Web Analytics and the Call Center


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I’m feeling a bit schizophrenic with my blog these days, bouncing between posts on digital database marketing, big data analytics, survey research (all our traditional milieu) and then back to Social Media Measurement. We’ve done so much interesting work in Social Media measurement recently and it’s a topic that I’ve hardly yet touched – so I feel compelled to continue that series. And yet, I hesitate to pull away from our main practice themes for any extended period of time. If you’re interested in the same range of topics that we at Semphonic are, it’s probably not much of a problem. If you’re interests are concentrated inside (or outside) of Social Media, then all this hopping around might be a bit annoying. I’m going to see if we can’t work out a solution – perhaps separate feeds by topic – since I fully expect to continue both themes.

Which is a long way around to saying that my topic today is a look at a classic digital measurement problem. Some of the most interesting work that we’ve done at Semphonic in the last few years has involved the integration of Call-Center and Web data for a couple of our largest clients. We rarely get a chance to talk about that work. It’s a shame that we generally aren’t free to say much about it, because it’s fascinating stuff involving warehouse integration, true customer-level analysis, and massive cost-savings – work that’s produced some of the largest ROIs that we’ve seen in our more than a decade of practice.

In this blog, I’m to show some sanitized data pulls that are representative of what we usually see in this type of analysis and explain some of the basic techniques and approaches. I’m going to draw from work for several different clients but all share a fairly similar structure – these are operational (non-marketing) Websites and Call-Centers. The goal here is not to sell product but to service customers and solve problems in the most efficient manner possible.

In this situation, the goal is generally call-avoidance. It’s much cheaper to service a customer via the Web than by call. A call for these clients will probably cost somewhere between $4-$7 to service. A Website visit – even with fairly generous cost-allocations – will cost only pennies.

Savings, however, are by no means guaranteed. A Website can actually generate more calls than it shifts if it isn’t well designed. Nor is it desirable to avoid every call. Some types of calls are difficult or even impossible to service on the Website and some customers are happier or more profitable when serviced in the Call-Center.

So it’s a good to start a Call-Center project with a “waterfall” analysis like this:

Call Center Waterfall

The idea in a Waterfall chart like this is simple. You start with the total universe of “All Calls”. You subtract out calls that are not applicable to anything on the Web – in this case about 1/3 of all calls. From the rest, you remove those that are deemed to have been unavoidable (here a very small percentage of the remaining calls).

This gives you your “Total Avoidable Universe” of calls. In this example, we’ve segmented these calls by Customer Type (the Gold/Red/Blue bars). The Customer Type is often critical to the call-avoidance strategy. For really high-value customers, we may have a strong bias toward keeping the high-touch, but more expensive Call-Center contact unless the customer really wants to self-service.

The next several break-outs refine these groups into those who use the Website and those who don’t. For non-users, we have a straightforward Activation Strategy – get these customers to try Web servicing when appropriate. For Web-users, we have a more challenging problem – figuring out why these visitors didn’t self-service online.

To understand that problem, we further divide this universe using Recency measures. There is always a population of lapsed Website users – and this population presents us with what is, essentially, a re-activation challenge.

For customers who don’t fall into this category, we like to further sub-segment by problem type. Understanding how many calls dealt with difficult to solve problems can help us understand how large the “slam-dunk” call avoidance opportunity is.

In this case, about 65% of calls are, in theory avoidable. Almost 70% of these avoidable calls represent an activation challenge since they came from customers who aren’t Web users. Of the remaining 30%, a heavy majority DO come from active Web users and about 1/3 are problems that are easily solved on the Web.

A diagram like this helps management understand the scope of the opportunity and understand where the biggest wins are. Here, it would probably make sense to target activation of non-Web Users (70% of avoidable calls) and easy problems of Active Users (10% of avoidable calls). The first is the largest opportunity and the second is the easiest win.

