Wondering What is Marketing Analytics? And why you should absolutely know this concept? Well, before we start let me tell you this: Marketing Analytics is the art of prospecting. It is impossible to know what’s best. You will always either find what is better or worse.
With that said, let’s jump right in the piece.
What is Marketing Analytics?
If I were to break this concept down for you. It would simply be a process of breaking down your marketing efforts into measurable chunks and then measuring each of these chunks to see if you’re investing your energy in right of the areas.
This process of measuring and analyzing your results to achieve the best return on your investment, is what you may call Marketing analytics.
Marketing analytics as a “concept” is new and only came around soon after broadcast media. We do not mean to say that in the period prior marketers mindlessly invested marketing budgets without tracking results. Albeit, that is what they did… They called it the “Spray and Pray” method. They would drop an ad in a newspaper and prayed for it to work.
In fact, Marketing wasn’t recognized as a discipline until 1902, when the University of Pennsylvania offered the first marketing course. A few years later broadcast media followed and with the combined capability of math and marketing, Marketing measurement (aka, Marketing Analytics) was born.
But traditional measuring techniques weren’t accurate, in a sense they took too much time for results… for example, you drop a print ad in a newspaper, the sales of the product in some regions increase, so you start to put more of similar ads in that specific geography. Works fine, only that the results were slow. Very slow. And by the time you get to know, the trend may already run out.
Over time this changed. With the advent of powerful technology today, now you don’t need to wait for a newspaper to give you answers. Social media does that for you. You know what is trending and where it is trending.
The Internet has also enabled you to curate and customize each and every ad that your current or potential customer sees today. Combining this capability with result-driven insights can improve your ROI greatly. For example, when you show a dietary supplement ad to someone who is overweight, he is highly likely to buy your product than someone who is not. Bam, high ROI!
Related: Take a look at how startups are valued
With the meaning and history clear, let’s now, take a few of the popular measuring techniques.
Popular Marketing analytics techniques
Different industries have different priorities in terms of numbers. Like, if we’re talking about an FMCG brand, your numbers will be different. You will very specifically look at your Sales/margin numbers in order to ensure you maximize ROI.
Similarly, if you are a bank, you will look at the number of customers and try to lower default rates in order to improve your profitability.
For a website that earns ad revenues, you can try and figure out how to improve traffic and ad CTRs.
What we’re basically trying to tell you is that depending on your business you can target different variables to maximize or minimize them. But there are a few basic techniques that are applicable across fields in order to improve your return, and these are what we look at today.
If I were to tell you this in a layman’s terms it is testing two variables to see which one does better. Just like offering ice-creams of two different flavours to a toddler to see which one he likes better. It is also called split testing.
How is this useful to a marketer? Suppose you are running an email marketing campaign, if you’ve done it before, you know you get metrics like CTR’s (Click through rates), open rates, etc. By tweaking your subject lines, starting lines in your emails you can see which one resonates well with your audience. If you see an improvement in your opening and reading rates, it means that the content is working.
For an FMCG brand, you can do tweaks to your packaging and pricing to see which one sells better.
Next, you can use this for your marketing campaigns to find out which one works better. Now campaigns usually are a lot more expensive than an email campaign, so it’s advisable to not divert too much from your core proposition.
Customer lifetime value (CLV):
Again, as simple as it sounds, it says how much money you will make from a customer before they stop buying from you. This metric is the combination of your customer acquisition costs, the total expected lifetime, and finally, the money you make during the period.
This metric is helpful for a marketer because if you try to sell your product to a wrong customer your fall is inevitable because of a lower CLV. An example of such a failure would be when you try to sell Beef burgers in India. Apart from backlash you’d face… you’d also struggle to find customers and naturally, your marketing spend would be high.
For a new product entering the market, the customer lifetime value is usually less because more education is needed. But as more and more people know about your product, your marketing spend decreases and your margin and retention period increase so does the customer lifetime value.
Here’s the formula marketers use to calculate CLV:
(where Margin is the money you make, retention rate is the industry-wide rate, discount rate is the money you spend to get this customer)
The case of Netflix
One of the well-known Marketing Analytics and segmentation case studies is of Netflix. It believes that their recommendation system is worth billions of dollars. And, it is ready to pay some great money to folks who improve it further.
According to Netflix, 80% of TV shows people watch are discovered via Netflix recommendation system. Fascinating, isn’t it? Experts suggest it’s the AI and machine learning at play. Netflix ensures that approximately 167 million of its paid users get unique content from its massive library of 15400 titles
While we’ve covered this topic in-depth here: Netflix Recommendation system, here’s how Marketing Analytics comes into play here. Netflix is believed to have segmented their users in 2000+ microsegments (“clusters”) and to each of these segments, they deliver personalized content. They identify what a specific cluster of users is watching and it recommends you the same show that folks in your clusters are watching – even if you have had no history of it.
Say, for example, you watch the movie The Hobbit, it will, sometimes, out of no-where recommend you to watch the Office because a lot of users in your cluster preferred watching it and you’re most likely too. Because the users in your cluster are likely to have the same behavioural traits like yourself. Similarly, if a lot of users in your segment don’t like a TV show, the system is then not to recommend it to you until the dynamics of your cluster change.
If you’re looking for more case studies, take a look at how the Weather channel rebranded itself to the Weather company – leveraging the power of digital. The other example is that of Burberry rebranding: How the brand went from being perceived as a gangwear to a luxury icon.
A/B Testing and CLV are a few of the methods that marketers use in order to improve their presence in the market, there obviously are other metrics that you may use depending on the area of work, these two are helpful across industries. If there’s something you’d like to add in here, feel free to reach out.