Glenn: Hi, everyone. I’m Glenn Gow, Founder & Advisor of Crimson Marketing. Welcome to Moneyball for Marketing where we talk about the incredible changes happening in marketing organizations around big data and marketing technology. We feature marketing technology insights from the top marketers in the world. The reference to Moneyball is from the story of how the Oakland A’s baseball team were able to win and win and win because they figured out how to use data and technology to their advantage. If you’d like to learn about how to use big data and marketing technology and marketing to help you win visit us at CrimsonMarketing.com or email us at info@CrimsonMarketing.com. And now on to our podcast.
Today I am very pleased to welcome Jacob Shama, the CEO and co-founder of Mintigo. So how does Mintigo describe themselves? Mintigo is a leader in enterprise predictive marketing, one of my favorite topics, and Mintigo leverages data science in every step of the enterprise marketing workflow. It predicts and recommends optimal marketing actions and identifies the best of marketing pipeline. Jacob, it’s a pleasure to have you here.
Jacob: It’s a pleasure to be here, thank you.
Glenn: We were talking earlier, Jacob, about how big data can be a real challenge for marketers and how data science can step in and help solve that challenge. Tell us a little bit about your thoughts about how data science can help marketers.
Jacob: Sure. So I think that data is both on one hand the challenge and on the other hand the solution. So in one hand you know every marketer is overflown with data, a lot of demand, a lot of traffic, a lot of information and how you can manage those. On the other hand if you leverage data science you can handle this data in a very efficient way. I think this is the real promise. So those are really exciting times for marketers because the big data and science has matured to the point where it’s really changed the way people market and sell.
Glenn: Good, and just to clarify when you talk about big data I think you’re really talking about prospects and customers, right?
Jacob: Yeah, exactly. The core of everything is you want to know your prospects, you want to know and understand your customers, and do the best actions to really win their engagement and eventually make sure that they convert and stay with you as a customer. So it’s really centric around the customers and having all the data and all the right actions in order to do successful marketing.
Glenn: Right, and I know marketers are hungry to take advantage of the data that’s available to them it’s just not always obvious to do that. I know you have a story or two to tell us on how companies are doing that. Please tell us a few.
Jacob: Yeah, sure. One of the ways to leverage big data is actually to understand what is your customer’s DNA. So, for example if you–
Glenn: I’m sorry, customer DNA you mean how a customer is made up? What are the components of a customer?
Jacob: Exactly so what are the unique attributes of a customer for a specific offer, for a specific product and this is a big challenge because there is this basic intuition that you can tell you know that my customers are mostly from a specific industry or from a specific area. Those are obvious [Inaudible 0:04:10] data, but when you turn that manual way of thinking into a scientific way of thinking then you really discover the entire picture. You immediately move from a few indicators to thousands of indicators that describe your customers and this is actually the promise of the data’s realm in marketing really understand what makes a good customer for a specific offer. So I’ll share with you a few examples.
Glenn: Okay, good.
Jacob: We have been with working NewStar, this is one of our customers, and we try to analyze their customer DNA for a specific offer. So the basic things [Inaudible 0:05:00] was we’re very obviously in terms of what are the unique feature of those customers. But doing that in a scientific way we discovered something that wasn’t so obvious. We discovered, for example, that having the certification for SSL, for example, was a very, very strong indicator for this specific offering. Meaning that if you know in advance that the prospect is using—having this certification it means that his probability to become your customer increases tremendously. This is something that obviously by intuition and by manual process you cannot do. So this is just a simple example of the power of letting machines do the discovery for you and really provide you with the insights that you need for better marketing and now you can act upon those link sites, meaning that you can leverage those indicators or segmentations for better content, for better engagement and so on and so forth. So this is one example.
Jacob: Another example is another customer of ours [Inaudible 0:06:11] actually they shared that in the model market event in Vegas, the Oracle one, so I can definitely share that with you. So they also try to figure what is the profile for their high end customers and it turns out customers that used MS Azure that use Oracle database and that use NetApp were the ones that really matched their high end offering. This wasn’t like an obvious observation. So now they took all those three indicators and they stopped working with that to better segment their house list and their demand flow and better address the different segments MS Azure users, Oracle database user, and NetApp. So it’s really the richness that you get you don’t really change the principles of marketing you just make it much more fast and much more richer to act upon those.
