The customer experience is arguably the most important part of your business. Without customers, you aren’t able to meet any of your business goals. Without a solid customer experience, you will have a hard time attracting and keeping new business.
How do we even start to tackle the customer experience and understand what our audience wants? One piece at a time. During this series, we’ll explore each step of the customer journey and how you can use predictive analytics to create more effective marketing plans for your customer experience.
Read the previous posts here:
- Planning: https://www.trustinsights.ai/blog/2018/10/powering-customer-experience-with-predictive-analytics-planning/
- Awareness: https://www.trustinsights.ai/blog/2018/10/powering-customer-experience-with-predictive-analytics-awareness/
- Consideration: https://www.trustinsights.ai/blog/2018/10/powering-customer-experience-with-predictive-analytics-consideration/
- Evaluation: https://www.trustinsights.ai/blog/2018/11/powering-customer-experience-with-predictive-analytics-evaluation/
- Purchase: https://www.trustinsights.ai/blog/2018/11/powering-customer-experience-with-predictive-analytics-purchase/
You have steady, predictable revenue coming in. Your customers will have expectations about service and support. Let’s dig into the ownership phase of the owner’s journey.
Customer Support Data
Depending on what kind of product or service you sell, you likely have a customer support system. This might be a ticketing system where a customer can report bugs or issues, or maybe you have a call center where a customer can call and talk to someone when they have questions or want to renew or upgrade their product.
The number of customer support calls your team gets may have a pattern or seasonality. You can assume that some of the spikes in volume will be tied to product releases and major commercial holidays. When you don’t know the anecdotal patterns, you’ll want to get a sense of when your phones will be ringing off the hook. When the phones will be non-stop you’ll want to make sure that you have proper staff coverage. When you’re looking at a lull in call volume you can use that time to make sure staff is trained up on the latest product features, policies, and procedures.
Another critical data point to look at is your ticketing system – this is where a customer can report issues. Using the predictive algorithm and your time-stamped ticketing data you can predict down to the 5-minute interval to find out what time of day people are contacting you the most. Maybe all of your tickets are coming in overnight but you don’t have staff looking or responding until the morning. This might be a good business case for a chatbot or other automated response system which will allow you to gather more data and keep your customers engaged. This might also be an indication that you need to stagger your support staff by time zones to make sure all of your customers have access to what they need.
As we move through the owner’s journey, the next phase we’ll explore is loyalty where we switch from time series analysis to driver analysis, determining the most important aspects of long-term, high-value customers.
You might also enjoy:
Get unique data, analysis, and perspectives on analytics, insights, machine learning, marketing, and AI in the weekly Trust Insights newsletter, Data in the Headlights. Subscribe now for free; new issues every Wednesday!
Want to learn more about data, analytics, and insights? Subscribe to In-Ear Insights, the Trust Insights podcast, with new 10-minute or less episodes every week.