Downloads
Revenue signals go way beyond renewal and churn KPIs. In order to maximize potential revenue, you need to get under the hood of your subscription product or service to understand revenue-related data.
These “revenue signals” are in your billing, product, and customer data and they show where your subscription business is at risk for lower revenue or where you losing potential revenue opportunities.
In this on-demand seminar, you will learn what to look at in your own data and analytics, what learnings you can glean from each revenue signal, what actions your subscription business should think about taking, and most importantly, the revenue impact of acting or not acting on each signal.
John Roney, leading expert on leveraging data to support decision-making for recurring revenue, will outline the seven data signals that every subscription and membership business should master to maximize potential revenue.
John will help attendees understand what data to look at to understand their own data and analytics. He will also educate attendees 1) what learnings they can glean from each revenue signal, 2) what actions a subscription business should think about taking, and 3) most importantly, the revenue impact of acting or not acting on each signal.
John Roney is president at Advanced Insight, a leading provider of audience insight tools for B2B and SaaS companies. Over the course of his career, John has been awarded for his innovative approach to implementing business solutions ranging from CRM, workflow, editorial systems and web technology. At Advanced Insight, John has worked with clients to super charge their use of decision making analytics through search and big data tools. Mr. Roney has over 30 years of experience working with organizations to effectively manage their retention activities. From working with directory and advertising businesses to assisting LexisNexis manage its subscription business, John has developed a thorough and effective approach to sales and renewal management. As an operational executive, John helped transform LexisNexis’ Martindale-Hubbell business from being solely a print publisher to a leading on-line provider of on-line marketing services as well as building systems and processes to manage all the new subscription offerings. John holds a BS in Accounting from The Pennsylvania State University.
Kathy: Hello, everybody. Welcome to Subscription Revenue Data Signals: How to Use Data to Understand Subscription Revenue Risk and Opportunity. I would like to welcome my guest today, John Roney. He is the president of Advanced Insight. Hi, John.
John: Hi, Kathy, how you doing today?
Kathy: I am doing great and I am thrilled, everybody’s getting online. We’ve got a great showing today. Before we get started, I just want to let everybody understand how knowledgeable John is on understanding data renewal analytics and everything we’re about to learn today. He has over 30 years of experience working with organizations to effectively manage their retention activity from working with directory and advertising businesses to assisting LexisNexis manage its subscription business from sales and renewal management. As an operational executive, John helped transform LexisNexis Martindale-Hubbell business from being solely a print publisher to a leading online publisher of online marketing services, and building systems and processes to manage all of the new subscription offerings.
Over the course of his career, John has been awarded for his innovative approach to implementing business solutions, ranging from CRM workflow, editorial systems and web technology. That really leads us to where he is now, as president of Advanced Insight, which is a provider of audience insight tools for business to business and staff companies, where he works with his clients to supercharge their use of decision making analytics through search and big data tools.
John: Okay, thank you, Kathy. As Kathy mentioned, I run a company called Advanced Insight. One of the products that we offer, she mentioned at the end there was Audience Insight 360, and that’s a product that we use to combine all of those different data sets they may be looking to combine. This is not a sales pitch for that product, though. What we’re going to be talking about are some data sets that you can pull information from, and manually bring together. I want to explain the actual data framework, and then get into the seven different signals. The first one I’m going to cover is, how can you use data to power these revenue signals? I have seven of these signals today, and some of them have two insights that come from the signal.
How to capture and expose the data. We’ll be talking a little bit about how to capture audience data in a practical way and in an inexpensive way, and then we get into the seven signals, which include low customer engagement. If you’re a B2B media publisher, or someone that has sold a subscription to a SaaS offering and you’re not getting the engagement that you want, how do you use information about what the audience is doing to ensure that you can make sure you get the renewal at renewal time? Low product utilization, in a similar vein, those are two areas of risk to revenue. If we look at the next two, missed up-sell opportunities and missed cross-sell opportunities, those are more opportunities in terms of finding ways to make incremental revenue.
