Piyanka is the founder of Aryng, a management consulting firm focused on analytics and the author of the Amazon bestseller “Behind Every Good Decision”. She focuses on developing her client’s internal organizational capability – people, process and tool, to leverage data for making smarter decisions.
Piyanka teaches us the simple questions that unlock the potential of data and analytics. If you have ever tracked anything then you need this episode.
Bronson: Welcome to another episode of Growth Hacker TV. I’m Bronson Taylor and today I have Priyanka Jain with us. Priyanka, thanks for coming on the program.
Piyanka: Thank you for having me.
Bronson: Yeah, absolutely. Now, Priyanka, you are the founder of E-Ring, a management consulting company focused on analytics. And you have multiple Fortune 100 clients, companies like Google, Apple, Microsoft. And you’re the author of the upcoming book Behind Every Good Decision and Actionable Guide to Data Driven Decision Making through Business Analytics. So basically, you sound like the perfect guest to have on growth after TV because everything you do is about data and analytics, isn’t it?
Piyanka: You make me sound so good. Thank you.
Bronson: And you’re the one doing all this stuff, not me. All right, let’s start by diving into your consulting work with Adrian. Because when you get to consult with Fortune 100 clients and a lot of other companies, you get to see just a lot of data and business intelligence and you just have more experience to pull from. So I want to kind of get inside of what you help these companies actually figure out and do. So first, do you have kind of a basic philosophy concerning data and analytics that you’re trying to get into the minds of these companies? Do you have kind of a set of core beliefs that you’re trying to get them on board with?
Piyanka: Right. Right. And we do. I think one of the big things for us is that and it’s also at the same time, we’re doing a lot of, what should I say, evangelism or analytics because it’s so overhyped, big data and so on and so forth. There is a lot of misconception here. So our work is both evangelizing and educating as well as driving impact for our clients. And and some of the things the way we do it is by kind of bringing home some of the true concepts versus, you know, here’s something cool to do, big data and predictive analytics and you need to get Hadoop cluster and so on. We try to nail down what is really true in this world of business. Still, the impact is coming from processes or following step by step method to drive decisions, to drive impact, not necessarily using cool technology and cool gardens as as it appears for most people who are outside of it, like, oh, it’s all about cool technology and cool algorithms does actually not. It’s it’s actually about it is about those things. It does leverage cool technology more than anything else. It is about how do you how do you follow a step by step method, a recipe of sorts to drive decision? And how do you ask good questions of data? Because it’s it is not a data doesn’t speak, you know, it really does not speak. It really responds. And so how can you really ask good, intelligent questions of your data? How can you lay out a good and agenda to get the questions, the questions you want to answered?
Bronson: Yeah, I love that quote. Data doesn’t speak. It only responds. I think we already have the quote of the episode right there. I love that. So you’re saying, you know, you’re not so much about the hype of big data. You’re not so much about Hadoop or this new technology. It’s more what’s the process to learn? So let me ask you kind of what is the process? I mean, what is the recipe that you said? What is that step by step method that when you when a corporation comes to you, you say, okay, this is the process to really utilize business intelligence. What does that look like?
Piyanka: Right. So there are two big frameworks that we talk about and that we help our clients with. One of the first one is called three key questions. In fact, if your audience and listeners are interested, they can download most of what I’m talking about as white papers on our website on adding dotcom. So the first framework is three key questions. And, and it’s a framework for laying out an optimal agenda. What kind of questions you should be asking your data. Let’s say you are a, you know, $50 million, you know, five year running. Not call it startup, but, you know, transform from startup, but yet not an enterprise. What kind of questions you should be asking you? Let’s say you have a base of hundred thousand customers. What? And lots of that. A lot of the transaction, what kind of questions you should be asking. And and for that, we have three key questions, which essentially is a framework of driving driving from your big goal, let’s say growth is your big goal for this year or maybe it’s revenue. How do you go from the goal out there to really understanding what drives those big things that you are driving for? And then what what what insight? What are the dynamics of your business so that you understand your business? You can move those levers and essentially have drive that impact. So there’s that three key questions. The other one we have is called Bother and Bother is an acronym stands for the Five Steps that you follow to get insights and drive impact from data. And so it is starts from business question people, business question. I understand the question. I lay out a plan that’s a second step analysis plan, then only collect relevant data, not big data, just a subset of the data. Then drive insights, get insights, use proper methodology and make recommendations and all toward that. There are two tracks in this. There’s the blue track, which is the algorithmic track record that all data science, the cool stuff, and then there’s a green track. How do decisions get made? How do you communicate? What what are stakeholders doing? How do you need to influence? What handshakes do you need to make? So what would what the tracks go parallel to this five steps for you to really drive impact in the organization from the insights that you get. So these are the two that we use.
