers.

Go beyond click rates and dive into behavior paths and CTA performance

While click rates are a valuable metric, they only scratch the surface of understanding your audience’s engagement. To truly optimize your marketing strategy, it’s essential to dive deeper into marketing analytics use cases and explore more nuanced behavior paths.

What calls to action (CTAs) have been most enticing to your audience in recent weeks and months? Behavior paths provide a comprehensive view of how recipients navigate through your emails, interact with forms, and peruse your website. Are they following the desired path, or are there drop-offs at certain stages? Understanding behavior paths allows you to identify bottlenecks and optimize the user journey.

Are certain CTAs driving more conversions, while others go unnoticed? By identifying high-performing CTAs, you can replicate their success in future campaigns. This data-driven approach enables you to create more effective and compelling email campaigns that resonate with your audience on a deeper level.

Use smart filters to zero in on specific groups of emails or dates

Navigating through vast amounts of email data can be overwhelming. Smart filters are like a compass to guide you through. Imagine you want to compare the performance of your email campaigns during different days, months, or key events. Smart filters enable you to quickly isolate the data for those specific timeframes. Are your summer campaigns more successful than your winter ones? Smart filters help you find out.

Likewise, you can use smart filters to segment your email campaigns by audience demographics, geographic locations, or other criteria. This level of granularity empowers you to tailor your marketing strategies for different audience segments and optimize your campaigns accordingly.

Whether you need to analyze specific campaigns, date ranges, or audience segments, smart filters simplify the process and enhance the precision of your marketing analytics.

Use AI to ask natural language questions to form hypotheses and test assumptions

One of the hardest things about data analysis is knowing which questions to ask. You know you have the right data to draw conclusions…you’re just not sure what you’re looking for yet.

This is where things get really exciting. The most cutting edge marketing automation platforms will allow you to ask natural language questions. Start with something simple and build on it. Which of my emails are performing best? Are my top 20 performing emails getting similar results for open rates and click through rates? Which of my emails have high open rates but low click through rates?

Before you know it, you’ve gone down the data rabbit hole and found a way to drive more engagement from your content. AI powered marketing automation gives you the power to field these kinds of open-ended questions and serve up useful data and charts to move you from confusion to clarity.

^{Marketing analytics software solutions should be built into your marketing automation platform.}

Collaboration is the cornerstone of a successful marketing team. It also leads to one of the best marketing analytics use cases. The best marketing analytics platforms let you create custom dashboards and share reports to facilitate seamless communication.

Pinning reports to your marketing analytics platform is like placing essential information on a bulletin board for easy reference. You can highlight key insights, top-performing campaigns, or critical trends that demand attention. This ensures that your team is always on the same page and aware of the most important data points.

Moreover, the ability to create dashboards and share reports fosters open communication, encourages data-driven decision-making, and empowers your team to collectively strategize for better outcomes. It ensures that everyone has access to the information they need to make informed choices and drive the success of your marketing campaigns.

No Need for Separate Marketing Analytics Software with Act-On Marketing Automation

As you read through this list, maybe you were nodding along enthusiastically, recognizing all the rich reporting options that your marketing automation platform gives you.

Or maybe you were stuck in a serious case of marketing FOMO, lamenting the lack of options at your disposal. If your marketing automation solution doesn’t do this for you, or is too difficult for the marketing team to fully utilize, maybe you should give Act-On a try. All of the use cases above are available with our advanced analytics capabilities.

Data Protection & Marketing Metrics in the Age of Privacy

Act-On — Tue, 11 Jan 2022 23:28:57 +0000

As consumers continue to demand better protection when they shop and interact online, new privacy practices and regulations are upending the ways marketers can collect and use data.

Apple, Google, and other tech giants are increasing their own focus on privacy to stay ahead of evolving regulations. From email privacy features to the continued demise of third-party cookies, even advanced marketers can feel overwhelmed by the impacts on the best practices and metrics we’ve relied on for years.

This new privacy landscape requires some flexibility, but also presents an opportunity for marketers to zero in on higher-quality data, better customer experiences, and more meaningful metrics.

The End of Reliable Email Open Rates

Since the release of iOS 15 in late 2021, users who updated their iPhones or iPads were automatically opted in to Apple’s Mail Privacy Protection (MPP) security feature. MPP hides users’ IP addresses and privately loads all remote email content received through the Apple Mail app.

When content loads privately, pixels contained within the email—the standard method of observing an email has been opened—can’t be tracked by email marketers. Instead, even if the recipient deletes or ignores an email that goes to their Apple inbox, every message will appear as an open. Since this change, open rates skyrocketed and click-to-open rates (the number of unique clicks your email receives divided by the number of unique opens) plummeted.

Bottom line: Two of the most frequently referenced email marketing metrics are no longer reliable for this sizable segment of email users.

How to Adjust to the Changing Email Metrics

1. Revise reporting to focus on engagement metrics

You can still measure open and click-to-open rates for non-Apple Mail subscribers in your reporting. Focus your reporting and attention on other metrics that signal reader engagement. Clicks are the primary measurement of email success, but conversions, form fills, unsubscribes, and website behavior are other useful metrics.

2. Update your sunsetting policies on email data

Maintaining a healthy email subscriber list continues to be essential for email deliverability, but most marketers have traditionally used open rates to measure contact engagement. Adjust these metrics to rely on clicks, but since that behavior usually comes later in the customer journey, you’ll likely want to set a longer timeframe to avoid sunsetting contacts who may be slower to click a link than open an email.

Additionally, consider using re-engagement campaigns to nudge users to take proactive steps to stay subscribed.

3. Check your automated email triggers

If your nurture flows rely on triggers based on open rates, they will be compromised for users who opt into MPP (or more accurately, fail to opt out). One way to adjust to the lessening reliability of open rates is to audit and update your automated email programs to use engagement metrics, or time-based triggers instead.

4. Update your A/B testing criteria or segments

Most email marketers use open rates to determine subject line winners in A/B tests. Either isolate those tests to exclude Apple Mail users, or adopt a different metric, like clicks.

5. Get your benchmarks in order

If click rates will become your new go-to metric, make sure you have solid benchmarks in place so you can continue to track performance over time. Comparing future clicks to past open rates won’t give you good insights, as open rates will always be significantly higher than clicks.

6. Encourage engagement within your emails

As you focus on clicks, you’ll need to double down on engagement best practices. Focus on clear CTAs, and consider adding in-email surveys or polls to keep users interacting with your content.

Missing Out on Email Device, Location, and Time Data

Open rates aren’t the only casualty of Apple’s MPP. With the blocking of invisible pixels and masking of IP addresses, certain types of individual subscriber data are unavailable as well. These include the time an email was opened, the location from which an email was opened, and the device used to open an email.

While these data points may seem more minor than open rates, you still want to adjust your email strategy and tactics to avoid negative experiences for Apple users.

