Speaker 1: Welcome to the SBI Podcast, offering CEOs, sales, and marketing leaders ideas to make the number.
Greg: Good morning, good afternoon, good evening, everybody. This is Greg Alexander, CEO and co-founder of SBI, a sales and marketing consulting company focused on helping you make your number. This is the SBI weekly podcast show, and this week I have an outstanding guest, Tom Kane.
Let me tell you a little bit about Tom. Tom is the vice president of worldwide sales and service operations for OpenText, which has eight thousand employees across the globe, does about $1.7 billion in annual revenue by providing application software to businesses, governments, educational organizations, and healthcare companies. Tom leads a global team of operations leaders who assist business unit leaders in the area of go-to-market strategy and systems and processes designed to accelerate business results. He has eighteen years of business experience and has been with OpenText for the last five. He received his MBA from the Ivey Business School at Western University.
Tom, welcome to the show.
Tom: Thank you very much, Greg. Glad to be here.
Greg: Okay. I am very excited to have you on the show, and one of the reasons, there are many, but one is many of our listeners are sales ops leaders inside software companies, and you lead the sales ops team. That’s part of your responsibility inside of one of the world’s largest software companies. In fact, according to PWC’s list of Global 100 software leaders, OpenText is now the fortieth largest software company in the world. Your point of view will be highly relevant to many of our podcast subscribers. Excited to have you here.
Let’s jump into today’s topic, and today’s topic is sales analytics. This is probably, if not the most requested topic for our show, certainly in the top three to five or so. Let me set up our show today with a little bit of a framework and then I’ll unpack this framework and ask your opinion on a few of these concepts. The framework we’ll use for our discussion today is what I call the progression of sales analytics. There’s four, there’s four progressive steps up the advancement curve, if you will.
The first is what we call descriptive analytics, which describes what has happened. The second is a little bit more advanced, and it’s diagnostic analytics, which tells us why things are happening. Third and more advanced is predictive analytics, which tells us what is going to happen. Then lastly, the most difficult but by far the most useful form of analytics is prescriptive analytics, which tells us what to do. Let’s unpack each of these a little bit and get Tom’s perspective on these.
If we start at the beginning, we have this descriptive analytics, which, again, describes us what has happened. Tom, based on your years of experience and what you’re doing there at OpenText, do you use descriptive analytics today?
Tom: Yeah, of course. I think everybody uses it. It’s probably the largest amount of traditional analytics that we do and I’m sure a lot of others do as well. It’s always good to understand history so that you don’t repeat it or that you can repeat it. I think that’s pretty important and we use this. Probably the majority, about eighty percent of the stuff that we do would probably fall into that category.
Greg: Okay. Is it traditional means such as KPIs in a dashboard that might be which reps hit goal, which ones didn’t, that kind of stuff?
Tom: Yeah. Certainly, that makes up a lot of it. The KPIs are a very important part of our business, and being in the information management business, of course, it really behooves us to make sure that we have a good handle on that, and I think we do. We use a lot of the tools available to us, the normal, the Excel exports of data and then moving around of dashboards. We also use tools like Salesforce.com and so on and use our accounting systems to help us build a three hundred sixty-degree pictures of both our salesforce as well as our customer buying patterns.
Greg: Okay, very good. That’s kind of base level. I would have been surprised if you said, no, we don’t use descriptive analytics, so we’re off to a running start. Let’s move on to the second form, which is more advanced but it’s diagnostic analytics, which tells us why things have happened. If you look at all that data that you have and it describes what’s taken place, now it’s time to diagnose it and maybe figure out why it’s taking place. Have you experimented with this at all?
Tom: Yeah. Certainly, we spend a good amount of time on root cause analysis and looking for trends and patterns. We’re very keen on keeping our eye on economic trends at a macro level and a micro level, understanding what’s happening in the industry and with our competitors. Really, the most important is understanding our customers’ businesses, which can play a pretty large part, things like regulatory changes and so on that could really increase buying behaviors or decrease buying behaviors, whatever the change may be. We do a lot of that and try and target really changes from the norm in trending and then try to dig down with a combination of data analysis as well as some subjective stories from our field support team and our field service team where they’re, of course, the customer-facing people and have the deepest knowledge of what’s happening in the business.
Greg: Yeah. OpenText is a very large company with thousands of customers. You said something there that was interesting to me. You talked about a three hundred sixty-degree view of the customer, which is something that I think many the listeners here are aspiring to but haven’t quite got there yet. It’s a really hard thing to do. Tell us a little bit about that. Have you accomplished that and how did you get there?