A lot of times, these signals are actually right in front of us in different departments, and sometimes don’t make it over to the right areas to work through the sales teams and the marketing teams. One of the keys of any subscription business is contract based and auto-renewals. We’ll cover how to manage through some of the declines in those, and some ideas and actions that I’ve seen that work to turn that around and make sure that you can not only manage the decline, but that you can also do up-selling. Missed opportunities is always an issue for subscription businesses. One of the things that we always get hung up on is looking at our customers through a lens of who they are, what industry they’re in, or what types of roles those individuals play. This is a different way to look at it in terms of what they’re actually doing, so they’re giving you their signals in terms of what you’re interested in, and how do we take advantage of that.
In addition, many of the people on the call have advertisers and sponsors, so how do we manage through the low sponsor and advertising sales close rates so that we can make sure that those stay study? Then the last piece I’ll cover is where to start. How do we start pulling these things together, and make them actionable? Then we’ll start with the perspective on where all the data comes from to make a successful program. We call it a 360 degree view of the customers. I’ve been doing this a long time. At LexisNexis Martindale-Hubbell, we actually had all of the interactions with the customers in terms of what they were doing on the website, what they were doing in terms of interactions with our products.
That’s really audience behavior. We want to understand that, and as I just mentioned, what we want to look at is marrying that, and what they’re actually doing. They’re already telling us their interests. If you’re a subscription company that wants to have trial users, someone downloads a trial, someone downloads a webinar, these are all signals. We’ll get into those specific examples as we go, but marrying that audience behavior with customer and prospect data, which comes from our ERP, CRM or marketing systems, really puts it on another level because now we know who they are, and we know what they’re interested in. The third leg on the stool, if you will, is the customer interactions. There’s interactions with customer service. There’s interactions with sales calls. Emails are going back and forth.
In some cases, we ask a customer how they rate customer service? Or how do they rate the products? All of these different things, and even webinars like this. How do we figure out from these webinars, and how many webinars, and what topics of the webinars people download to get a sense of what they’re interested in. Integrating all of this data together with the customer interests. As I said, the customer interactions, which again are emails, marketing automation type functions, surveys, calls to customer service. Marrying that all together with the customer profiles and the product purchases. Now, this is really a key success factor for enterprise type solutions, and we have customers that are in that space, but in many cases we have customers that have just started with one of these sets or actually done manual matching between these, just to get the insights and the revenue signals that we’ll get into today.
All right, so how do we capture and integrate the data? At first, I was going to leave this slide to the end, but I thought I’d cover some of the ideas. I mean the first piece, which many properties are not doing quite a good enough job on, is capturing first-party data. Capturing that information, and what their interactions are with us, and then matching that to establish the 360 degree view of the customers and prospects. Then, this is sort of some ideas for implementation. All of us use Google Analytics. Not very many use their event tracking model, and what the event tracking model does is works through this program. We actually get a sense of what the user is doing, and how they’re interacting with it. For example, let’s say that we have a specific function that someone is working on, or not working on in the event tracking model, which pretty much sets up as a category action and a label.
What that means is that in a certain category, an example that Google uses, which I think is an effective one, is that someone looked at a video. Now we know the video, that that was the main section of the site that they were working on. An action would be, did they download it, or did they view it? The label would be what they actually looked at. When you implement something like this Google Analytics event tracking, it makes it fairly easy to get reporting because you can look at, “Okay, what’s the activity on a specific video, or a specific article?” Did they download it, or did they just view it?
Lastly, we have the major category, which we can look at on major functions. Bringing this together in terms of audience analytics is very critical, and very important. What we’ve done for customers is put in their user IDs, in terms of the number. You can’t put in people’s names in the free version of Google Analytics, but what you can do is you can put a user number in, which you can then match to later. There’s other tools that do similar things. PIWIK is an open source product that stores the data locally, or in your private cloud. Also has event tracking involved in that program. Webtrends, Woopra, and then some of the more expensive ones, the marketing cloud solutions and data management platforms like Adobe, IBM or Oracle.