This type of table-set is also important in situations where an organization is debating the right mix of Call-Center and Web. Some organizations have strong bias toward self-service because of the cost-savings. Not everybody feels that way. There are organizations that pride themselves on the ability of their customers to talk directly to them. Fair enough. But here’s the rub. Some customers don’t want to do that. If you’re goal is really good customer service, it’s not enough to say you have great call-centers. Lots of customers (including me) prefer to service online when it’s possible. This type of Waterfall analysis with customer segmentation can help you identify customers whose needs aren’t met appropriately – whether that’s in the Call Center or on the Website.

To do this analysis requires a join between Call-Center data and Web data. That isn’t always possible. However, where you have Websites that have logged in capabilities, you can make this link. If you have customers logging in to track order status, to solve operational problems, or manage their relationship with you, then you have the ability to create this join for at least a subset of your Website traffic.

By joining Web data to Call Center data, we’re able to create interesting analysis at the individual customer or company level. That waterfall chart isn’t just for analysis. Using those segmentations, we can target the actual customers who fall into each of these buckets. That lets us target frequent call-center users that are inactive or have never used the Website and are not in the highest value customer segment. And that’s pretty much what we do. It’s a logical first step when building a call-avoidance program and we’ve had terrific success with it.

What about that segment of Active Website users with “easy” problems who somehow still ended up calling the Website?

Here’s a look at the time-lapse between the Website visit and the call to get a sense of how long you have to react to these types of situations:

Call Center Time Lapse

Notice the absolute cliff at around 15 minutes after the last Website touch. Calls peak between 1 and 3 minutes after the last Website touch.

There are a couple of really interesting learnings in this view. First, it provides ample evidence of the relationship between the Web visit and the call. Where your (or your customer’s goal) is self-service, these sessions represent a clear failure. Second, it suggests that your window for call-avoidance is very short. You either have to fix the Website so that it solves the problem or you have to be able to react in something like real-time to send out an email or initiate a chat.

Waiting even an hour to address this problem will doom the effort. The vast majority of calls will long since have taken place.

This is just one of many cases where your time to response is critical in the online world. The need for (and role) of real-time and the demands of a real-time technology stack for decision-making are topics that I plan to tackle in more depth over the next month or so. This Website to Call-Center is a terrific illustration of the need part of the equation. In ecommerce, you rarely know how quickly a customer rolls off your Website and into another decision. But with Call-Center, the time-lapse between Web and Call is completely measurable.

A real customer-level join of Call-Center and Web data makes three analysis tracks possible. First, it allows you to separate out and individually identify customer-segments that SHOULD be self-servicing specific problems and aren’t. You can target these customer segments with activation or re-activation messaging. Second, analysis of Website to call sessions by call-type let’s you predict how impactful activations are likely to be on Call-Center volume and to calculate the actual savings from your Web effort. Finally, you can use the bridge between failed sessions and calls to identify Web usability problems. You can even target surveys at this population if the web behavioral data isn’t providing a clear picture.

You’re looking at the entire spectrum of analytics in those three efforts: from what amounts to traditional database marketing (albeit with a cost-avoidance twist) to predictive analytics to classic Website optimization.

Call-avoidance may not be the glamorous side of the Web. There isn’t a lot of scope for clever design or branding. But there are world-class returns on investment and a significant and obvious set of analysis paths that use data integrated at the customer level. To my mind, that makes it one of the best areas for an analytics department to focus on and one of the top integrations to consider when it comes to making warehouse and big data decisions in the online world.

Republished with author's permission from original post.

Gary Angel
Gary is the CEO of Digital Mortar. DM is the leading platform for in-store customer journey analytics. It provides near real-time reporting and analysis of how stores performed including full in-store funnel analysis, segmented customer journey analysis, staff evaluation and optimization, and compliance reporting. Prior to founding Digital Mortar, Gary led Ernst & Young's Digital Analytics practice. His previous company, Semphonic, was acquired by EY in 2013.


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