Did you know there are about 200 Facebook Ad metrics? That’s way too much to keep your eyes on. A smarter approach is to focus on a few metrics and ignore the rest until you need them. But how do you know which ones are really worth your constant attention? Let’s find out…

Every Facebook Advertiser Struggles with Metrics

You are not the only one who is lost in the maze of Facebook ad metrics. Every day, my team at MeasurementMarketing.io answers dozens of questions from business owners and agencies about this topic.

• I read somewhere that metric X is important but is that true?
• Why would I even track metric Y?
• Can I really ignore metric Z? 

These kinds of questions are important, but they are often asked at the wrong moment. 

The key to understanding which Facebook Ad metrics matter the most to you, is to see them as possible answers to questions you have about Facebook campaigns.

Let’s dive in…

Are my Facebook Campaigns Profitable?

Paid ads are like an investment. You pour money into ads and hope that you will get more money back. 

But like any other investment, there is a difference between hope and reality. 

One metric in Facebook Ads Manager will partially answer whether your ads are performing as you had hoped.

Return On Ad Spend (ROAS)

This metric tells you how much money you get back from every dollar you spent on Facebook ads. 

It is calculated with the following formula:

Revenue / Ad spend

For example: (your revenue) $1,000 / $500 (spent on ads) = ROAS 2

That means that for every dollar you spent on Facebook ads, the platform  generated $2 revenue. 

All that sounds great, but keep the following in mind:

• Revenue and profit are different things. So, you will have to do your own calculations to find out if your Facebook ads are actually making profit for you.
• To calculate the real Return On Investment (ROI) of Facebook paid campaigns, you need to include costs for setting up and managing your ads. 
• This metric is especially useful to ecommerce stores because they sell directly and know for which price. For service providers, ROAS is harder to calculate because it is hard to assign a price for someone who, for example, signs up to a newsletter. 
• Facebook knows a lot about you, but you need to assign values to conversions. I cover that a bit further below. 

How Much do My Facebook Ads Cost?

Running ads costs money. To keep track of how much, you can use over 60 Facebook Ad metrics. Here are some interesting ones that can give you valuable insights.

Amount Spent

This metric tells you how much money you have already spent on a Facebook ad or campaign. 

Although you can set daily budgets to keep your budget under control, it is absolutely worth checking this metric regularly. If the amount is low, for example, that can mean nobody is seeing or clicking on your ads. 

Cost Per Mille (CPM)

This metric answers the question how much it costs to show your ad 1,000 times. If you run awareness campaigns, it is useful for two reasons:

• CPM is a metric that is used by other ad platforms or websites that sell advertising space. It makes it easy to compare the price to advertise on different platforms. On the other hand, it doesn’t tell anything about how profitable the ads are. 
• The CPM also lets advertisers easily compare the cost of different campaigns on the same platform. If, for example, the CPM for one Facebook campaign is $10 and $5 for another, it is worth diving deeper to understand what causes this price difference. Is it because of the timing? The copy of the ad? The audience? The frequency? Etc.

Cost Per Impression

This metric tells you how much every impression of an ad on Facebook costs you. It is not a very important one from the digital marketer’s helicopter point of view. 

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But I included it anyway to illustrate that Facebook has metrics that can give answers to more complicated questions you didn’t come up with before. 

Prices per unit also put things in a different perspective. Knowing that every bite you take from, let’s say a Philly Cheesesteak (Can you tell I’m from Philly?!?), costs you 0.25 cents, may either spoil or add more taste to your meal. 

Cost Per Click (CPC)

Facebook has two metrics for clicks. CPC links are more important than CPC All, because it tells you how much a link to your landing page costs. A click that is, for example, included in CPC All is when someone clicks to see more of your ad copy. 

CPCs fluctuate and the price Facebook charges you depends on factors such as timing, audience size, the services or products you promote, and so on. 

Yet, the CPC is a powerful metric that is worth keeping your eyes on:

• It gives you a clear idea of how cheap or expensive clicks to your site or web shop are.If, for example, you pay $10 per click to sell a $3 product, it may be time to rethink your paid advertising strategy completely. 
• A high CPC may also be a sign that your landing page has an issue. I will get back to that further below. 
• CPC is also a useful metric to compare the performance of campaigns over time, or to find out which ads are repeatable or need optimization. 

Cost Per Action (CPA)

Ideally, people take action when they see your Facebook ad. That can, for instance, be a click to your landing page, watching a video, sharing your page, and so on. 

The CPA metric shows you how much these actions cost. It is also good to:

• Use the CPA as an internal benchmark. Simply put: if you can decrease it, you will get more at a lower cost. 
• Compare the CPAs of different actions. If you  take the bigger picture into account, it may turn out that you have been running ads to trigger people to take actions that don’t boost your business.

Cost Per Conversion

Another metric that is definitely worth your attention is the Cost Per Conversion. If you know, for example, that your paid ads cost you $5 for someone to add a product to the shopping cart, that will give you a good idea whether the campaign is profitable or requires fine-tuning.  

Do My Facebook Ads Actually Contribute to My Goals?

The best way to find out if your Facebook ads help you actually achieve your campaign goals is to look at conversion metrics. 

Conversions are important actions that people take, like adding a product to the basket, filling in a form, signing up for a trial account, and so on.

Conversion Rate

The conversion rate is the percentage of people who click on your ad and do what you want them to do. Let’s assume 100 people click on your product ad and 50 of them add the product to your cart, the conversion rate will be 50%.

That may sound exciting, but if none of them actually buys your product, the conversion rate for your sales goal will be 0%.

It is therefore important to think about your goals and conversions before you dive into metrics. 

How Much Value do My Facebook Ads Generate?

In Facebook Ads, you can assign a ton of conversion values for every goal you want to achieve.

Even if you don’t sell products or courses online, you may profit from assigning a value to conversions, like the Contact conversion value or Leads Conversion Value.

Total Conversion Value

The total conversion value is self-explanatory. But it can also be misleading. If you define, for example, a Content views conversion Value or App activations conversion value, you may get a total skewed version of what your conversions actually are worth. 

Is My Facebook Target Audience Even Interested in My Ads?

Although Facebook is a great advertising platform to reach your ideal audience, your ads may not be appealing to them. The following metrics can help you find that out quickly.

CTR (Click Through Rate)

The click through rate metrics is the calculated percentage of clicks compared to how many times your ad was displayed.

If, for example, your ad was shown 1,000 times and the link to your site was clicked 10 times, your CTR is 1%. 