Tom: You know what, I think we’re a good sample of probably most of the people listening today. We’d love to say that we’ve aspired to that, but, as you said, it’s an extremely hard thing to do. With the pace of change in the industry, keeping on top of that is a challenge unto itself, but the tools are very difficult. Building integrations between all of our systems to try and get a real firm understanding of where the dollars are spent and the types of buying activities is a challenge. I think we’ve done a pretty good job of that. It’s never perfect. It’s never what you want it to be. You’re always aspiring to get that one extra little piece of data that you’re searching for, the unicorn in the pile.
We have built a lot of integrated systems and we pull them all together with our dashboards and our views and really having operations from the other departments and working together with finance to build a pretty good view. Of course, our CEO being an ex-Oracle person understands the value of data and understands the value of good systems. He’s helped really drive a lot of that culture throughout the company.
Greg: One of the things I liked about your answer, too, was you discussed paying attention to what’s happening in your customers’ business not just your business. The example you used is there could be a change in the regulatory environment that either positively or negatively impacts your customer, which may trigger them entering a new buying cycle and represent an opportunity for you. Scraping public data sources and weaning the noise from the signal, if you will, and getting that into something manageable and useful like in a comp plan is super powerful if you can do it, but, again, it’s really hard to do. Tell us a little bit about what you’ve done there and how’s it going so far.
Tom: When I talk about that, it’s more for us about understanding how to react in the marketplace, and you can look no further than the current oil prices. While many people could be terrified that a lot of the oil companies have paused and really slowed down quite a bit, we look at it as a fantastic opportunity. We know that a lot of the oil companies will be anxious right now to take costs out of their systems, and that’s where we can come to the table with a lot of business solutions. Just on that high-level surface, we can just pay attention by reading the paper as a good example and then being able to move quickly to either hire in those areas such as, let’s say, a Calgary, a Houston area, is important to keep agile and keep quick, and especially as a larger company, we always want to keep that. That’s really where our data helps us to start to analyze the slowdowns and buying patterns and then understand it.
From a regulatory point of view, we’ve actually invested in a few resources this year under the direction of our CEO, who’s really … Their whole job is to keep on top of all of the changes going on in the regulatory industry where we do very, very well, things like the banking industry and governments and financial industry. It goes beyond the data as well. It’s just making sure that you’re keeping up to date with all of the trends in the market and then feed that back into your systems. Mainly on the hiring side, because, as you know, the quota process is a yearly process so it’s a little bit slower on the cycle than something like hiring some extra people into the areas where we think there’s an opportunity quickly.
Greg: Yeah. This responsibility of paying attention to these macro environments, and we’ll stick with the use case here of change in oil prices, and I’ve certainly seen this impact several of my clients. Is that the responsibility of the local sales team? Is that something that is centralized in your group, in marketing? Who owns that and then how does that get packaged up into some messaging that the sales team can use when engaging with customers?
Tom: It’s not a clear ownership in any one particular place in our company. We like to work as a collaborative team. We work very, very closely with the marketing folks. We work very, very closely with the financial folks, and, of course, getting the information back from the boots on the street and moving that into the organization where the executive team and the senior leadership can make these decisions fairly quickly. I think a lot of it is mainly living in the operations world where we’re putting together a lot of particular data and information around some of these buying patterns. Then marketing comes to the table with what they’re seeing in the industry as they keep on top of the trends and events and conferences that they go to. It’s really a collaborative effort as opposed to a single source.
Greg: Okay, got it. It makes sense because there are so many different sources of information. Putting that on one team is probably not the smartest thing in the world to do, so I can certainly understand that.
Tom: Yeah, it certainly doesn’t contribute to work-life balance if it was one person’s responsibility. He would be up all night and day reading the papers.
Greg: Yeah. No question. Yeah. I don’t think anyone is looking for things to add to their to-do list.
Greg: All right. We’re going to take a quick break here, and the reason for the break is I want to draw attention to the SBI Magazine, which comes out now every quarter. In this edition, we have a very interesting article from the head of sales ops at Rackspace, a gentleman by the name of Scott White. He talks about what he calls mining for gold inside of sales analytics reports, and it’s a really interesting article. I think some of the podcast listeners are not getting the magazine, so I wanted to make sure they knew how to do so, so here’s some information on SBI Magazine.
Speaker 4: Making your number is hard. Your problems are complex. Complex problems need complex solutions. Introducing the SBI Magazine. Read in-depth stories written by award-winning journalists about how your peers have overcome their problems to make the numbers. When you need more than a tweet, social post, or blog article, turn to the SBI Magazine. Go to salesbenchmarkindex.com to subscribe.
