Imagine you’re at a party, surrounded by people from all walks of life. You have a chance to make new friends and connections, but first, you need to find common ground. In much the same way, the world of marketing thrives on finding commonality among disparate data points, connecting the dots to tell a cohesive story. That’s where normalisation enters the picture, helping marketers like you make better data-driven decisions.
In today’s marketing landscape, data is king and you’re expected to wield this power to drive growth, engagement, and ROI. But with great power comes great responsibility, and that means ensuring the data you use is accurate, comparable, and primed for analysis.
In this post, I’ll walk you through the principle concept of normalisation and how important it is to everyone in working marketing.
Let’s kick things off by pulling back the curtain on normalisation. What if you needed to compare the performance of two marketing campaigns, but one has a budget that’s ten times larger than the other? Comparing their raw numbers would be like comparing apples and oranges, or in this case, apples and really, really big oranges. That’s where normalisation steps in, transforming those data points into a common scale so you can make meaningful comparisons and uncover valuable insights.
Normalisation, in its essence, is the process of adjusting data from different sources or scales to a common, comparable level. It’s like having a universal translator for your data, ensuring that all data points speak the same language, allowing you to find patterns, trends, and relationships with ease.
For example, let’s say that you need to compare the engagement levels of two social media campaigns. Each campaign was conducted on a different platform: Campaign A on Platform X and Campaign B on Platform Y. The engagement metric for Platform X is measured in “likes,” while the engagement metric for Platform Y is measured in “hearts.” The raw data is as follows:
- Campaign A (Platform X): 2,000 likes
- Campaign B (Platform Y): 8,000 hearts
To make a meaningful comparison between the two campaigns, we need to normalise the engagement data. In this example, we’ll use the Min-Max scaling technique (see below).
First, we’ll gather some information about the minimum and maximum possible engagement values for each platform:
- Platform X: Min = 0 likes, Max = 5,000 likes
- Platform Y: Min = 0 hearts, Max = 20,000 hearts
Next, we’ll apply the Min-Max scaling formula to normalise the engagement values for each campaign:
Normalised Value = (Actual Value – Min) / (Max – Min)
- Campaign A Normalised Engagement: (2,000 – 0) / (5,000 – 0) = 0.4
- Campaign B Normalised Engagement: (8,000 – 0) / (20,000 – 0) = 0.4
Now that the engagement values for both campaigns have been normalised to a common scale (ranging from 0 to 1), we can see that they both have an equal engagement level of 0.4. This allows us to make a fair comparison between the two campaigns, despite the different metrics used by each platform.
The need for normalisation in data analysis
Normalisation is important for many reasons, here’s just are a few of them:
- Comparability of data: In marketing, you often work with data from various sources, like social media platforms, web analytics, and customer surveys. Normalisation helps you harmonise these diverse data sets, making it easier to spot trends and make data-driven decisions.
- Removal of biases and distortions: Normalisation helps mitigate biases that may arise from different data collection methods or external factors. This way, you can trust that the insights you glean are based on a fair and accurate representation of your data.
- Improving data quality: By smoothing out disparities, normalisation can also help enhance the overall quality of your data, giving you the confidence to make data-backed decisions that drive your marketing strategy forward.
Types of normalisation
Here are some of the most popular normalisation techniques that can help you unlock the true potential of your data:
- Min-Max scaling: This method involves scaling the data by adjusting its range to a predefined minimum and maximum value, typically between 0 and 1.
- Z-score standardisation: By calculating the number of standard deviations each data point is from the mean, this method ensures that the transformed data has a mean of 0 and a standard deviation of 1 (this is usually my preferred method).
- Log transformation: This approach is particularly useful for reducing the impact of outliers or dealing with data that follows a power-law distribution.
- Box-Cox transformation: This technique can help stabilise the variance of data and make it more suitable for linear modelling, which is especially useful when working with time series data.
Now let’s take a closer look at how normalisation can empower you to make more informed, data-driven decisions.