Glenn: So I think what you’re saying that it’s possible that there’s a sales rep who kind of figured that out and is pursuing that kind of business, but what this does it actually proves the connection between certain elements about a customer because you can see who’s really buying from you and it helps point marketing and sales to those exact same kind of customers. And I think what I hear you saying is it’s machine learning that really enables that to happen.
Jacob: Exactly. Exactly, so it really takes all the guessing or intuition out of the process and it really becomes a scientific process meaning that—those are high awards, but the very core principle of predictions it’s simply a machine. It simply learned the past and predict the future. This is what it does. So it takes all the old data that you have learn in using machine learning and then it predicts what will happen in the future in terms the fit, in terms of best actions and so on. By moving from a manual process to a more automated process you can do everything at scale. So if you have a million contacts you can get all those insights through these million contacts that you have and then the sales guy really gets the ones that truly match the offer he’s trying to sell and all the information he needs for better engagement.
Glenn: So I think you and I were talking earlier about the Pareto principle and how this helps you if you have access to that data and the intelligence behind that data you can now focus on the 20 percent that you know is going to work for you and ignore the 80 percent that is inefficient.
Jacob: Yeah, this is really amazing. Going from a customer to a customer we see this exact phenomenon. This is amazing because the meaning of that is we can show that 80 percent of the marketing budget is wasted. Why is that? Because if you take a house list of the company or you take the demand flow of the company and you analyze that against the customer DNA, their customer DNA you will see that around 80 percent of the prospects don’t really match the customer DNA. It means that if you spend one dollar on those 80 percent you’re wasting your money. So by actually zooming in the 20, in some cases it’s actually 10 percent of your demand traffic off your house list you get an amazing impact both from efficiency perspective meaning spending money on the right prospects and, of course, eventually on convergence because now every dollar matters so you get better performance in terms of convergence and it’s huge. Think about saving 80 percent of your marketing budget, this is a very strong value.
Glenn: Excellent. And all this comes from an understanding I think you said of who are the customers you have today and why are they buying from you and how much do they buy from you and so you didn’t use the phrase look-alike modeling, but is that really what you’re doing then if you turn around and try to find people who are like those customers?
Jacob: In machine learning there are different classifications of machine learning but the principle is very similar, well aligned with what you have said. The machine actually learns what were the profiles of your customers for a specific offer and then it tries to predict what will be a good match from your house list or from your demand flow. So it’s really kind of look-alike in-between the past and the future. I would say that doing predictions is not that difficult. You can guess. If you have a question you can guess that 50 percent of the answer will be yes and 50 percent of the answer will be no.
The key for accurate predictions are actually the data itself. So, for example, if you know that in the last 10 questions the answer was yes then the probability of the next question that the answer will be yes is much higher. So past history really can tell you a lot about the future and this is the basic principle of predictive marketing. The better data you have, the more reach data you have the better prediction you will generate. And this is why it’s so important not only to leverage your own data, you know the data that you have in house, but rather to leverage all of the data that you have outside the web, the social data on each and every account and each and every prospect, and this is actually what we do in Mintigo we collect all available data for all companies and all the potential prospects and we leverage that in the predictive process to get the best impact. In many aspects the web and the social is becoming your CRM. This is what happens because instead of having your internal CRM you have this web which is your CRM, you just need to know how to leverage that.
Glenn: Yeah, and earlier you and I spoke about something you were talking about called full coverage meaning how does B2B learn from what B2C is doing and gathering even more information. Tell us a little bit about your vision for that.
Jacob: Yeah, that’s a very important domain that we see is trending toward—actually immigration into one class of data. So it’s currently you have data for B2C and you have data for B2B, eventually what will happen you will have data for B2P meaning that business to people and really it doesn’t really matter what aspect of your profile you’re analyzing whether it’s the business part or the consumer part, the personal part, eventually what you really want to know is for each prospect, for each and every customer you want to have a full picture of everything, both the hobbies and what I’m interested in, but also what company I’m working for and what is the profile of this company and so on. So if you are, for example, a bank, Citi Bank for example, definitely if you add those two components for each and prospect you will have a better understanding and a better ability to really generate the conversation if you have kind of holistic data sources rather than a very segmented one. And this, for example, will allow you to better assess, for example, the [Inaudible 0:14:52] size for example myself.