Once you make a commitment into those, it’s a significantly higher level of commitment and engagement, but with that comes a reward, as well. Then there’s some new things coming out. These are three examples. Lytics is a customer data platform where they bring all the information together, as I showed in that previous chart with the 360 degree view. One Count is another one that does it, and our Insight360 product for audience does it as well. Matching is always a challenge, but as I mentioned, in some cases you can just do a manual matching between different data sets to gain insights for the sales and marketing teams. Automated matching obviously goes a little bit further, and then having these types of applications like a marketing cloud or a customer data platform for pulling multidimensional analysis and segmentation.
As you’ll see as I go through here, I’ll be speaking about how to use segmentation to understand different sets of users, where they are on the customer journey, and how you can pull that together to an effective list for either sales or marketing. All right, the first revenue signal is low customer engagement. This is an actual chart for one of our customers. The names have been changed to protect the innocent. Libero is a pretend company name, but this is an actual customer of ours that came and saw this trend. They had one user that was relatively engaged, their usage was going down. Everyone else was pretty low, and there was a complete drop-off by one of their users. This is the format in which I’m going to go through all of these. The revenue signals, the low usage. The subscriber usage is very low, or more important, dropping.
What do we do about that? What you can proactively discover is, hey, what’s going on? In some cases, people left the company. That’s one of the things that can happen, where someone is a pretty active user, and they just drop off. What you can do then is you can obviously reach out to the company and make sure that that’s what occurred, and someone else can use the seat. The advantage of that is that you’re managing the renewal process much earlier, rather than waiting to the end and dropping a renewal notice on the customer and they say, “We just couldn’t get the value.” In other cases, it’s really training. Some people may not know how to use functions, or one thing frustrates them and they never go back to it. You can very clearly manage that into a success story.
Then, in yet other cases, the value proposition really isn’t enforced, or isn’t real clear to the customer. You can reinforce that value proposition. You can offer to do one-on-one sessions with any of these users. It makes it very effective in terms of saving those renewals. This is a quote from one of our customers that these insights are invaluable for site license renewals. The one thing that we do, and other products, I think do, but it’s been very successful for us is we have an alerts function. That alerts function will actually show when things drop off, so you don’t have to necessarily check it every day. You can actually set an alert which will tell you when, like in this particular example when Solomon Powers fell off.
Another thing that people are not really doing a lot of is really looking at satisfaction scores. There’s a lot of companies that do customer service sat scores, but then overall product scores. It’s amazing, the people that are in product and everything are also looking at these, but sometimes these particular insights and the fact that there’s the satisfaction don’t really flow back into the revenue process. I like to call these leading indicators. The customer is showing signs of dissatisfaction. The survey results, for example, are not good in any way, or there’s been multiple calls to support, which is really in your CRM system. Once it hits a certain level, that needs to go back to the account executives, who can then action on that. What they can do, obviously, is reach out to the customer, and determine the reason for that leading indicator.
Okay, what were your issues with the product? Why did you call support so often? In some cases, just analyzing why support is being called so often. The end result is that the revenue is saved long before the contract end date. As I mentioned before, not just sending out a renewal notice and getting a cancellation, you’re proactively managing it. This is a continuation of that example that I gave you, where you can see in the first quarter, you might see the chart thing is much bigger. We have three users where the hockey stick is going in the right direction. We just have one user that’s starting to come along, Jonas Morrow, and yet another who is still flat, so there’s more work to be done. But clearly, a postmortem on this one is a very good pickup.
Kathy: Hey, John?
John: Yeah.
Kathy: I’m listening to you, and I can imagine that some of the people who are on the call are subscription companies with higher volume than perhaps the examples that you’re showing, who setting up an alert or monitoring, how are you seeing people taking action? Are they automating some of the action, or is it much more of a manual type of process in terms of flagging a segment of users that might be dissatisfied, or flagging a set of users that have called customer service 10X more times? How are you seeing people managing the action part?
John: Well, it’s definitely automated in many cases. In this case, I’m giving a pretty simple example, just to illustrate it, but in many cases you can have hundreds or thousands of people that are not responding the way that you want. You can push those off into whatever systems, whether it’s your marketing automation system to send emails, push the action over into Sales Force, for example. Those are, primarily at scale, pretty easy to automate in either of those cases. I will say also that just even trying to determine the signal can be difficult at scale. What a lot of our customers will do is say, “Okay, we have a thousand users which are on their subscription license, and we just want to know which users are going down so that you’re not doing a manual review of all of these charts every day.”