The toughest part is to decide whether your CTR is good or bad. One way to know this is to run several ads simultaneously and see which one has the highest CTR. 

But this approach is risky too. A higher CTR may not result in higher conversions.

Relevance Score

Facebook assigns a relevance score between 1 and 10 to your ads. The higher the score, the more relevant the ad is for your audience, according to Facebook.

Ads can break or make your campaigns. A picture, the copy, but also how many times it is shown are all details that can make or break your campaign. The following metrics help you better understand how your ads are doing.

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Ad Frequency

This metric tells you how many times the ad has been displayed on average in the Facebook feed of your target audience. 

Mind that this metric can mean many different things depending on the type of campaign you are running.

• With brand awareness campaigns, you can show your ad more before people get tired of it.
• If you are running a lead gen campaign, people usually get annoyed when they see the same ad too many times in a short period of time. 

The list of metrics will help answer the important questions you, your business or customers have about paid marketing campaigns on Facebook

Alas, these metrics cannot give all the answers you need to run successful paid campaigns… 

The 4 Biggest Mistakes Facebook Advertisers can Make

The MeasurementMarketing.io team has taught and supported hundreds of businesses with measuring and optimizing their marketing campaigns for success. 

There are 4 mistakes that keep returning and I figured it’s worth dropping them here so you won’t need to make these mistakes yourself…

Mistake 1: Misunderstanding Metrics

Like any other industry, digital marketing is filled with jargon. It’s easy to misunderstand what something is and is not.

Metrics are often confused with: 

• Business goals 
• Key Performance Indicators (KPIs)
• Dimensions
• Segments

Metrics are just the numbers you add, subtract, multiply, and divide.

Dimensions, on the other hand, are how you sort those numbers.

For example, you might have a “Dimension” that is the Traffic Source and then the “Metric” might be the number of users from that traffic source.

Always remember though, you’ll always first start with a question in mind and then you jump into the data to find the answer (never the other way around!).

Mistake 2: Ignoring Data from Facebook 

Most businesses understand that data is important. But in two situations, it is tough to make data-driven decisions.

Analysis Paralysis

Facebook Ad Manager contains a lot of data, but that is often overwhelming. Not all businesses have the know-how or resources to even look at numbers, charts, graphs and therefore simply ignore them.

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Focus on just ONE THING at a time.  I like to take the advice I learned from my buddy Jeff Sauer at DataDrivenU.com…

“Assign one KPI per team member.”

This keeps it really simple.  If it’s just you, focus on the ONE metric that needs the most improvement.  As your team grows, you can expand your focus (because you’ll have more people to help!).

No Access to Real-Time Data 

This happens, for example, when an external party is running ads and reports monthly. By the time decision makers know what’s going on, the monthly Facebook marketing budget is already gone. 

Businesses that ignore, or don’t have access to Facebook data, lose a lot more than money.

The target audience may, for example, have seen a Facebook ad too many times. It will be an expensive challenge to turn that around.

Mistake 3: Focus on the Wrong or too Many Metrics

Facebook, and other ad platforms, make it very easy to set up your first campaign. They promise you will get results without having to lift a finger. 

And then reality kicks in. 

At one point, you need to understand the true value of data. 

But as I said in the beginning of this article, it can feel overwhelming, confusing or for some, not enough. 

The opposite reaction of analysis paralysis is wanting to have even more data to make complete data-driven decisions. 

Facebook Ads has a ton of them available, like 

• Photo views
• Unique achievements unlocked
• Unique ratings submitted
• Cost per unique level completed
• Etc. 

The question is…

Do you really need all that data to drive your business forward?

In other words, ask yourself, “Is this useful?”

This brings us to the last mistake (which actually might sound contradictory)…

Mistake 4: Ignoring Data from Other Sources

Customers start their journey after they have clicked on your Facebook ad. But as you know, a lot can go wrong when the user lands on a site or web shop.

Think, for example, of:

• The contact form may not be working. 
• The site might not be optimized for a specific device.
• The conversion tracking may not be set up correctly.
• The landing page may not be aligned with the message of the ad.
• Your actual revenue may differ from what Facebook or other platforms, like Google Analytics 4, shows. 

I am not claiming that Facebook Ad metrics are worthless, but you need to pick them carefully. 

Sometimes the best “source of truth” will definitely be Facebook Ads.  But sometimes (often!) it won’t be the best source for the answers you’re looking for.

To measure your actual revenue, for example, it is wiser to rely on data from your cart, or (even better!) your merchant processor (platforms, like PayPal, Stripe, Authorize.net, etc.).

Conclusion: 

Facebook Ad metrics are very powerful to 

• Measure the performance of your campaigns
• Get insights on how to improve your campaigns
• Control your paid ads budget on the biggest social media platform
• Reach the right audience with the right message at the right moment
• Achieve your business goals

But Facebook Ad metrics reveal only one part of the complicated customer journey. 

If you want to stay ahead of your competitors, as a business or marketing agency, then make sure you:

• Track only the most valuable Facebook Ad metrics
• Include metrics from other platforms and tools to make profound decisions
• Give your team access to the data they need to do their job
• Present everything in a shared dashboard that’s explains itself

This is the secret sauce of businesses that thrive in the complicated digital marketing landscape. 

I hope this information will help you become a better Facebook marketer or give your business a better understanding of Facebook Ad metrics and how they fit in the bigger picture of digital marketing.

The post 12 Facebook Ad Metrics Worth Your Attention appeared first on DigitalMarketer.

Have you ever wondered if the article you’re reading online was written by a human or an AI? 

In today’s quickly evolving digital landscape, distinguishing between human-crafted and AI-generated content is becoming increasingly challenging. 

As AI technology rapidly advances, the lines are blurring, leaving many to question: Can we really trust AI content detectors to tell the difference? 

In this article, we’ll deep dive into the world of AI content detection, exploring its capabilities, limitations, and discuss Google’s view of AI content generation.

What Is AI Content Detection?

AI Content Detection refers to the process and tools used to identify whether a piece of writing was created by an AI program or a human. 

These tools use specific algorithms and machine learning techniques to analyze the nuances and patterns in the writing that are typically associated with AI-generated content.

Why was AI Writing Detection Created?

AI content detectors were created to identify and differentiate between content generated by artificial intelligence and content created by humans, helping maintain authenticity and address concerns related to misinformation, plagiarism, and the ethical use of AI-generated content in journalism, academia, and literature. 

There are several key reasons behind the creation of AI writing detectors:

Maintaining Authenticity: In a world where authenticity is highly valued, especially in journalism, academia, and literature, ensuring that content is genuinely human-produced is important for many people. 

