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Adam’s colourful journey through anthropology and army careers into Sales Ops 

Every operation uses data, whether it’s choosing a Starbucks store location, learning about anthropology, or being in the army. The common theme in Adam’s career across all fields he ventured into was data. Throughout his career, his ability to analyze proved to be his “highlight skill”. Adam also applied this skill to his work with Boeing’s test and evaluation team, where they had to replace legacy BI systems and unify data from multiple sources.

Using data at Tableau to help sales reps be more effective

Data is used in Tableau to make decisions across the sales cycle, from sourcing leads to closing opportunities. A key function of using data is finding knowledge gaps in this end-to-end process and eliminating any follow-up/hand-off delays. 

Tableau’s Sales Ops structure 

Strategic Account Executives have a crew of resources to help their customers understand and deploy Tableau at an enterprise level. They also have account executives focused on the small and medium business (SMB) category. The sales ops team helps all levels of sales teams monitor stats and direct them using data-driven strategies.

Tableau’s Sales Ops focus in 2021 

Tableau had the opportunity to serve public sector businesses in 2020. The US government’s infrastructure plan will affect the business of their public sector clients. In 2021, they are looking to explore the impact of this plan on their business.

Examples of Tableau using data to improve Sales Ops 

Adam’s team uses granular pipeline coverage data to talk to their VP and offer help to improve stats. The goal of the conversation is to “flag the issues,” using their soft skills to convey their willingness to help. They also use historical close rates (up to one and three years) and linearity to validate their forecasts. 

Becoming data-driven in Sales Ops 

Data-driven is not just an adjective, it’s a way of thinking and a core pillar of your lifestyle. Adam likens this shift to switching to healthier food. At a company level, having a few evangelists with a data-driven decision-making approach helps grow the culture within a company. This approach should permeate all employees across a company and not just teams who need it as a part of their job.

The key to enabling all of this is having a real grasp on the customer’s journey and deeply knowing how you can help them succeed from the data. 

Cleanup of historical data

Cleaning up dirty data can be frustrating, but your priority should be simply cleaning up the relevant data set that will affect results and your goal. You should not be focused on changing historical data that will have no business value, as it will waste time and resources with little to no impact. To get value from data, you don’t need it to be perfect. Small imperfections can often be explained and managed.

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