Kathy: Great.
John: Okay, the second signal relates a little bit more to actual product pieces, specific product pieces. Here’s a low product utilization. Usage is going in the wrong direction. In this particular case, this is a product that really seemed to get a lot of activity, and then seemed to drop off. This can be for one company, one user, or in aggregate, so a lot of our customers use the tools to manage through the product. In this case, something clearly needs to be done, because usage is going in the wrong direction. What people do is proactively discover what happened. Either the function wasn’t working properly, or it’s something that was not noticed at first, and now you’re getting that customer feedback. The customer is finding alternatives out there, so there might be a pricing issue, or a value proposition issue. And of course, the revenue result we’re looking for is to save that renewal revenue.
Another, this is sort of the flip side of that, and I wanted to cover it because a couple of my customers have really used this effectively. Guess what? The customers are really getting value, and we want to find the people that are engaged, which customers are engaged. We want to go to them and figure out what the value proposition is, what value are they getting? Instead of just looking at this as a potential loss of revenue, I’m going to look at it as an opportunity. What some of my customers have done is proactively set up focus groups to bring different individuals together, and I just showed a list of 10 users there, people that are very engaged. One is incredibly engaged, almost five times more usage than the next potential user.
Improve the messaging. How do we get that information that we’re hearing from these particular people that are getting value? Too many times over the years, I’ve seen people that are in product that are not listening and taking the feedback from the customers as clearly as they should be. Then email the thinking, and that type of marketing out to customers and prospects. There’s the improved value proposition, and this is my favorite quote. “Our customers sold us on the value,” and then they were able to push that back out into the marketplace and gain additional seats and utilization. Here’s the result of that. You can see that actions were taken here, and then that the product usage started to tick back up. A lot of work to be done yet, but definitely positive.
Okay, missing up-sell opportunities. That is basically, we have loyalty already. How do we take advantage of it? Usage is showing strong interest with the existing user base. Let’s reach out to these customers and up-sell additional seats. What this particular chart shows is that we have three, or it could be 300 different users that are all getting value. Clearly, there’s probably pent up demand at that subscriber, where you can sell additional seats. In this case, it’s incremental revenue. Okay, here is a missed cross-sell opportunity. This one comes up a lot with our publishers, and functions in SaaS companies that will find that some of their users are very engaged in their generic product, and they can see what topics that they’re interested in, and in other cases they’re not. They’re interested in just one.
What we can see here is the usages in topical categories are higher than average. An example here would be technology news and productivity articles are something that’s clearly of interest to this person in an individual basis. What you can do is, you can reach out to that person directly, or again, find people that are in these different categories and create automated mail lists or feeds into your Salesforce or whatever sales automation tool that you’re using, and reach out to them in one way or another, and sell into these adjacent products. This has been a real home run for a lot of our customers. What we’re finding is that additional product revenue is really flowing through. I can see one of the questions that came through that I wanted to address now is, how do you score low product utilization?
The way we’ve done it is help our customers, and I thought it was topical to bring this on now, is our customers will see averages across all their different users. So you can do some statistical analysis, or you can just use your gut that users are not really engaged as much as you’d like. It’s really a one-off, and each individual case is different, but by using averages and seeing what the typical usage is, generally gives you a sense of where everyone else should be, and how you can measure low, versus high, versus average. All right, let’s see. The decline in contract-based auto renewal revenue. This is a situation where we’re all facing this, all the time, and the first chart I showed here is the burn down rate of renewals by quarter.
You can see, the next quarter’s coming up, and we’re still only probably about 60% cleared on those renewals, so these should be coming out of your Salesforce, or other systems to let you know that you’re not closing things fast enough for the calendar month remaining. I’ve been around sales teams a long time, so some of the learnings that I’ve had is that you need very clear renewal channel plans. The year starts off, and everyone’s happy, but we’re just really not a clear plan. What my teams, and what the businesses I’ve worked for have tended to do is find the least cost method for the early renewals, and then define the timing and be flexible as you go forward.