Combatting Misinformation: With the rise of AI tools, there’s a risk of their misuse in spreading misinformation. AI content detectors were created in an attempt to combat this.

Upholding Quality Standards: While AI has made significant strides in content generation, it still lacks some of the nuances, depth, and emotional connection that human writing offers.

Educational Integrity: In academic settings, AI detectors play a vital role in upholding the integrity of educational assessments by ensuring that students’ submissions are their own work and not generated by AI tools.

How Does AI Detection Work?

Perplexity and Burstiness

AI generation and detection tools often use concepts like ‘perplexity’ and ‘burstiness’ to identify AI-generated text. 

Perplexity measures the deviation of a sentence from expected “next word” predictions. In simpler terms, it checks if the text follows predictable patterns typical of AI writing. When a text frequently employs predicted “next words,” it’s likely generated by an AI writing tool.

Burstiness refers to the variability in sentence length and complexity. AI-written texts tend to have less variability than human-written ones, often sticking to a more uniform structure. 

Both these metrics help in differentiating between human and AI writing styles.

Classifiers and Embeddings

Classifiers are algorithms that categorize text into different groups. 

In the case of AI detection, they classify text as either AI-generated or human-written. These classifiers are trained on large datasets of both human and AI-generated texts.

Embeddings are representations of text in a numerical format, allowing the AI to understand and process written content as data. By analyzing these embeddings, AI detection tools can spot patterns and nuances typical of AI-generated texts.

Temperature

Temperature is a term borrowed from statistical mechanics, but in the context of AI, it relates to the randomness in the text generation process. 

Lower temperature results in more predictable and conservative text, while higher temperature leads to more varied and creative outputs. AI detection tools can analyze the temperature of a text, identifying whether it was likely written by an AI operating at a certain temperature setting. 

This is particularly useful for distinguishing between texts generated by AI with different creativity levels, but its detection accuracy begins to degrade the higher the temperature.

AI Watermarks

A newer approach in AI detection is the use of AI watermarks. Some AI writing tools embed subtle, almost imperceptible patterns or signals in the text they generate. 

These can be specific word choices, punctuation patterns, or sentence structures. AI detectors can look for these watermarks to identify if the content is AI-generated. 

While this method is still evolving, it represents a direct way for AI systems to ‘mark’ their output, making detection easier.

The Accuracy of AI Writing Detection

Assessing the Reliability of AI Detectors

These detectors are designed to identify text generated by AI tools, such as ChatGPT, and are used by educators to check for plagiarism and by moderators to remove AI content. 

However, they are still experimental and have been found to be somewhat unreliable. 

OpenAI, the creator of ChatGPT, has stated that AI content detectors have not proven to reliably distinguish between AI-generated and human-generated content, and they have a tendency to misidentify human-written text as AI-generated. 

Additionally, experiments with popular AI content detection tools have shown instances of false negatives and false positives, making these tools less than 100% trustworthy. 

The detectors can easily fail if the AI output was prompted to be less predictable or was edited or paraphrased after being generated. Therefore, due to these limitations, AI content detectors are not considered a foolproof solution for detecting AI-generated content.

Limitations and Shortcomings of AI Content Detection Tools

No technology is without its limitations, and AI detectors are no exception. 

Here are some key shortcomings:

• False positives/negatives: Sometimes, these tools can mistakenly flag human-written content as AI-generated and vice versa.
• Dependence on training data: The tools might struggle with texts that are significantly different from their training data.
• Adapting to evolving AI styles: As AI writing tools evolve, the detectors need to continuously update to keep pace or get left behind.
• Lack of understanding of intent and context: AI detectors can sometimes miss the subtleties of human intent or the context within which the content was created.

Real Examples of How AI Detection is Flawed

AI detectors, while increasingly interesting, are not infallible. Several instances highlight their limitations and the challenges in distinguishing between human and AI-written content accurately. 

University of Maryland AI Detection Research Findings

University of Maryland researchers, Soheil Feizi and Furong Huang, have conducted research on the detectability of AI-generated content. 

They found that “Current detectors of AI aren’t reliable in practical scenarios,” with significant limitations in their ability to distinguish between human-made and machine-generated text.

Feizi also discusses the two types of errors that impact the reliability of AI text detectors: type I, where human text is incorrectly identified as AI-generated, and type II, where AI-generated text is not detected at all.

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He provides an example of a recent type I error where AI detection software incorrectly flagged the U.S. Constitution as AI-generated, illustrating the potential consequences of relying too heavily on flawed AI detectors.

As you increase the sensitivity of the instrument to catch more Al-generated text, you can’t avoid raising the number of false positives to what he considers an unacceptable level. 

So far, he says, it’s impossible to get one without the other. And as the statistical distribution of words in AI-generated text edges closer to that of humans —that is, as it becomes more convincing —he says the detectors will only become less accurate. 

He also found that paraphrasing baffles Al detectors, rendering their judgments “almost random.” “I don’t think the future is bright for these detectors,” Feizi says.

UC Davis Student Falsely Accused

A student at UC Davis, Louise Stivers, fell prey to the university’s efforts to identify and eliminate assignments and tests done by AI.

She had used Turnitin, an anti-plagiarism tool, for her assignments, but a new Turnitin detection tool flagged a portion of her work as AI-written, leading to an academic misconduct investigation.

Stivers had to go through a bureaucratic process to prove her innocence, which took more than two weeks and negatively affected her grades.

AI Detectors vs. Plagiarism Checkers

When considering the tools used in content verification, it’s essential to distinguish between AI detectors and plagiarism checkers as they serve different purposes.

AI Detectors: AI detectors are tools designed to identify whether a piece of content is generated by an AI or a human. They use various algorithms to analyze writing style, tone, and structure. These detectors often look for patterns that are typically associated with AI-generated text, such as uniformity in sentence structure, lack of personal anecdotes, or certain repetitive phrases.

Plagiarism Checkers: On the other hand, plagiarism checkers are primarily used to find instances where content has been copied or closely paraphrased from existing sources. These tools scan databases and the internet to compare the submitted text against already published materials, thus identifying potential plagiarism.

The key difference lies in their function: while AI detectors focus on the origin of the content (AI vs. human), plagiarism checkers are concerned with the originality and authenticity of the content against existing works.

Common Mistakes in AI-Generated Text

AI-generated text has improved significantly, but it can occasionally produce strange results. 