How to Address Missing Subscriber Metrics

1. Check on your send time optimization tools

Many email service providers use time-opened data to develop their “magic” recommendations about the best time to send emails to your contacts. Talk to your email provider about how they’re adjusting their send rate optimization to ensure the data driving those recommendations isn’t compromised by Apple MPP users. (For example, Act-On’s Adaptive Send feature incorporates other engagement behaviors like form fills and webpage views.)

2. Review and update reporting that includes device and location

If your reporting includes metrics on device or location, remove it altogether, or at the very least add a note about this data now being incomplete.

3. Check for compromised localized or time-specific email content

Fun countdown clocks that rely on time-opened data and handy location-specific content like weather reports or nearby attractions will be unavailable or wildly inaccurate for users who adopt MPP. Review your dynamic email content and either isolate those blocks to non-Apple users—or remove them altogether to play it safe.

4. Collect your own location data

Location data is really useful, but if you aren’t getting that data as easily as you might have before, one tip is to ask the user to share it with you. Add a question about your subscribers’ location in your form, in your onboarding email, or within your preference center.

Bonus metric tip: Forward tracking is also going away with Apple MPP. If you include this metric in your reporting, consider removing the metric altogether.

Updating Ad Performance Metrics to Combat the Loss of Third-Party Cookies

By the end of 2023, Google promised to no longer support third-party cookies in Chrome. These cookies made programmatic ad targeting possible across the open web, monitoring users’ website behaviors and showing them relevant ads everywhere they go.

Safari and Mozilla already did away with third-party cookies due to privacy concerns, so this change wasn’t a big surprise—but it is a big deal. (Keep in mind, however, that the first-party cookies you can use to track behavior on your own website aren’t going anywhere.)

Generally speaking, this means that advertising platforms lost performance signals from third-party cookies, and brands find it more difficult to connect the dots between ad impressions and eventual conversions. Informative metrics like view-through conversions aren’t accessible across browsers. While ad platforms like Google are developing machine learning models to fill the gap, and programmatic ad companies revise their products to adjust to this new normal, measuring and optimizing ad performance will look different in the near future.

One additional consideration: frequency capping. Right now, most ad buyers rely on cross-browser and cross-device cookies to limit the number of times a user is exposed to the same ad—because overexposure can lead to fatigue. Without cookies, frequency capping will become much more difficult, putting brand sentiment at risk.

How to Navigate a Cookieless Future

1. Fortify and improve your first-party data collection

You’ve probably heard this time and again in conversations about cookies and privacy, but that’s because it’s simply unavoidable. You must collect and put first-party user data to work. Form fills, email interactions, content downloads, website visits, SMS opt-ins, subscription preferences, custom touchpoints—you can collect all of these data points directly from your audience and use them to segment, score, map your customer journey, and personalize content experiences. In a future without third-party cookies, robust first-party data is an absolute must.

2. Implement offline conversion tracking across ad platforms

Right now, most ad platforms use machine learning to optimize performance based on conversion tracking. As third-party cookies go away, that data will become less reliable. You can help address this gap by implementing offline conversion tracking, or server-side conversion tracking, which allows you to integrate your first-party CRM data with your ad platforms.

With this approach, you can start tracking leads, MQLs, and movement through the funnel directly within the respective platforms. That allows those platforms’ machine learning models to optimize your performance better, according to your own first-party data. As digital marketing expert Gwynne Ohm described in our recent webinar on cookieless marketing, here’s what this looks like right now across the major players:

Google: There’s a seamless integration between Google and Salesforce, or marketing technologists can use tools like Zapier to help integrate other CRMs.
LinkedIn: Enable enhanced conversion tracking to support first-party cookie tracking on landing pages, and consider using LinkedIn’s native lead gen forms to collect data directly in the platform and sync to your CRM.
Facebook: Become an early adopter of Facebook’s new conversion API that shares data directly through their server (versus a browser), so you capture more downstream data. And similar to LinkedIn, Facebook’s native lead gen form is available to improve conversion tracking.

3. Talk to your intent data provider

If you work with an intent data provider, chances are they’re working on a plan to accommodate these changes and continue delivering valuable insights to customers like you. But, it’s never too early to have a conversation and understand how these changes will impact the volume and quality of the data they’ll be providing.

4. Keep your eye on frequency capping

There’s no easy solution for the danger of overexposure, but keep a close watch on how your respective platforms are approaching frequency capping in this new frontier. Marketing leaders are sounding the alarm about the risks of ignoring frequency capping without cookies, so it’s likely the industry will respond with new approaches—but you may need to micro-manage across platforms or implement more conservative limits in the meantime.

Consumer Privacy Shifts: An Opportunity to Update Your Marketing Metrics

Adapting to new technologies and customer behaviors is part of what can make a good marketer great. Changing expectations around consumer privacy can provide motivation to refocus efforts on improving your first-party data collection and utilization. The data your customers directly provide is more reliable and more actionable than any information you receive from external sources. You can use it to fuel better personalization and cutting-edge marketing automation, driving meaningful outcomes and delivering better experiences for your customers—while respecting their privacy and earning their trust.

A/B Testing for Beginners: Simple Steps to Better Campaigns

Act-On — Thu, 21 Nov 2019 00:00:00 +0000

Introduction

Today, we’re on a quest to help you eliminate the guesswork by outlining how your marketing team can benefit from practicing A/B testing. Keep reading if you’re ready to start gathering data that will help you optimize engagement and drive sustainable results.

Having too many good ideas is a great problem to have, but knowing which of those will resonate best with your audience is not. Choosing the wrong subject line, content asset, or design can lead to lackluster results and send your hard work down the drain. While most of us are allowed a marketing miss or two, one too many could lead to wasted efforts and missed opportunities for ROI.

If you’re like me, you probably prefer to stick to creative tasks and leave data and analytics to others on your team. The problem with that approach is that it leads to random acts of marketing that might not always correlate with the interests and pain points of your target audience or where they are in the sales funnel. So, in order to get the results you want, you need to launch personalized marketing efforts that engage, nurture, and convert your audience.

So what can marketers do to hit the nail on the head when it comes to producing effective and engaging marketing efforts? To start, you have to stop looking at the process of gathering and analyzing data as an unbearable and impossible task, and start thinking of it as a source of inspiration. When fully leveraged, good data opens the door for you to produce creative campaigns that generate results. By taking time to test your efforts and gather insights, you’re carving the way toward producing a more targeted and effective digital marketing strategy.

TL;DR: A/B testing helps marketers eliminate guesswork by comparing two variations of an asset to determine what resonates best with their audience. When paired with marketing automation software, it becomes easier to continuously test subject lines, CTAs, pricing, ads, and more—leading to optimized engagement, better conversions, and smarter overall marketing strategies.

What is A/B Testing

A/B testing allows you to compare two versions of the same asset with one distinct variable to determine which one resonates best with your audience and will garner better results. If you want to reap the full rewards of A/B testing, it is important to make this process a continuous effort. You can continue to test the same asset (also known as the constant, this is the version that usually generates the best results) against a new contender every time.

Why Should You Implement It?