For example, if a salesperson is trying to make a sale near the end of the period, clearly those will have to be reassigned through territorial reassignment back to the tele-sales person that can actually get to them. Then review the team analytics. Make sure these are all front and center, and shared across the organization. Defining these channels, keeping very detailed burn down charts, utilizing all the sales channels, as I mentioned, to close the gaps, and fallout approach for the late commitments. Obviously the result of that is retained revenue, which is really critical, and everybody on the call I’m sure has heard this 10 times, at least, but it’s much, much harder to get a new customer than it is to retain an existing one, so managing this process is really important.
All right, the next one is the missed registrations. We’ve all been in this boat. User stops short of registering at checkout. I think it’s somewhere in the neighborhood of 68%, on average, that people put things in a cart and then change their mind. This is a real, major issue. I think that’s the industry average. So, the users are stopping short of actual registering and/or paying. The email campaign to come back to these users, there’s a lot of tools out there that do that as part of e-commerce systems and others, but just the format of writing the emails is really critical as well. The other thing you can do, and we’re doing some things now with predictive or prescriptive analytics. We try and help you figure out the discount that’s required, because we can see the discounts that have worked in the past.
But a lot of people are just going back and saying to the customer, “We noticed that you did not register. Please come in and do so, or buy our product.” Sometimes an incentive, or at least an incentive on the second or third attempt is really important. Then, from a product perspective, determine where these users are dropping off in the site flow and fix that pattern. So, gaining these types of insights of how the person landed in that particular spot versus others that potentially did, and let’s say close that, there’s only a 30 or 40% drop-off. So, trying to find a way to make the product and the process of ordering more effective, and incremental revenue is the result there.
This one I’d like to cover a few minutes on, because it’s more emerging. This goes to what we had spoken about before, is looking at the activity and how the customer or prospect is interacting with us. I put it under missed registrations, because we were all trying to find new names and new people to sell to. In capturing the interest in the customer journey, what type of sales terms are they looking at? Which articles did they read? Which topics were those articles on? In many cases, for software as a service companies, which webinars did they look at? Which white papers did they go after, and did they do any trials or downloads? You can see from the graphic there, at first, in marketing, we’re all trying to go through brand awareness, and then converting them in this flow as we go through.
You can see, we can send out different messages to different individuals based on their interests, and where they are on the customer journey. This is a way of creating focused segmentation. That’s one of the interesting terms that’s being thrown around now, is account based marketing, to take individuals and put them into much more micro segments, or hyper focused segments, to make sure that the messaging that goes back to them really is focused. That can be in the form of content. It can be in terms of forms of offers, but you have a lot of information as to what people are looking out, and what topic, so let’s tailor and target those messages. Targeting the content, as I mentioned, to those particular segments is critical. Clearly you can get incremental revenue here by converting these prospects into real customers, and the game goes from there. Once you’ve acquired them, now you need to grow and retain them. Either through newsletters, or any other social information, through social media or promotions.
Another one to look at here, this is slightly different. Number seven relates more to advertising, so if you’re a B2B media customer or company and you’re selling advertising, this is an area where you can really gain insight into which people are really looking at the ads, and in many cases, a lot of the companies I’ve worked for, really this is sort of a blind spot where they don’t understand the audience well enough to capture these demographic profiles and find out what they’re viewing on, and which advertising they’re clicking through. So, what are some of the actions that we can do to work through this? Improve the value that users are engaged with, the advertising content.
You can see from this chart over here, this is an area where we’re explaining to the advertiser that these are the types of companies that click through your advertising, and you can view it as impressions as well. What’s the revenue of those particular customers? You get a clear demographic sense here of who’s looking at the advertising, and who’s clicking through. This has been a major pick-up for a lot of my customers, and in some cases, they’ll bring ad server information over. We just implemented a customer that uses the Facebook API, which we are able to bring over the fact that that user viewed something, and we marry that up to our demographic information and report this back out to the advertiser. Saved and incremental revenue, both are key benefits of this particular data signal.
