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Email marketing is one of the essential marketing tactics. With an ROI of over 3700% (according to Hubspot) and with 80% of business professionals swearing that it improves customer retention, it’s easy to see why email marketing should never be discounted.
However, companies won’t see meaningful improvement and results even with a superb campaign unless they carefully analyze the data. In this article, we’ll explain why marketing data is so effective and show you how to best utilize it for success.
How Important Email Marketing Data Is to Marketers
The best way to start analyzing email data is to refresh on the basics. Email marketing is a varied tool that has numerous implications and uses in the marketing world. It can be used to ascertain several important statistics. Your best bet when starting an email marketing data analysis is to ask which questions you would like answered. If used correctly, this type of marketing data can answer all sorts of questions and make statistics crystal clear for future use. Here are some questions a business often asks from their marketing data:
- How many people open the emails we send? What are the open rates?
- How many people will click on the links?
- Who are your most loyal customers? What kind of emails do they open?
- Which links are the most popular?
- At what time of day or week do people open emails most?
- How many people purchase the products advertised or linked?
- Do people unsubscribe from the emails? What are the unsubscribe rates?
- How much does an average email campaign make in revenue? What is their ROI?
- What is the average revenue per email subscriber?
- What is the email-to-lead conversion rate from successful campaigns?
- Why do some campaigns make less revenue than others?
- Are there segments with better click-through rates than others? If yes, why?
These are only but a fraction of possible questions to ask when analyzing marketing data. The type of questions you want to be answered will guide you through the analysis and ensure you only choose the appropriate data subset and avoid overcomplications. They will show you what type of content customers consume most and how to optimize future campaigns based on the results.
Email Marketing Data Types and How to Utilize Them
When we talk about email marketing data, it’s easy to think of everything as one big jumble of information that needs to be disentangled. However, the data-driven marketing strategy approach generally recognizes three distinct data types, each with its use cases and framework. When asking the critical marketing questions, consider which of the following data you will need to analyze.
Behavior Analysis
The first part of the data trinity is behavioral analysis data. This data answers how users behave towards email content and what kind of content they tend to consume most. Behavioral data can answer the following:
- How many people open the emails? What is the average open rate?
- What subject lines perform the best?
- How many people have clicked on links?
- What links are most popular?
- At what time of day or week are the emails opened most?
- When do people unsubscribe from the campaigns?
Behavioral data points can usually be found on the email provider’s platform. This data type is usually easy to interpret but can be challenging to improve on. Sometimes, the best you can do is infer how users might behave in the future and hope for the best.
Outcome Metrics
Outcome metrics data is usually what most businesses tend to focus on when analyzing campaigns. These metrics will answer the campaign results and how well each campaign did compare to its cost.
For some companies, the end is all that matters. Metrics such as open rates and click-through rates (CTR) can be secured from Google Analytics or ESPs. Revenue and ROI can usually be pulled from a few different sources based on how the email campaign is set up. These important metrics usually answer revenue-based questions:
- What is the average ROI per campaign?
- What is the average revenue per subscriber?
- What is the email-to-lead conversion rate from successful campaigns?
- Do some campaigns make less than others?
While outcome metrics are the epitome of “the end justifying the means,” it’s vital to understand that this data might not be as useful when optimizing future campaigns. However, it can point to a failing campaign, and further analysis of other data points may uncover the reasons behind its lack of success.
Experience Metrics
Last but not least, the experience metrics answer the ever-important “why.” Correctly using experience metrics data will allow marketers to get into the consumers’ heads and optimize campaigns. You can use this data to cater to a broader customer base or make the most out of loyal users.
There are a few ways to gather experience metrics, usually separated into passive and active data. Passive data collection only uses user activity and indirect data on how they interact with the emails and links. Active data collection utilizes user responses, usually via surveys, to get a more accurate picture of wants and needs. Experience metrics will answer these questions:
- Who are your most loyal customers? What kind of emails do they open?
- Why do people unsubscribe from emails?
- What do the users expect from the company?
- What kind of products or services do customers think we excel at?
Experience metrics are usually the driving data subset behind optimizing future campaign efforts and can easily spill over into other marketing strategies. However, collecting and analyzing this data is less straightforward and requires more nuance and a personalized approach.
Analyzing the Data Collected
With so much data at your disposal, it can be difficult to separate useful metrics. Raw data is only as useful as the ability to process it, and sometimes it can be helpful to discard a portion for later and focus on the essentials. Here are our tips on how to analyze data.
Focus on the Campaign Goals
Each email campaign has a specific goal. When considering data analysis, think about what the campaign aims to achieve and use appropriate data collected from it.
A lead generation campaign will have different data points than an eCommerce or behavior analysis campaign. One of the best ways to zero in on the goal is to start with questions you want to be answered then work backward. The questions will point you to the type of data you need to use, which will guide you to the platforms required.
Define the Audience
When sending a campaign to users, data can be misleading if there’s a significant gap between users and how they approach emails. For example, trying to figure out the best time users open emails can be challenging if you work with customers in different time zones. For data collection, segment the audience according to one or more of the following:
- Geography (location, time zone)
- Demographics (age, gender, occupation, and more)
- Behavior (how they interact with the brand and campaigns)
- Industry and company size (if you provide services to other businesses)
Not segmenting users, or segmenting incorrectly, can be a common pitfall, so consider what data you need, then divide and conquer.
Combine Tools
More data is not always better, but you’ll usually need more than one data source to get enough information. Basic metrics like email open rates and click-through rates are often easy to pull from the ESP and Google Analytics. However, you can use other data-gathering tools, like CrazyEgg, UserZoom, or GetFeedback, for more nuanced behavior and experience data.
Stats Can Matter
When combing through data, some points will inevitably be more meaningful than others. Outcome metrics are fairly easy to calculate, so start from there and expand into other data types. Consider the following:
Conversion Rates
Conversion rates refer to how many users take the desired action, like opening links and clicking opt-in buttons.
Return of Investment
The ROI is a great way to measure initial campaign effectiveness and is one of the easiest stats to pull. It’s calculated as ROI=(Profit-Cost)/Cost. You can multiply the result by 100 to get percentages. For example, a $1,000 campaign that made $10,000 in revenue has a 900% ROI.
Customer Lifetime Value (CLV)
CLV is how much profit the brand makes from a customer. This one is a bit trickier and requires knowing their average purchase amounts, average purchasing frequency, and how long a customer interacts with the brand.
Cost Per Acquisition (CPA)
CPA is how much the company spends to get a customer. Email marketing campaigns often use this formula: CPA=(Campaign cost)/(New paying customers). CPA can indicate the success of a lead generation campaign.
Design Based on Results
When the results are in, you can use them to optimize how you interact with future customers and improve the user experience. Data-driven web design has only recently become popular in the marketing world, but it’s an excellent way to employ the gathered knowledge.
Putting it all Together
Data-driven marketing requires leveraging a substantial amount of data. However, you can start small and improve slowly and advance email campaigns based on previous results. Taking a data-driven approach to analyzing email marketing data will accelerate your conversion rate and allow email marketers to make optimal choices.
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