Repeating this process will help you uncover insights regarding your audience’s preferences and enable you to optimize your efforts for better engagement and conversions. While there might be a great amount of guesswork the first go around, you’ll eventually learn what your audience wants to hear and see — and when. And the more you practice A/B testing, the more your focus will shift from figuring out what works to fine-tuning campaigns and efforts that you already know will be a big hit.

What Should You A/B Test, and How Often?

You can A/B test pretty much anything, but we recommend that you focus on the variables that are likely to pack the most punch to begin with and then move on to more minute details.

For example, if you’re trying to generate better results from your email marketing, you should consider the buyer journey as you work on A/B testing different elements. Your first goal should be to get your email delivered and opened, so your subject line is a good place to start. Once you’ve gathered enough data to understand what kind of subject lines resonate with your audience, you can move on to testing copy and CTAs. The end goal is to optimize your efforts so that they motivate your customers to keep progressing through the entire customer journey.

As to how often you should A/B test your efforts, that’s really up to you, but we recommend not to let your efforts remain stagnant for too long. At Act-On, we meet monthly to review the results of our campaigns and optimize them accordingly. We’re very fortunate that our marketing automation platform makes it easy to A/B test elements in email, landing pages, and forms so that we can ensure that every single effort we launch is tailored to pique the interest of our audience, encourage them to engage, and convince them to convert.

What Can You Do With the Insights You Collect

As we’ve mentioned a few times, the greatest benefit of A/B testing is that it allows you to optimize your efforts. A mistake that many marketers make, however, is not looking at the bigger picture when analyzing their results. Whether you’re testing a subject line, preview text, design, or CTA, you should use your findings to inform your overall marketing strategy and business efforts. Think about where else you can apply your findings aside from the variable at hand. These are a few areas where you can leverage the results of your A/B testing for maximum impact:

Content Strategy: Using the results you gather from A/B testing CTAs and topics is a great way to determine which direction to take when it comes to developing your content strategy. If you notice that certain content pieces or topics are generating a lot of buzz across the board, that’s a good indicator that you should produce similar content in the future.
Sales Funnel Optimization: Have you noticed that, despite the fact that you’ve invested a great amount of time A/B testing and optimizing your efforts, your leads still seem to disappear at certain points in the customer journey? If so, you should take a step back to determine what kind of changes you can make (beyond testing variables) to improve the customer journey and keep your target audience moving through the sales funnel.
Pricing Strategy: Comparing your pricing with your competitors is a good place to start if you want to determine how much to charge customers for products or services. A/B testing prices with your own customers, however, can provide more thorough insight into how much your customers are willing to pay for what you have to offer. This practice can help you figure out a pricing strategy moving forward and also provide information about things you can do to increase the value of your offerings in the eyes of your consumers.

Paid Search: Whether you’re a B2B or B2C organization, chances are you rely on some sort of paid ads to capture the attention of your target audience and generate new opportunities. A/B testing can help you determine the best social media platform to use to promote your ads, the most effective placement, and even which keywords to use. This will not only lead to better conversions but also help you effectively manage where you allocate your budget.
Email Marketing: If you’re not already A/B testing your email marketing efforts, then you should start doing so as soon as possible for multiple reasons. To start, you can’t see results from your email efforts if your messages are not getting delivered, opened, and read.

In addition to email deliverability best practices, you need to ensure that everything from your subject line to your copy, design, and CTAs all resonate with your audience and motivate them to convert. Testing each email element individually can help you uncover many insights about your target audience’s preferences, which you can use to inform and optimize your email marketing strategy over time.

A/B Testing Example: Everlane Pricing Strategy

You don’t have to be sneaky about testing pricing. Sustainable clothing company Everlane, for example, has a pretty interesting approach to A/B testing pricing on their website. The company’s “Choose What You Pay” section gives customers the option to pick one of three listed prices on overstock items. This enables the company to get rid of surplus stock by offering discounted prices while collecting important data that tells them how much customers are willing to pay for their products.

Get More from A/B Testing with Act-On

Let’s be honest, this is probably not the first time you’ve heard about the benefits of A/B testing, but you’re probably not making it a consistent effort because of the amount of time and resources it takes to test variables, gather data, and analyze your results. Implementing this practice and leveraging your findings doesn’t have to be a tedious task, however — especially if you’re using a comprehensive marketing automation software such as Act-On.

Act-On not only makes the A/B testing process a breeze — allowing you to test practically any variable on everything from emails to landing pages to your website — but it also allows you to put the insights you gather into action easily. Our platform’s analytics and reporting features enable you to consolidate your data and create reports so you can easily analyze your results. To top it all off, Act-On provides the tools you need to easily build email nurture campaigns and landing pages, score leads, segment your audience, and more.

If you’d like to learn more about how Act-On’s powerful marketing automation platform can help you enhance every single aspect of your marketing strategy, we invite you to schedule a demo with one of our digital marketing experts.

Summary

A/B testing is one of the most effective ways to optimize marketing efforts, improve engagement, and drive ROI. By testing small variations—like subject lines, CTAs, or pricing—marketers can uncover insights about audience preferences and apply them across email, content, paid search, and the entire customer journey. The more consistently you test, the more refined your campaigns become, shifting from trial-and-error to fine-tuned strategies that deliver sustainable results. With marketing automation platforms like Act-On, A/B testing becomes faster, easier, and more actionable, empowering teams to launch personalized campaigns and maximize their impact.

The Beginner’s Guide to Tracking and Analyzing Google Ads Metrics

Act-On — Tue, 04 Jun 2019 07:00:00 +0000

When I began using Google Ads at my first job out of college, I was scared senseless. I knew that I’d seen those distracting, cumbersome ads litter my search engine results page (SERP, although I didn’t know that was the acronym back then) and foul up the actual content I was trying to read on blogs and websites, but I didn’t know they served any real purpose or that anyone actually bothered to click on them — much less convert.

I didn’t really see the value in pay-per-click (PPC) advertising, and I couldn’t understand why my employer was so hell-bent on throwing away all that money! Worse yet, I was now tasked with finding a way to actually get people to click on those ads and make some sort of decision that guided them toward a purchase!

I had a lot to learn and an uphill climb in front of me, but as I began slogging through the training suite in what was then called Google AdWords, I slowly realized the potential of this digital marketing channel. Less than one year later, our monthly leads from PPC had nearly tripled, and we were gaining new business left and right.

Moral of the story: Just because something is difficult or you don’t immediately see its value doesn’t mean you should abandon the tactic altogether. Instead, you should dig in deep to learn more about it and how to use it to improve your business and increase your revenue, which is what we’re going to do in this blog.

No other marketing strategy lends itself to data collection, interpretation, and optimization better or more frequently than Google Ads. Today, we’re going to take a closer look at a few essential PPC key performance indicators, how to track them, and how to leverage the past for future success.

Which Google Ads Metrics Marketers Should Be Tracking

It’s important to track and monitor all of your marketing strategies, but since most companies dedicate significant budget toward their PPC campaigns and Google Ads is such a data-rich platform, you should be reviewing your progress on a daily basis to seize opportunities and avoid wasted spend.

