Decision-making, marketing, product management, business, innovation, culture. Data-driven “everything” is a trend. We live in the era of big data, there is a ton of data about everything and there are tools such as Google Analytics that provide those data for free.
Nowadays using data in your job is common. I use data in my everyday job as well. Most of the professionals that are working in digital industries such as marketing & product management adopted data-driven strategies due to its significant advantages:
- Using data, you can avoid opinions without solid argumentation even though someone is HiPPO.
- We can’t do everything. In a sea of ideas and projects, strategy and data can act as a compass that will lead us to implement the most important ones by prioritizing them according to their value.
- We are now able to measure the true impact of our actions. By applying the right methods, we can identify accurately enough if our efforts are generating positive business outcomes or not.
- By using data & engineering, we can automate a lot of jobs, saving tons of hours of manual work, while we maintain the same performance (ex. Adwords & Facebook campaigns scripts that open and close campaigns automatically)
Along with data adoption, another trend emerged as well. People are trying to use data for EVERY decision they take. This is not necessarily bad, but as Kleovoulos, an ancient Greek firmly said: “all in good measure, all in moderation, moderation in the best thing” (English translation of: “παν μέτρον άριστον”). This quote applies perfectly in this situation as well, simply because using data in all of our decisions is wrong. Here are some good examples of when you should use and when you should avoid use data for your decisions:
The right question is not how can we measure everything.
The right questions is: What should we measure?
Measure what matters
Everything can be measured. Literally, from a business standpoint everything can be measured. And if we can’t measure something, we can assign or create a correlated. Is this right though? Let me give you an example:
During 2018 World Cup, Apple’s earbuds were everywhere. Apple was not even in the sponsor list for World Cup. Millions of people around the world saw popular football stars wearing Apple’s latest invention. How can you measure the effect of that? One answer could be that we can check the overall product sales during the World Cup. Another one -more sophisticated- could be to check which days the earbuds showed on TV and allocate the number of incremental product sales of this day, compared to a similar day without the World cup placement. But how can we be sure about the true impact on sales? If you ask, why should we know the percentage, simply because we need to know if this could be a good investment to do in the next World Cup or in the next football tournament in general.
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There are a lot of similar examples out there especially in the offline marketing and the branding industry that can’t be measured properly. Here are some examples from product management:
How can you measure the impact of an animation in your “add to cart” button?
How can you measure the impact of a nice page transition you developed, everytime a user proceeds to check out?
We must be brave enough to resist measuring that kind of initiatives even if our manager asks to evaluate this situation. Avinash call those initiatives “faith based“. Those projects should be evaluated regarding their alignment with our focus and strategy and not with performance metrics.
Apples vs Oranges Syndrome
This one is very common. Monthly marketing budget ROI report is one of the most important reports. If only marketing spend, consisted of only click-based channels. We would check clicks, sessions and conversions and allocate our budget to the campaigns that had the best performance.
Unfortunately marketing channels mix includes many non click-based channels such as TV. TV marketing is an expensive channel, it is 100% paid so we need to make sure that money is well spent. To get things even more complicated, we do two types of TV campaigns, branded campaigns and direct campaigns.
Branded campaigns are creative campaigns that highlight our USPs (unique selling points) and generate general awareness while direct campaigns highlight specific offers for a limited time. We always have hard times to evaluate brand campaigns, but direct campaigns performance is easily tracked because they drive immediate traction due to high buying intention.
Comparing branded campaigns with direct campaigns would led us to biased results against branded campaigns, so we compare branded campaigns versus past branded campaigns to set the right benchmark and understand what works better. The same methodology is applied to direct campaigns as well.
Understand how data is collected
Not all data is the same. In our business we have behavioral data -Google Analytics-, backend data -database-, qualitative data -reviews, chat logs- and other. Understanding how we collect data is important.
A nice example is direct traffic and how it is calculated in Google Analytics. According to its definition: “Direct traffic is defined as URL’s that people either type in directly or reach via their browser bookmarks.” So direct traffic happens every time someone types your website’s URL which most of the times is a good thing because the user memorized the website and did not have to search for it. Direct traffic is part of “branded performance” (along with along with brand SEO & brand SEM) which is considered as one of the best channel groups with relatively high conversion rates.
What many people are not aware of is that direct traffic act as a fallback channel as well for sessions that are simply untracked or unrecognized which may cannibalize the performance of channel overall, leading to wrong conclusions.A bad “untracked A good way to keep your direct traffic “clean” is to add utm’s in all your marketing campaigns, even the links you share as organic posts on social media.
Data-driven decisions can boost or destroy your business, depending on the way you’ll use data. Put effort and time to understand your data structure and what should be measured and what is not to avoid unhappy situations that although you think they are “data-driven” they will led you to failure.