Greg: Okay. Welcome back, everybody. This is Greg Alexander, CEO of SBI. Today, I’m joined by Tom Kane, who is the vice president of worldwide sales and service ops for OpenText. Prior to the break, we were discussing the topic of sales analytics, and we covered two components of it, which was descriptive analytics, which describes what has happened, and there was diagnostic analytics, which tells us why things happened.
I want to progress the conversation now to predictive analytics, which tells us what is going to happen and what is likely going to happen. This is getting a lot of traction right now. We hear the hype around big data and we see software companies out there peddling predictive analytics applications specifically targeted at the salesforce. They’re suggesting that if you get to predictive analytics, then the productivity of the sales team can go up quite a bit. Tom, what’s your opinion on predictive analytics?
Tom: For me, I’ve always thought about predictive analytics and always used them and developed some of the tools on our own, within our own group. You can look no farther than our last acquisition. We bought a predictive analytic company called Actuate, and that would feed directly into our product and look at all the content and information that we store on behalf of enterprise companies and doing the analysis on that.
We’re also taking the approach of how can we use this new and pretty exciting tool within our own organization and specifically in sales, and we’ve embarked on a journey of starting to build some analytics tool along with the developers there to see how we can use it because we basically have already started that. We’ve got some predictive forecasting tools. We’ve got some predictive pipeline tools and we start to look at really vectoring out into the future and drawing the continual trend line to see if there has been any changes in behavior and can we predict that out into the future and then act upon it.
Greg: Yeah. I was unaware that you guys acquired Actuate. Tell me a little bit about what that product does.
Tom: It’s really in the space of the Tableau and Qlik-type …
Tom: … analytics tools. It’s more based to the enterprise in embedded analytics where it sits inside of an enterprise tool like ours. If you take our trading group, for instance, where we’re doing B-to-B transactions through EDI on a trading grid, it would be a wonderful addition to say how do we do analytics on all of the transactions moving through that grid to paint a picture, let’s say, for the car companies on what their buying patterns are with their vendors and how their vendors are performing to do basically the same thing you’re discussing, which is to try and predict some of the future and where they might have a problem with supply chain, for instance.
Greg: Yeah. I just came off a project myself and we tried to do this for our client and it was moderately successful. Let me explain what the project was and the obstacles that we had, and maybe you’ll have an opinion on how to get around some of these. This was a company that sold big-ticket items to large enterprises. The transaction count was probably less than a hundred a year, but the average transaction was over $20 million. We went back and looked over the last five years what the behavior of these customers were. Before they bought, what were they doing, how did they engage with us, what activities did they respond to. All the way down to things like e-mail open rates and responses to certain marketing offers and attribution models for lead gen programs. It was rather extensive.
Then from that, we built this thing called the perfect prospect profile. We then projected that on a group of companies that this client had not done business with yet and ranked them top to bottom, best to worst, based on who was most likely to buy over what time period. We gave that to a control group inside of a salesforce and said, “Have at it with these and see what happens.” The prediction accuracy of the predictive analytics was average at best. It was really hard to do things like assign attribution to certain activities. There were so many things that were subjective in nature, and trying to prove those with some type of empirical data was really hard.
Any thoughts on that? How do you get to the reliable data, I guess is what I’m asking.
Tom: I think we’ve experienced a very similar thing as well. Of course, once you start drilling into your sales regions, the data gets much, much smaller datasets and statistically it starts to be irrelevant or you get wild variations and swing. You’re actually really correct, because we’ve started playing around with some of these type of analytics tools. We’re assigning a positive to a negative score to various pieces of data within our CRM database and trying to come up with exactly what you’re talking about, which is the positive buying behaviors and signals that may be stronger than other certain deals that you’re working on and where to apply your resources.
I think generally you have to have a pretty good dataset. I think you’re right. It may be a real holy grail for somebody out there who’s working on that. I’m sure it would be a valuable tool if you can get a low volume or a low dataset of business and be able to apply that predictive analytics to it on a consistently accurate basis.
Greg: Yeah. I think we all in the community just have to keep experimenting with it and eventually we’ll probably get there.
Greg: Okay. We’re going to take a break here, and I want to draw the audience’s attention to a podcast I recently recorded with Lee Wood, who is the sales office leader at Thomson Reuters. I’m mentioning that on this podcast because Lee and Tom share something in common, and that is they lead a team, a global team, and they support business unit leaders. Lee discusses the organizational model and how to design large sales ops group that’s geographically dispersed and matrix them into the business unit leaders. If you’re not subscribed to the podcast and you want to get podcasts like this one and the one that I just mentioned, here’s how to do that.