One of our customers even was able to prove the value to their specific customers for their directory listings, and the quote was that they got their investments back in two months. Some of that is pent up, clearly, where the user wasn’t getting any information, and they start getting information and it really starts to pay off. All right, the other thing you can do, and I just put a little slide here because this is a really crude user interface we’re using with some of our customers to explain how you can actually play out the right advertising and content for specific users, and therefore drawing in more of the demographic that that advertiser wants. Clearly, if you were getting heavy traction, and you seem to be getting a lot more click-through rates in terms of a percentage basis on professional services companies, then that’s really where you should push those ads out in front of those particular individuals.
The example I have on the left here talks about individuals in the company sector, and here are the articles they are reading, or the topics that they’re reading, and then we find users with the same profile and recommend different content, or different advertising. So, capturing that first person content, and using it to determine the relevant ads to show the user. Doing that through APIs, now there are other recommendation engines out there. This is not a novel concept, but I see a lot of B2B media companies not really doing it very effectively. Then, this generally comes through with a significant improvement in click-through rates, obviously because you’re targeting the particular segments and the particular individuals that are going to be most interested in it.
It’s both on a demographic basis that you can do it, and then you can also do it on a topical basis. Not to mention that you’re going to have an improved user experience, which in itself, is going to be incremental revenue on the subscription side. Okay, what I’d like to cover next is how to get started. There’s all different types of ways to get going, and in some cases, to get some successes, and then eventually get to scale, as a couple of people were asking earlier. The audience usage really helps enable revenue signal one, two, three and four. That was some time ago, so that would be managing to the low customer engagement, low product utilization, missed up-sell and cross-sell opportunities.
The burn down rate chart, coming from both your CRM system and ERP can primarily be handled by those two systems, and the information and analytics that can come out of those. In some cases, customers will not be happy with the package that’s bolted on to their CRM or their ERP so they can bring those forward into some type of BI tool, like a Tableau. We have a lot of customers who bring this kind of information into our tool, because it’s a lot easier to slice and dice than some other packages. In terms of missed registration and customer journey, a lot of the Google Analytics or other tools which are managing your audience really come into play here. I guess the point I’m trying to make is instead of this looking like a blot out the sun 360 degree view that most people, when they start discussing this, tell me that sounds very expensive, it can be very effective to just get started working inside those particular, in those particular repositories as well.
Then the low sponsor or ad close rate. Obviously you can get some analytics from your ad server. One way you can do that through an ad server is, as you’re populating the ads, you can put different information in. So if you’re using DoubleClick, and you’re pushing information into DoubleClick in terms of what type of advertising it is, what topic it is, and do work through that particular system, you can accomplish that. Then you can import the ad server data, if you want, into the overall audience database, or audience section of the database. Then marching towards what seems to be coming on pretty strong is a customer data platform which has all these pieces integrated, as I started with, or making the full commitment to that and stepping through it so it’s not really a blot out the sun type exercise.
Okay, that’s the formal piece of the presentation that I’ve put together. I hope you enjoyed it, and I think I’ll turn it back to Kathy now.
Kathy: Thank you, John. I want to thank everybody. We’ve been getting some great questions during your presentation, and I thank everybody for all those questions. If you have any other ones, start putting them into your chat box now, and we will get to them.
Here’s a question. Can somebody get started with all this analysis from, whether it’s Google Analytics, or pooling from your CRM, can you use Excel? Can you use a simple tool other than what is perceived to be an expensive data analysis tool? I’m just curious what your thoughts are on getting started.
John: Yeah, I think that’s a great idea. I mean, all of the packages have great export options. Even some customers that eventually have come on with our product in terms of customer data platform started with the Google Analytics, and then just downloaded that into Excel to do the filtering, and the charting, and everything. I think that’s a great place to start, and in some cases it’s preferred because you’re getting your hands on the data. The one thing I found over the years with data is that the more you explore with it, the more you learn, and you’re always hungry for more, so you’re defining the answers that you want as you go through that process in Excel. Absolutely.