Bronson: Yeah, I like both those a lot and they both start with questions. One framework is all questions and the other one it begins with a B, business questions. In your experience, it must be the questions at the very beginning. Really change the whole the whole discussion. It changes everything. If you ask the right questions, you probably get to the right spot, you ask the wrong questions, you probably get to the right spot. Is that why you guys do that?
Piyanka: That’s exactly right. That’s exactly it. It’s like it’s like let’s say you were, you were captain of a ship. Mm. And, and, and, you know, you were basically a treasure hunter and you have a crew. And basically I come around and I’m an investor and I say, Hey, Bronson, if you can help me find this, you know, plate fleet, which sunk in 1857, it was a it was a lot of big ruby and thing from Thailand. If you can help me find that, you know, I find your entire trip and here’s here’s the price dollar for you. Okay. So here’s I’ve given you a very, very specific aim of it, such a goal that two ways to approach it broadly speaking, one way is you can set up like Columbus, right? You can say, well, all right, cool crew, let’s get onboard, let’s start sailing. And you just dove into Pacific Ocean or, you know, get onto a boat and go to Pacific Ocean and you, like, start looking for the complete fleet, the sunken treasure. How likely are you to find that?
Bronson: Not very.
Piyanka: Not very. All you can be like home, Sherlock Holmes. And you say, okay, where? Where, what were the trade routes? Where did that likely think? You know what? When was it last seen? What are the different heights of the ocean, so and so forth. You can be like Sherlock Holmes nailed down. Exactly. Because you’re looking for plate feet. You nailed down exactly. You know, top five locations you’re most likely to find. Then you basically send your submarines or your deep sea dove without the boat. How likely are you to find your goal then?
Bronson: Yeah, very much more likely.
Piyanka: Much more likely. And if you don’t find it, you fail fast too. So that’s the difference. Analytics is the Sherlock Holmes approach saying if this is the question, then this is my approach. Let’s say the question was, you know, go find this killer. Well, it’s a completely different you were going to look for it in different place. But if you were Columbus, you’re going to do the same process, right? So your goal, your question guides you to what steps you take.
Bronson: Yeah. Like, for example, a lot the many questions come to mind as kind of quintessential. These are really good questions that actually apply to business or is are questions you can say in these are really bad questions. So what’s the kind of question that you just feel like you’re asking the wrong thing?
Piyanka: Yeah, I mean, in general, anything which is nice to have, which is not actionable, anything, would you would you say it would be nice to know what do our customers on an average spend? Let’s say that’s a question. And if I were to ask you. Oh, okay. And what are you going to do about it if you don’t have an answer to that? That’s a really bad question. Gotcha. So if it’s not actionable to begin with, it’s not. If it’s not if it’s not something you can take action on, then it’s not well thought through. Now, if you were to answer the question and you said, well, you know, depending on the average price I may bundle it with, you know, I may do some partnership with another channel to see what makes sense. Okay, now we’re talking business. Yeah, but you’re going to do something about it. So in general, that’s the rule. Each each business, let’s say to two gaming companies. Hmm. Both roughly 50 to $100 million is kind of in the same space. Still will have their different stuff. You can have their very different agenda, analytics agenda, still very different set of questions. Why? Because they may have a different philosophy. They may have a different, you know, on one end, you know, they may one one of the companies may have 1% paying base, another company maybe 100% paying this right that nobody can play unless you at least put $5 or something like all of that. I mean, this that one thing will completely change the dynamics of the business, right? So the questions you will ask you the same to gaming company, similar customer base. Still your analytics will be completely different. Yeah. So it’s not one question is right or wrong, it’s about what is the context and what’s your business. Yeah. And where you’re going with that.
