5/1/2023 0 Comments Notion database![]() ![]() So, when I think about a couple of our capability areas such as data analysis, research, and insights, if you think about the goal of those teams, they produce analytics and recommendations. The best way to for us to do that is to really think about connecting through emotion. Connectivity to those needs and their ambitions and the challenges that they face are very human, which means that we need to connect in a more human way. Those are all human goals and human needs. Howard: Why is emotion now even more important to creating human-led connection?įurze: At the end of the day, everything we're talking about and everything we're trying to achieve as a team is in pursuit of our customers’ goals. We are now made up of six capability areas and our mission is to combine the power of data, technology and behavioral science to unlock growth for our customers. So, we sought out how we could think about our customers’ challenges holistically and present them with a series of solutions or consultancy options that could be presented in a more unified way? We knew we had the skills, experience, and knowledge, but decided to bring this team together to set the course for an even more ambitious organization anchored around what our customers need to have to drive growth and innovation. There were a lot of subject matter experts, but We needed a unified strategy to unite them. With all that said, we'd previously had specialist teams, but they sat in different functions with different focus areas operating independently. We see that now as companies are trying to navigate and better understand the headwinds that they're facing from the economic downturn. That’s where we found an insatiable appetite for insights from our customers to help them better navigate the complex macro environment. What we noted at the time was we had these specialists across our organization and the demand for their skills and the solutions they produced were unprecedented, particularly, things like our Customer Insights team. We also recognized that we needed to have a unified Go to Market approach for how we deliver that type of value to our customers. Right? What we recognized is that in order to do that, it requires a lot of specialist skills, and a lot of specialist knowledge on top of those products and services. The inception of this team was the recognition that as we developed more sophisticated solutions and products for our customers and we invested in the product roadmap, we needed to equally invest in spaces to help our customers unlock value from those solutions. Howard: Tell me about how you created the customer science team and what’s been your greatest accomplishment to date?įurze: We've been a team now for 18 months, so a year and a half old. You've got sales operations teams that are now highly analytical. We now even have analytics specialists in the sales team and function. What’s super interesting is that analytical organizations are now in lots of different parts of companies where they didn't previously have that type of capability. They are much closer to the center of decision making than they have been in the past. Instead, we’re seeing at LinkedIn with our customers and ourselves that analytics teams are now more tightly aligned to the commercial arms of businesses. Also, on the technology side, there were a lot of advancements that allowed us to start to mine the data, store the data at speed and then add a lot more intelligence tools at the application layer.įrom a company standpoint, the biggest changes that I've seen are that analytical/quant functions are no longer the back end functions that sit behind technology teams. We were able to get clickstream data no longer just transactional CRM database data, or third party data, We started to get a lot more first party data with a lot of volume, variety, and velocity and that was significant. It was at this time we were just starting to get a handle on the idea of what it meant to have not just the volume of data that we're working with for the first time, but also the variety. One, is clearly the data tsunami, right? This came our way around 15 years ago. ![]()
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