The world is seeing a resurgence in healthy and sustainable eating. In the 2014 Lempert Report, Phil Lempert predicts that adults are increasingly motivated towards improving their eating quality or as he calls it “better for you snacking”.
This dietary shift has fueled the growth of subscription-based snack provider Love with Food. Based in San Francisco, Love With Food delivers organic or all-natural snacks to your door for about $10 a month. They have been using Zopim for the past year.
Analytics Can Plan your Future
Earlier this month we spoke to Ira, Customer Happiness Manger at Love With Food, and their experience using Zopim and our newly launched Analytics dashboard.
Ira explained that her team manages all incoming support requests, including phone calls, chats and emails. During busy periods of the month Ira and her team can serve upto 60 chats a day, while simultaneously fielding phone calls and replying to emails.
With so many chats coming in, it’s important for Ira, her team, and the rest of the company to be able to predict chat load and prepare for it accordingly. In the past, they relied on intuition to figure out which days would be the busiest (e.g. during promotional periods).
However since using Analytics, Ira has been able to take some of the guesswork out of it. With Analytics, Ira can quickly pull up chat data for the past month and compare it to the month before. This helps her monitor the overall company growth and forecast future chat volume. If the trends suggest that the number of chats will be increasing, they can allocate more resources during those periods.
Chat Stats – The Heart of Analytics
The Chat Stats graph is what Ira uses to compile the meat of her report. With this graph, she can see:
- The number of chats served by her team
- The number of missed chats during the same period
- And timing trends such as wait and response times.
The number of “Chats Missed” is a particularly useful indicator of when Love with Food needs to have more agents to handle chats. This coupled with the “Wait Time” can show when customers have been made to wait excessively.
The Chat Stats graph also helps Ira spot spikes in trends or explain anomalies. These can then be used to identify any technical or support issues that need to be fixed or any revisions that need to be made for their marketing and promotional materials.
How Long is the Ideal Chat?
Ira also tracks the “Chat Duration” to see how fast they are able to respond to customers on average. This is an important metric as it’s used to determine the average issue resolution time. In the future, this can be used to benchmark the ideal length of a chat.
Finally, Love with Food uses the Chat Sources graph to identify how many of their chats were inbound (started by customers) vs. outbound (initiated by a Trigger or agent).
Although Analytics can be used for many purposes, Love with Food has found a simple use case for it – finding out how much the chats are increasing on a month to month basis. This is a simple use case, but one which most companies should start thinking about to better optimize their customer service workflows and ensure there’s always adequate staff.
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