Now this is going to be what’s called a “living document”, because this is one of those ever-changing, ever-evolving, ever-explored areas and the list of tangible benefits keeps on growing and growing. So let’s see how many more we can add?
Clara Shih in The Facebook Era, gives us a great place to start:
- Cost savings from customers responding to other customers. Count how many questions or issues the customer community addressed instead of your support staff. Automate this based on message threading and Twitter @replies to figure out who responded to whom. Then multiply this by your average cost of a support call (effectively, average call length times the average number of calls to resolution times a prorated customer support rep salary).
- Multiplier effect from each customer solution being broadcast to everyone and made searchable. Add a multiplier to account for people who would have contacted your call centre had another customer not publicly responded with a solution to a similar question earlier. This is harder to determine precisely, but you can appromximate it by the number of searches on a support community site that did not result in a question being asked (implying that the person searching found a satisfactory solution).
- Savings from an improved knowledge base from community-generated content. Solutions contributed by customer experts not only help in real time, but they also expand and improve the support knowledge base over time so that future portal or call centre cases are resolved more quickly. One way to approximate the impact of community-driven knowledge base improvements is to track how often and which cases are resolved using customer-submitted solutions. Then compare average customer satisfaction and resolution time of those cases against similar cases in which a traditional solution was used.
- Average case resolution time. Compared to phone calls and even live chat, tweets (b/c of their 140-character limit) are short and to the point, forcing customers and support reps to eliminate banter and cut to the case. Early data shows that this is drastically reducing the average time for case resolution for many support organizations that have added Twitter to their channel mix. To calculate your average case resolution time, have a team of reps go through at least a few dozen tweets (the more tweets, the more accurate your measurement) and respond to each. Divide the time they spent by the number of tweeted issues they resolved, and that is your approximate per-caseresolution time. It is interesting to compare this to your average resolution times for other support channels such as phone or email.
These are awesome, thank you Clara! Let’s build and add more…