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Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Bob Selfridge, CEO and CTO of TMMData.
Find Bob Online:
Email: bob.selfridge@tmmdata.com
[00:02]
Hi, you’re listening to the Happy Market Research Podcast. I’m Jamin, your host. Today I’ve got Bob with TMM Data. We are live at Predictive Analytics World, Marketing Analytics World, Health Care.
[00:16]
There’s a giant list…
[00:17]
There’s a giant list.
[0:18]
of events. Email is in there somewhere. Deep Analytics, there’s lots of good stuff here.
[00:23]
The email one to me is kind of interesting. I’m like, “Gosh, email’s dying.”
[00:28]
You think? You think so?
[00:30]
Yeah, open rates are decreasing. There’s actually divisions of companies now that aren’t even responding to emails. They only use Slack and other…anyway. So, Bob, thanks for being on the show.
[00:42]
Thank you for having me. I appreciate it.
[00:44]
Let’s start out. Talk a little bit about TMM Data. What do you guys do
[00:48]
Sure. We’re about a 13-year-old company. I started it back in 2008 on my front porch, closed in the building, brought people in: one of those good, old-fashioned, kind of bootstrap things. We really started TMM was originally Track My Marketing. So we were all about channel marketing and, ah… Well, at that point, it was multi-channel; now it’s omni-channel ‘cause we have to have cool new words. But being able to set this up so that you could do reporting.
[01:14]
That was like early pre-social media focus market too.
[01:18]
Right, right. 2008: I mean it was around, but nobody was really diving in. It was more of a fun thing to have that us old folks were starting to play with more so than day-to-day operational thing that it is now. But we started measuring that around 2012. My phrase is, as I like to say, “It’s about the data dummy, not about specifically marketing data or analytics.” And we really refocused the company to becoming a data-integration company. We saw, while campaign management and marketing analytics is still very the core of what we do, we find that a lot of folks are still struggling with simple things. They’ve got spreadsheets coming in email; they’ve got, of course, 7,000 marketing technologies out there, floating out there in the atmosphere that they need to pull that data in and merge it and meld it and marry it and all the cool phrases we use now. So, our goal is to make it easier for analysts, whether they be predictive analysts, marketing analysts, financial analysts, just analysts that are fighting. We just did a survey with Digital Analytics Association here last year. People are spending 40% to 60% of their day just copying and pasting, cleaning data to start doing their work. And our ultimate mission at the company, our official mission statement is “Meet data needs painlessly,” very short and sweet.
[02:32]
Oh, I love that.
[02:33]
But we want to be able to allow analysts to come in at 8 in the morning and start doing their job instead of spending four to six hours cleaning up the data to then move to the next step to start doing their job. So, that’s kind of ultimately our goal in life.
[02:47]
An ROI on that is really easy to get to. That’s the nice part.
[02:50]
Well, it is. Unfortunately, for us and unfortunately for some of the analysts, the really good analysts, end up doing the extra work and working into the wee hours. And the senior management still get all their reports because they just put in extra hours. So the ROI to the individuals on the ground, certainly the feet on the ground really know that there’s a great ROI to it ‘cause it saves them a lot of time. Sometimes, although it’s getting much easier in the last two years, selling it up because they’re going to want to get my reports now. And it’s like, yeah, but you could get double the amount of reports, and better reports, had you not had to spend your whole day copying and pasting.
[03:25]
And the reality is there’s such high turnover among the people that are generating those reports. And if you can have a little bit better quality of life… To your earlier point, even if… ‘Cause cleaning and structuring data sucks; it’s not very fun, right? It’s very grindy kind of work. And the less of that you can do, the more interesting stuff you can do.
[03:47]
Absolutely. Wow, it’s funny you say that because we actually like to say we like to be the plumbers, is what we want to do with our platform. So, like real plumbers, it’s not real friendly work; nobody likes it, but everybody has to have it. So, that’s ultimately where we want to be in an organization.
[04:07]
Oh, that’s great. So, who’s your ideal customer?