Bronson: Yeah. And one of the things that seems like also hear you saying is in your frameworks, you focus a lot. So it’s not big data, it’s relevant data to whatever we’re trying to do. It’s not let’s find the answer to every fun question. It’s less ask a few right questions to find the answers to those. If there’s something we can do about it. So really, it seems like a lot of what you probably do is give people permission to ignore a lot of things in the world so they can focus on the few things that matter. Is that.
Piyanka: Right? All right. You said so. Rightly so. Yeah.
Bronson: Exactly like that. Let me ask you this. I know you don’t focus on the technology. So this may be a short answer. We’ll see. Is there any kind of core sets of technology that you do just always install that you go into a company, say, okay, you need this, you need this, you need that? Or is it really just kind of it’s so different and it’s not really that meaningful to you?
Piyanka: Yeah. No. You know, so these days, I mean, from where we’ve come from, 15 years, 20 years ago, when you look at the by technology you had, you know, you would submit. I’m talking about the graphical user interface, you know, where you would just like if you think about business object in years ago or even before, you know, you would cut and slice the data and then you would submit it and then take forever for you to record for the query to run. And then you’re waiting, waiting, you know, maybe you come the next day or something. I mean, I’m exaggerating, but, you know, the point being, the technology at that time was not that cool. It was not up to par. It was not that user friendly. Now, technology, most whatever be it to you have whatever stack you have as long as it’s designed. So if you thought through the information architecture, it will it should be user friendly, it should return the queries quickly. It should be, you know, you should be able to get the data you want quickly out of that. It should be intuitive. It should be, you know, available in all the channels, all the, you know, mobile and your PC and so and so forth. All of those are table stakes now. So to us, really, it doesn’t matter when you go into the organization, it doesn’t matter what kind of a tool they’re using, what kind of underneath stack they’re using. They can be using Oracle database, they can be using it a.S.A.P. They can use using kind of data. On top of that, they can have cubes, aggregated cubes which are, you know, Pentaho or Tableau, whatever you have. As long as you designed it. Well, it will it should be very functional and it should. The, the the the way to measure its success is, is it, is it giving the people who are making decisions, is it giving them access to data easily? Are they excited about what they have at hand? Or are they saying, oh, we have to wait for another analyst to pull the data for us? That’s a failure of your, you know, your system, your your data architect into the system. And it’s mostly not about technology. It’s not a technology that is failing because technology is that power. You know, whatever you’re using, it’s actually the underlying design that you’ve done.
Bronson: Yeah, I think about the acronym BEDIA that you gave us earlier, and it seems like as long as the technology eventually gets you to insights and recommendations, the rest doesn’t really matter. We can be smart about it as long as we can get to the end of that acronym and end up with insights from these tools, then we’re probably going to go easily.
Piyanka: And that’s easily. I mean, the point and I keep saying that because there’s another company, another client of ours, I think it’s a closer to $10 billion organization. And essentially the head of product has about 20 product managers and is it’s funny. So the head of product is basically saying, I hire only the product managers who X equals skills. Okay. All right. So basically, you are basically going to get sequel analyst who have done some product, but that’s what you’re going to get. And that’s, you know, and maybe for that business, it’s fine. But the point is that you’re narrowing down your scope. And just imagine if you’re expecting your product managers to be guiding sequel quickly and getting the data back and then like cleaning it up or whatever else, when do you really expect them to move product? So yeah, right. And so I think be careful about that. If it is not easily accessible. You expecting people to write cyclicality or not wait too long or have extra, you know, get extra training for the tool? I mean, little bit training may be needed, but you know, like, you know, it’s so hard to use. And if it’s not easy to use, people will not use it. They will find ways not to use it. So how can you make if if your tools work, it needs to be easily. It needs to give easy access to data and needs to be able to easily visualize because it’s all visual. Yeah. If you if I give you a spreadsheet and has, you know, a thousand columns and and thousand rolls or whatever else, and you look through that and it’s not visualize. It’s it’s it’s daunting. Mm hmm. It’s really daunting. So, you know, you do need to have easy access and visualization of it.
