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.

There’s so much data, from so many different sources, with so many different reporting tools, that you could just drown in reports, attribution, and meetings. With so much noise out there, it’s important that you look at the data in a certain way. There’s important information hidden in the metrics that will help direct your digital marketing strategy.

In this article I’m going to walk you through this technique that I’ve been using for 25 years, called MAA.

Metrics, Analysis, Action

MAA stands for metrics, analysis, action.

Let me show you how powerful it is when you use this technique on any kind of data set you have. It could be SEO data, website data, email data, conversion data, shopping cart data.

The Data Doc is in…

Think of this as if you are a surgeon in the emergency room. You must follow these three steps.

• Collect vitals.
• Diagnose.
• Treat.

First you collect the vitals. It could be heart rate, blood pressure, respiratory rate, x-rays things like that. These are the numbers that clue you in to the cause of the problem.

The second phase is the diagnosis. In this phase you interpret all the vitals that you collected. Based on the data, you make the determination of a heart attack, broken bone, virus, etc. The key point is that the diagnosis is based on the data.

From that diagnosis, you create the treatment plan. The plan might include surgery, medications, a recovery plan, etc. But the list of things to be done to make the patient healthier is based upon the findings and the diagnosis.

The marketing analytics data you collect leads directly to analysis of the problem. That then leads directly to the action. What I will show you in this article is a number of examples from a variety of digital marketing projects. This works whether you’re working on a large or small project.

Data vs Analytics

Lots of people think that they have analytics because they have Google Analytics installed on their website.

But let me tell you a dirty secret.

There are no analytics in Google Analytics. It’s just Google charts. It should be called Google Chart-Maker.

Marketing analytics is figuring out what’s actually going on. It’s the interpretation of the data. Interpreting the data tells you why sales went up or down. It helps you discover why conversion rates went up or down. Analyzing the data answers questions like:

• Why did people buy or not buy?
• Why did a competitor take a certain action?
• Where are we losing customers along the customer journey?
• Is our content hitting or missing with our customers?

Analytics is more than making charts and collecting data. And action is the next step after marketing analytics.

The way we see it, if you are not taking action based on the analytics, which was based on the data, then whatever you’re doing is random.

Returning to our analogy, not everyone should take the same pill. If you’ve got a broken bone, you shouldn’t take the same medication as someone who has a headache. So the action that you take, the optimization, should be contingent upon the analysis, which should go straight back to the data that you gathered.

Most people make the mistake of just trying to look at lots of data. This Metrics Analysis Action framework is the easiest way to figure out what you really need to do versus what’s noisy.

MAA Framework Case Study: Ecommerce

If you are in ecommerce, lead gen, or any kind of performance marketing, then you’re going to start with the action, mapped back to the analysis, and back to the metrics.

Because the actions are all the things that you could do.

So make a list of the things that you could do.

• You can play with the website.
• You can change your budgets.
• You can change ads.
• You can optimize creatives.
• You can work with influencers.
• You can buy another tool.
• You can change bids.

Think of all the actions that you could take. Start with the end in mind.

Once you decide on the action, look for the trigger. In other words, when analyzing the data, what diagnosis will cause you to take that prescribed action?

That’s where you have automated rules on Google, Facebook, or Shopify. Wherever you’re looking at data, you can set up these rules.

For example, if your cost per acquisition goes above $50, then turn the ad set off. If someone leaves a positive review on Yelp, then reach out to them to say thank you.

So if a certain thing happens, then here’s the particular action.

Then there’s a limited number of things that you could do, so you don’t have to look at everything. And then if you need to determine if that triggering condition is true, then what data do you need?

Data, Analytics, and Attribution

On the far left of this image, we have plumbing. Plumbing is collecting the data from different tags in tag manager, UTM parameters, pixels that are firing, and other events inside an app.

These are the things that people are doing. For example, opening an email. When that happens, you get plenty of email marketing data. But the data doesn’t mean anything unless you can tie it to a goal.

How do you tie data to a goal?

Here’s a lifetime value example…

Seeds of Life sells flowers to people who’ve experienced the death of a loved one. The lifetime value (LTV) of a customer is $150. What can they do to increase the LTV?

They might offer a referral bonus, free shipping for orders over a hundred dollars, etc. Their goals, checked against the marketing analytics, will determine the direction of their next marketing campaign.

The important thing is to define the goals and measure them against the data. If the data doesn’t tie to the achievement of a particular goal, then you have to ask, “why are we even collecting that data?”

We’re not searching for a needle in a haystack, here. Although, that’s what most people do with their reporting.

Most people log into Google analytics, or whatever they use to pull in all the data from all the different places. And then they just hunt and peck and wander around and look for interesting things.

They look at the data then filter down to this date for that particular segment and this part of the country. It’s like the lotto, like the power ball where you choose six random balls to try to win the million dollar jackpot.

You want to have your goals before you figure out the plumbing.

Don’t Make the Same Mistakes with Analytics

Large and small companies make the same mistakes. They tend to go after impressions or click through rate or secondary metrics when the primary metric, the business goal, is more important than a diagnostic, secondary metric.