When strategizing your campaigns, determining which metrics to track is mission critical. You’ll need to decide which KPIs to assign to each individual campaign from the outset in order to align Google Analytics and Google Ads prior to launch. This way, you can track all the most important information from the get-go without missing out on any previous data, which will allow you to demonstrate the value of your campaigns to key stakeholders at your company.

Here are the four most important metrics you should be tracking in every campaign:

1) Position

Average ad position is specific to search campaigns and tells you where your ad is showing up on different search engine results pages (SERPs). Ad position is based on Ad Rank, which is determined by multiplying your ad’s max cost per impression (CPM) by your Quality Score — a vital metric that measures the value of your ad for users through an algorithm that accounts for several factors, including:

CTR (see below)
Keyword relevance
Landing page relevance
Ad copy relevance
Overall Google Ads performance

1) Google Ads Basics

It’s unclear how each of these factors are weighed, but CTR seems to be the most important element of the algorithm. Achieving a Quality Score of 7 or higher will decrease your CPC (see below) and improve your Ad Rank and position. Securing a top three position on a SERP is extremely important in search campaigns, as 46% of all clicks go to one of those three spots (1).

2) Google Ads Impressions

An impression registers every time your ad is shown online — whether as a display, search, or email ad. The number of impressions for each ad doesn’t necessarily indicate success, as a user doesn’t have to interact with the ad in any way for it to count as an impression. However, knowing your impression share (which is determined by dividing the number of impressions your campaign receives by the number of eligible impressions) can provide useful information into how much of the digital market you own for a given ad.

3) Google Ad Clicks

When your ads are shown online, users have the option to click on them to learn more about whatever messaging you’re trying to convey. Every time they do, you will be charged an amount that varies based on your bid strategy and the competition surrounding that keyword or placement. Understanding how many clicks your ads are receiving, at what rate, and for what amount is an essential component of any successful Google Ads campaign because doing so allows you to manage your budget, pause ineffective campaigns, and focus on where you’re seeing the most success. Further, your click-through rate (CTR) will impact your Quality Score, as mentioned above.

4) Conversions

At the end of the day, conversion-related metrics are the most important PPC KPIs of all. In B2B marketing, form fills are the most common conversions. And it’s these users who you will want to target with carefully considered nurture campaigns based on their responses.

When you set up your campaigns, you’ll want to make sure you’re tracking not only the number of conversions you acquire, but also how much it cost for each acquisition. To determine your cost-per-acquisition (CPA), just multiply the total cost of your conversions by how many conversions you’ve generated. You should also be monitoring your conversion rate, as the volume of conversions you’re receiving might look great on paper but is actually grossly out of whack with how many conversions you should be receiving based on the number of clicks your ads are generating.

THANKS FOR READING!
Check out our additional related content:

How to Set Up, Launch and Run a Paid Ad Campaign

Setting Up Conversion Tracking in Google Ads

Many new PPC marketers fail to set up conversion tracking properly from the outset of their campaigns and thus have no way of monitoring their most important KPI. To help you avoid that mistake, here’s a simple primer on how to set up conversion tracking in Google Ads.

There are two main steps to accomplishing this; the first of which is to create the conversion tracking pixel. Here’s how!

From your main dashboard in Google Ads, click on the “Tools” tab. Under the measurement menu, choose “Conversions.”
Next, create a new conversion type by first choosing which type of conversions you want to track (sales, app installs, calls, or importing conversions from another system).
Name the conversion and choose a category (Purchase/Sale, Sign-up, Lead, View of a key page, or Other).
Then set a value for these conversions — either the same or different values for each and whether to count each conversion per interaction or just one.
Finally, choose your preferred conversion window so your conversions are attributed properly.

Once you’ve created your conversion tracking pixel, it’s time to actually install it on different pages on your website and other digital properties (such as your dedicated PPC landing pages).

When it’s time to place the pixel, you’ll first see your global snippet, which you can place on all relevant web pages to be used for remarketing purposes as well as conversion tracking.
Next, you’ll copy the Event Code, which will track different types of conversions for further detailed data.
You’ll place this code directly after the global snippet on whichever pages you choose to track.
The global snippet should remain the same on all pages, but if you want to track different types of conversions, you should create individual event codes for each relevant page on your website and associated digital properties.

Although this might seem complicated, it should only take 10-15 minutes and will automate all of your conversion tracking efforts so you never miss an opportunity to assess your progress and report major successes to key stakeholders.

Easily Integrate Your Google Ads Account with Act-On

Well-built PPC campaigns are one of the most effective digital marketing strategies and are a great way to get all of your amazing content in the right hands, especially when your ad groups are targeted based on personas, demographics, and behavior. But if you’re not keeping a close eye on the metrics above, you won’t be able to optimize your campaigns to their full potential, and you could lose a mint in wasted spend.

Fortunately, Act-On has a seamless integration with Google Ads that allows marketers such as yourself to measure the success of your campaigns and also gain visibility into which search terms potential customers are using to find your products and services. To learn more about Act-On’s Google Ads integration, please click here.

Effective Email Marketing Uses Data, Behavior, and Customer Personas

Act-On — Tue, 02 Apr 2019 00:00:00 +0000

Most of us know that email is one of the most effective tools marketers have to nurture their audience, gain new leads, and drive conversions. Getting the most out of email, however, requires the right email marketing strategy, and developing that strategy involves more work than just setting business goals and determining topics.

The email marketing approach that works best for your organization will vary based on numerous factors that influence audience engagement and deliverability. You also have to go beyond a one-size-fits-all approach to make sure you’re addressing the various people involved in the buying process. Keeping these multiple personas in mind is crucial to helping you tailor your efforts to meet their specific needs while improving the chances of your emails getting delivered, opened, and read.

Since deliverability best practices and audience preference are fluid, finding the right path toward email marketing success can be daunting. Today, we are on a mission to remove the mystery involved in creating an effective email marketing strategy and equip you with the tools and knowledge you need to figure out a recipe that gets results.

Keep reading to learn how to use data, behavior, and customer personas to develop an email marketing strategy that improves inbox placement and captures your audiences’ attention.

Segment Your Audience to Improve Personalization and Deliver Relevant Content

Personalizing your marketing efforts is necessary if you want to maximize engagement and, in turn, improve your inbox placement. You can’t expect all of your contacts to be at the same decision stage or share similar pain points, and sending an email that doesn’t resonate can result in poor engagement or, worse yet, get sent to the spam folder. Delivering content that matches your recipient’s needs and preferences will prove your business’ value and help them progress from one stage in the buyer journey to the next.

Segmenting your contacts appropriately is the first step toward delivering a more personalized customer experience. Although segmentation has long been regarded as a huge undertaking by marketers, grouping your contacts into lists doesn’t have to be laborious and time-consuming. The right marketing automation tool (such as Act-On), removes much of the work involved by enabling you to automatically segment your contacts based on their behavior as well as the information you’ve gathered through form fills.