Using normalisation in digital marketing agencies
If you work at a digital marketing agency, then you could use normalisation to compare the performance of different customers’ campaigns by adjusting the key performance indicators (KPIs) to a common scale. This allows for meaningful comparisons, even if the campaigns vary in terms of budget, duration, target audience, or marketing channels. Here’s how the process might work:
- Identify relevant KPIs: Determine the key performance indicators that are most relevant to the campaigns you want to compare. These may include metrics such as click-through rate (CTR), conversion rate, cost per click (CPC), or return on ad spend (ROAS).
- Collect data: Gather the data for each customer’s campaign across the selected KPIs. Make sure the data is accurate and up-to-date.
- Choose the appropriate normalisation technique: Based on the characteristics of your data (e.g., distribution, presence of outliers), select the most suitable normalisation technique, such as min-max scaling or Z-score standardisation.
- Normalise the KPIs: Apply the chosen technique to the KPIs of each customer’s campaign, adjusting the values to a common scale. This could be done using a tool like Microsoft Excel or more advanced analytics software.
- Compare campaign performance: With the KPIs normalised, you can now make meaningful comparisons between the different customers’ campaigns. Look for patterns and trends that may indicate the relative success of each campaign. For example, you might compare the normalised conversion rates to see which campaign is more effective at turning leads into customers.
- Draw insights and make recommendations: Use the normalised data to identify strengths and weaknesses in each customer’s campaign. Based on these insights, make recommendations for improvements or adjustments to help each customer achieve better results. This might include reallocating budget, tweaking ad creatives, or adjusting targeting parameters.
Implementing normalisation in your marketing analytics process
So, you’re ready to embrace the power of normalisation and take your marketing analytics to new heights. But where do you start? In this section, we’ll walk you through the steps to implement normalisation in your marketing analytics process, helping you unlock the full potential of your data.
Identifying the appropriate normalisation technique
As we’ve discussed earlier, there are several normalisation techniques at your disposal, each with its own strengths and weaknesses. Identifying the appropriate normalisation technique for your specific data set and marketing application is essential for obtaining accurate and meaningful results.
Here are some guidelines to help you select the right method:
- Understand your data: Before choosing a normalisation technique, it’s crucial to have a thorough understanding of your data set. Examine the distribution, scale, and presence of outliers, as well as the nature of the variables you’re working with (e.g., continuous, discrete, or categorical).
- Consider your marketing objectives: Different normalisation techniques are suited to different marketing applications. For example, if you’re comparing marketing channels with different scales, Min-Max scaling might be a good fit. However, if you need to deal with data that has a skewed distribution and outliers, log transformation or Box-Cox transformation might be more appropriate.
- Min-Max scaling: This method is suitable when you need to bring all features to the same scale (e.g., 0 to 1) without changing their original distribution. Min-Max scaling is sensitive to outliers, so it’s best used with data sets that have a relatively uniform distribution and minimal outliers.
- Z-score standardisation: This technique is ideal when you want to transform your data so it has a mean of 0 and a standard deviation of 1. Z-score standardisation is less sensitive to outliers than Min-Max scaling and works well with data that follows a normal (Gaussian) distribution [think bell curve] or when you need to compare variables with different units of measurement.
- Log transformation: Use this method when dealing with data that has a skewed distribution, contains outliers, or follows a power-law distribution (e.g., income, web traffic). Log transformation can help reduce the impact of extreme values and make the data more suitable for linear modelling.
- Box-Cox transformation: This technique is useful for stabilising the variance of data and making it more suitable for linear modelling, especially when working with time series data. Box-Cox transformation requires that your data be strictly positive, so you may need to add a constant value to your data set if it contains zero or negative values.
- Experiment and validate: Don’t be afraid to try different normalisation techniques and compare the results. Use cross-validation or hold-out samples to assess the performance of your chosen method and ensure that it accurately reflects the underlying patterns and relationships within your data.
By carefully considering these factors and guidelines, you can select the appropriate normalisation technique for your specific marketing application and data set. Keep in mind that you may need to use different techniques for different data sets or marketing applications, depending on their unique characteristics and objectives.
