A lizard would make a terrible paperweight. Placing one on a stack of notes with the intent of it staying put and weighing them down would immediately yield disappointment. Conversely, a rock would make a terrible lizard. Staring at one, expecting it to dart here and there and catch flies will prove rather futile.
I sometimes think of this when I’m in the throes of my day-to-day as a Customer Success Manager. Analytics platforms, even when immensely powerful and easy to use, may be seen through varying perspectives: Data scientists may be much more likely to click and scroll and meander through a UI, understanding the necessary evils of both data manipulation and wonky user experiences, and always believing in both the results presented and their downstream usage. End-users, especially in the Outdoor Power Equipment (OPE) industry will inevitably be much less inclined to spend any time digging through UI to get to what they need, and infinitely more skeptical of the data and somewhat hesitant about what to do with it. In the middle, I find myself gaining more and more of the end-user’s perspective, trying to allay their suspicions and drive up usage- initially taking the data scientists’ perspective as the ideal state.
Sometimes this feels like trying to teach a rock to be a lizard.
As a Customer Success Manager I believe that recognizing the abilities and needs of the end user, and customizing both the experience and the service to meet those attributes (along with persuading the data scientists to think like an end-user during development) leads to the best outcome. Harnessing the infinite customizability of a SaaS platform such as ours, we can respond directly to the needs and wants of the end user – which are never broadcast in terms of a platform, data, or user interface. But I believe we can go further: Initially, we gave them the ability to manipulate the data as if they had a blank canvas. Our data scientists could do it, so why not the end user? With the blank canvas came a blank stare in response, and no usage. We then showed them data and analysis that we know points directly at opportunities for selling more product through existing dealers, and the response we received was ‘This data is great but… I want to sell more product through existing dealers.’
We were expecting a lizard, and were surprised when a rock didn’t know how to catch flies.
However, if we approach the platform and the data analysis from the perspective of the end user, asking ourselves at the outset ‘How can I sell more product through existing dealers, or find new opportunities for expansion’ we can tailor and automate the platform’s response in the easiest, most direct manner. Structuring our offerings around these concrete needs and providing the easiest pathway from login to insight is the surest way to get them to see the value of the data, believe in the veracity of the data, and use it again and again from use case to use case across their entire territory.
This same mentality applies not only to our industry of analytics and insight, but to the OPE industry as a whole, as they must think like a customer (or end-user) in order to make sure they have the right product at the right dealer at the right time, so that the desired customer base is served and high-value repeat customers increase. By gearing our development and support around the axle of the skills attributes and needs of the user at the very outset, we can drive them towards knowing more about their customer base and actioning real tactics and strategy to increase sales and capitalize upon opportunity. Knowing the difference between a rock and a lizard is step one, making it the best rock it can be is where we’re going.