Bronson: Now. Now, let me ask you this. You know, you work with a lot of big companies. Would your advice to a small company be different than. The advice you give to larger companies are the things that are teaching universal laws or to startups have a different set of rules because of their unique situation. How do you see that in terms of analytics and business intelligence?
Piyanka: That’s a great question. And I think at the at the fundamental level, the universal laws apply by their stands because question stands, I think, as applied to the world. Whereas what I see from two sets up are two sets of clients. What I see with startups is they’re running very fast. A lot of them in the name of data analytics something. And then if we meet them and they’re already doing data analytics are doing too much, too fast, not learning, not pausing, not falling through the process in the name of We are agile, we don’t do process throughout the process. They are not learning. And it’s true for a smaller organization, they go from 100 to 200 very quickly. Right. They can do 100 X growth very quickly. Their customer base can change. So they need to learn fast, but they need to learn systematically. And that’s one of the challenges I see with smaller organizations that they are not learning in a systematic way which can sustain them. They’re often moving too fast and often not clear about what they’re learning. Some of them are doing too much testing, especially the Silicon Valley. High tech companies are all about AP testing, AP testing, and they’re just doing too much testing. It’s crazy insidiously. And it’s basically, you know, and they’re going with we out. This is a lot of them. They’re not getting much value out of it. So that’s one side of it. For the Fortune 100 companies, the advice is, I mean, tactically, the advice is completely different. Move faster. Don’t wait on yourself. Q Yeah, they’re moving too slow and they are somewhat limited by okay, we, I have to wait for my question to get prioritized in the next cube are for it to get answered for me to take action in the next quarter, you know, and it’s like losing opportunity, you know, another $50 million or smaller stock. I was eating your lunch. You if you don’t move fast, I mean, there are lots of examples even in gaming company scenario that we only talk about, there are small organizations that, you know, you don’t some big, big companies like Stripe. So I think the fundamentals remain the same. But it is, it is where they are, what their natural constraints are.
Bronson: Yeah. So it’s the same frameworks, the same philosophy. Startups need to move slower. Big companies need to move faster.
Piyanka: Yeah. So a lot of systematic, I should say. You can move fast. This move, it’s systematic.
Bronson: Yeah, that’s good. I like the way you put that. That’s a very. Okay, so in your experience, you’ve got to, you know, go into these companies, you really implement kind of data and analytics, you know, mindset and tools and you really walk them through that process. What have been some of the biggest wins, some of the biggest successes that you’ve seen that you can say, you know, this really is attributable because of our work here or because of their work with analytics or something. Give me some kind of inspiration. So we feel the need to go back and really get some of the stuff on straight.
Piyanka: Right, right. Right. I mean, there are lots of stories out there and we can also talk about. So, for example, there’s a retailer that we software retailer that we work with and and their and they are focused on creative professionals. And essentially they have a product and one of the very popular products. And, you know, they they marketed to their base, you know, let’s say their base is X and you know, over all the years they were released version was an 8.0, 9.0, 10.0 and they’re kind of basically selling it to the same base. They have some ways of doing trial of the product to to grow the base, but it grows in a step function. So we engaged with them and we essentially they had, they have other products and they they had a huge, I think, close to 50 million plus huge database of people who were not related to this particular product that we were talking about. But they had done something with the company, know some some trial or, you know, some event participation or something like that. And it was completely left untapped. So what we did was we took that entire database and we used predictive analytics and we essentially scored that entire database with the propensity to buy this product. Mm hmm. With a look like, you know, like look like modeling. Sure. And and essentially, it turned out that if we if we and they and then they then we