Here are some common mistakes that can be a giveaway:

• Lack of Depth in Subject Matter: AI can struggle with deeply understanding nuanced or complex topics, leading to surface-level treatment of subjects.
• Repetition: AI sometimes gets stuck in loops, repeating the same ideas or phrases, which can make the content feel redundant.
• Inconsistencies in Narrative or Argument: AI can lose track of the overall narrative or argument, resulting in inconsistencies or contradictory statements.
• Generic Phrasing: AI tends to use more generic phrases and may lack the unique voice or style of a human writer.
• Difficulty with Contextual Nuances: AI can miss the mark on cultural, contextual, or idiomatic expressions, leading to awkward or incorrect usage.

AI Detection in SEO

Within the world of SEO, content quality has always been one of the major ranking factors.

With the advent of AI-generated content, there’s been much speculation and discussion about how this fits into Google’s framework for ranking and evaluating content.

Here, we’ll explore Google’s stance on AI content and what it means for SEOs.

Google’s Stance on AI Content

Google’s primary goal has always been to provide the best possible search experience for its users. This includes presenting relevant, valuable, and high-quality content in its search results.

Google’s policy on AI-generated content is fairly straightforward: it doesn’t need a special label to indicate it’s AI-generated. Instead, Google focuses on the quality and helpfulness of the content, no matter how it’s made.

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They advise creators to focus on producing original, high-quality, people-first content that demonstrates experience, expertise, authoritativeness, and trustworthiness (E-E-A-T).

Google has made it clear that AI-generated content is not against its guidelines and has the ability to deliver helpful information and enhance user experience, however, they obviously oppose the use of AI to generate deceptive, malicious, or inappropriate content.

Implications for SEO Strategy

Given Google’s position, the use of AI in content creation can be seen as a tool rather than a shortcut. The key is to ensure that the AI-generated content:

Addresses User Intent: The content should directly answer the queries and needs of the users.

Maintains High Quality: AI content should be well-researched, factually accurate, and free from errors.

Offers Unique Insights: Even though AI can generate content, adding unique perspectives or expert insights can set the content apart.

Broader Applications and Future Outlook

As we dive into the future of AI writing and content detection, it’s clear that we’re standing at the brink of a technological revolution. 

AI isn’t just a fleeting trend; it’s rapidly becoming an integral part of the digital landscape. But as AI writing evolves, it’s unclear as to whether or not AI detection will be able to keep up.

The Future of AI Writing and Content Detection

The future of AI writing is trending towards more sophisticated, nuanced, and context-aware outputs. 

As AI algorithms become more advanced, they are learning to mimic human writing styles with greater accuracy, making it challenging to distinguish between human and AI-generated content.

In response to these advancements, AI detection tools are also evolving. The focus is shifting towards more complex algorithms that can analyze writing styles, patterns, and inconsistencies that are typically subtle and difficult to catch. 

However, as AI writing tools become more adept at mimicking human idiosyncrasies in writing, the task of detection becomes increasingly challenging.

The post AI Anxiety – Does AI Detection Really Work? appeared first on DigitalMarketer.

In today’s digital age, businesses are constantly seeking innovative ways to improve their lead generation strategies. Traditional methods can be time-consuming and may not always yield the desired results. However, with advancements in artificial intelligence (AI), businesses now have the opportunity to enhance their lead generation efforts and drive better outcomes. In this article, we will explore eight key ways to leverage AI to improve lead generation and propel your business forward.

Personalized Content Recommendations

AI-powered algorithms have the ability to analyze vast amounts of data to understand user preferences and behaviors. By leveraging AI, businesses can deliver personalized content recommendations to potential leads, increasing engagement and conversion rates.

AI algorithms can analyze a lead’s browsing history, social media activity, and other relevant data points to suggest content that aligns with their interests and needs. This targeted approach ensures that leads receive content that resonates with them, enhancing the overall customer experience and increasing the likelihood of generating quality leads.

Chatbots for Instant Engagement

AI-powered chatbots have revolutionized customer engagement by providing instant and personalized interactions. When integrated into lead generation strategies, chatbots can engage with website visitors, answer queries, and gather relevant information. Chatbots can use natural language processing to understand and respond to user inquiries, providing a seamless and efficient user experience.

By automating initial interactions, businesses can capture leads’ contact information and qualify them based on their responses. This not only streamlines the lead generation process but also ensures that leads receive prompt assistance, enhancing their overall experience with your brand.

Natural Language Processing for Lead Qualification

AI-powered natural language processing (NLP) techniques can help businesses automate lead qualification processes. NLP algorithms can analyze and extract information from leads’ responses, such as email inquiries or form submissions, to determine their level of interest and qualification.

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By automating lead qualification, businesses can save time and resources while ensuring that only the most qualified leads are pursued further. NLP can help categorize leads based on their intent, sentiment, and specific criteria, enabling businesses to prioritize follow-up actions and improve the efficiency of their lead generation efforts.

Predictive Lead Scoring

Lead scoring is a critical aspect of AI lead generation, as it helps businesses prioritize and focus their efforts on the most promising leads. AI-powered predictive lead scoring takes this process to the next level by using machine learning algorithms to analyze historical data and identify patterns that indicate lead quality.

These algorithms can analyze a wide range of data points, such as demographic information, past interactions, and purchase behavior, to predict a lead’s likelihood of converting. By leveraging AI for lead scoring, businesses can allocate their resources more effectively and focus on leads with the highest potential, improving overall conversion rates.

Automated Email Campaigns

Email marketing continues to be a powerful tool for lead generation. However, manually managing email campaigns can be time-consuming and prone to human error. AI-powered solutions can automate various aspects of email marketing, such as email scheduling, personalization, and segmentation.

AI algorithms can analyze lead data to determine the most appropriate time to send emails, personalize email content based on individual preferences, and segment leads into targeted groups for more relevant messaging. By automating these processes, businesses can optimize their email campaigns, deliver personalized experiences to leads, and increase the chances of converting them into customers.

Voice Search Optimization

With the increasing popularity of voice assistants and smart speakers, optimizing lead generation strategies for voice search is becoming essential. AI can help businesses adapt their content and SEO strategies to align with voice search queries. AI-powered algorithms can analyze voice search patterns and understand the intent behind queries to provide relevant and accurate information.

By optimizing content for voice search, businesses can increase their visibility in voice search results and capture leads who prefer using voice assistants for information retrieval.

Intelligent Lead Scouting

AI can also be leveraged for intelligent lead scouting, which involves identifying and targeting potential leads that match a specific set of criteria. AI algorithms can analyze large amounts of data from various sources, including social media platforms, business directories, and public records, to identify leads that meet predefined characteristics.

