As decision science carves out a wider niche in the field of data science, which has seen vast growth as a discipline— we are seeing a change in the way teams use the wealth of data at their disposal. Data analysts and reporters have been acquiring, organizing and cleaning data for years. Now, with better reporting and collaboration, data scientists can take that data, analyze it, and use it to make more informed decisions.
While this current structure works, it continues to evolve and improve. Machine learning is now enabling businesses to move from a reactive and cause-and-effect mindset to a more predictive mindset. Machine learning and deep learning models are getting better every day when it comes to predictions, but the decision to focus on the future should always come from a data science team. With this need in mind, companies are invest in teams with the skills to take the available data and apply it to potential future situations.
At Clearlink, our evolution from a reactive decision science team to a proactive team has been a natural progression as our team has evolved. As we continually advanced data capabilities and added key talents, we were able to stay dynamic instead of getting stuck in a reactive mindset. The positive results we’ve seen reinforce the value of moving from reactive to proactive, wherever your data science team is currently located.
Start with a solid foundation
Clearlink’s first foray into data science formed a small team. In just a few years, we have experienced significant growth and now enjoy a close relationship with the company’s marketing and sales divisions. When we started, however, we needed to establish a framework that would allow us to react to needs in real time before we even thought about being proactive. This meant building a large data warehouse over time. As we built the warehouse and gathered data, we were able to start reporting, learning, and reacting.
This kind of reporting was certainly valuable – being able to understand and react to past customer behavior helped us improve the experience of future customers. Unfortunately, this still left current customers with experiences that we couldn’t change in real time. By looking back rather than forward, we got good ideas and direction, but left customers feeling frustrated in the moment. With more advanced analytics, we’ve been able to start predicting customer journeys before they happen, allowing us to help people in real time rather than after seeing them give up.
Proactively change customer experiences: traffic control
A proactive approach specifically helped us balance call traffic and marketing campaigns. Typically, when one of our sales teams is overwhelmed with call volume, that level of volume triggers specific PPC campaigns to stop. This limits the number of customers driven to the call center (only to experience long wait times and potentially abandoning the queue) and prevents our marketing team from overspending on campaigns we don’t need. . This process identifies when traffic is at capacity and helps us reduce accordingly.
Historically, this process involved looking at a specific moment in time and assessing our situation. If we looked at a specific time and saw a drop in calls, we would increase the number of PPC ads we served to increase call volume. Five minutes later, however, the calls could increase again. Our commercial agents would be overwhelmed and we would stop these same campaigns. This approach works, but too often it involves looking back and reacting instead of thinking ahead.
Now, machine learning can anticipate these fluctuations in call volume and help us avoid overreaction. Based on the data we already have, we have built models that show the volume of calls we should expect in the next 15 minutes, as well as the number of agents who should be available in the next 15 minutes. This allows us to plan in advance how to balance agent availability with marketing campaigns, giving us time to make data-backed strategic choices in advance.
With a more proactive approach, we can deliver a smarter customer experience by managing call volume more consistently. This streamlines the customer journey and therefore strengthens our brand. When customers benefit, the business benefits. Additionally, this approach facilitates more strategic spending. We’re stopping campaigns that aren’t generating additional revenue and we’re lowering our customers’ abandonment rate, allowing us to optimize our investments in the areas that will help us the most.
Encourage a proactive approach
If your team is still in a reactive mindset, encourage them to think differently to pave the way for a more proactive approach.
- Use case studies: Show your team how machine learning and predictive analytics have helped other businesses. If possible, create hypothetical case studies illustrating how proactive thinking could help solve a problem for your team.
- Talk about what the customer is going through: Describing up close exactly how your customers feel when interacting with your brand is a great way to shed light on new issues or help your team see areas for improvement that they may have previously missed.
- Show your team the potential (or real) impact: Data science teams love analytics, but their motivation ultimately comes from the impact of those analytics. Motivate them by identifying opportunities for your team to make a difference, then showing them positive results for the bottom line and for the customer.
At Clearlink, the example of traffic control is part of an AI layer that underpins our customer journeys, which aims to make every step informed – proactively – for the customer, based on everything that happened before. This real-time ability to positively impact people through data science has really inspired our decision science team to adopt a more proactive mindset. This has a significant impact on our bottom line and has a positive impact on both the business and the customers, which continues to be an added motivation to stay true to this new way of thinking.
If your team is struggling, keep in mind that the transition doesn’t have to happen overnight. Build the right foundation, strive to use data to make a difference in the business, and try to start thinking and planning ahead. Your team will follow your lead and also begin to take a proactive direction.
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