tested with a campaign to that database, which we had scored. And so the top 30% we mailed to top 30% there. And. That that response rate was comparable to their own base. So that was tremendous. I mean, you don’t imagine a base which has never done anything with you right now. And I think that this product, that almost comparable response rate to the to their base and and they increase their market universe by four times. So just imagine the revenue impact that you can have immediately and and for all the future, because that that basically means you have your core base and now you have the base that you have. Now your market universe has become full time. So that’s one story that we have. We have many stories. In fact, we share a lot of these in the book, which is coming out behind every good decision. And I think it’s coming out either in September or October so folks can look for it. We share a lot of those stories. And but if you you know, these are some personal stories. But if you look at the domain out there, I mean, a lot of us use LinkedIn. Mm hmm. And if you just use see LinkedIn like that, keep adding new product, new you can call it product. But like, let’s say you do searches, right? And you do searches and you say, okay, you know, how do you want to filter? Do you want to filter by this in your job, seniority, whatever else they’ve been adding? You know, if you remember LinkedIn, I don’t know, five years ago. Awesome. You know, initially when it started, it was a very simple form. You had first name, last name, job profile company and a few other things. Now, if you see all those things which have been added and they keep, you know, all that is coming from analytics and these are simple analytics, it’s not like they necessarily are using, you know, advanced algorithms and predictive analytics. These are simple business analytics. You look at Amazon, if you go shop, audit and sales people, people who have viewed this product have also viewed these other products or whatever else on this percentages have bought essentially bought this product. All this is simple analytics, but imagine that, that itself, I, I don’t, I don’t work for Amazon, so I don’t have those numbers, but I can imagine that would be things that, that app, that little app, which is simple and they think, okay, you know, it’s a simple bivariate rate. All of the people who bought this, how many what, what, what, what of the people have seen this, how many, whatever they have done, if they have if they have converted you just showing that number and that is bound to increase your conversion X percent. I don’t know what that percentage is, but you know, so there are lots of these. I mean, it’s we use analytics in our daily life. We are using it all the time or we are interfacing with it all the time. When you see some offers in mail calls, I don’t know if you get like.
Piyanka: Yeah, cool. This is one of those retailers they like. They have figured out their couponing system right there, ten, 15, 10%, 15%, 20%, 30%. They have a cyclic way of getting their customer base to their shop. Right. It’s just unbelievable what they being able to do. And so we are on the on the we are experiencing it on the other side because when you receive 30%, you wait for the 30% and you you go to this to shop at gold only when you get your 30%, right. Yeah. What have they done? They have essentially said we don’t care about seasonality. We will get the customers. We want to come to our shop when we want them because they can essentially send us 30% whenever they we like. And so they have divided their customer base and they have seen who responds and who doesn’t respond. And to those who respond, they have figured out when they should get a cyclic way of getting them to 30%. Yeah. I mean, amazing. Just just and simple. It’s not like they’ve used some really cool, you know, advanced algorithms here. These are simple, simple stuff. So it’s a really great examples.
Bronson: Absolutely. And those are examples we can learn from because they’re not so far out of reach. You know, it seems like hearing you talk about kind of your philosophy, all this stuff, there’s a lot of soft skills involved and a lot of hard skills involved. You know, in the soft skills, there’s asking the right question, getting insights. But the hard skills are there to knowing how to build a lookalike audience, knowing how to score an audience so that, you know, that creative, professional group, you know, knowing which ones might also buy this other thing. Yeah. What does that mean, practically? Does that mean that to do this? Well, you have to be left brain and right brained, or does it just mean that the team as a whole needs some left raiders and some right brainers?
