#118: No Need For Complicated Analytics Dashboards

Please support the show if you liked today's episode:

• By rating and reviewing the show in the app that you are listening to, you will enable us to invite bigger and more impactful guests.
• Please subscribe and get notifications when new episodes are released. Sign up with your email below.
• Share the podcast with your family, friends, and co-workers.
• Tag the podcast on Instagram @clauslauter and let me know what you like about it.
• If you like the content and would like to support the podcast, you can buy me a coffee here.

Claus Lauter: Welcome to episode 118 of the E-Commerce Coffee Break podcast. Today our talk was Jonathan Albrecht, co-founder and chief product officer@blip.ai about why there is no need to follow complicated analytics dashboards anymore. This is an interesting episode, so stay tuned and let's get started.

Claus Lauter: Hello, and welcome to another episode of the eCommerce Coffee Break. As a store owner, you have to wear many hats. For instance, product creation, marketing, fulfillment, customer sort, finances, you name it. On top of that, you have to be good with Google Analytics, data analytics to measure the KPIs of your business.

But not everyone can read data well. So how do you gain insight into your Shopify store performance without being a data analyst? That's a topic we're going to talk about today, and with me as a guest on the show. I have Jonathan Halbrecht. He's the and chief product officer of blip.ie. That's BLYP.IE

about where there is a need for a dashboard, or if you can make this actually easier. Jonathan is an experienced analyst, consultant, advisor, and team leader with deep roots in eCommerce. He has over a decade of experience in designing solutions that support data driven decisions through powerful insights.

As former VP of client consultation at Nielsen and founder for Data Driven Companies, Jonathan co-founded BLIP to enable e-commerce SMBs to leverage data analytics at an enterprise level. So let's say hello to Jonathan. Hi Jonathan. How are you today? Hey, how's it

Jonathan Halbrecht: going? Thanks for having.

Claus Lauter: Jonathan, share a little bit of what you do, where you're coming from, what got you into data.

Just give us a bit of an outline. .

Jonathan Halbrecht: As you mentioned, I'm, , , the co-founder and chief product officer at blip and you know my background, I say that in the past. , 20 years I've been, helping people make sense from their data. I did it for, , multiple hats.

I did it, , multiple roles. , for me, data's always been something I really like and really love, and I've found that I'm. Also really good at explaining, data and the stores behind the data for people that I work with. , I also had my own econ , store, , back in 2006.

I imported oil paintings from China and I sold them over the Internets, , in Israel in 2006. Before, Facebook, before, iPhones before mobile advertising, , It wasn't a really successful one. , but I gained a lot , of insights, with two years that I did that.

So I had a lot of, e-comm experience. And then, also that a lot of data experience, where everything combined for me, , when we founded blip, , is combining those two passions or these two things tonight that I had some experience.

Claus Lauter: you gave a good example there.

A lot of, , , small, medium business owners are coming from a complete different walk of life. , they started with a side hustle and now they're running a business. , they might be coming from a creative side and now they have to deal with data. So where does BLIP help with that?

Jonathan Halbrecht: What we noticed is that, everybody knows that to run a really good, , store or, , these days you need to be really good with your data.

, and then you're making data during decisions and need to, either run iterations or, look at, know, opportunities for optimizations and things like that. , but the, what we found is actually that, wild data's everywhere and there's a lot of tools out there to, slice and dice it , and do the analysis and run reports.

What most people are struggling with is actually, , reading into the numbers , and making sense of it. So it's not about the availability of the data or the availability of the tools by which to do that. It's the data literacy or just resources, time or anything like that, , to actually sit down and understand.

What those numbers mean for you and how you're able , to use them for your benefit. It depends on the size of the store. , most of the, smaller stores or medium tier stores maybe don't have a big team of, data scientists or analysts to sit down and do that for them. And even so, when it's, the analysis itself is, is human base, it's very, , time consuming and is a part of the reason why we, set up blip, which is.

more like an interpretation layer that sits on top of your data and helps you make sense out of it , and really find those opportunities for optimizations or prevent revenue leakage or, keep you safe, whereas it's something that you either don't know or don't have the time to run for yourself on an ongoing basis, 24 7 and things like that.

What we provide is basically not another, dashboard with like numbers reports for you to, , go through. , we provide the actual, , summary of what that data means to your business. What is the potential impact on your business , and moreover what should you be doing about it.

So we really strive to, take as much off the plate of the whole data analysis progress process , for decision makers that they can understand their data and know what to do about it. So this is where BLIP fits in this whole process.