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Greg: Okay. Welcome back, everybody. This is Greg Alexander with SBI, and today I’m with Tom Kane, who is the vice president of worldwide sales and service ops at OpenText. We’re discussing the topic of sales analytics and so far we’ve been through three components of it, descriptive analytics, which says what has happened, diagnostic analytics, which tells us why things happened. We just spoke about predictive analytics, which tells us what is going to happen.
Lastly in our framework here is the prescriptive analytics, which tells us what to do. The idea here is we know what’s happened, we know why it’s happened, we know what might happen, and in order for us to accomplish what might happen, if that’s the desired outcome, then there’s certain actions that we need to take. If any of the listeners have done any study on sales analytics or analytics in general, they know that this is the holy grail.
Tom, OpenText is on the bleeding edge in many areas. Have you gone this far out? Have you started getting into prescriptive analytics?
Tom: As a matter of fact, we’re working with a small startup company. I’m particularly working with them in a very small environment. We are looking at many points of data and assigning some scores to our deals in our pipeline so that we can help our sales managers understand where to apply very valuable resources in this day and age. Not only that, but really look at the elements of the deal, much like you would do in a deal review, but using data to say we can improve our chances and probability of us winning this deal by doing these five things.
Tom: If we’re able to accomplish that, our score will go up, and strictly by the math of it all, we should have a better chance of winning.
Greg: Yeah. That’s the promise of it. Maybe when you get further along here with your experiment, I’d love to have you back on the show to talk about that. I’ve seen some startup activity in this area as well. It tends to be concentrated around content marketing. What I mean by that is let’s say you have a sale methodology, I don’t know, it has five steps in it and it’s in your CRM tool. As a sales rep moves from one phase to the next of a sales opportunity, the tool is pushing proactively pieces of content that have a higher probability of helping a rep win the deal because it was the content that was used on the last victory that looked like that one. See what I mean?
Tom: Exactly. We have certainly discussed these ideas and being able to push that envelope a little bit. Since we’re in the content business, it lends itself hand in hand not only to help us win deals but perhaps develop new product.
Greg: Yeah. To me, I think that use case there of proactively pushing the best content to the rep at the right moment, that has so much promise because one of the challenges we see with sales organizations … You guys probably have this yourself. If I’m a sales rep, there’s just so much content and where do I find it and what’s current, what’s old, what do I use? Do I create my own? Who do I go to? Do I go to the overlay group? Do I go to the product group? Do I go to engineering? Do I go to presales professional services? It can be quite a challenge just locating the right sales collateral. Do you guys have that challenge?
Tom: Of course. I think that our tools like salesforce have helped to bridge that gap where sales reps from all parts of the globe can talk to one another, but it still inherently has that challenge inside of it. Being able to narrow down the focus and get that specific piece of content that’s going to help your situation today, that’s really the challenge that we’re trying to overcome.
Greg: Yeah, it really is.
Greg: Let’s talk a little bit about data architecture. When I say that, I mean it in layman’s terms. I don’t want to geek out on information architectures here. Generally speaking, especially if you think back to your earlier comments regarding the three hundred sixty-degree view of the customer, could you describe maybe the high level, what the basics of your data architecture is?
Tom: Yeah. I don’t think it’s a whole lot different than anybody’s out there. We want to know as much as we can about the customer and the account, everything from their legal entity structure and the hierarchy of all their subsidiaries down to their buying patterns and latest contact. We want to understand what they’re paying for, what they’ve bought in the past. We want to understand where their footprint is. Really, that equation hasn’t changed in sales in quite some time. The only difference really is to be able to create that data, to be able to get to it very, very quickly rather than doing it the traditional ways on papers and templates.
Being able to bridge the gap between areas such as our customer support function and our professional services function and accounting function, being able to tie all that together. Of course, the master data for a customer is key to success in the organization. We’ve struggled with that, but we’ve made some quite large advances in the last couple of years. We really just started with our billing centers. That seemed to be the key. It was the one piece of data that crossed all boundaries, where we invoice to. Once we have that key piece of data, then we can start to pull together all of our systems so that we can start to grab pieces of data from everybody and put together a nice picture of our accounts.
