by Lance Fried
Agent retention has long been one of the most important topic conversations for contact center managers and supervisors.
Recruiting and training expenses alone can cost an organization a fortune. Not to mention the ongoing agent morale issues that go hand in hand with high agent turnover rates.
Social agent turnover and the costs associated with high rates are caused by new fundamental channel-specific issues that traditional contact center management have to now consider. Additionally, the ROI of social engagement is being used as a driver for expanding enterprise social care initiatives as budgets continue to shift from Marketing to Care Groups– it becomes clear how pivotal social care solutions will be in driving success and meeting objectives.
The social contact center is unique in the way organizations can reduce turnover, increase retention, boost morale, and cut costs across the board.
Let us discuss one of the main challenges social engagement command centers face; SPAM
Spam Spam Spam
One of the most prominent differences between a social command center and a traditional contact center is the volume of spam agents face. Slogging through social spam to find the people who truly deserve a reply based on enterprise business rules can be a nightmare for agents and social customer service operations. In some cases, agents can be dealing with 85-90% of all content being spam, and only 10-15% actionable. This overwhelming amount of spam can lead to agent burnout, decreased morale, low productivity and ultimately agent turnover.
Imagine searching for a needle in a haystack. Ok, now imagine that it’s YOUR JOB to find as many needles in a haystack as you can all day everyday- not to mention your job performance is measured on how many needles you find. Yikes, sounds like you need one of these:
So what’s the social spam eliminating giant magnate equivalent? Well, we have found that Natural Language Processing and Business Rules Engines are the best way to identify and isolate the actionable social posts that matter the most.
NLP engines are taught to understand what types of posts are topically relevant and which are spam. Engines such as these can deliver intelligent filtering and established rule triggers that are trained to tag certain posts as spam.
For example, if a popular tweet that mentions your brand is retweeted 72 times, it is highly unlikely that any of those 72 retweeters need a response. This is where the rules engine can automatically tag retweets as spam so agents don’t need to sort through the same post 72 times.
Other examples include automatically tagging posts that are identified as news items, job postings, or any other topic that would be, from a care perspective, irrelevant or unactionable to respond to.
In addition to this first level of sorting unactionable and actionable items, it is beneficial to go even deeper, adding a second level of sorting. This second level involves a further categorization of posts so agents can filter to work on a specific topic grouping, type of consumer sentiment, or level of social and enterprise influence.
For example, a business rule can be set to prioritize all posts that are tagged as sentiment= angry, influence= high. This way, agents can respond faster to those who matter most.
There are numerous benefits to implementing a social engagement solution that has NLP and rules engines as part of the out-of-the-box offering, but reducing burnout and improving agent morale, productivity, and turnover by making agents’ jobs easier seems to be a good start to boosting agent retention.