Piyanka: And that’s a great question. And I may take it in a different direction than you probably were going to. Let me answer your question specifically, and then I want to caveat that, because it may leave the audience thinking something different than what I intend to do. So how will we you do it. Not everybody is a super simple man, and not everybody would have a very, very balanced right and left. But those who do tend to do very well in analytics. So that is true because there is a green flag that is a blue track. Those who are able to influence and communicate effectively make the right hand shake and follow a process and do analytics. You know, they tend to do well in analytics. But all of this is learnable. So at the bottom of all this, this is all a recipe. If you can follow a recipe, if you can. If I were to say, do you know how to make fettuccine? You if you’ve made it before you like. Yeah, I do. And if you haven’t, let’s assume you haven’t made it. Like, okay, give me a recipe and I can try it. The first time you make a decision, it would be perhaps okay. And depending on your cooking skills, I don’t know that they’re very small. There is no restaurant. Okay, second time, third time, fourth time. This time you would get really good because you’re following a standard set recipe. It’s not like it’s not rocket science. So analytics is like that or analytics as applied to business. What I call business analytics is like that. What the blue track, which is a data science track under the decision stone track. Science track is learnable so far for the for the statisticians in the audience. Those are done. You know, those who have those who can do regression in their sleep, they often need to learn the green track, you know, how to communicate their findings, how to influence, how to bring stakeholders together. And those of us, especially, you know, product managers, marketing managers, operations managers, we often good on the green track, right? We’ve done this many times. In fact, we’ve been successful only because we know how to communicate. We know how to get people together with us, build our product, the product, the app that we want. Right? So we have good with that, but we don’t have necessarily the skills. But that skill doesn’t necessarily need you don’t. Okay. So let me back up. 80% of the business problem can be solved by very simple methods, simple analytics methodologies. They are roughly aggregate analysis, correlation analysis, sizing and estimation is another one of them. And trend analysis, these four are pretty much can solve most of your problem. And this is learnable. This is doable in Excel. If you have access to data in whatever form and you can add that tool or or Excel, you can get the data in Excel, you can do analysis yourself very easily. Those the data science data. Yeah. So it’s learnable. Very easily learnable. So it’s not like you have to be statistician. Yeah. To be effectively able to use analytics or to, to drive decisions based on data as a product manager, if you’re a product manager and, and or a marketing manager or operations manager or whatever have you, and you want to use data to drive insights you can by just following the recipe. Yeah. And this recipe, by the way, is completely outlined in our the book, which is coming.
Bronson: Up that doesn’t ask for those for data and science. Things you just mentioned are going to be in the book.
Piyanka: It is all there in the book. Okay.
Bronson: Because I was like, where do you find that information? Because that’s what I want to know now is tell me those four things in a little more detail. So that’s awesome. We’ll definitely get that book.
Piyanka: Okay. So yeah.
Bronson: Let’s, let’s talk about the book a little bit. So it’s called Behind Every Good Decision. So tell us about the title. You know, it doesn’t sound like a hardcore data analytics book when it’s called Behind Every Good Decision word that title come from. What does that mean?
Piyanka: That Typekit came from analytics, from testing. So I read a book from Tim Gravel called How to Sell a thousand copies of the book or something like that. And in that book he talks about how to choose a title. And that was it was timely. I picked that book right when I was when we were writing this book and last year and I think his book came out in June sometime. And and essentially it’s it’s a it’s a multi it’s it’s an automated test. And this is what we did was we basically asked all our all our base outright, as you know, hey, we’re writing this book. And what we what we are trying to do with the book, what would you suggest the title should be? And people send their request. They they send the request. You know, I think it should be this, that and all that. So we’ve gotten like, I would say, 100 plus titles of different permutation. And. Are you there with me? Yeah, yeah, yeah. Okay. All right. And so then we essentially did a B testing. There is a site called Pick two and big four dot com and essentially does, you know, you show it to a to a random audience and B and it’s they tend to choose between the two and they they tell you why. So that’s how we came up with it, you know, and then we move stuff around to really come up with behind every good decision now behind every good decision one by three X over the next title, which was suggested by our publisher. So the above the does what was suggested by the publisher was had analytics in it and kind of like some of those things which you would expect and analysis book by one by four times because at the end of the day now that we look. Back. At the end of the day, does anybody care about analytics? Even I don’t care about. I care about.
Bronson: Good decision making. That’s it. They care. That’s what that’s what makes our business move forward.
Piyanka: Exactly. Exactly. I really don’t care. I mean, you can give me guidance. I mean, if you have a crystal ball, give me a good answer. I don’t care how you’re getting it. And this is one systematic way of getting it right. So analytics is your crystal ball and if you do it systematically, it is your crystal ball. So that is what it is about. This is about how do organizations make good decision? How can you as an entrepreneur, as a startup or as a fortune, as a product manager, as a leader, how can you do your part so you can move the business forward? That is what this book is about.
