Using Attensity for Crisis Management
October 14, 2009 • Author:
Catherine van Zuylen
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0 comments
Tags: Brand Equity, Customer Service, Risk Management, Voice of the Customer
With the launch of our new Attensity Cloud product, I wanted to start blogging about some new capabilities that adding "real-time" data analysis can do for an organization, with the help of our partners at Radian6. One of these capabilities is in the field of Crisis Management.
One of the last things a company wants to face is a crisis - a sudden spike in negative conversations about a company or its brand. But when this happens, you not only want to know about it as soon as possible, but you want to be able to respond.
Using Attensity Cloud plus Attensity VOC, you’ll be able to find people talking about your company and the possible issue, in real-time as the discussions happen on the web discovered on the web, and then dig deeper to discover the effects the crisis might have on your company.
For example, using Attensity Cloud's Topic Trend graph, you can watch the buzz around your brand, as well as whether the tone of posts containing your brand are positive or negative. By opening a conversation cloud, you can see what might have caused the outrage or praise. In this case, you can see a peak of negative twitter content around DirecTV's decision to drop Versus:
But just knowing that people don't like a decision (or to be more precise, are using negative terms in conjunction with your company name) doesn't tell you the whole story. For deeper analysis, you can then export those posts into Attensity VOC to reveal the specifics of what people are saying. For example, consider these tweets:
1. "I hate Versus for not being on directv anymore."
2. "I HATE DIRECTV FOR BEING STUPID WITH VERSUS."
3. "I'm not mad at DirecTV anymore, because after I complained to DirecTV about them dropping Versus, they gave me a $120 credit."
4. "I'm cancelling DirecTV if they don't bring Versus back. I'm switching to AT&T."
Each expresses a different opinion and requires a different action.
While statements like #1 do not require action from DirecTV, Attensity's automatic fact extraction provides deeper analysis: How many people blame Versus as opposed to DirecTV for the standoff? (compare the fact in #1: Versus:hate vs fact in #2 DirecTV:hate)
Statement #3 expresses that someone used to be mad at DirecTV, but their attitude was changed. (I:be mad at [not]:DirecTV) Compare this against a statement like "Even though DirecTV gave me a $100 credit, I'm still very mad at them" (I:be still very mad at[more][again]:DirecTV). In both cases, the trigger is a credit event; in one it worked, in one it didn't, which warrants more follow up (either through direct contact with the customer or by doing further analysis - does a $120 credit work while a $100 credit does not, for example?)
Statement #4 is one which requires immediate action. In it is expressed an "intent to leave", as well as a switching event to a competitor. Using either Attensity Cloud or Attensity Respond, customer service can reach out to the customer to see if anything can be done to prevent the switch. Attensity Respond will automatically route these kinds of posts to the right person in the organization to respond.
So you can see how the Attensity suite can help you with the listen-analyze-respond loop in crisis management.
Got an application/need you'd like us to cover here next? Let us know in the comments below and we'll tackle it!
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