Your Automated Data Scientist | Jeremy Levy from Indicative

Typically, if you want to gain sophisticated insights into your marketing and sales data, you’d need data scientists and engineers to custom code a solution.

This can be expensive and time-consuming. But Jeremy Levy wants to make it unnecessary with his company, Indicative.

In this edition of UpTech Report, Jeremy talks about Indicative’s product, which offers an automated customer analytics platform specifically designed for product managers, marketers, and data analysts.

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Jeremy Levy is the CEO and Co-Founder of Indicative a Customer Analytics platform for product and marketing teams. He is a serial entrepreneur and a veteran of New York City’s Silicon Alley. Jeremy Co-Founded Xtify, the first Mobile CRM for the Enterprise, acquired by IBM in 2013.  He also Co-Founded MeetMoi, a pioneering location-based dating service for mobile sold to in 2014.

DISCLAIMER: Below is an AI generated transcript. There could be a few typos but it should be at least 90% accurate. Watch video or listen to the podcast for the full experience!

Jeremy Levy 0:00
How do we take the enormous amounts of data that we’re collecting across all of our channels across our product? And how do we synthesize that into something actionable from a product and marketing perspective?

Alexander Ferguson 0:16
Jeremy, excited to chat with you today to begin, can you share very briefly five seconds? What is indicative?

Jeremy Levy 0:23
Credit? So indicative is a customer analytics platform designed specifically for product and marketing teams, to allow them to understand, analyze, and ultimately act on data surrounding the customer journey?

Alexander Ferguson 0:34
How did you arrive to building this solution? What was the problem you initially saw? And then you’re like, we got to solve this bit of

Jeremy Levy 0:43
a long story, I’ll try to give you the short version, the short version effectively, in a prior company that I had founded and was operating, we wanted to be as driven as we possibly could. And we struggled with the notion of how do we take the enormous amounts of data that we’re collecting across all of our channels across our product? And how do we synthesize that into something actionable from a product and marketing perspective? Long story short, we looked at products and market, and nothing really satisfied our use cases. And we decided to roll something ourselves internally. And I think like a lot of products end up being born out of necessity. And so we effectively spun out indicative from another startup. And then began building the customer knowledge platform that we want it

Alexander Ferguson 1:29
the best use cases, for sure, or come out of a problem that you initially see. So basically, data analytics turning into actionable tactics, is that kind of the nuts and bolts? How do you then can give a use case of that in action for

Jeremy Levy 1:44
so? Absolutely. So it’s even more complicated than that. So when you think about this notion of data, there is an ever growing amount that we’re generating as a society as a company, the ability for us to generate data has increased with the use of new channels, new touchpoints, with customers that generates data, but harnessing that information is really difficult. So from a traditional sense, if you’re storing this data in a data warehouse, synthesizing that into something actionable is very hard, because the customer journey analytics, or typically, time series analytics isn’t the kind of thing you can just write a SQL query for. So I’ll just say that, again, this type of analytics is not something you just whip up a SQL query for. And so the traditional BI tools around understanding customer journey, don’t really have the ability to answer these questions by themselves. So what does that mean? Historically, the way that’s been solved, is by having engineers or data scientists, data analysts write both code and sequel that synthesize this information into analysis that then yields quote, unquote, insights. What our platform does, is does that for you automatically. So our platform is both a visualization tool and an analysis platform. So examples of that would be if you want to look at say, simple KPIs like signup, or pageviews, or even purchases, that’s very easy to do. But understanding how a signup or page view or even much more sophisticated interactions with your business, open up an email, different nuances of your product, how those actually interact to drive outcomes is really challenging. So maybe a very simple example is, you know, if you’re looking at a actually, we have a customer who’s one of the major insurance companies who sells life insurance, their onboarding process is very complex, because their goal is to bribe you with a quote within the first few minutes of using their application. So they have to ask 20 or so very detailed questions. So now they can easily look at how many people start that process, they can look at how many people answer each one of those questions, but understanding the nuances of how people are answering those questions where they’re dropping off. And what are the different influences of the nuance there that contribute to someone ultimately, arriving at a quote, and then purchasing life insurance for them is really hard. They’ve used our platform to understand those individual behavioral nuances, the combinations there that lead to greater outcomes, and then take that information and apply to both how they’re changing an onboarding process, but also even how they’re acquiring customers understanding of some channels have a higher efficacy towards this completion goal, as opposed to just people who show up at a landing page.

Alexander Ferguson 4:30
This quantity of data that’s being created is only growing. So companies that have these, this data warehouses used to talk about, there’s tools that it comes into one of those Google Analytics, so you have all these different applications, but they don’t really help you know, what to do with it. And you’re saying previously, they would have to create their own ways of pulling it out and developing it. Yours is supposed to just simplify that sounds like integration is a big thing then into whatever platforms they’re already using. Tell me a bit more about the technology. How does it work? How does it seamlessly integrate hasn’t made it easier for them.

