Q4 is the most happening season for E-commerce brands for undeniable reasons. Brands prepare for the holiday or peak season for several months. Businesses certainly do not want to make the same mistakes as the previous years. Some core aspects, such as logistics, inventory management, marketing spending, and email marketing campaigns, get a lot of attention and thorough research.
Whereas the customer service readiness is often not thought through, this often leads to a burnt-out team at the end of the holiday season. The most common readiness approach is to increase the headcount and hire an interim team to manage the support load. Some probably invest in chatbots to handle the volumes, only to frustrate the end customer and leave a bad reputation.
How can customer support and service teams adopt a data-driven approach to better handle their preparation to finish the year on a high? Let’s get started. We have built a simple calculator for you to input key metrics and walk away with the forecasting plan.
1. Calculate the last six month’s Ticket to Order Ratio
Let’s kick start by understanding why the Ticket to Order Ratio is an important metric to understand the efficiency of a customer service function. In a world of social media shopping, one-click checkouts, and hyper-personalized shopping experiences, it is pretty outdated to measure the success of a customer service team purely based on metrics such as CSAT and resolution times.
Those are evergreen metrics to stay on top of your support team’s performance, it doesn’t entirely give you a holistic view of the customer experience journey you have laid out for your customers. We are heading to a reality where the best customer experience is the one with no agent interaction. This expectation shift forces brands to solve customer experience beyond agent-based conversations and ticket resolutions.
Ticket to Order is the accurate yardstick of your customer support effectiveness for modern buyers. It forces CS teams to ask, ‘How to avoid the support queries’?
The ratio varies based on a few factors, such as your target audience's demographics, the buying process's complexity, and the overall post-purchase experience you have set up for your customers.
Brands that are just starting to streamline their support process, typically brands with <1M in revenue, would see a very high Ticket to Order ratio of ¾ or 75%. As more systems and processes are in place, this number eventually comes down to about ½, which is still very steep. Support teams should aspire to get it down to 10-15%.
Calculate your Ticket to Order Ratio for the last nine months.
2. Calculate your average agent efficiency
Short-term hiring is a commonly adopted approach to handle the holiday spike. But before we resort to that solution, let’s identify if there is room to improve the productivity of the existing agents.
Agent efficiency gives you a realistic idea of how much you can expect from a live agent. Again this metric depends on some obvious factors such as the complexity of the products, seasonality, the choice of support channels you have enabled for your customers.
And there are usually overlooked reasons, such as agent enablement, ease of information gathering, the tech stack, and ease of collaboration between agents.
Calculate agent efficiency for the last nine months. If you have a large CS unit, it might make sense to break this down by channels and the nature of queries solved.
3. Simulate the expected spike in orders
The easiest way to calculate this would be to gain access to the marketing and growth plans for Q4. But since we are already too deep in building a data-driven CS org, let’s turn to last year’s metrics to simulate this year’s expected growth better.
Compare the nine months of 2022 to 2021 to calculate the growth rate in orders and revenue. And now, compare the ticket trends for the same time. If it’s linear, then you are sorted. You could just apply the growth rate your brand has been experiencing this year and use it to simulate Q4 orders and revenue. This simulation is purely indicative and not accurate; your marketing plans would be closer to reality!
Our calculator will just simplify this for you. Your time and mind space is better spent thinking about your customers :)
4. Forecast support load
You now access three key metrics :
- Ticket-to-Order Ratio
- Expected spike in orders
- Agent efficiency
You will now have a good sense of the expected ticket spike and how many agents you would need to cover. But then, before you go that route, there are two questions we encourage you to ask:
- How to avoid the ticket spike?
- How to increase the number of tickets one agent can handle?
And yes, the answers are in the following chapters..