It gets a little more complicated when you have audience data, and you’re trying to marry that up with data that’s demographics, but there’s other ways to push that forward in that as you’re cutting an audience record, if you know who the user is, you can put those demographics down into each log record. Which skews the counts a little bit for unique and distinct customers, but it gives you a good sense of usage within different demographics.
Kathy: That’s a great tip. I have a question here which is really a request to speak a little bit more regarding the value and use of Google Analytics event tracking.
John: Okay. I think the best way to answer that is to really talk about Google Analytics in general. I mean, you can put in some custom dimensions, but what you don’t really get is an easy way to do the analytics in the tool itself. I mentioned the category action label piece of it. Maybe the best way to do it is to just talk about what one of my customers did, and the evolution of where they got. They had Google Analytics, they knew where people were coming from, but they didn’t quite have clear ideas of what sections of the site people were using. They didn’t have clear ideas of different actions that different users would take. In their particular case, people could look at a video, or they could download the video, so it was a matter of getting information on that.
At the high end, Google Analytics is more of managing your pages and behavior. You can do some of this, though, I will say, with some custom dimensions, but what they were able to do then was really drill into which sections of the site were being used, which ones were not. Which users were focusing on them. In that case, we use customer dimensions to show them what type of member was looking at it. In their case, they had different buyers or other media companies as customers, and they were able to report very quickly, in aggregate through Google Analytics, which particular users were getting value, and which ones were not. So, it puts it on steroids, and I would be remiss if I didn’t mention that Google Analytics 360 goes yet a step further. That’s a significant investment, but from what I’m hearing, people are getting a lot of value out of it, which is very similar to my 360 degree view with advertising, and marketing, and all those pieces including audience put together.
It’s sort of an evolution in just using basic analytics to do page views. It’s actually really understanding how people are interacting with you. Another example on the customer journey would be to really drill in on webinar usage and topics of webinar. That is much easier to accomplish with event tracking than it is in base Google Analytics.
Kathy: I have another question on skill sets. What expertise do I need in my company to really do all this analysis? Can I use a financial analyst, a data analyst? What do I need to be looking at really make sure I can set this up, and look at this correctly?
John: That’s a little difficult to say, because it ranges all the way from financial analysis through to the data scientists putting together regression models to figure out, if I do action A or B, what will be the result? There’s all types, the whole data science thing has really come on in using audience data to put together prescriptive actions we should be taking. I think just a strong person who understands the business, and understands what they’re viewing. You could either be an account executive, or, not a salesperson, but someone that owns the product to be able to decipher it. I mean, it’s great if you can afford a data analyst, but like I said, it runs the gamut.
Some of the larger media and SaaS companies have data scientists who are figuring out, what’s the predictive nature? What are we going to sell, if nothing else changes next month? And all the way to prescriptive, to say, “What should I be doing to get the best return?” It’s a little hard to answer, but I think you can actually get there pretty quickly with just a solid business owner that understands Excel.
Kathy: Here’s another question, John. What are the costs of some of the audience capture tools? You did mention Google Analytics, and some of the other Google tools. The second part of the question is, how easy are they to integrate with my CMS system?
John: That’s great. I didn’t really cover cost too much through this, but Google Analytics is free up to 15 million page views a month, so that gives a lot of us a little breathing room in terms of how we can use it. I mean, some of the setup can be a challenge, but one of the advantages of GA, and even the event tracking model is that they have modules, or plug-ins, I should say, for a lot of the CMS tools. Whether you’re using a commercial CMS tool, or whether you’re using one like WordPress, or Joomla or Drupal or something like that. They all have plug-ins to do the category action label event tracking. That’s relatively low cost, and it goes up from there.
I think our product is relatively low priced, and I know some of the other customer data platforms, clearly being a customer of Adobe, I know Adobe’s prices are pretty high, but the good news is that you can actually use a very effective tool like Google Analytics to really get started at low cost.
Kathy: It runs the gamut based on, honestly, what you need and what your drivers are. I have a question from an individual, first of all a compliment, great presentation. This individual has a simple, subscription-based newsletter that actually was just acquired, and they email their information weekly to their subscribers. The question is, what is the best way to see which articles are read and opened in the newsletter? Is it through Google Analytics, and tapping into some other revenue signal sources, I guess is what the question is.