This approach helps businesses identify new and untapped markets, discover leads that may have otherwise gone unnoticed, and expand their reach. By using AI for intelligent lead scouting, businesses can uncover new opportunities and increase their chances of finding high-quality leads.

Data Analytics and Insights

AI-driven data analytics tools provide businesses with powerful insights into lead generation strategies. These tools can analyze vast amounts of data in real-time, uncovering patterns, trends, and correlations that human analysts may overlook.

AI algorithms can identify the most effective channels for lead generation, analyze customer behavior, and provide actionable recommendations for improving lead conversion rates. By leveraging AI-powered analytics, businesses can make data-driven decisions, optimize their lead generation efforts, and continuously improve their strategies based on actionable
insights.

Leveraging AI can significantly enhance lead generation efforts and drive better results for businesses.

By using AI to deliver personalized content recommendations, implementing chatbots for instant engagement, utilizing NLP and voice search optimization, leveraging predictive lead scoring and scouting, automating email campaigns, and utilizing AI-driven data analytics, businesses can optimize their lead generation strategies, improve conversion rates, and ultimately drive business growth.

Embrace the power of AI and unlock its potential to transform your lead generation efforts into a more efficient and effective process.

The post 8 Ways To Leverage AI To Improve Lead Generation appeared first on DigitalMarketer.

In the world of digital marketing, data is king. With so much information available, it can be overwhelming to try and make sense of it all. One of the best ways to gain insight into digital marketing trends is through the use of graphs.

Graphs can help to visualize complex data sets and identify patterns that may not be immediately apparent when looking at raw data. In this article, we will explore the top nine graphs for revealing digital marketing trends.

Line Graphs For Digital Trends

Line graphs are one of the most commonly used graphs in digital marketing. They are particularly useful for showing how a particular metric has changed over time. For example, a line graph could be used to show how website traffic has changed over the course of a year.

By plotting data points over time, it is easy to see any trends or patterns that may have emerged. Line graphs can also be used to compare data sets over time, such as comparing the performance of two different marketing campaigns.

Chord Diagrams Connecting Different Marketing Channels

Chord diagrams are a type of visualization that show the connections between different variables. They are often used to show the relationship between different parts of a complex system or network.

In digital marketing, chord diagrams can be used to show how different channels (such as social media, email marketing, and search engine marketing) are related to each other. By visualizing the connections between different channels, businesses can optimize their marketing mix and ensure that each channel is working together to achieve their marketing goals.

Scatter Plots for Digital Correlations

Scatter plots are often used in digital marketing to show the relationship between two different metrics. For example, a scatter plot, designed by a graph creator, could be used to show how the bounce rate on a website correlates with the time spent on the site. 

By plotting data points on an x and y axis, it is easy to see any correlations that may exist between the two metrics. Scatter plots can also be used to identify any outliers within a data set.

Bubble Charts Show How Differing Variables Relate to Each other

Bubble charts are similar to scatter plots, but they add a third variable to the mix by varying the size of the bubbles based on a third data point. This can be a useful way to visualize trends and patterns in complex data sets.

In digital marketing, bubble charts can be used to show how different variables (such as ad spend, click-through rate, and conversion rate) are related to each other.

Bar Graphs for Quick Comparisons

Bar graphs are another common graph used in digital marketing. They are particularly useful for comparing different data sets. For example, a bar graph could be used to compare the conversion rates of two different landing pages.

By presenting data in a visual format, it is easy to see which landing page is performing better. Bar graphs can also be used to compare data sets over time, such as comparing the number of leads generated by two different marketing campaigns.

Heat Maps Revealing Behavior

Heat maps are a unique type of graph that are particularly useful for analyzing website user behavior. Heat maps show how users interact with different parts of a website by using different colors to represent user engagement.

For example, a heat map could be used to show which parts of a landing page receive the most clicks. By analyzing heat maps, marketers can identify areas of a website that may need to be optimized to improve user engagement.

Pie Charts For Categorical Divisions

Pie charts are often used in digital marketing to show how a particular metric is divided among different categories. For example, a pie chart could be used to show how a company’s social media followers are divided among different age groups.

Pie charts are particularly useful for highlighting the most significant categories within a data set. However, it is important to keep in mind that pie charts can be difficult to read when there are too many categories.

Funnel Charts Reveal Bottlenecks

Funnel charts are a type of chart that shows how many users or customers move through a series of steps in a process. They are often used in digital marketing to track the conversion rate at each stage of a sales funnel.

By visualizing the drop-off rate at each stage of the funnel, businesses can identify potential roadblocks or bottlenecks in the conversion process and take steps to optimize their marketing strategy.

Gantt Charts for Keeping Campaigns on Schedule

Gantt charts are a type of bar chart that show the duration of each task in a project, as well as the start and end dates. They are commonly used in project management to track progress and deadlines.

In digital marketing, Gantt charts can be used to plan and track the progress of marketing campaigns. By breaking down a campaign into smaller tasks and assigning deadlines to each one, businesses can ensure that their marketing efforts stay on track and meet their goals.

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Conclusion

In conclusion, digital marketing is a complex field that requires businesses to track and analyze a large amount of data. Charts and graphs are essential tools for visualizing this data and identifying trends and patterns.

By using the right types of charts and graphs, businesses can gain insights into their marketing performance and make data-driven decisions to optimize their marketing strategy.

From line graphs and scatter plots to heatmaps and chord diagrams, there are a variety of charts and graphs that businesses can use to reveal digital marketing trends and stay ahead of the competition.

The post Data-driven Marketing: How Graphs & Charts Transform Digital Strategies appeared first on DigitalMarketer.

There are two core metrics that should drive a lot of the decisions you have in your organization; churn & sales. A great agency is constantly studying these two numbers diagnosing them from every angle learning specific areas of opportunity. 

The more you are able to understand these numbers and what they are composed of the better you’ll be equipped to making the right decisions for your business.

In this report, we want to look at churn, which is something we’ve been studying for about 10 years across two different agencies. The first one was scaled to over 1,000 clients and the second one we’ve scaled to over 200 full time employees in just 5 years. 

When you’re a young agency, churn is so important because 1-2 clients can represent a large portion of your income, however as you scale, the same is true. Imagine you’re an agency like Hite and you’re doing $500,000 per month in MRR.

If you have 10% churn monthly, you’ll need to do $50k in new sales just to break even. If you can create an environment where you’re more likely to have 5% churn, if you do $50,000 in sales you’ll grow by 5%.

Understanding why clients leave and acting on it, isn’t only the key to scaling. Agencies with lower churn, partake in other benefits such as receiving more referrals & a much higher evaluation when it comes to selling the business. 