Glenn: Right, so you may have information about the company they work for, but if you layer on information about the individual you now just have a richer understanding of that individual so that you can market to them differently.
Jacob: Exactly, and if you add also on top of that the intent, if you for example show interest on a specific database, on a specific security technology by downloading a paper or by doing something actively this is another layer of information on top of my profile that really can generate a better profiling and a better match in-between profile and a specific offer.
Glenn: Okay, so it’s not only historically information it’s real-time information.
Glenn: Right, so if you see you’re already interacting with me digitally and you can see I’m taking certain actions you now have a richer data set and maybe based on the timing of those actions you know what to do in terms of a way to interact with me differently based on what I’m doing.
Jacob: Exactly, and this going back to holistic, the end-to-end coverage of every prospect this gives you another dimension to understanding what the best offering will be, what the best action—how to communicate—it’s kind of three layers. You want to find the right audience and then you need to figure out what is the best engagement in terms of the messaging, the offering, how you communicate with them, and then what is the best channel to use in order to get the best response and all of those can actually be driven by data science. This is why it’s so exciting because it’s really changed the way people market. Instead of kind of [Inaudible 0:16:54] manually the process, deciding that you need to give two points for this action, two points for this profile, now everything is done seamlessly in a [Inaudible 0:17:05] to conversion and U.S. marketers are becoming more strategic in terms of the guidance for this process and everything from that point is actually driven by science.
Glenn: Something occurred to me is that not only by mining my current customer data can I understand who’s mostly likely to respond to an offer, but I can also begin to understand who are my most valuable customers.
Glenn: And if I can measure the lifetime value of a customer and based on that I can start to really hone in not just people who will buy from me but people that I want to buy from me because they become a long-term customer of mine and they become a highly profitable customer of mine.
Jacob: Yeah, definitely. So as long as you have this data science platform for marketer all those solutions are actually applications. You can optimize for specific product. You can optimize for specific targeting of revenue and you can optimize for a specific type of customer. So the high end customers or the ones that are willing to spend the most are actually profiled and predicting marketing platform could really find those profiles. This is the power of it. This is all math. You can generate models for each and every organization you are trying to do and everything is done [Inaudible 0:18:36] I guess that eventually this will go to a customer success platform, prediction of retention in turn and so on and so on.
Glenn: All right, this is extremely helpful and as we near the end of this, Jacob, I know you had some comments about the future and I would really love to hear your vision for what’s going to be happening over the next few years.
Jacob: Yeah, sure I would love to. So I think the kind of difference is very obvious. We are really heading into a self-driving marketing car–
Glenn: A self-driving marketing car, did I get that right?
Jacob: Yeah, exactly, and from my experience talking with marketer although I know that scientific decisions are usually better than the human decision people still need this control over the process. So you know the self-driving car are more safe than human driving car.
Glenn: I’ve heard that before.
Jacob: Yeah, but if I take you or myself and we would sit in—today, in a self-driving car I’m sure that we would feel kind of an urge to have this control ability. So I think today people still want this and we do have it within our platform the ability to actually bypass the scientific recommendation for profile or for indicators and so on, but in a few, I guess, two or three years you will see that people will be more receptive to actually have a full scale self-driving car that actually manages your entire funnel from first touch to conversion and then later from conversion to customer management, actually the up sell and cross sell and retention. So this is really exciting and data science is really ready for that. I think that it’s more of the maturity of people to observe this data to the maximum scale of depth.
Glenn: Well, I’m looking forward to the future as it relates where predictive can take us and, Jacob, thank you so much. I’ve learned a great deal in our conversation today.
Jacob: Thanks a lot, that was a real pleasure.
Glenn: I look forward to talking to you soon.
Jacob: Thanks a lot.
Glenn: If you like this podcast please subscribe and rate us on iTunes and tell your friends about us. You can also go to our website, CrimsonMarketing.com, and sign up for our free monthly newsletter featuring the very best of our marketing insights, featured Moneyball for Marketing podcasts, and one of our favorite features called, “Bad Marketing,” or email me at info@CrimsonMarketing.com. Thanks for listening to Moneyball for Marketing from Crimson Marketing. Have a great week and let us know if we can help you in any way.