While there are dozens of ways to segment your contacts, a good place to start is by their position at their organization and stage in the sales cycle. Once you begin to engage with your contacts and learn more about their specific preferences, you can use this information to segment them into a new list and enter them into a new automated email campaign.

Look at Your Data to See What’s Working (and What’s Not)

Accurate and thorough customer personas provide a good starting point toward delivering personalized emails that resonate with your audience and motivate engagement. Data, however, enables you to go the extra mile and get even more granular with your efforts by providing visibility and insight into whether your subject lines, CTAs, graphics, and other email elements resonate with your recipients.

You can begin collecting data and gain even more detailed insights into your customers’ preferences by conducting A/B testing for every email you send. Send at least two versions of each email to measure which components inspire your audience to open and click through. Once enough time has elapsed and you’ve gathered sufficient data, use these insights to revise your emails to highlight the components that seem to be driving engagement for your audience.

There are plenty of analytics tools available, so you don’t have to be a data scientist to thoroughly measure the performance of your emails. If you’re using Act-On, Data Studio allows you to view delivery, bounce, and open rates and click maps so you can gain a more detailed sense of how your content is influencing the deliverability and engagement of your emails.

Avoid Email Fatigue By Monitoring Audience Behavior

Email fatigue is a real thing, and (in addition to negatively impacting engagement) it can result in an uptick of opt-outs and spam complaints, which are the two fastest and most common routes to a damaged email reputation. Monitoring audience behavior for spikes in spam complaints, emails, and lack of engagement can help you adjust your timing and cadence to avoid bombarding your audience with unwanted emails in the future.

In addition to considering audience preferences and engagement, you can avoid causing email fatigue by limiting your sending during certain times of the year when your audience is likely to receive an influx of emails, such as the holiday season. Another good rule of thumb to prevent email fatigue is to avoid over-sending in the first place. Even if your contacts sign up to your email list and choose to receive communications from you, they might change their mind if you start flooding their inbox. Therefore, it’s best practice to use forms to learn more about what and how often they want to hear from you.

Continuously Revisit Your Strategy for Best Results

As with all your marketing efforts, your email marketing strategy should never remain stagnant. Your customers and best practices for deliverability are constantly changing, so it’s best to periodically revisit your email marketing strategy to reflect these changes for optimal results. This will help you stay ahead of the competition and ensure that you’re always getting the most bang for your buck when it comes to your email marketing efforts.

If you’d like to learn more about how you can use data, behavior, and personas to develop an effective email marketing strategy, check out our eBook Deliverability 101: Your Guide to Developing an Email Strategy That Improves Deliverability and Drives Results (also linked below).

Creative Ways to Use & Present Data

Act-On — Thu, 14 Feb 2019 00:00:00 +0000

Introduction

“My Favorite Murder was the favorite podcast of 327,676 people on Spotify in 2018. Careful, one of them might be behind you.”

That is one of the many eye-catching, social sharing, earned-media nuggets Spotify gleaned from its listener data and shared as part of its 2018 “Wrapped” campaign, which included digital, social, and out-of-home billboards.

Do you want to use your data for something other than an eye chart in the monthly board report?

Do that. We get it: big data is awesome, but no one enjoys reading a bunch of KPI charts on a slide deck (except for maybe the folks in finance). Here are five creative ways to use your data to improve your bottom line.

TL;DR: Data isn’t just for KPI charts — finding creative ways to use and present data can fuel marketing campaigns that drive engagement, brand awareness, and sales. By analyzing customer behavior, identifying success factors, optimizing top-performing content, visualizing insights, and pursuing small, continuous improvements, brands can turn raw data into compelling stories and actionable strategies.

Spotify’s “Wrapped” Campaign

Spotify‘s Wrapped campaign first rolled out in 2016, and it has been an annual favorite ever since.

From 2016: “Dear person who played ‘Sorry’ 42 times on Valentine’s Day, what did you do?”
From 2017: “2018 Goals: Deliver burns as well as the person who streamed “Bad Liar” 86 times the day Sean Spicer resigned.”
From 2018: “God is a man” vs. “God is a woman,” according to fan-made playlists: Man – 9 playlists; Woman – 28,802 playlists

Now that is a creative use of the data you already have. Spotify isn’t the only brand that’s done this.

Pepto-Bismol’s “Celebrating Life” Campaign

In 2010, Pepto-Bismol heard people talking on Facebook about needing Pepto-Bismol on weekend mornings. So they created their “Celebrating Life” campaigns ads that indirectly reminded folks that taking Pepto-Bismol ahead of time can help prevent hangovers. The result was an 11 percent increase in market share.“

Data is leading a lot of the creative process right now. Budgets are tighter, there’s more competition than ever, and people don’t have the luxury to make mistakes and fail forward,” said Brent Poer, president and executive creative director for Zenith in New York in an AdWeek article about how marketers were using customer data to drive faster, smarter, and more creative storytelling.

What stories can you tell with your data? Think like these brands to uncover quirky opportunities that exist to build brand awareness and drive sales.

Act-On’s “Fundamental Three”

In 2017, Act-On’s customer support team wanted to learn the commonalities of our most successful customers. Unfortunately, this isn’t yet a report you can generate in Salesforce.

“We collected data on just about every customer that we have,” said Phil Bosley, CEO of Tactical MA and formerly lead marketing strategist at Act-On. “First, we began to look for patterns. We used frequency tables and histograms. Then we used ANOVA comparative studies with IBM’s SPSS software. Ultimately, we really took this to a scientific level to understand, at its core, what was it that made an Act-On customer successful. What was it that made an Act-On customer love their experience, and what were the determining factors that somebody would love Act-On so much that after the first year they renewed for a second year, after the second year they renewed for a third year, so that we could see that longevity in relationship.”

The result was what we call the Fundamental Three:

Installing the Act-On tracking beacon to their websites to begin tracking who specifically was visiting their website (even anonymous visitors)
Integrating Act-On forms to begin gating valuable content and converting those anonymous visitors into known leads
Regularly emailing at least 20 percent of each marketing list and nurturing those leads throughout their journeys until they were ready to buy (and then continue nurturing those customer relationships)

Businesses that failed to follow the Fundamental Three were more likely not to renew their contracts. So what did we do next? Started educating our customers on the importance of the Fundamental Three, of course, by creating customer nurture email campaigns, tutorials on Act-On University, and more.

Can you identify why your customers are successful when using your product or service? Use your NPS or other customer feedback data to help you identify which customers will be your champions, which customers may need a little help toward becoming your champions, and which customers are going to churn.

You can also use that data to identify any roadblocks your customers may be experiencing that prevent their success. Ask yourself: could those be addressed with a product update or a simple video tutorial?

Ask Yourself, “What Should I Do Next?”

Most marketers can tell you which pages and blog posts are most popular on their site. They can tell you how long prospects are spending on the site and how many pages they view per session. But many fail to make that sort of data actionable; they fail to ask, “What should I do next?”

Make a list of those most popular pages and posts to see which ones are driving conversions to leads. If your most popular pages and posts are not driving conversions, you need to ask yourself why and how you can flip the script.

