I’d like to share some of my lessons learned from my career working with data in marketing, focusing around the things that I tried which I believe added value to the business.
- Share what you have learned
In this field of analytics and marketing, it is pretty common to find people who have learned things keep those knowledge and insights for themselves as if that belongs to that person. This is not unique to people in analytics, but that is a pretty lame move in general. Your team and people working in the company are trying to perform, and analytics are important function of this entire business process. The biggest part of analytics people’s job is to support solving business problems through data, evangelize data and insights, support the business move forward achieving it’s goals. Without sharing the data and insights, there is no evangelizing.
Also when you assume what you have shared is being translated to others, don’t assume they have shared with others. Many companies have great internal resources and capabilities that allows employees to share what’s going on. If not, then I’m sorry to hear that. In tools like Jive, it is important to create your own area and start publishing your learnings. You can always reference that source, and communicate to the wider audience. This is obviously not the only way, but the idea is try and find ways to share more than that one point of contact, boss, CMO, etc. What good it is to not share your awesome work and let it sit and decay.
- Be curious, build a habit to find answers and apply
Every job, great jobs come with opportunities to learn. Sometimes it may come in a form of unexpected things you have to deal with, or things encountered through new projects, or even come by terminologies encountered through work place, experts, school, and peers. Never be shy to ask, there are no dumb questions. There is Google for us to search wealth of knowledge online as well, and we should take every advantage of it. Up until this point, I hope people do search for their own answers when encountered with questions that you want answers to. Curiosity in a dictionary states the meaning ‘a strong desire to know or learn something’, and for me, this is one of the must have behavior in analytics expert.
Another key thing is to ‘apply’. One of the common characteristics I see in successful people in any industry is that they don’t just consume things, but they apply their learnings and practice what is in their mind to help drive tangible or real impact. Example could be… don’t just talk about tools and wish it get’s purchased or implemented, find ways to bring in a proof of concept deal, and prove the value of the tool.
- Communicate and be clear
Communicate your findings, but reserve for the insights. Don’t communicate for the sake of communication. When you communicate, you want to make sure you’re respecting every moment of people’s valuable time. What that means is, be clear as much as possible. Whatever you find through data, you don’t want that to be miss interpreted or taken action in a wrong way from what you originally recommended. If you have found something interesting, but not necessarily gives an answer to the business question, steer the communication to drive testing, or excite others to ‘test it out’. A/B testing tools are so cheap now a days and it is no longer something we should be wait for. Clear communication needs to have a direction, and expected action or outcome that is lead by you.
- Always be clear on the problem you’re trying to help solve.
Unless you have some spare time and just playing around with data, don’t waste your time solving and analyzing problems your team, department, and company is not solving for. If a data practice you’re involved isn’t involved in the problem business is not trying to solve, that is a super low priority thing to work on. When analyzing data and concluding your findings, make sure to go back, and ask if the finding helps solve the core business problem or not.
It doesn’t help to be confused on the problem business is trying to solve either. If you’re trying to analyze data for some problem that is way off from the real problem, then that’ll be a huge waste of time. Make sure the problem is clear and you really understand it.
In a knut shell…
I might have learned the hard way to understand the key behavior points listed here (which is not limited to), but hope you find this list of key acumens required in marketing analytics helpful.
My apologies if this came out to be me puking out things many people already know, but I thought it was important for me to put this out and see if this helps for others. Being in this field of data for over 12 years, there is definitely more to it, but I guess I’ll stop here and write more in near future.