I love looking at cost per mille, or CPM, in advertising. For example, how much are you paying per thousand impressions? What is the trigger or check engine light, to let you know whether the algorithm is penalizing you for having a low click through rate, low quality score, low relevance score, etc.

Analyzing a marketing campaign in this way may show that something else is wrong.

Please don’t make the same mistake thinking that a secondary metric like click through rate, cost per click, quality score, or CPM is more important than the main business metric.

Profit, lifetime value, or cost of acquisition should be the goals that tie to your content and targeting.

Plumbing, Goals, Content, Targeting, Amplification, Optimization…

Here’s an example (above) of a marketing campaign we ran for our friend, Brennan.

At the very top are the financial metrics, specifically profit. There’s some kind of margin with or without cost of goods and services or overhead.

Then we have revenue minus costs.

Revenue is driven by factors like conversion rate, LTV, and how well you use things like recency and frequency to increase revenue.

Then there’s costs: people costs, ad costs, software costs, other kinds of costs.

On the revenue side, units (high price vs low price) multiplied by volume (clicks and/or conversion rate) is your revenue.

On the cost side, let’s say you run all your digital marketing campaigns on a cost per click basis. You can break that down to different fixed and variable costs. So we know if we double the number of clicks we’re buying from Google, we’re going to pay twice as much. Multiply the cost by the number of clicks you get for the overall cost of that campaign.

This decomposition pyramid helps you figure out the data you need to collect using secondary diagnostic metrics.

Start to think about how those different metrics will help you uncover the main issue to focus on right now.

MAA Framework: Case Study

Let’s look at how this actually applies when you’re looking at tabular data.

In this example (above), we’re looking at a lot of information. There are 132 ad sets here. That means we have all this information for 132 projects…

• Data
• Campaigns
• Ads
• Landing pages
• Messages

This happens to be a set of Facebook campaigns, but it could easily be any social media platform or other traffic source.

We use a concept called “Top N” to select a manageable number of ad sets to work with. Why? Because it’s intimidating to try and look at ALL of them to diagnose the problem or issue.

You don’t have time to look at every single keyword, creative, or landing page. The idea of Top N is to look at the top, best- or worst-performing ad sets and ignore the rest. This is just another way of using the 80/20 rule or prioritizing your work.

I find that when you use the Top N technique on any large dataset you can quickly zero in on the most important thing.

In this case, we can see that this very first ad spent $10,000 out of $43,000. That means 25% of all of the money being spent is inside that one ad out of the 132 ads total.

Look a little more closely and you’ll see the top five already account for 60% of the total spend.

That’s not uncommon. In lots of cases the top three to five ads will account for about half of your ad spend.

Applying the Top N Method

I like to start by doing Top N on spend, because that’s where I can identify a “bleeder” (a high-spend ad with very low return).

Then I look at what drove the most revenue or had the highest number of conversions. Because then I can find where the winners are.

Then I look at clicks, leads, or other metrics that are important to the business.

Using this method, I kill the losing ads and amplify the winning ads.

Let’s say you were to sort just by conversions or revenue. If you do that, then you could have an ad that’s wasting lots of money that doesn’t make it into the top four or five for your most important metrics.

So I use Top N for three or four metrics in succession. Each time it reorders the ad sets or ads or creatives or whatever it is that you’re looking at.

You can use this method to determine ad performance in just three minutes.

Find and Fix the Issue

If something’s out of whack, it could require a big change or it could be something wrong with the tracking.

It could be iOS 14, or the pixel wasn’t on that landing page. It could be the data didn’t come through and it’s delayed. There’s all kinds of things that could play into why numbers aren’t adding up.

A lot of people freak out when sales are way down. Understandable. But many times it’s because of some silly issue. So before you pull the fire alarm, just think, does that really make sense?

I like this particular ad here.

There’s no way we spent this amount of money with no return. So we know there’s an issue. And we know with social media platforms like TikTok, Twitter, and Facebook, their systems often will not show data.

We know that because of the iOS 14 update, impressions and clicks are reported on different frequencies. So you might see a bunch of spend show up before the conversions show up or vice versa.

Make sure it’s statistically significant. Also make sure that you have enough data, so you don’t jump to any conclusions.

We’ve seen these systems spiral out of control. For example, let’s say you decide to reduce the bid amount on a marketing channel when the ROI falls below a certain amount. That seems logical. But if you’re only looking at revenue, not conversions, you might kill off a marketing campaign that was actually working quite well.

Imagine if it all boiled down to a hiccup in the data that caused the downward spiral. Not good. So be careful about that.

Now, if you see that a metric is out of whack and the data looks good, then ask yourself why that campaign isn’t performing as well.

Data and Instinct for the Win

Don’t let everything you do be completely automated and dependent upon rules. A successful marketing strategy requires a human touch.

Don’t set so many rules that the software automatically terminates your ads.