John: It depends a little bit on the package that you have. I mean clearly, open rates and those things you can get through your mail provider, but the actual articles themselves, as long as there’s tagging that will push that information to Google Analytics, that will definitely work. We have a program, and many other of the providers out there have open APIs and services where you can just push the data, and then it gets aggregated automatically through a lot of our processing, and then right into our reporting tools. But on the low end, Google Analytics is a great way to do that, but it depends a little bit on what your newsletter program can and can’t do.
Kathy: Here’s a question about segmentation. Can you talk a little bit more about how you create segments based on a customer journey?
John: Sure. In many cases, what you’re looking for there is an aggregated view. For example, I’ll explain a little bit about how our tool works. You can do some of this in Excel, but the way our tool works is, since it’s multidimensional analysis, you can say, “All right, I want to find all the people that have looked at this webinar.” Then you can say, “Okay, from there I want to see, of those individuals,” so I select those individuals to look and see what is the next step that they took, or any other webinars, or have they downloaded any whitepapers. So, you’re just stepping through, getting a tighter and tighter mix of people with a reasonable number of, you don’t want to always get it down to a hyper focused group of one, but you want to get it down to, say, 50 individuals that are classified in a certain area.
Anyway, that’s the way you have to do it. I mean, you can do these things in Excel, as well. You’re just taking various lists, and then comparing the lists, and finding people that have looked in either different topical areas of webinars versus people that have responded to an email, people that are reading certain articles in the newsletter. I mean, it’s really, a lot of these tools now are so strong at doing detailed segmentation. It’s really quite exciting. I think I might have mentioned it briefly before, but the account based marketing is what really takes this to the next level. Now you know, and you can see, because you really don’t know what people are doing until you bring these segments together on the customer journey. These set of people have gone through the set, and then you go through and on that particular set you really tailor the content and the messaging.
Kathy: Great. Now, we are going to be just taking a few more questions. If you have them, get them in. I do have a question here about leveraging API. For example, Facebook API, can you talk more about how you could possibly use that within the learnings and the modeling?
John: Yeah, for sure. I have a customer who does quite a bit of advertising for their advertisers inside of Facebook. They have native advertising on their particular B2B media property, and they also have ads that are running on Facebook that they’re playing out for those particular advertisers. What they do, then, at the end of the month, this is their schedule. At the end of the month, they read through the API and they find all the different users and everything they can find out about that user in terms of how many saw it through an impression, and then how many clicked through it. So, bringing that data together with your information on your native advertising really gives you a complete set of value. Then a lot of the tools will give you high level roll-ups. Okay, these are how many click-throughs you had in our native site. This is how many you saw on Facebook, etc.
Kathy: That’s good. If that answered your question, let us know. Then let’s see. I think I have another question on account based marketing, but I think you answered that. Then I’m just going to ask this one last one. Can you say more about the minimum amount of data and technology to get started, and to really get something valuable from this process?
John: Let’s see. Well, one way, it depends on some skill in your development staff, but if the development staff can just tag certain things, even before you get to the event tracking, which I think really adds exponential value, but you could put custom variables into each tag that’s already going to Google Analytics, so that you can do roll-up reporting on that, and just a decent understanding of Excel. That’s the minimum minimum, I think, but being able to do something like event tracking or load up something in PIWIK, which is sort of another step, but then you own the data, and you can push it in. So, the logging is the one piece, and the analysis, Excel would be the two minimums, I would say.
Kathy: That’s great. If you have any other questions, obviously let us know. For everybody attending today, thank you. We know that taking an hour of your day is a big commitment. John, thank you for your insight on this important topic. Do you have any last remarks or guidance for people attending today?
John: No, but beings that I’m a crazy person when it comes to data, best of luck and let us know if we can help.
Kathy: Great. So everybody knows, we are going to leave the chat window open if you have any other questions, and we will follow up on any open questions we didn’t get to, and any new questions that you submit now. We will follow up with you directly on them. So there you have it. Have a great day everyone, and thank you.