Bronson: I love the idea of that title, and here’s something I actually tell my team. Sometimes I tell them If we can make 100 right decisions in a row, we’re going to dominate. I mean I mean, because it’s impossible to do that, you know, I mean, you can’t make 100 right decisions in a row. But just the idea that all this whole thing is all business is about is just one decision after another. And if I can string together enough good decisions, yeah, it’s going to be hard not to take market share.
Piyanka: Right. And you would make you will make bad decision because at the end of the day, it’s all hypothesis driven, right? Then you are going in looking for that politically treasure. You are putting your bets. You will fail four out of five times. Are you are you maybe five out of five times? But you will fail faster so that you can say, okay, now that I haven’t found my goal, where the next five bets that I can put my dollar and my effort and my team and my resources. And so it’s it’s about failing and failing faster and then succeeding. You will fail, but you will fail faster so that you don’t waste time. And then a year later you realize, okay, I’m going to sell this business because it doesn’t work.
Bronson: Yeah. So who’s this book written for? Is it for the CEOs? Is it for founders? Is it for established middle managers who should be reading this?
Piyanka: It’s essentially for four set of client readers. One of them first one of them is is middle managers and people in the trenches. Right. People who are in their day to day making decisions. They’re thinking about a marketing manager’s thinking, I have 100, $100,000 marketing budget. Why should I spend it next. Mm. It’s for those people who are, who have, who are making decision on day to day and they need they, they haven’t, they haven’t, they have access to that and they have an opportunity to optimize that. Mm hmm. So that is it’s the first like the middle manager. And so basically what we we call business professionals, then it’s for business leaders, people in leadership roles who are managing personnel who need to figure it out. What do I do with my team? How do I actually compete on data? Because everybody’s competing on data. How do I do that? It’s it’s for them. There’s a section for them in there. And it also gives them an idea of what is that? What is this really about? What does analytics really about? Like you talked about, it’s all hype. It is actually. And they need to really see what it is. Not beyond the hype. Sure. Then it is for data scientists and it’s professionals. A lot of them, as I was saying, are good at the blue track, the data science track. They often miss the green track. And I speak at this conference called Predictive Analytics Board and an E metrics and a big percentage of the crowd are for predictive analytics world is data scientist. And if you ask all of them and as I talk to them, I’ve been talking to them for like last five years and I talk to them and ask them what’s the number one problem that they have? I’m building the best of the model and nobody gives a shit. That’s the biggest problem they have. They don’t have necessarily the green skills, green track skills, the decision science skills. They they’re doing good work, but it’s going to waste because that the whole thing about building alignment with the stakeholders, influencing communication that’s all missing so they can, they have a lot to learn and that’s what this book will do. And then the fourth set of readers this book is aiming to target is a we have actually a university professor which teaches these upcoming professionals, people who are taking courses. And it’s for them for people who like to you know, I’m I’m coming in, I’m majoring into marketing, I’m majoring into product. And maybe it’s a it’s an executive program. It’s or maybe it’s an MBA program with with a focus on that. It’s for them to learn how analytics in the real world or actually decision making in the real world. I would say no matter how can you use it effectively. So it does work for them. So it’s it’s a little bit of a wide audience, but we have something for them for each one of these.
Bronson: This group. Yeah, well it sounds great. I’m going to buy it personally and read it because I want to know those four things and how to dove into those a little more. So that’s what got my interest. But I know it sounds great though, because this has been a great interview. Have one last question for you. It’s a question that I always end with with all my guests, which is what’s the best advice you have for any startup that’s trying to grow?
Piyanka: Do growth hacking. There you go. There is a method to this madness. In fact, you and I connected perhaps with a blog which I wrote on Forbes, which has a video component to it, and that’s where I’m actually teaching. How do you do growth hacking? There’s a whole method to the madness. So do growth hacking if you want to grow the growth hacking systematically.
Bronson: Yeah, I like it. Parker, thank you so much again for coming on Growth Narrative.
Piyanka: Thank you very much for having me.
Get the strategies, motivation, and in-depth interview with all the details every week!