[04:10]
Traditionally, it’s been marketing departments for large enterprises or medium-to-large enterprises. Some of our larger companies we work with like T-Mobile and Sony and Comcast, they’re marketing organizations that have a great IT technology environment where they work but the marketing information that they need access to is not a real high priority. You know when there’s frustration there I say, “Look, the engineers at Comcast are keeping the internet running for about 75% of the country. They have higher priorities than the analytics data from yesterday that they want to try a new regression test on.” So, what we want to do is fill that gap. We want to be friendly to IT because if we make IT uncomfortable with us or we try to shadow IT (those kind of things), it becomes a real problem for the organization, and it causes hardship for the folks we’re working with. So ultimately, we want to be IT-friendly, but we want to be able to fill those gaps that IT can’t fulfill for marketing or the business side of the shop. And we’re finding more and more as BI departments are coming of age, we’re seeing that a lot in the last couple years where the middle ground either an IT person’s been assigned to run an analytics/BI group (‘cause we got to make the business folks happy) and try to fulfill the needs of both of those worlds at once. That’s kind of where we’re coming in, and it’s been a good model there, which is kind of our second group, so to speak: so marketing departments directly and now the new BI/analytics groups that are being created in their centers of excellence, etc., throughout the industry. We work with them to be (I hate to say “middle ware” ‘cause that’s a bad word, or it was traditionally) but uh…
[05:53]
I’m sure there’ll be a new fancy way, word. Don’t worry.
[05:59]
We’ll come up with something.
[06:01]
We will, absolutely. We did clouds, one of my big pet peeves, terms. We had remote servers before. But anyway… so, do you have a favorite customer story?
[06:13]
Yeah, probably one of our oldest customers is Comcast. Of course, for those in the country that aren’t aware of who they are, they’re Xfinity now that’s what they kind of rebranded. But they’ve got phone services now; they have mobile services; they’ve got VoIP for telephone; they got internet security systems, and, shockingly, they have cable TV still. That still exists too. And one of the challenges they’ve always had is trying to figure out what is actually happening with some of their customers. A really cool thing just from simple data-integration perspective that we’re able to do with them is one of their struggles has always been cost of customer services, right, on those. And one of the neat little projects we were able to do was: We get notified when a new customer they call it connected, when I new customer gets plugged in. So, we have access to those records. So, we’re able to take that information, merge that with the service records to know when a truck physically went to a house and plugged somebody in. And then the third piece is when did that customer log in and get their magic Xfinity account so they could start doing their cool stuff on Xfinity. Those three pieces of information started saying things like from order to logging in; that kind of told us the maturity level of the customer they found. That’s where we found. So, somebody the day they got their internet if they had their account within 6 hours, they’re probably pretty technically savvy. And it just so happened that those people, all of their service requests were bottomed out; they were self-served; they didn’t want to deal with it. The folks that took six days became another level, and what they would start doing is immediately the very first time they opened their internet up, when they went in, they would put them to the Help page on how to set up their account so that they could move them along.
[07:57]
That’s huge.
[07:58]
So, you knew the segmentation. And then the people that waited 30 days, they actually were starting to work through processes to do like a follow-up call because until they have that account there’s a lot of things they couldn’t do. And, honestly, they couldn’t be tracked to know whether they were good customers or bad customers. More importantly, were they self-served or did they need extra help?
[08:15]
I love that!
[08:16]
If it had been more than 30 days since they had their account (This is my personal favorite), we actually did a segment so that the very first page they came to was not the help-tech stuff. They went to how-to-program-your-remote page ‘cause if 30 days to get an account, chances are they were struggling with how to even get the remote set up to watch TV. So that was the first thing they saw versus the knowledge base. And being able to do that… And they were able… The mix of all of that reduced cost by almost two million dollars a month in services and calls to support centers.
[08:45]
And you know, by the way, the most important thing is the customer experience is better.
[8:49]
Correct. The customers weren’t frustrated because most of their calls from users was, “How do I program this stupid remote?” because they’d already tried and been frustrated.
[08:56]
It’s very frustrating. Yeah, exactly.
[08:58]
It was just a really neat story. And it was just a couple different data points from four different data systems, right? None of them talked to each other because they didn’t need to. Normally, you order online; a ticket goes to a trucking system; the contractor goes to a house, plugs it in, and says, “Done.” And none of that needed to be aligned, but once you start aligning it, you can do cool stuff.
[09:16]
When you think about like… So, there’s different business owners there that you’re having to integrate as well, right? It would be easy if it was just data, wouldn’t it?
[09:22]
Right.
[09:23]
But the human part is… So, you must have had like a champion internally at Comcast in order to… that could navigate those channels successfully.
[09:32]
Well, yeah. We actually were very lucky: We had a person that we worked with for three or four years there, who left the company, went to chase something else, and then got bored, and turns out we had a position; so, we were able to hire her. So, we didn’t hire her from a customer. We were trying to help… And she went back to serve the customer.
