Kerry Uhlenhake is a sales analyst at Spring Venture Group. In this role, he helps facilitate open lines of communication between sales and data to help initiate transparency between both teams.
By Kerry Uhlenhake
I have held many sales jobs in a variety of companies. But what SVG does is something I had never seen in my career: Leadership is fully dedicated to providing transparency between sales and data science.
From my experience working on a variety of sales teams — both ones that don’t provide transparency to ones that do — I have learned that transparency between agents and data is truly imperative to the success of both the individual and the business they work for.
Transparency means allowing teams outside of your technology department to have access to the abundance of data that is collected. This helps establish a culture of trust, enhances cross-department communication, and betters the bottom line.
Transparency builds trust
In many sales departments around the world, data science is the great and powerful Oz behind a giant curtain. Agents are unsure where their leads come from, questions are raised about their legitimacy, and a disconnect occurs amongst departments.
Transparency between data and sales is crucial to building mutual trust and inspiring growth. Simply removing this disconnect allows for a greater feeling of community and teamwork. If information is readily available that can provide context or background to performance, employees are more willing to buy into initiatives.
Transparency enhances communication
It’s easy to blame data science for bad sales performance, either by claiming the leads were faulty or by assuming that each team has its own objectives. With data science, a lot can get lost in translation; my role allows me to guide the conversation and “translate” data into sales. I use my sales experience to my advantage as I communicate across departments about initiatives and setbacks.
But, opening the doors to the full set of data is not beneficial to an agent. It’s imperative that data science teams facilitate a conversation that communicates what the data means and how it affects the sales teams, without overburdening them with information they don’t need.
An unavoidable side effect of transparency is feedback. With a mutual level of trust comes a new line of communication and cross-department sharing. Sometimes this can inspire innovation. Communication from other departments — especially those using the data and tools — helps shape data science in ways that may not have been considered before.
Whether it’s greater buy-in from other departments or the sharing of new ideas, the communication that comes from transparency is a positive reinforcer of a company’s values and a good way to facilitate more teamwork.
Transparency betters output
Open data is a growing movement across the world. Whether it means providing performance metrics to the financial market or offering greater weather insights, many experts and open-data advocates believe that if data is readily available, crowdsourcing becomes easier and solutions become greater.
We gather an abundance of data points at SVG, from agent close rates to lead-specific insights. An analyst can make use of some of this data, designing algorithms around agent and lead performance. But if an agent has access to that same information and could make micro changes in their script or macro changes to their performance, the data can then take a new life.
If our analysts were the only ones using data, the potential for growth is minimal. By providing wider access to data, our agents become inspired to make better, faster decisions based in proven facts — decisions that can improve both their bottom line and the company’s.
Transparency in data is crucial, which is why it’s the next step for many innovative tech companies all across the world. As a company, providing open data should not mean giving pure, full access; it’s imperative that data experts communicate how to interpret data points in a way that impacts them. This sharing of both data and its meaning allows companies to improve their internal and external output.
Ultimately, by empowering employees to take more of a role in their success, a company’s data gets a broader return on investment and can make a far greater impact than before.