Why analytics needs an impact-driven approach
The amount of data and its complexity are rising constantly, making it harder and more expensive for businesses to find the right places to create value and drive impact.
When your marketing team is running thousands of campaigns in different regions, countries, and platforms, there are millions of hypotheses that can be tested and numerous interventions that can be made. Or there are hundreds of different touch-points in a website that might be affecting the way consumers engage or churn and it differs for each consumer segment.
This means that your metrics such as return on ad spend, conversion rate, or basket size are changing on a regular basis. But do you know why they are changing? Do you know what to do in order to move the needle in your favor? If you want to create impact, new learnings need to be implemented in your marketing and sales efforts every day. Even for well-performing efforts, failing to track and make incremental changes could result in downturn in the longer run.
Using traditional business intelligence tools means that you need to know the right question to ask to get started. And it can be both frustrating and time-consuming even to identify the right question. While business teams throw different questions at data teams hoping to identify the reason for metrics changes, data teams are overwhelmed with the requests. And more often than not, by the time the right question and the root causes are found, it is too late to act on it, missing the window to create value.
To get to the why, businesses need speed and scale to be able to transform through data, a way to reduce time to insights from weeks to minutes, and an accurate view of priorities to empower team members to focus on what really matters.
This is where the power of machine learning comes into play: Every possibility is analyzed within minutes, actionable insights are put into business teams’ hands in real-time, and no opportunities are left on the table.
Given that the abundance of data will only get greater, businesses, especially the ones in fast-growing and fragmented markets, need to navigate this complexity well and fast to stay competitive.
Let’s take a look into our international, cross-platform campaign example and assume that you are seeing a global uplift of 47% in ROI (Figure 1). That’s an impressive outcome, but to keep up the performance, you need to diagnose what is driving this increase.
When analyzing the ROI of this campaign, what if you could understand how much every sub-campaign or sub-segment contributes to the ROI of your global marketing efforts within seconds? And you could easily view the information around the platforms, countries and demographics to have a better context? What if you could even cross-check the change in ROI with the changes in other related metrics such as CPI, predicted revenue, or in-app conversions?
I'm sure you agree that this would be great. But you may be thinking: "Can't I do this already today in my dashboards?" In fact, no... Because dashboards are great at showing you changes, but they don't help you see how it all fits together.
Let’s take a look at Figure 2 below, where you can see a simple visualization of ROI change by country. You are most likely going to direct your attention to Finland, Ireland, and Spain as they have the most steep changes. What if I were to tell you that the main contributors to the Global ROI are actually Croatia, Austria, and Belgium, and Finland is actually one of the smallest contributors?
As the Figure 2 didn’t take into account the size of the marketing spend and revenue change into account, it diverted your attention, instead of focusing it on the high-impact countries. Looking at individual changes loses track of how much each piece really moves the needle for your business. When you do take the full picture into account, you can understand by how much each country has been pulling the global ROI up or down. We have summarised the results of such an analysis in Figure 3 below.
Working on a small set like this, similar insights can be found through manual slicing and dicing as teams will know the usual suspects to go after, but this becomes impossible as datasets grow, more factors and subfactors come into play and performance comparisons over time are needed. Depending on gut feeling will only lead to missed opportunities or lost savings.
Just because you have complex data, working with it doesn't have to be complex as well. Decision intelligence platforms powered by machine learning can help simplify and automate your data processes. This means:
Kausa accelerates data exploration, delivering actionable insights in seconds by testing all hypotheses comprehensively and continuously.