For instance, on our site, we have one blog post that is consistently the 800lb gorilla in terms of driving visitor traffic to the website. But with the exception of a call-to-action (CTA) at the bottom of the post, we hadn’t really optimized the post for conversions. That’s changed. It now has relevant CTAs, including on-demand webinars, throughout the post. And we’ve made sure to send some of that post’s SEO value to other relevant pages via internal linking. That investment of just a few hours of work is now generating leads on a monthly basis. Cha-Ching!

Besides optimizing your best-performing blog posts, you can also improve or leverage your best performing eBooks, videos, and other gated content. You can also see whether you can rescue poorly performing content or whether it just needs to be re-directed to another related page.

Simplify & Visualize

“Complexity is your enemy. Any fool can make something complicated. It is hard to make something simple.” – Richard Branson

I admit it: I’m guilty of sending managers spreadsheets with rows and columns of numbers and formulas. Instead, I should have been looking for ways to present the data in a more visual, easily digestible way.

1% Performance Improvements

In 2006, back when Netflix was still the mail-in DVD king and Big Data wasn’t yet a thing, the company offered its $1 million Netflix Prize to see if anyone could improve their recommendation algorithm. The formula had been introduced in 2000, and internal efforts to improve upon it had hit a wall. Computer science geeks everywhere went gaga, and, 13 years later, the term “binge-watching” is universal, and the updated algorithms help Netflix save $1 billion in customer retention each year. If you’re still struggling to uncover new creative ways to use data, you should consider applying the theory of marginal gains, which basically states that consistently pursuing 1 percent improvements will ultimately result in sweet awesomeness.

Gather your team together for coffee or a working happy hour to brainstorm which data-driven stories you’d like to tell. Then, identify the data points you need to tell those stories in a way that truly engages and motivates your target audience. If you’re unsure of which data points to focus on, you could add a brief survey to your monthly newsletter, hold a series of focus group interviews at your roadshows and events, or even work with a consultant to develop an original research project for your company.

Within a few short months, you’ll likely have ample data to tell that story. Who knows? It may just be the thing that anchors your marketing activities for 2019 and beyond.

Summary

Brands like Spotify and Pepto-Bismol show that data-driven marketing can go beyond reporting and drive highly creative campaigns that capture attention and increase engagement. The key is identifying patterns that explain customer success, making data actionable, and optimizing top-performing content. Simplifying complex data into visual, easily digestible insights, and pursuing incremental improvements can also generate significant ROI over time. By asking “what should I do next?” and leveraging both qualitative and quantitative data, marketers can uncover opportunities to tell compelling stories that resonate with audiences and improve the bottom line.

Essential B2B Marketing Metrics Every Team Should Track

Act-On — Tue, 07 Aug 2018 00:00:00 +0000

Introduction

One of the biggest investments a company makes is in its marketing organization. The pressure on marketers to say how these investments are paying off is enormous, and it’s going to keep growing. It’s important to slow down and identify the B2B marketing metrics that are meaningful to you and your organization.

Marketers use this data to shape campaigns, figure out their team’s impact on revenue, and justify budgets, among other applications.

Those trends mean that B2B marketing metrics aren’t just nice to have – they’re absolutely essential. Having the right marketing analytics and reporting at the right time can reveal how your campaigns perform, where you’re spending has the greatest impact, and how your campaigns impact the sales pipeline. Marketers need to know which metrics enable them to explain — and sell — a marketing plan to their CEO and CFO.

“At the end of the day, you can’t buy a beer with an MQL. You can buy a beer with a closed deal,” said Matt Heinz in an interview with Act-On’s Rethink Marketing podcast. “And so the more we can help marketers align behind metrics you can buy a beer with ‒ and ideally how that changes the way they operationalize and prioritize our efforts ‒ the better.”

TL;DR: Marketers must focus on the right B2B marketing metrics to prove impact, justify budgets, and align with sales. The most valuable data falls into two categories: revenue metrics (big-picture results tied to revenue and profit) and program metrics (day-to-day campaign performance). Keeping measurement simple, aligning with sales, and leveraging automation helps marketers connect efforts directly to business outcomes.

Picking the Right B2B Marketing Metrics: Keep it Simple!

Part of the B2B marketing metrics challenge for marketers is choosing what to assess. The good news is that you don’t need to track and analyze every possible data point to build a successful measurement strategy. In fact, the best course is usually to take a simple approach: Concentrate on a relatively small set of clear metrics that you can understand and put to work right away.

“The goal is to make better marketing decisions,” said Aaron Bird, CEO of Bizible in an interview on Act-On’s Rethink Marketing podcast. “It’s to make the right investments that take the right prospects and get them into the hands of the sales team so the sales team can then close them. And, in some cases, maybe even help the sales team accelerate or increase win rates on those deals.”

In that spirit, we’re going to focus here on two categories of B2B marketing metrics: revenue metrics and program metrics. Some people think of these in terms of “strategic” big-picture metrics versus “tactical” day-to-day metrics. But it may be more useful to think of them this way:

Revenue metrics are what you’ll show to your CEO, CFO, and board to document your contribution to revenue and profit growth.
Program metrics are what you’ll use internally to gauge the impact of your campaigns, database management, and sales-marketing alignment.

Let’s look at both types of B2B marketing metrics in greater detail and discuss some specific examples.

Revenue Metrics: Painting the Big Picture

Revenue metrics are easy to understand when you engage in a simple thought exercise:

Pretend that you’re being asked to explain your marketing plan to your CEO and CFO. What kinds of metrics tell a story that they will understand and embrace – especially when the time comes to justify your budget?

The answer to this question begins with your marketing funnel and continues through your company’s sales pipeline. It’s extremely important to quantify your marketing team’s impact in terms of converting leads to closed deals and revenue.

“A lot of marketers produce metrics that only measure activity, such as inquiries or leads generated,” said Jon Russo, Founder of B2B Fusion Group. “That’s meaningless at the executive level; it needs to be translated into revenue impact.”

Here are some key revenue-related metrics that allow you to accomplish this goal.

A) Marketing Lead Metrics

Inquiries or Raw Leads are often the first metric that matters to a CEO, since this is the point where your marketing team actually begins its qualification process and separates the “suspects” from the “prospects.”

A related metric involves net new leads added to a marketing contact database. This number – for example, 5,000 new names added per quarter – allows marketers to demonstrate that they can generate the raw material required to feed a company’s funnel. Marketing Qualified Leads (MQLs) represent the next step into the marketing funnel, where individual prospects show the right level of buying intent to pass them along to sales.

B) Sales Lead Metrics

Sales Accepted Leads (SALs) are MQLs that the sales team has qualified and moved into the sales pipeline. SALs are an important indicator that marketing and sales are on the same page about what they consider a qualified lead. These criteria usually involve factors like job titles and firmographics, such as “CIOs of companies with 100 or more employees,” or online behavior, such as “people who downloaded at least three pieces of content and visited our website more than twice in the past month.”