[09:49]
So she knew…
[09:51]
So she knew the players to go chase down. We had three or four champions: one from each of those channels of data. It was very helpful to have that road map, so to speak, to go chase. So we got lucky in that case. But there was a corporate sponsor that said, “Look, there’s only so much… With the competitive nature of the internet as it is, the margins there are not very high ‘cause they’re all competing very heavily between Fios and Comcast and now MyFi and others coming out. Satellite’s gotten much better, much bigger recently. So they got to figure out where they’re going to make more money. Sometimes, it’s not about making more money; it’s about spending less money. We had a champion in the customer-success department, who was new, who said, “Look, there’s got to be a better way to do this ‘cause we’re just losing money hand-over-fist for things that seem unnecessary: fighting with people over remotes. Why can’t we just get them what they need, you know?” Having forward thinkers like that, and that’s tough at a lot of organizations.
A company the size of Comcast or Sony, they clearly have really good people they can recruit. At the conference here, as we’ve talked to folks throughout the week, it’s been interesting ‘cause it really depends on the maturity level of who’s running your analytics department how deep they can go. Someone, “I have my marketing data; I want to throw it somewhere and have pretty reports come up and I don’t know what to look for. So give it to me.” And others are coming to us with, “I work for a university, but I’m in the research lab for the medical department, and I’ve got all these specific needs. Please don’t tell me you’re just giving me reports. I need to be able to play with the data, look at the data, work with it.” And we try to be more of a blank slate, which sometimes is not good for immature – not “immature” – new to the industry, right? Some industries are just catching up, and they’re just trying to figure out how their ad spends are. That’s where they’re at. So we tend to work with the more mature, larger enterprise that can go deep.
[11:43]
That’s makes a lot of sense. So, the conference’s been good?
[11:46]
So far, so good, yeah. It’s been a neat mix with the different folks. It is a little tough when you see somebody up, you got to kind of analyze, “OK, which conference are they attending? Which portion of the conference are they attending?”
[11:56]
Eight conferences.
[11:57]
There’s eight different people. The email folks have a different status they want to talk about. Then the predictive versus the deep analytics want to go super technical, etc. I’m very lucky: health care, they want to go security and privacy. Finance is like nothing can ever leave the building, you know. So it’s been… That’s tough at a conference like this ‘cause there’s a great…, but it’s also a great mix. Sometimes people like to play “Stump the Chump.” That’s the word I like to use. I welcome it. Yes, I really do ‘cause you get those deep analytics and predictive folks that are like hard-nosed coders and PhDs in data science, and they love to ask that kind of stuff. And I’ve got a couple new acronyms this week that I had never heard of. So I got to look them up and learn something new; so, that was good. I hope they come back ‘cause I promised them I’d look it up and give them an answer. So, yeah, it’s kind of fun. And then I get to play “Stump the Chump” ‘cause I had one super smart guy that… PMML, I think is the language: Predictive Modeling Meta Language or…
[13:01]
I’ve never heard of it.
[13:02]
I had not either, but it was a PhD from a university. So, the next guy was another PhD, and I’m like, “So, have you ever heard of PMML?” So I got to play it back and, when he said, “No,” I felt much better about myself. Turns out it’s just another format in XML. So there’s really no magic to it. I’m talking geek now but…
[13:22]
I love that. I love that.
[13:25]
“Stump the Chump”.
[13:25]
It’s so funny.
[13:26]
We’re still using SQL, right? It’s amazing to me, like 30 years. Is that what it’s been? It’s been a long time.
[13:33]
Well, yeah. And then we created a whole industry of no SQL so that we could then build apps that let you do SQL and no SQL because that’s what we needed to do. And my poor marketing staff are very confused when they said, “Well, how do you do no SQL or how do you do SQL with the no SQL?” She like, “I thought was no SQL.” But we get there. it’s tough. We like to make up new terms. That’s always good.
[13:54]
If somebody wants to get in contact with you at TMM Data, how would they do that?
[13:59
Simplest way – go to our website, www.TMMDATA or call our phone number, 855-55FORDATA. See, easy, that’s kind of fun.
[14:11]
I like that.
[14:10]
Feel free to visit our website. We’re actually also do several other shows. So you’ll probably catch us at other shows as well.
[14:17]
And we will include that information in the show notes as well so people can have at their fingertips a click to the website. Bob, thanks so much for being on the Happy Market Research Podcast today.
[14:27]
Thank you very much. It’s been great talking to you.
[14:29]
Everyone else who’s listening, if you found value, please, please, please take the 30 seconds to screenshot this and another minute to post it on LinkedIn, Twitter, wherever your social personas exist. Be greatly appreciated. Have a wonderful rest of your day.