Instead, take a moment to look at how far out of bounds the ad performance is. It could be that you launched a new campaign and you’re doing an AB test or some kind of split test. The winner stays on and continues to win, even when other ads are losing, because you’re trying to find another winner to take its place.

If the cost per acquisition is high, then you can break that down using the metrics decomposition pyramid.

For example, the cost per acquisition will double if:

• the conversion rate is cut in half and the cost per click is the same
• the cost per click doubles and the conversion rate is the same

The cost per acquisition remains the same if either factor doubles while the other one is cut in half.

Always look at your marketing analytics when the cost per conversion goes up. Determine whether it’s because of the cost per click or the conversion rate.

When you run ads using objective-based bidding you don’t have to worry as much about cost per click, click through rate, or conversion rate because the artificial intelligence behind the ad platform is going to seek your target metric.

If the target metric is out of whack, you can decompose it into the underlying metrics.

That’s true for organic traffic. But it’s not as true for paid traffic because the systems are getting smarter and can optimize for the objective you set. Either way you should still look.

Balancing Metrics

This method gets you to look at metrics that matter according to our business goals. It gets you to think about and analyze why the data might be good or bad. And it gets you to outline the actions you’re going to take when goals aren’t being met. Over time you’ll find that the same pairing of metrics change alongside each other. So let’s talk about what these balancing metrics are.

One company we were working with was spending a hundred thousand dollars a month on advertising. When they were unhappy with the return, the analyst on the project adjusted the Google ad campaign. All of a sudden the cost per conversion dropped from $20 per lead to $7 per lead.

But I wanted to know how and why it dropped so dramatically. I found out that this person went into the Google ads campaign and turned off all the campaigns except for the brand search terms. Of course it was going to convert super well!

But the balancing metric was volume. When the analyst “fixed” the cost per conversion, the number of leads dropped from 5,000 leads a month to maybe a thousand leads a month.

The key takeaway here is that if you optimize one metric blindly, you can fool yourself into thinking everything is better when in reality another metric took a nosedive.

Analyzing Like a Scientist, but NOT a Rocket Scientist

Metrics don’t matter, unless there’s a clear analysis that can come from the information. Remember, you’re seeking a diagnosis.

Think like a surgeon or scientist. Start with a hypothesis. If a certain thing happens, what will you do to correct it and what outcome do you expect? If there’s no potential action based on some metric, there’s no need to gather the metrics.

I see companies spend most of their efforts collecting data. No one even knows why they’re using the data. Be strategic and ask, “what are we doing with this data? Is there some meaningful action we’re going to take?”

Maybe there’s another metric that would measure the goal better.

The point of analytics is to figure out whether something is worthwhile. Most of the data you thought was important, doesn’t even matter.

I’ll give you one example. Our client was a large company, but this works for small companies, too.

We were working with an airline, taking one database and matching it against another. They wanted to know things like whether a customer that goes skiing has kids and what their income was.

They wanted predictive models to uncover which customers would be most likely to sign up for their credit card or buy flowers or upgrade or travel to new destinations.

We went all in on the idea that more data is better. After all the time and money spent on sophisticated data models, what we found was that the best predictor of people flying more was past purchase behavior. Not a surprise, right?

In this case, purchase behavior predicted purchase behavior. And the fact that they drove a station wagon, or liked to eat Haagen-Dazs ice cream, might be interesting but it had very little impact on their flying behavior.

Moral of the story, you might find that the most obvious thing is the best place to start optimizing in your business as well. Start thinking about what kind of “if-then” logic you can implement. And don’t dismiss the really simple idea just because it’s simple.

The MAA Framework is Not Just for Advertising

Collecting data allows you to put if-then sequences in place across your business. In Google and Facebook you can set up automated rules using if-then logic. For example, one might be for conversions. If conversions fall below a certain number, then an automated action would be taken or an alert might be sent to whoever’s in charge of that area to let them know there is something that needs their attention.

Here is a table of common if-then scenarios we’ve come across. Start small by looking at just a few of these things.

You’ll find a lot of value when you look at the patterns. For example, look at posts with the highest engagement versus posts with the lowest engagement. What can you learn? What do the high-engagement posts have in common? Is there a cross-over with the low-engagement posts?

Don’t spend all your time messing around inside the tools. Even Google’s head of analytics said that 90% of every dollar you spend on analytics should be on people and 10% should be on the tools.

We see a lot of businesses do the opposite. They spend 90% on tools and 10% on people. The hard truth is, the most sophisticated tools are useless without someone who knows how to make sense of the numbers.

To ensure success, set the framework in place. Make it clear that everyone is accountable for the results.

Summary

I hope the metrics, analysis, action framework I’ve just introduced you to encourages you. Data and analytics aren’t really that technical. You don’t have to collect a ton of data, build regression models, or feed your AI any recipes.

Customers buy this over that. It’s not math. It’s not huge databases. It’s not engineering.

The MAA framework is all about understanding the numbers in the context of business performance and goals. Tracking metrics should always begin with the business strategy in mind. 

The post Digital Marketing Data and How to Optimize Like a Champ appeared first on DigitalMarketer.