Jeremy Levy 5:02
So it’s worthwhile to take a second talk about how our, our industry is evolving. So by and large, if you wanted to have a data warehouse, you know, 567 years ago, you know, this was, if you wanted to do that, you’d have to contact IBM and do a multi million dollar contract. And as I’ve everyone obviously knows, from infrastructure perspective, the cloud has taken over all of tech. Today, if you want a data warehouse, then you know, literally, in the span of this conversation we’re having, we could have signed up for AWS or Google Cloud and you know, provisioned a enterprise quality data warehouse. And so in the past, because the technology was inaccessible, many companies in our space, when they collect your data, or when you send them your data, they store it in their own proprietary data warehouse. Today, anyone can have a data warehouse, whether you’re, you know, a two person startup in a garage, or you are, you know, the largest of companies. What makes indicative unique, what makes us special is that we’re the only analytics platform that connects directly to a data warehouse, and therefore alleviating the need to have to duplicate your data into a third party’s proprietary store. And secondly, because we automate this type of analysis, like I mentioned earlier, it alleviates the need for product teams to have to depend on data teams in order to quickly iterate with data in their product decisions.

Alexander Ferguson 6:33
What so this living out to a reality of a product manager being able to have access to this? Is it just faster execution of all right now, data is automatically come in, I can make decisions and adjustments now. Within a week or a month versus what used to take me longer. What’s that that time change?

Jeremy Levy 6:51
Well, I think the effect of this has been that product by and large. We used to think of it as an art, you know, I have a knack for product, we should do this, I think that’s going to work. And I’m obviously I’m speaking a little bit, you know, hyperbole here, what’s happened is we now have the ability to harness this data. So product has become much more of a science in and of itself. So how do we apply that in whatever your question was? What is the timeline, if you acknowledge that we should be using data to drive your product, historically, understanding the nuances of how people are using your product used to be a process that could take multiple days, two weeks to reach a single conclusion today, and I think the way we believe in this is that you should be able to make many decisions, or at least get access to information at your fingertips that helps inform those decisions. And, you know, for people who work with data, it’s never just there’s one answer. Typically, it’s you know, you generate an analysis or a chart. And you say, Huh, that’s interesting. And then you say, I want to look at this 10 or 20, different other ways and other runs. In other words, rerun that analysis, but tweak it slightly and see if you can, you know, look at it from a slightly different angle and help that inform more decisions. And so by providing access to this type of data, this type of analysis at your fingertips means that we can make smarter decisions about our product and our business faster, which I think is it’s a, it’s obvious, then that can also lead to better outcomes, more success stories, more revenue, ultimately, for our business.

Alexander Ferguson 8:28
Where do you see indicative in the near term, next year? So where are you guys headed in the long term, next five years.

Jeremy Levy 8:35
So I think about sort of the evolution of how we use data. Starting with this notion of having a data warehouse just means that we’re able to have this data, there’s been an explosion in the CDP space, which means we can now capture more and more data. The first wave of utilizing this data is predominantly around descriptive based analytics. Show me the counts show me, you know, the the counts in different dimensions of that data, we’re moving towards a place and we’re indicative focused is focused for analysis revenue in the future is, while our toolset today provides a lot of the ability to look at it from different angles, you’re ultimately searching for those light bulbs for those insights, light and make the decisions. And as it turns out, within our domain of analytics, a lot of the use cases our customers have for girls to the vertical they’re in all typically fall around increasing engagement, decreasing churn and how to influence outcomes. And so our goal with our product, as we look to the future, is to move our toolset from one that provides you with more knobs and dials to get into to come up with your own conclusions to one where we can actually tell you what the insights are because we know the basic patterns. And so when people come to typical BI tools or custom analytics tools, they go in they ask those 20 questions to help reach a hypothesis. We know what you want to achieve. Why can’t we just tell you exactly what the answer is that you’re searching for. And so when we think about sort of the time to value aspect, that’s almost what I was alluding to earlier, we’re trying to look at how do we get that time to value aspect even shorter?

Alexander Ferguson 10:14
And when when, when is that reality going to come in where you’ll be able to give that analysis never even have to search through the data?

Jeremy Levy 10:21
You know? That’s a hard question to answer, right. I don’t think there’s any product in the market in any space. That is that is able to give you crystal clear crystal ball predictions on what’s going to happen to the future. So I think, you know, I think aspirationally we’re all going there. And I think in very narrow domains, there is really interesting stuff going on around making those predictions. But if if you know, if you want to, if you want sort of my my quick take on at least indicative in our ecosystem. There are bits and pieces where we can do that today. There are bits and pieces where we do that in our platform today. I think sort of the Holy Grail here is, is still many years away. And fortunately, you know, when I when I think about the data ecosystem in and of itself, it’s evolving incredibly quickly, which is really exciting. But in terms of the overall evolution, we’re still really at the nascent stages, we’re just beginning to really harness data in any meaningful way I think of I think the opportunity or the value is still very much in front of us rather than where we are today or where we’re coming from.

Alexander Ferguson 11:27
Appreciate the time to share this Jeremy. For those who want to learn more, definitely check out That concludes the audio version of this episode. To see the original and more visit our UpTech Report YouTube channel. If you know a tech company, we should interview you can nominate them at UpTech Or if you just prefer to listen, make sure you subscribe to this series on Apple podcasts, Spotify or your favorite podcasting app.



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