Applying normalisation to your data set
Once your data is clean and ready for action, it’s time to apply your chosen normalisation technique. You can use programming languages like Python or R to transform your data into a common scale but you can also use Excel (or Google Sheets). Here’s some basic instructions on how to do use Min-Max scaling and Z-score standardisation methods using Excel (my preferred spreadsheet):
Follow these steps to apply Min-Max scaling to your data in Excel:
- Organise your data: Ensure that your data is arranged in columns, with each row representing an observation and each column representing a feature or variable.
- Create new columns for normalised data: To the right of your original data, create new columns that will hold the normalised values. Label these columns accordingly.
- Calculate the minimum and maximum values: In an empty cell, use the MIN() and MAX() functions to find the minimum and maximum values for each feature. For example, if your data is in column A, use “=MIN(A:A)” and “=MAX(A:A)”.
- Apply the Min-Max scaling formula: In the first cell of the new normalised data column, use the following formula to calculate the normalised value:
=(Original Value – MIN) / (MAX – MIN)
Replace “Original Value” with the cell reference of the original data point, and “MIN” and “MAX” with the cell references of the minimum and maximum values you calculated in step 3. Copy this formula down the entire column.
- Repeat for all features: Repeat steps 3 and 4 for each feature or variable in your data set.
Follow these steps to apply Z-score standardisation to your data in Excel:
- Organise your data: As with Min-Max scaling, ensure that your data is arranged in columns, with each row representing an observation and each column representing a feature or variable.
- Create new columns for standardised data: To the right of your original data, create new columns that will hold the standardised values. Label these columns accordingly.
- Calculate the mean and standard deviation: In an empty cell, use the AVERAGE() and STDEV.P() functions to find the mean and standard deviation for each feature. For example, if your data is in column A, use “=AVERAGE(A:A)” and “=STDEV.P(A:A)”.
- Apply the Z-score standardisation formula: In the first cell of the new standardised data column, use the following formula to calculate the standardised value:
=(Original Value – Mean) / Standard Deviation
Replace “Original Value” with the cell reference of the original data point, and “Mean” and “Standard Deviation” with the cell references of the mean and standard deviation values you calculated in step 3. Copy this formula down the entire column.
- Repeat for all features: Repeat steps 3 and 4 for each feature or variable in your data set.
- Here’s an step by step example
Interpreting and utilising normalised data
Now that your data has been normalised, it’s time to put it to work! Use your transformed data to create compelling visualisations, build predictive models, and inform your marketing strategies. Remember, normalised data is just a means to an end – the true power lies in your ability to interpret and act upon the insights it reveals.
The risk of over-normalisation
While normalisation can help you uncover hidden patterns and trends, it’s essential not to overdo it. Over-normalisation occurs when data is transformed excessively or inappropriately, leading to a loss of valuable information or the creation of misleading patterns.
Consider a marketing data set containing the following information about customers:
- Number of items purchased
- Total spending
- Customer satisfaction rating (1 to 5 stars)
Suppose you decide to normalise all features, including the customer satisfaction rating, which is already on a standard scale. By applying normalisation to the customer satisfaction rating, you may unintentionally obscure the relationship between the rating and the other features, as it could create artificial distances between the ratings. This could lead to incorrect conclusions about the relationship between satisfaction and other variables, such as total spending.
In this case, normalising the customer satisfaction rating is an example of over-normalisation. Instead, it would be more appropriate to only normalise only the continuous features (age, number of items purchased, and total spending) which are on different scales and have varying ranges. This would help preserve the original meaning of the satisfaction rating while still allowing for a better comparison of the other features in the data set.
I hope this has been a useful overview of normalisation and how useful it can be in marketing; here are some of key points:
- Normalisation is the process of adjusting data from different sources or scales to a common, comparable level, enabling you to make better data-driven decisions – with comparison being the most frequent reason.
- To implement normalisation in your marketing analytics process, start by choosing the right technique, cleaning and preparing your data, applying normalisation, and interpreting the transformed data in context.
- Be mindful of potential challenges and limitations, such as data quality concerns and the risk of over-normalisation.
As a marketer in the digital age, embracing normalisation can help you harness the power of data to make smarter, more informed decisions that drive growth and success. I highly recommend everyone to start actively using normalisation in your marketing efforts.
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