First, we'll explain what a marketing funnel is. In the pre-digital era, there was the following stream by which we achieved goals - by lowering the number of interested people and narrowing the target group. It looked like this:
1. Awareness
2. Consideration
3. Conversion
When the third step happens and the goal is achieved, the funnel would usually ends there. Then everything would start from scratch, and then digital happened. A moment has happened in which we can measure everything. And so due to that sequence of events and the evolution of marketing, the Retention part was created, so the funnel looked like this, except that the famous Retention came in the 4th place.
Why is Retention an important part of the funnel, one might even say the most important? Because it's about people we already know. We often have their email, first and last name, gender, demographic, and geographic data. If they bought something from you, you know what they bought and when. You can anticipate their future intentions.
The best thing about this stage is that we know so many things about them, and with smart communication, we can make them advocates of our brand. This is a real safe full of diamonds for any serious marketer. A safe of diamonds, because it saves you money that you would invest in campaigns to attract new sales and new potential customers.
Where is the data you ask? You're not really asking, but this is a good form of storytelling, so they say. But if you're wondering, I'd tell you that data is the central brain of optimizing any marketing funnel. Believe it or not, absolutely every part of the marketing or sales funnel can be optimized if:
1. We collect data in the right way,
2. If we visualize them in the right way,
3. If we analyze them in the right way i
4. If we extract insights from them in the right way.
Why do I say the right way? Data management is a rough word, but very important in the story of data. Now we will go point by point and how to do it all correctly to get the best possible result.
How do we collect data the right way?
First and foremost, we need to have a place where we collect data. Most often it is Google Analytics when we have a site or application as the main destination. We may also collect data in advertising platforms, some of the most common being Google Ads, Meta Ads (Facebook and Instagram), as well as many others. In addition to Google Analytics, there are alternatives such as Matomo Analytics, Adobe Analytics, or Plausible Analytics (as an open-source alternative).
Once we know where we are collecting data, the next step is to collect the data properly. In data analysis, there is a term - data noise. Noise gives data improper collection that can be embodied in the form of poorly named or duplicated events, improper analytics settings, or improper grouping of channels. All of these can introduce noise into the data.
If you want to avoid this, sign up for the Digital Marketing program, or if you just want to educate yourself on setting up the new Google Analytics 4, sign up for a one-day workshop.
How to visualize data in the right way?
You would probably say the following; I select a chart and display the data. Choosing a chart is a good answer, but which chart? In data analysis, there are rules for which graphs represent which data. As an example, a line graph should be used when showing trends, volatility, acceleration, and deceleration, especially when looking period by period. It is especially important when we look at the period by period, that we take the same days of the week, according to the same days of the week of the previous period.
The waterfall chart is used when we have a defined goal, so we show deviations in plus or minus with this chart. An example could be that we sell a certain product and make a report for the previous period to show us during which periods we exceeded the set goal, in what quantity, and in what income.
The bar chart is one of the most commonly used. There we have horizontal, vertical, grouped, and stacked. They are usually used for top lists, for top lists that include comparisons of a maximum of two categories, as well as stacked, which has its application when we want to compare several categories.
Finally, without going into fine details, we have the famous pie chart. A favorite graph of most PowerPoint presentations, it can find its application in tracking percentages. A note about percentages, especially when you have a small sample, although it is recommended almost always. Keep the absolute figures in sight as well, because percentages often mask the picture, distort it and show an incorrect situation.
Among the more advanced ones, we have a Gauge chart, Scatter plot, Spider chart, Area chart, Bubble plot, Box plot, Bullet chart, Funnel chart, Treemap chart, and so on, but we will stop here. I must mention that knowing how to use graphs is very important if you use some Business intelligence tools like Power Bi, Tableau, or Looker from Google Kitchen.
How to analyze the data in the right way?
Now we have come almost to the point. The real crux goes in the last chapter. And now let's get down to analyzing the data. In order to implement it, we should first respect the following principle called CARE, which is the opposite of CRAP. CARE stands for Collect, Analyze, Recommend and Execute/Experiment, while CRAP stands for Collect, Report, Avoid Analysis and Postpone actions. A fellow analyst, Mikko Piippo, is responsible for these definitions.
If we listen to Mika and if we listen to other experts, data analysis starts primarily from the quality of data sources. After that, the correct choice of visualization helps us, and when we have successfully mastered those two steps, then analysis follows. Many people think it's a boring part, it's actually very interesting. Here is an example.
We have a person who bought an Oral-B toothbrush from us. What can the analysis tell us further? If we analyze, we will use several types of analysis. Let's say we analyze by diagnosing. First, we would determine what series the brush is. Let's make it a Series 9 IO brush type. The brush belongs to the high-value segment, which is more than 50% more expensive than the average and common value of electric toothbrushes. This shows us that the buyer most likely belongs to the upper class.
If we go further and analyze, possibly the purchase history of this person, we will be able to determine with greater probability whether he is really in a higher class. If we don't determine that it is, we can go towards him taking good care of his teeth. If so, then we know that through email marketing we can offer this person brush accessories, a smart device that monitors the quality of washing, and then track this person's reactions.
We are now entering into prescriptive analysis, and in the last sentence into predictive analysis. These are all types of analytics that we use in a chain to learn as much as possible about our users or customers, then we will save the actions that come from the data analysis and finally make predictions about the future intentions of our users or customers.
How to extract insights the right way?
Well, here lies the secret of the data. We can collect everything nicely, sort it correctly, map it correctly, visualize it perfectly, and perform certain storytelling on the data, but everything falls apart if we don't present a strong enough insight that will affect the business in front of the one who listens to us.
There is an unwritten rule that an analyst is not the one who prepares 15 slides of graphs and data. A real analyst is one who prepares strong enough insights that change ways of thinking and approach work in a positive direction. Sometimes it is enough to have two, or even one slide. Quality of insight must always come first.
So how do we get them out? Literally explained looks like this. Imagine you have a wall. The insight is somewhere behind that wall. You will dig through the wall as you know how to reach the insight. You will see if he has answered all your questions when you reach him. If not, you'll keep digging and you'll do it until you've answered all the questions. It may sound like a well-worn phrase, but there is no conclusion that cannot be reached. There's only one way you get there, or you don't. That is what separates a good analyst from a not-so-good one.
Conclusion
And finally, how do we optimize that famous marketing funnel? By analyzing two things always. First, we analyze people who pass from stage to stage, while the second, much more important, is to analyze people who did not enter the funnel, especially the reasons why they did not enter the funnel. Your emphasis should be on the bottom of the funnel, especially when it comes to conversion and user retention. You can learn more about it in the Digital Marketing course at Molèn Academy.
Trust the data. Trust the insights. Data should drive business and should be recommendations as well as advice. If you see that the data does not do this, either you are not using it in the above ways or you do not have it in adequate quality or quantity.
Data will and already is, saving the world. Be part of the solution! Use your data!