Hite is constantly focused on understanding the why behind our growth & this is essential for your business if you want to scale in 2023.

Churn is critical, especially as you scale for churn is a representation of the quality of your product, service, & customers.

Every agency is constantly battling both the increase of sales and the decrease of churn.

Defining Churn? 

Churn can be broken down in a lot a ways, but for agencies, the most common two churn metrics you’ll see is Client Churn & Financial Churn. These two churn types can be define these two churns as followed: 

For Client Churn we will look at the monthly turnover of clients regardless of financial impact.

For example, If in January you had 10 clients pay you then in February only 8 of them paid you, that would be a turnover of 2 clients and equal 20% churn. In this example it would not matter how much each client represented financially. 

For Financial Churn, we look at the monthly turnover of revenue regardless of clients.

For example, if in January you had $20,000 in recurring collected MRR and in February you only collected 18,000 of that $20,000, it would represent a 10% churn rate. 

Understanding the difference between these two numbers is crucial, let’s look at the following list of clients. 

MRR

Client A $1,000

Client B $5,000

Client C $2,000

Client D $3,000

If we were to lose Client B, you would have 25% client churn, however you’d have 50% financial churn. There could be a very large difference in these numbers especially as you scale. 

The Problem With Researching Churn

Doing research on churn for agencies doesn’t come easily. First off, about 80% of agencies that exist today would be defined as micro agencies, doing less than $15,000 in monthly revenue of which the vast majority do not keep up with, nor have any data on their numbers, especially when it comes to churn. 

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If you take into consideration those that do keep great track of their numbers, between those they may manage and report back churn in many different ways, even beyond the above numbers.

For example, there is a well known agency that is doing several $100m in annual revenue that keeps track of their financial churn, but in their own way focusing more on net growth vs. churn.

In their model, they look at how much was lost, and measure that against what was upsold in order to come up with a net churn. 

With that said, we believe that this report takes all those data points into consideration arriving to tangible and definitive results.

The post How to Reduce Churn appeared first on DigitalMarketer.

2022 was not an easy year, with a lot of declines in key metrics, particularly in the middle of the year. Q4 gave us a reason for optimism though, so will the momentum keep going or will 2023 continue financial uncertainty? 

Last year was a watershed moment in the history of ecommerce. While the 2010s saw the rapid expansion of online shopping thanks to developments in mobile devices, the expansion of social media influence, and a massive shift in consumer behavior, this decade will see an even greater change thanks to AI.

Here’s what we found.

How We Aggregate:

Hawke Media uses the data from its proprietary marketing technology platform, HawkeAI. HawkeAI aggregates data across 1000s of businesses’ marketing channels and $100’s of millions in annual media spend to compile these data benchmarks. 

Ecommerce Businesses:

Web Analytics 

• Revenue was down 5% YoY, which predominantly came from declines in Q2 and Q3 (Q3 was down 16%). We saw stable numbers for Q4 YoY.
  • Question for 2023: Does Q4 stabilization represent a broader trend of moving towards growth and predictability, or was it simply a deal-driven BFCM period (which was up 16% compared to BFCM 2021) that is propping up an otherwise stagnant revenue report? 
• Average order value (AOV) increased significantly YoY (31%). A portion of this is likely due to inflation, as the AOV increased as 2022 went on, as opposed to 2021 where AOV was consistent throughout the year, even during the Q4 peak retail period.
• Another potential reason for an increase in AOV is a continued emphasis on buy-now-pay-later usage. We saw a 78% increase in buy-now-pay-later usage during BFCM despite rising interest rates.
  • Question for 2023: Will the rising cost of debt curtail the use of BPNL option, and ultimately curtail inflation in general? Using bundling and complementary product recommendations (‘you may also like…’) will be key in maintaining/growing AOV without simply increasing prices. 
• Sessions overall were down 5%, which aligns with the revenue decline as well, while the bounce rate held steady.
  • Question for 2023: As Google Analytics switches to engagement-based metrics it will be interesting to see what metrics and benchmarks will be found in order to assess the quality of a website’s traffic. The simplest answer is of course transactions! 

• The quality of those sessions was also down, as transaction rate decreased by 24%. This was offset by the AOV increase (i.e. those that did buy, spent more). These transaction rates were bottoming out at 2% in Q2 and Q3 of this year, compared with all quarters of 2021 being above 3%.
  • Question for 2023: With media budgets tightening, optimizing web traffic to conversion is crucial. Where/how brands invest to generate a more optimized site will be key (checkout process, site speed, landing page/promos, etc.). 

Organic Channels

• Email marketing saw a gradual decline over the course of 2022 on multiple performance metrics, including both quantity of sessions and quality of sessions. Total sessions from email declined 12% but were flat for most of the year until Q4. Similarly, transaction rates on email declined from 4% to 3% YoY.
  • Question for 2023: With these declining results, how can brands attract new email sign-ups and tailor content to not see high unsubscribe rates? 

• Organic social content also took a hit this year, with both sessions and transaction rates down. Sessions particularly have been on a steady decline since the start of 2021, decreasing almost every quarter (except for Q4 naturally). This could be indicative of either less content being produced, or audiences that are more particular or selective in what they click on as pandemic restrictions lift and people are not surfing social media the same. Of course, the other possibility is a continued challenge in attribution from various updates to tracking. 
• Affiliate was a bright spot for 2022, with a 16% increase in sessions and a 35% increase in transactions. 

