My suggestion is to make sure you document any third party (even commercial) code and how you’re using it across your applications; and then have an attorney review the appropriate license agreements.

Speech Analytics is about figuring out what people are saying.

Can a customer interaction look like one thing to you, but feel like something else to your customer? Let’s say that your trusty ACD report shows you that a customer’s handle time was 300 seconds, and 300 seconds happens to be your Handle Time Goal. That would seem acceptable, right? But what if 200 of those seconds the customer was on hold? How would that variable impact the customers perceived experience? Or hypothetically the call was answered in the goal of 20 seconds, but that was only after spending 2 frustrating minutes in the IVR?

The heat index takes variables like humidity and wind and makes a relative, plus or minus, adjustment to the absolute temperature value to reflect how it is actually perceived by people. Sound like a reasonable approach?

If it seems like I’ve been harping on customer experience measurement lately, its because I am. Stick with me here…

The difference in customer experience between 93% Service Level and 100% Service Level is negligible. But the difference in staffing cost to us is huge.

Now, I’ll spare you my full rant about Service Level (you can find it here) but I think this is indicative of a larger perception and education problem in the call center industry. Simply put: Service Level is NOT a measure of Customer Experience. It’s an opaque metric.

Let’s assume for purposes of this argument that the service level goal is 93% calls answered within 20 seconds. By decreasing the goal from 100% to 93% you’re saying that it’s okay for 7% of your customers to sit in queue for longer than 20 seconds. Logically, and most likely what the Workforce Manager was thinking, the effect on customer experience by being answered in 19 seconds vs. 21 seconds is unnoticeable. However the real impact to Customer Experience between 93% and 100% is actually immeasurable from Service Level alone. You have no way of knowing how many of the calls in queue longer than 20 seconds were answered in 21 seconds or how many were answered in 20 minutes and 21 seconds.

Try explaining to the irate customer that listened to hold music for twenty minutes that his difference in customer experience was “negligible”.

DimDim has a free hosted offering which I’ll probably test out for a non-profit I’m working with. Although, they should really partner with somebody like http://www.freeconferencecall.com/ to integrate free voice conferencing in as well (currently they support VOIP via a headset plugged into your PC).

As compelling as free web conferencing is, hosted web collaboration is a rather commoditized market. However, there OEM/ISV approach could be hot. The ability to embed web collab right into applications (like I wanted to do w/ Business Intelligence) could create a new market segment.

Call it Pervasive Collaboration.

Take out the garbage, I am not talking about employees or customers, I am actually talking about reports and data. One of my pet peeves, and I could go on for hours but I will go on for 45 seconds, is when you walk into a call center and you see the reports that supervisors are looking at every day and the first column you see is calls answered,  and this is for an agent. Johnny had 27 calls yesterday and was logged in for 15 hours, blah, blah, bah, blah. Step back and ask yourself what value you are getting out of this information. So take your 30 column report and pare it down to four or five columns that you can actually impact and actually take action on. If you can’t impact whether an agent is logged on for six hours or seven hours, get rid of the column. Just say, you know what, what was their schedule adherence, or what was their hold %? In other words, what are the columns that you can influence? Then write out the business value for each column on the report. Are you going to see service level on there, or outbound calls? Write down why you need to see that so you can articulate that back to the people that are managing to that data every day and why it is important.

I couldn’t have said it better myself

No campaign has ever raised this much in a single month in the history of presidential primaries. But more important than the total is how we did it — more than 90% of donations were $100 or less, and more than 385,000 new donors in February pushed us past our goal of more than 1,000,000 people owning a piece of this campaign.

My first reaction was “Wow! Go Baby Go”! Then my second thought was, “Wow! That’s roughly 900,000 donors we can’t track…”

The Federal Elections Commission (FEC) regulates that a campaign has to disclose all donors contributing more than $200 to Candidate. I downloaded the most recent FEC database and found only 84K contributors to the Obama Campaign at a time when he was claiming north of 500,000k. That means the vast majority of campaign money is originating virtually anonymously.

This raises questions around current Campaign Finance laws and if they will need to be revisited or reporting limits adjusted to adapt to current trends. My sense is that inevitably they will, but whether that’s good or bad for us is its own animal.

For example, last summer I contributed a whopping $25 to the Obama campaign. Should my Name, Address and Contribution Amount have to exist in a publicly accessible database as a matter of Campaign Finance Reform? From a privacy standpoint my reaction is “Not just no, but hell no”. But as a fan of Government Transparency, shouldn’t we be able to have visibility into the money flows of these campaigns? My answer is “yes”. So then, where in lies the balance?

The gray matter that exists at the intersection of Personal Privacy and Government Transparency when you participate in “public” activity will no doubt be the subject of much debate when the dust settles on this election. My gut says the issue will be raised from which ever side looses in November.

Originally I just wanted to play around with the data, so I filtered based on the greatest band of all time, Van Halen.

Unexpectedly, a couple of things struck me about the results:

1. The top two most played songs both appear on the album 1984, but 1984 is the second most played album: Seems a bit counter intuitive at first, but upon closer inspection we discover that the album Van Halen’s songs may be played fewer times, but there are more of them. The aggregate is greater by more than 300 song plays. There is a lesson to be learned here about product distribution, and a pretty good example of Chris Anderson’s Long Tail theory in action.
2. Why the big disparity between the “David Lee Roth Van Halen” and the “Sammy Hagar Van Halen”? My assumption is that this is most likely because Van Halen is on tour right now with David Lee Roth. No doubt radio stations are heavily promoting this tour by spinning the vintage favorites. My hypothesis though is one that is impossible to prove given the current data set. One would “just need to know” that these guys are on tour to draw that conclusion. Without this information, what conclusions would you draw? Would they be accurate? Is mine accurate?

Now, an interesting exercise would be to take the chart below that displays where these songs are being played and overlay the tour schedule. Also, the data is only available from Feb 24, 2007 to current. What would a wider data set show us? Is the distribution reversed when they’re on tour with Sammy Hagar? What about when they’re not on tour?

This example demonstrates that the unlocked power of analytics is not just about spotting trends that you otherwise would not have, but its often in finding and qualifying external (and sometimes non-structured) data points and quantifying their impact. It also causes you to ask questions and seek answers that you otherwise wouldn’t: What long tails are hiding in your data? How can you leverage them? What external events influence your business? How do you qualify them, and quantify their impact?

What else do you not know you need to know?

as a private label to Citibank Credit Card holders for $49 a year

 
Needless to say I’ve changed a bit since then, and so has our beloved industry. When I started this gig, all outbound campaigns we’re manually dialed and managed from color coded labels that were stuck on “sales sheets” and “callback cards”. And things like VOIP, WFM, CRM, Business  Intelligence, Performance Management and Analytics weren’t even glimmers in our eyes yet.

Ironically enough, some things haven’t changed much. In February 1991, a Bush was in the White House, we were at War in Iraq, and the U.S. was in a recession…

On that note,  “Smile and Dial”

-Chris