Greg: Yeah. You’re right. The parent-child hierarchy is just so critical here, and it typically sits within the billing system. Believe it or not, I know it sounds obvious to you that it should be set up that way. For example, how do I handle GE? You got GE corporate, then you got their business units and so on and so on. Some of the people that are listening to us haven’t gotten to the point yet where they’ve assigned the parent-child hierarchy. It sounds like you have done that, which is great, and that’s certainly a best practice.
Tom: Yeah. Interesting along that lines is we’ve taken what I’m hoping to be a unique approach and maybe it’s not with some of the listeners out there. We’ve got a data subscription, as many companies do, to help prospecting. We’ve used some of the legal hierarchies within that system and tied it into our own. Just started cross-referencing against who we’ve done business with. That has really brought us leaps and bounds forward into helping our sales reps understand the company’s architecture, our customers’ architecture.
Greg: Yeah, okay. All right, we’re going to take one more break and we have one more segment after this, and I want to get into the technology stack that supports all this and then maybe discuss an action plan that the readers can walk away with. Heading into the break, I wanted to mention a blog article that’s doing very well right now. In fact, it’s been read over ten thousand times, and it’s titled How to Establish a List of Priorities for Sales Ops by Developing a Sales Ops Charter. That’s quite a long title. You wouldn’t think a blog with that long of a title would do as well as it’s doing, but it’s doing very well. If you’re not subscribed to the blog and that type of post is interesting to you, here’s some information on how to subscribe.
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Greg: Okay. Welcome back, everybody. This is our third and final segment with Tom Kane, who is the vice president of worldwide sales and service ops for OpenText, one of the world’s largest software companies. We’ve been having a conversation regarding sales analytics and we walked through a discussion framework around how to process this information. We could talk about this forever. In fact, the thirty-minute podcast doesn’t do this topic justice by any stretch.
Tom, if I was to ask you to speak directly to the audience and say based on everything that you’ve done there, maybe the two or three things to look for, the next two or three areas to focus on once you got to basic competency applying sales analytics, where would you point them?
Tom: I’m a big fan of quick iteration. I know there’s a lot of talk nowadays about agile development and things like that. I really believe in that. I’ve come from a lean background. I like to get something stood up very quickly and then iterate on what I need to do in real time. Honestly, there’s no better tool than Excel to do something like that. I see the mistake that a lot of people make over and over again is building these wonderful castles of data cubes and databases and all these wonderful things.
In all honesty, technology in society moves very, very quickly nowadays and you need a very, very flexible tool to adapt to those changes. Sometimes the simplest tools are the best until we get something that we really, really can use as a cornerstone of a KPI or a dashboard. My tool of choice early on is Excel. Then as we get more and more sophisticated, we start to move it into a more traditional application like an OLAP cube or a specific database or an analytics tool.
Greg: You mentioned lean. I love the concept of lean as a way to improve the cycle time of certain processes. Are you applying lean in general to sales ops?
Tom: Certainly. We looked at it from the order processing point of view, and that’s been a three-year journey, three- or four-year journey where we’ve looked at the order-to-cash process end to end, and we continue to evolve it and we continue to get it faster and faster. We have good partners in finance to help us do that. We take it right from the customer point of view from first contact all the way to invoice. Yeah, actually, we’ve looked at value stream mapping in a lot of those areas and had multiple projects over multiple years, and we continue to iterate very quickly as we learn more things and as the customer preferences shift. Yes, we’ve used that over and over again.
Greg: That’s fantastic and the application of it specific to order to cash, that’s a perfect application and there’s so many things that we can do there to improve that process. I’m making a note for myself here. At some point in the future, I’ll have you back on to talk specifically about that.
Okay. Unfortunately, we’re out of time here. Let me conclude by suggesting some additional resources to the audience who might have an interest in this topic of sales analytics. You go to our site, salesbenchmarkindex.com, click on About Us, and then click on Our Services. There’s a few services that we have that are specific to sales analytics, and they are data planning, forecast and pipeline management, reporting, sales operations, sales support, and sales systems. If you click on any of those items, you’ll see the methodology, the deliverables, some case studies, some educational material, et cetera. That’s something you can do if you want to explore further. Of course, if you just type in sales analytics into Google, there’s lots and lots of information out there.
To the listeners, thanks so much for joining this week’s podcast. I hope you got as much out of it as I did. Tom, on behalf of everybody, I wanted to personally thank you for generously giving your time to us. You’ve contributed to our field and your wisdom is very much appreciated.
Tom: I appreciate it and look forward to a lot of the podcasts. I use them myself and find them very valuable.
Greg: Okay, great. Thanks again, Tom.
Tom: Thank you.
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