Paid Channels 

• Google Ads
  • Spend YoY increased 3%, but distribution was very different
    • 2021 saw a linear increase quarter over quarter in spend 
    • 2022 saw a significant drop off in Q3, and while Q4 increased over Q3, Q4 YoY was down 15% 
    • Media budgets were definitely impact by economic climate and lifting of restrictions 
    • Question for 2023: with a year of hopefully no restrictions, and continued economic uncertainty, where will ‘the bottom’ be in terms of spend, when will we see the ramp up? 
  • Clicks moved in line with spend (up 4%), with predictability/steadiness in CPCs YoY, which is helpful for forecasting in uncertain times. Using a ‘bottom-up’ approach of starting with a CPC type metric to establish sessions expected from Google is likely more reliable than doing a ‘top-down’ approach to forecasting (i.e. where are we going to generate $X of revenue from) 
  • Eyeballs got more expensive on key visual networks for Google (YouTube and Display), with increases of around 30% in CPMs on those networks. Ultimately both also had lower conversion rates (approx. 15% drop in conversion rates on both).
    • These CPMs are still lower than the typical social media platforms, so they still represent a cost-effective option to generate impressions.
• Meta Ads
  • Meta Ad spending also increased 4% YoY, but was more linear in growth, with spend increasing each quarter over quarter in 2022. 
  • Meta Ads also sees a more significant drop off from Q4 to Q1 than Google (Facebook dropped off 14%, Google dropped 4%). This is likely indicative of more seasonality in spending on Meta Ads during peak retail, whereas Google is seen as more of the ‘baseline’ spend to capture highest intent. 
  • Question for 2023: Will we see this same drop off in spend in Facebook Q1 this year as last year, or will the positive CPMs/CPCs from Q4 on Facebook mean advertisers stay with the platform? 
  • The increase in spend on Facebook was almost entirely due to a YoY increase in Facebook spend in Q4, while the rest of the year was mostly flat. This is likely the result of the 64% and 47% YoY increases we saw in spending on TikTok and Pinterest respectively. 
  • Question for 2023: How much of the diversification of social media advertising will continue? The numbers would suggest that any new budgets are being allocated to these new platforms and that Google and Facebook budgets are being treated as optimized/maxed-out. 
• Other platforms
  • CPMs on TikTok and Pinterest are increasing as more budget shifts to these platforms. For example, CPMs on TikTok increased from $4 to $8 YoY. The CPAs still though are lower than Meta, so until those become more aligned it is likely that these platforms will take more of any increases in ad spend.
    • Question: When will these platforms reach the saturation point and competition of Meta and Google? Based on these trends, we would expect to see that by end of 2023.

“Non-discretionary” Ecommerce: 

Includes: food/drink, healthcare, B2B

Web Analytics 

• The conversion rate from visits to the site increased from 2.6% 2.9% YoY. This is indicative of two key points:
  • Once people land on a site with products of this nature (i.e. items that are more essential or inelastic in demand), they have a higher likelihood of purchasing than sites with more discretionary products, which had a conversion rate of 2.2% in 2022). 
  • There were also fewer sessions for website selling these products, so while conversion rates were up, total transactions declined by 6%. 
• Another telling piece of information is the average order value declined YoY, so even though those sessions had higher intent to purchase, the average sale was worth less. This is potentially due to price sensitivity from economic conditions where these products are needed but easily substituted for a lower cost alternative. 

Organic & Paid Channels

• To support the idea of shoppers looking for lower cost alternatives, we saw an interesting trend in where sessions came from.
  • Typically more ‘loyalty’ based channels such as email, direct, (i.e. someone opens a browser and goes to that page), and organic search were all down YoY. 
  • Conversely, sessions increased from paid search, social and referral sources. These channels can typically be attributed to more people searching out for new alternatives, as opposed to just automatically buying from known brands. 
• Unsurprisingly, conversion rates associated with the more loyalty based channels stayed consistent, with all of them staying within .2% of their previous year number (i.e. someone who clicks on an email from a brand they know were just as likely to buy from that brand in 2021 as 2022, but fewer people were clicking in the first place).
• Also perhaps unsurprisingly, but validates the conclusions above, is that the ‘browsing’ channels of social and referral that drove more traffic, saw declines in conversion rates as more people were searching out new buying options.
  • The channel that bucked this trend was paid search, which saw sessions go up 8% and still maintained a conversion rate YoY (3.6%) 
  • That said, social and referral conversion rates, while down from 2021, were still above paid search (both around 5.5%). 
  • This could be an indicator that buyers looking for alternatives ‘trust’ or find better recommendations from those social or referral sources than a plain old google search (i.e. perhaps they trust social proof more than google’s search algorithm)

“Discretionary” Ecommerce: 

Includes: arts/entertainment, beauty, fitness, home and garden and apparel) 

Web Analytics 

• discretionary products, which had a conversion rate drop from 2.5% (which was in line with non-discretionary in 2021) down to 2.2% in 2022.
  • This makes sense given the economic climate that buyers are going to ‘shop around’ and also makes sense that discretionary items are purchased less frequently than non-discretionary
• BUT, what is striking within the data is that the average order value of purchasing discretionary items went up by 20%, so while conversion rates dropped, those that bought spent more money. As discussed above, there are a few possibilities for this, including buy-now-pay-later usage.
  • This is especially interesting since the spike in AOV is heavily skewed to Q4 of this year. AOV was up 41% in Q4 of 2022 compared to Q4 of 2021, which cannot be explained away by inflation. For Q1+Q2 2022, AOV was only up 9% compared to Q1+Q2 2021, which is very closely correlated with inflation rates (around 7% YoY). 
  • Question for 2023: Was this spike in Q4 pent up demand from people tightening spending the rest of the year, or is this going to continue? 

Organic Channels

• The only channel that saw an increase in sessions and transactions was affiliates. Given the discretionary nature of these products, it likely makes sense that affiliate marketing performs well since affiliate marketing predicates itself on social proof, testimonials, and are often more creative in nature from a copy/visual perspective. 
• Email had an interesting mix in that sessions were actually up, but revenue and transactions were down. So, unlike non-discretionary goods, consumers were still willing and active in engaging with emails, but were not able to be converted. The most telling stat to this point is that in Q4 2021 email had a conversion rate of 4.4%, and in Q4 2022 that fell all the way to 2.7%, meaning that shoppers were looking for the deals, and were more selective on what deals they actioned.
  • This is substantiated by an AOV increase in Q4 2022 compared to Q4 2021 of 25%, so when they did find a deal they liked, they took action in a big way!  
  • Question for 2023: How often can full retail price be realized? Are shoppers only willing to pull the trigger on deals? How can you structure your sales/products to maximize average order value?  

Paid Channels

• We saw declines in ad spending on both Facebook and Google, but Facebook’s was more significant (27% decline) vs Google (4% decline).
  • Facebook did see improvements in CPM, CPC and CPA as a result of this decline in spend (less competition). As mentioned above, this spend was reallocated to other social platforms. 
  • What is interesting is conversions reported actually went up 7%, showing that Facebook had likely reached a point of diminishing returns and inefficient. By peeling back the spend a bit, the more efficient/likely buyers still engaged and bought. 
  • Question for 2023: are there more efficiencies to be gained by shifting spend, ro are the other platforms soon going to reach a tipping point of saturation themselves? Let this be a case study/lesson in the inefficiency of not diversifying your spend enough!

Lead-Generation Businesses  

Quick note: the definition of a ‘goal completion’ or ‘conversion’ when it comes to lead-gen is greatly varying and subjective to each individual business.