C) Sales Qualified Leads (SQLs)

Sales Qualified Leads (SQLs) have been moved into the sales pipeline and get actively worked by sales reps. This is a critical B2B marketing metric for both the sales and marketing team: It’s the point where leads are entered into Salesforce.com or some other CRM. As a result, this is also the point where a lead is often associated with a potential revenue value.

Many CRM systems or third-party metrics tools allow a sales team to measure this revenue potential as it moves through the pipeline. When this information is combined with data on a sales team’s historical close rates, it’s possible to make accurate revenue forecasts – a key figure for anyone concerned with revenue-focused metrics.

D) Conversion Metrics

Conversions from one funnel segment to the next are actually a function of the other metrics we discuss here. Higher conversion rates, especially as leads turn into opportunities and then customers, indicate a more efficient marketing effort that delivers what the sales team needs to hit its numbers.

The same applies to velocity metrics that measure how long it takes for leads and opportunities to move through each stage of the marketing funnel or sales pipeline. Velocity metrics can provide important hints about which marketing activities have the best impact on ROI. Higher velocity usually indicates more efficient marketing activities that generate faster, higher ROI.

Nurturing Metrics

Lead nurturing gives you a way to stay engaged with leads that aren’t ready to buy yet but will be in the future. Re-engagement metrics cover situations such as leads that don’t score high enough to convert to MQLs, or SALs that turn out not to be valid opportunities. The better you are at placing these leads in a nurturing campaign, and ultimately moving them back into the sales pipeline, the more you’ll contribute to revenue growth.

With all of these, it is important that sales and marketing are aligned on definitions. Kari Seas, a senior demand gen consultant from Seas Marketing, said many B2B marketers struggle in this area, and that it important to define success as a joint effort with sales and the executive team.

“This can be challenging. We all recognize that. But you do need to work with your sales counterparts to determine the success metrics that support the entire business, instead of just telling how well marketing operates internally,” Seas told Act-On’s Rethink Marketing. “That doesn’t mean it’s not important to monitor and measure marketing’s operational effectiveness. It absolutely is. We all want a high performing marketing machine. But you do need to connect marketing’s efforts to the business outcomes to maximize the penetration of your target market and make sure you’re well-aligned with all of the existing sales efforts.”

Program Metrics: Dealing with the Details

Your CEO may not want to hear the details about which programs or campaigns deliver the best results, but your marketing team certainly does. After all, your day-to-day program execution – everything from email and social media to webinars and website content – provides the raw material that ultimately drives your strategic revenue-building efforts.

It’s impossible to list all of the metrics that you can extract from email campaigns, web analytics, webinar attendance, and other sources. But there are some general measurement criteria that you can use to sort through them all:

A) Benchmarking Metrics

Marketers track a wide variety of day-to-day program activities because they’re easy to measure – and because almost everybody else measures them, too. These include benchmarks such as:

email open rates and click-through rates
website visits and page views
content asset downloads
website form completion and abandonment rates

These numbers can be very useful. If your email open rates, for example, are lower than the industry average, then it’s time to examine your email campaigns for potential problems. The same is true for web analytics, especially when you compare current data versus historical trends. Just be careful not to dwell upon these metrics, because they don’t always have a direct impact on marketing campaign performance.

Social media mentions, connections, “likes,” and conversations are similar to other benchmarking metrics; you’re often comparing your metrics to industry averages, your competitors’ numbers, or your own historical data. Many marketing automation tools allow B2B marketers to track social activity on Twitter, LinkedIn, and Facebook and benchmark their own activity against their competitors’ doings. The key here, as with benchmarking metrics, is not to confuse social media success with bottom-line impact. It’s one thing to celebrate a record number of Twitter followers; it’s quite another to demonstrate just how those followers convert into leads, opportunities, and revenue for a B2B organization.

C) Lead Source Metrics

Any quality marketing automation platform allows companies to create multiple-attribution systems to decide which campaigns actually generate prospects. For most companies, however, simpler single-attribution systems work just fine.

Single attribution – deciding, for example, whether a new prospect was recruited via an email campaign or direct mail effort – allows you to do some relatively simple calculations for the investment required per prospect. That, in turn, allows you to calculate the ROI for your campaigns.

D) Database and Data-Quality Metrics

Data quality issues are a growing problem for marketing organizations, as databases with outdated or inaccurate records tend to increase costs and drive down campaign ROI. Tracking metrics such as database size, average lead age, and performance by database/list source can tell you whether there are potentially serious data quality problems lurking in your marketing database.

The Payoff of Marketing Measurement

Most marketers know that metrics are important, and they already attempt to track at least some of the data points discussed here. The real payoff, however, comes when a B2B marketing organization learns how to automate its data-collection process and to present this data to tell a coherent story about its contributions.

Marketing automation offers tools to identify, track, and analyze key metrics, allowing marketers to spend their valuable time on tasks other than spreadsheets and other manual tracking methods.

B2B Marketing metrics are still a work in progress and an always-moving target for even the most successful companies. As a result, it’s important for any marketing team to experiment with its own metrics and test new approaches. But for today’s marketing organizations, it’s clear that effective measurement is a tool you can’t afford to work without.

Summary

In today’s competitive landscape, B2B marketers face increasing pressure to prove ROI and tie their efforts directly to business outcomes. The most effective approach is to focus on two core categories of metrics: revenue metrics, which demonstrate marketing’s contribution to revenue growth and resonate with executives, and program metrics, which help teams fine-tune campaigns and operations. By keeping measurement simple, aligning success definitions with sales, and using marketing automation to streamline reporting, organizations can transform raw data into powerful insights that guide better decisions, accelerate deals, and justify marketing investments.

Best Practices for Pie Charts and Bar Graphs

Act-On — Thu, 26 Jan 2017 00:00:00 +0000

As business people and marketers, we traffic in data. It’s what we use to make decisions (even gut decisions). Data is also how we communicate the status of things and how we make the case for change.

As logical, results-oriented business folk, we like to be data-based.

This principle is so prevalent now that it shows up in a number of disciplines. There’s data-driven marketing. Data storytelling. It all ties nicely into the trend of visual content, too.

Yup – all our pie charts and bar graphs are a type of visual content. We even have a nice word to blend data and visual content: “data visualization,” aka, “data viz.”

Given how powerful and ubiquitous data visualization is, wouldn’t you like to be better at it? If so, learn our pie chart best practices and other tips below on how to make your data more convincing.

These proven best practices will help. To keep things short, I’ve limited my suggestions to the two most commonly used data visualization devices: pie charts and bar graphs.

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What are Pie Charts Used For?

Pie charts are used to represent data visually as proportional slices of a circular chart, making it easy to compare parts of a whole. Each slice represents a category, and its size corresponds to the percentage or proportion it contributes to the total. They are most effective for displaying simple, limited datasets where the focus is on showing relative sizes or distributions.

Are Pie Charts Bad?

Quite a few data visualization experts are intensely opposed to pie charts. Walter Hickey opens his blog post, “The Worst Chart In The World” with this sentence: “The pie chart is easily the worst way to convey information ever developed in the history of data visualization.”

