Digital Marketing - Study Notes:
Comparative data analysis
Comparative data analysis is an item-by-item comparison of two or more comparable data sets. It examines the variance between the data sets over the same or a different period of time, and it shows the percentage or actual change.
It’s an important technique for validating hypotheses or tests, and for reporting on performance. When you compare data, you can draw insights, such as how mobile traffic compares to desktop traffic over a set period of time or year on year etc. Then you can try to work out the basis for the difference in performance. It might be down to the customer type, the product, or another reason.
By comparing data sets, you can follow a path of enquiry to find out more about the patterns you’re seeing in the data. Marketers use time comparisons quite often; for example, they might compare this month to last month or this year to last year. It seems simple but it’s really important type of analysis.
To compare data over time:
1. Choose your date range.
2. Choose your data sets for each date range.
3. To see the actual difference, simply subtract the older data set from the new data set.
4. To see the percentage difference, use this formula:
o (new metric - old metric)/old metric x 100 = percentage difference.
Making decisions using comparative data
Developing forecasts using comparative data is a key technique for digital strategists. To do this, follow these steps:
1. First, choose recent comparable data sets (for example, last month versus the month before last), and work out the percentage difference in the metrics between your months. You can use this difference to validate your forecast at the end.
2. Next, to account for seasonal trends, compare last month to the same period last year (for example, April this year versus April last year). The percentage difference in the annual metrics is your yearly multiplier.
3. The third step is to take a full month’s data from last year (for example, May of last year), and multiply it by the yearly multiplier that you worked out in step 2. This gives you a forecast of what future months look like based on yearly growth.
4. Finally, it’s important to validate your yearly multiplier and forecast against your current percentage difference, which you worked out in the first step. By comparing your forecast against your current percentage difference, this allows you to sense check if your forecast seems out of step with what you’re seeing over the last few weeks
Strategists can also develop forecasts using historical metrics. For ecommerce, this involves forecasting outcomes using one or two static metrics, and one variable metric.
Cathal Melinn
Cathal Melinn is a well-known Digital Marketing Director, commercial analyst, and eommerce specialist with over 15 years’ experience.
Cathal is a respected international conference speaker, course lecturer, and digital trainer. He specializes in driving complete understanding from students across a number of digital marketing disciplines including: paid and organic search (PPC and SEO), analytics, strategy and planning, social media, reporting, and optimization. Cathal works with digital professionals in over 80 countries and teaches at all levels of experience from beginner to advanced.
Alongside his training and course work, Cathal runs his own digital marketing agency and is considered an analytics and revenue-generating guru - at enterprise level. He has extensive local and international experience working with top B2B and B2C brands across multiple industries.
Over his career, Cathal has worked client-side too, with digital marketing agencies and media owners, for brands including HSBC, Amazon, Apple, Red Bull, Dell, Vodafone, Compare the Market, Aer Lingus, and Expedia.
He can be reached on LinkedIn here.
Kevin Reid
Kevin is a Senior Training Consultant and the Owner of Personal Skills Training and the Owner and Lead Coach of Kevin J Reid Communications Coaching and the Communications Director of The Counsel.
With over twenty years of experience in Irish and International business with an emphasis on business communications training and coaching, he is a much in demand trainer and clients include CEO’s, general managers, sales teams, individuals and entire organisations.
With deep expertise in interpersonal communication through training and coaching and in a nurturing yet challenging environment, Kevin supports teams and individuals through facilitation and theory instruction to empower themselves to achieve their communication objectives. This empowerment results in creativity, confidence building and the generation of a learning culture of continuous self-improvement.
By the end of this topic, you should be able to:
- Critically analyse the process of using analytics tools to create insights
- Critically evaluate the role of Artificial Intelligence (AI) and Machine Learning (ML) tools in enhancing marketing strategy
- Evaluate Customer Relationship Management (CRM) data and its use in informing business decisions