Web Analytics 

• Goal completions YoY for the first three quarters of the year were up a modest 6% until Q4 which was significantly higher (28%). This is impressive given sessions and bounce rates were relatively flat YoY. In other words, those that went, had intent as indicated by an increase in goal completion rate.
  • Question for 2023: With the switch to Google Analytics 4, these benchmarks will become challenging to monitor and the definition of ‘goal completion’ will become morphed into event-based actions. No one really knows what will happen but having GA4 set-up and running on sites today is imperative to get some sense of baseline performance under the new system. 

Organic Channel 

• Visitors had more intent across multiple channels, which is the opposite of ecommerce trends. For lead-gen businesses, both email and social content generated more goal completions than 2021 (21% and 25% respectively). This is despite social having a 24% decline in sessions.
  • Question: Is the decline in sessions due to tracking limitations? Or is it simply consumer sentiment? 
• Ultimately, seeing the goal completions increasing is the key benchmark to look at. 

Paid Channel 

• Google Ads

• YoY was up 3% as well, but Q4 was the lowest spend since Q1 of 2021. This is despite very good results overall from the spending, with conversions from Google Ads improving YoY in every quarter of 2022. 
• Question for 2023: What will be needed to ever get brands to invest heavier in Google Ads Network, or is there any appetite at all? Is it saturated? Results would suggest investing in it…Another trick though is performance max campaigns. While they are reporting increases in conversions on performance max campaigns, are those leads becoming sales at the point of sale or in your CRM? Marketers have seen mixed results from these campaigns to date.
  • This concern is backed up by a reported 25% increase in conversion rate on cross-network campaigns, which has (artificially?) driven down CPAs by 36% YoY.. 

• Meta Ads

• Meta spend decreased 13% YoY, but this was dramatically split between the year. In the first half of 2022, the spend was very consistent with the spend in the first half of 2021. However, in the second half the spend declined 28% YoY. Those that stayed invested on the platform though did see declining CPMs (14% decrease) and CPCs (5%). 
• Pinterest saw an increase in spend of 10%, and TikTok increased as well, which likely drove the decrease from Meta. Pinterest still does have a lower CPM than Meta ($6 compared to $7), but that gap has closed from $5 and $8 respectively last year. The gap in cost per click has also closed between TikTok/Pinterest and meta.

Lead-Generation Businesses – B2B specific

Quick note: the definition of a ‘goal completion’ or ‘conversion’ when it comes to lead-gen is greatly varying and subjective to each individual business.

Web Analytics & Organic Channels

• Despite the increase in paid media that we discuss below, sessions YoY were within 2% of last year’s total. This indicates that the spend did not have a significant impact on overall traffic. 
• Goal completion rates as well were also down about 10%, which indicates that the increased spend and re-allocation of budget did not necessarily lead to higher quality traffic hitting the site. 
• A B2B favorite for marketing is email of course. We did see a 13% increase in sessions that came from email, but a goal completion rate that declined by 25%. If the goal of email marketing is to generate brand awareness and engagement with the content that you are sharing (e.g. monthly newsletters, product updates, etc) then mission accomplished for B2B marketers this year, from a loyalty and engagement standpoint email worked!
  • If you were trying to use email marketing to get prospects to fill out a lead gen form or activate a trial, then overall that is not what email performance was delivering. 
• orOrganic search was a similar story in some regards, we saw an improving bounce rate on organic search sessions, so people were finding the content they wanted more often and engaging with it, but goal completion rate for organic search was down. Again, that ‘goal completion’ may not be the objective and with long sales cycles, etc it is tough to conclude on this but from a direct attribution perspective of someone read a blog and then signed up to be pitched services was not happening as frequently as I’m sure some marketers would like. 

Paid Channel 

• Let’s actually talk about LinkedIn here, since this is effectively the only category of businesses that care about it  
• LinkedIn from an engagement with ads perspective did well in 2022. While media spend did increase by 25%, we saw a larger increase in clicks (i.e. CPC actually went down) and an improvement in click-through rate as well.
  • To compare this to the movement in ‘conversions’ is tricky knowing that B2B sales cycles can be longer and the definition of a conversion stops at the website experience and rarely be truly connected back to the real source of truth for B2B companies, the almighty CRM! 
  • For what it’s worth, we did actually see conversions stay flat despite this increase in clicks.
• Across Google and Facebook we did also see increases in media spend. The google increase was spread across all google ad networks and not concentrated to any one tactic, indicating this was the bi-product of broadly applied budget changes and not a tactic or result specific re-allocation.
  • Question for 2023: Will increased spending in digital continue in our new virtual norm, or as restrictions are lifted will spend get redirected back to the traditional B2B staples of trade shows and in-person sponsorships/networking? 
• While spending did increase overall in 2022, it was not linear by any means. The increase was predominantly generated by increases in Q1 and Q2 2022 compared to 2021. For example, Google spending for H1 increased by 40% (remember Jan-June 2021 was still very much pandemic-restricted and plenty of uncertainty around supply chains, etc still existed). Conversely, H2 spending was flat YoY.

Overall, from 2022 there are a few takeaways:

• Customer Lifetime Value is more important than ever – with the cost of paid media increasing, it’s more important than ever to keep the customers you get. This means improving product/service quality, enhancing customer experience, and maintaining contact with customers between purchases by providing value-adding information through content.

• You MUST HAVE a cohesive and comprehensive marketing strategy – With AI tools increasing the productivity of marketing managers and allowing all marketers to produce content faster than ever while also managing paid media channels more effectively, the marketer that can produce the best overall strategy will win.

• You need to dial in ALL CHANNELS – As you can see with the data, the effectiveness of paid media, email marketing, and social media is shifting widely between platforms. This means that predicting which channel to focus on will be more difficult. The best marketers will need to both understand and execute cohesive campaigns that span multiple channels to ensure that their message gets through.

• Position your products as a “must-have” – Wallets are a little tighter, and decisions are being made on what is a “must-have” purchase, and what is a “nice-to-have”. In difficult economic times like we’re experiencing currently, customers are looking for what they can cut. The better you can position your products and services as necessary to the customer, the better off your bottom line will be. When in doubt, show how your products can do one or more of the big three for your customers: Save them time, Save (or make) them money, Improve their quality of life

• Get strategic about customer acquisition – With ROAS becoming less predictable and having potentially longer timelines before proving profitable, it’s a good idea to offset your customer acquisition costs by forming strategic partnerships, affiliates, and influencers who can provide new customers regularly at breakeven or better to remain cash flow positive.

The post Ecommerce Data Benchmark Report 2023 appeared first on DigitalMarketer.