Ouch.

Even Edward Tufte, possibly the most respected voice in data viz, says “pie charts are bad and the only thing worse than one, is lots of them.”

So why are pie charts bad?

Basically, it’s because they’re hard to read. They’re hard to read because our brains aren’t great at interpreting the relevant size of different slices of the pie. If the sections of the chart are similar, we can’t easily tell which piece is bigger.

If you’ve got only two or three slices of the pie, and all those slices are very different, then you don’t have much of a problem. But that’s not the case with most pie charts. All too often, we try to squeeze too much data into them – not only do we end up with lots of slices, we get too many similarly sized slices. That’s when it becomes hard to tell the difference between, say, a slice that’s 23% of the pie versus a slice that’s 28%.

So, is this really such a big deal? Are they really warping our perception so badly?

Well, consider this row of charts. They’re supposed to illustrate changes over time:

Pie charts aren’t great for showing changes over time.

The main takeaway is that it’s difficult to use pie charts to show both the changes over time and how big the different categories are.

Pie Chart Best Practices

Yeah… so pie charts have issues. But I’m guessing you’re still going to have to use them from time to time, whether it’s because your boss said so, or because it’s difficult to depict your information any other way.

So, if you must use one, try to abide by these pie chart best practices:

1. Don’t use more than five sections

The most important aspect of pie chart best practices involves the amount of data used. Too many skinny slices are hard to read. They cloud the information as much as they reveal it.

So, do this:

A good pie chart with just the right amount of information.

Not this:

A bad pie chart with too much information.

2. Place the largest slices from “12” at the top (like on a clock) and work your way around the circle

Like this:

Good chart with the data correctly starting at ’12 o’clock’.

Not this:

Bad chart with data not starting at 12 o’clock.

3. Avoid comparing one pie chart to another

Remember that example above, with the three pie charts showing changes over time? That goes again pie chart best practices as it’s not the right time to use them. Because even one pie is hard to read, lining up a row of them for people to compare makes things even worse. If you want to compare two sets of data like that, use a stacked bar chart like this:

4. Don’t use 3-D pie charts

These also go against pie chart best practices as they make some slices of the pie seem larger than others. This makes the chart even harder to read, and possibly downright deceptive.

In the example above, the purple slice looks larger than the dark blue slice in the back, but maybe they’re the same size, due to the foreshortening of the blue slice. The angles are hard to measure, too.

I don’t advise this (of course), but if you wanted to manipulate information – to deliberately make that dark blue slice look smaller than the purple slice ‒ a 3D chart could do that well.

When to Use a Pie Chart

Before you swear off pie charts forever, know this: There is an exception to the no-pie chart rule. Really. The data viz experts say so. When you’ve got 2-3 data points that are significantly different, then it’s fine. You can have your pie at that time. This is the one instance when pie charts are helpful ‒ they’re good at showing people what a fraction of something looks like.

For example, this is a reasonable use of a pie chart:

Common mistakes with bar graphs

After pie charts, the next most common chart we see in business is the bar graph. These are not as reviled as the poor pie chart, but they’re associated with some frequently made mistakes. Here are the worst offenders:

1. Sideways labels

Here’s an example of a bad bar graph with sideways labels:

Fortunately, this is an easy fix. Just switch the information on the axis so the long labels can be read horizontally.

2. Data points aren’t ranked by size

The charts right above don’t break this rule. In both the vertical and horizontal examples, the bars that are longest are up top, and they descend orderly to the shortest bar.

This lends a sense of visual consistency, but more importantly, it makes it much easier to understand how the measurements compare.

A graph like this breaks the rule.

It makes the viewer have to guess or squint to see how often the different excuses are made.

Of course, if you’re using a bar graph to show changes over time, then you should prioritize the time increments and put them in order, rather than forcing the bar lengths to line up.

3. Avoid shadows or 3D elements on your graph

It just distracts from the information. Keep the imagery of any data visualization as simple as humanly possible. You want the star of your information to be the information itself – not anything else.

While I adore Social Media Examiner’s annual industry report, I wish they wouldn’t make their graphs 3D.

This is a small mistake, but it makes every data visualization experts’ list of no-nos. Those lines behind the bars on a bar graph? They should go. They’re only cluttering up your graph.

So do this:

Instead of this:

The clearer we can communicate our message, the more likely we are to be heard. Pie charts and line graphs can do that ‒ but only in specific circumstances, and only if they’re depicted in a straightforward and clean way. These charts distill a lot of information, but the simpler they appear, the more impactful they become.

Frequently Asked Questions

1. What is a pie chart?

A pie chart is a circular statistical graphic divided into slices, where each slice represents a proportion of a whole. The size of each slice corresponds to the percentage or fraction it contributes to the total dataset, making it easy to visualize and compare parts of a whole. Pie charts are commonly used for simple datasets to show proportions, distributions, or relative sizes among categories.

2. What are the best pie chart alternatives?

The best alternatives to pie charts include:

Bar Charts: Ideal for comparing individual values across categories with clearer visual precision.
Stacked Bar Charts: Useful for showing proportions within categories while maintaining total values.
Column Charts: Similar to bar charts but oriented vertically, good for comparing data across time or categories.
Donut Charts: A variation of pie charts with a central hole, better for additional labeling.
Treemaps: Show hierarchical data as nested rectangles, ideal for complex datasets.
100% Stacked Bar Charts: Focus on the proportional breakdown of each category.
Waterfall Charts: Demonstrate cumulative impacts of sequential data changes.

These options improve clarity, especially for complex datasets or when precise comparisons are needed.

3. What are the three limitations of pie charts?

Difficulty Comparing Similar Sizes: It can be challenging to accurately compare slices that are close in size, reducing clarity in data interpretation.
Limited Data Categories: They work best with a small number of categories; too many slices can make the chart cluttered and hard to read.
Lack of Precision: They are less effective at showing precise values or trends compared to bar or line charts, as they rely on visual estimation.

4. How to make pie charts in Google Sheets?

Enter Data: Open Google Sheets and input your data into two columns: one for categories and one for values.
Select Data: Highlight the data you want to include in the chart.
Insert Chart: Click on the Insert menu, then choose Chart.
Choose Chart Type: In the Chart Editor on the right, select Pie Chart under the Chart Type dropdown.
Customize: Use the Chart Editor to adjust labels, colors, and other visual elements.
Save/Export: Once done, save your sheet or export the chart by right-clicking on it.

5. How to Make Pie Charts in Excel?

Enter Data: Open Excel and input your data in two adjacent columns: one for categories and one for values.
Select Data: Highlight the data range to be used in the pie chart.
Insert Chart: Go to the Insert tab, click on the Pie Chart icon in the Charts group, and select a pie chart style (e.g., 2D, 3D).
Customize: Use the Chart Tools ribbon to modify the title, legend, colors, or labels.
Save/Export: Save your Excel file or copy the chart for use elsewhere.

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Analytics and Reporting Archives - Act-On

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Formalized data security

Improved spend optimization

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More human error

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Poor decision-making

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