Claus Lauter: in other words, I can stop staring at dashboards.

Not understanding what I'm looking at, but BLIP helps me with understanding what's happening on the other side. How does BLIP do that? ?

Jonathan Halbrecht: we can run, , tens or dozens , of analysis at the same time, whereas you need to look one by one reports and find that.

, how we do that is, we connect to the data stack of the current data stack , of stores. We work currently with Shopify stores specifically, and we connect to Shopify data, to Google Analytics. We now connect to play. We connect to, , Facebook ads , and more, , coming in. . And then we do a lot of data cleaning and crunching behind the scenes.

We create, , data warehouse for each store, on the back end. And then we do a lot of classifications to understand each store what are , the individual characteristics of that store. And we really want , the analysis and the insights , to fit the nature of each store individually.

Some stores have like lots of skews, some don. Some, , have a, long purchase cycle, some, smaller ones. And we really want that now to fit each store characteristics, and give them really something that is valuable and makes sense to them. , so after we aggregate everything, we have a lot of different types of analysis that we start running and we look at different areas of the business.

We look at, marketing and store and zero. We look at customer segmentation. We look at products and opportunities for bundling and everything like that. We look at discount code, so we run multiple analysis at the same time and understanding where are the opportunities? Like , some of the cases, nothing is wrong, that you're doing really well and there's nothing to report.

, but on the other ones we find where those opportunities are and we start floating them , and we start bringing 'em to the attention of, the users. So that's in very, like high, like broad strokes. Some of the things , that we do.

Claus Lauter: So how does Blip liberty information, I understand I'm not looking at the screen anymore.

Jonathan Halbrecht: We believe in two things. One is that we need to be really proactive and we need to find you where you are. Some of my experience working with analytical, companies in the past and, Nielsen, everything like that, is that, a tool is only as good as much like as you use it and if it fits , your current, , workflow and things like that.

We try to reach , and be proactive about where we reach, , where clients are at we do have an app. You can log into the app and you can, , experience that. It's almost like an inbox type of experience where you have all these insights and everything like that.

But we also send out emails and we're also working on a Slack integration so if a big teams are sitting within Slack and then , a blip can come up and fit in and have a channel that people can start, , talking about this and sharing it across the team. So , we're currently.

Emails in the app and working on Slack one, but in the future it might be a service integration and things like that. So we want to find you where you are at, right, where you're running your business, rather than just sit there and wait for you to come in for us and consume that insight.

Claus Lauter: Okay. So I reckon there's a lot of AI involved in providing this information because usually it's like, I don't know what I don't know. So I don't know what questions to ask. How does AI help you or blip with delivering the right information in the right moment?

Jonathan Halbrecht: There's some AI behind this.

, some of it is just, heuristics based. , and we try to, be able to identify, , Almost like simulate what an analyst would do, right? So if you had Alice on your team, what would they come up with? Like first, Like how would they identify what is the right thing , to bring right now?

So it could be based on, urgency, right? So if we see. Like a drop in your conversion funnel. It's like something you need , to take care of right away right now. , and then we try to look at whether there is a direct monitor impact on what we're showing you.

So for example, if we show you. , looking at some kind of customer segmentation and finding, , customers who may be, long term loyalists who haven't shown up in a long while, and there's a, like a lot big potential out there. So we try to assess what the monitor impact might be in your business and floats the ones that have, higher impact compared to others, for example.

So we look at multiple characteristics of an analysis and try to find like what is the right one to float in the right time. On the flip side, I'll say that, Part of, you're also trying to be very, , aware of mindset of our users, right? , people are managing a store, they're like customers and it's something stuck with customs.

And then you have customer support and then you like product delivery and they have a shooting for a brand. Like you have a lot of things on your plate right now. So we're trying not to bombard people with like, Hey, you. 10 red flags you need to take care of right now. Because we realize that cognitively people cannot take care of so many things at the same time.

So in a way, we're trying to pace it and say, , we're gonna send you a report just like for your Monday report and see everything, how was going on, but then maybe give you one or two things you can focus on this week. and the most important one we think of and do this week to improve your business even by slightly.

And then we find. , when you give them , to users that like a piecemeal and like step by step, it's easier for them to mentally know. Okay. , I know there's one thing that BLIP told me I should probably look at, , or maybe try to fix this week or next week. And then it's not, like, hey, there are 10 things, the ground's burning or something.

Claus Lauter: Very good point. Yeah. I see that a lot of store owners, , have to deal with, , too many things and they're looking for clarity and it's very difficult. So I think your approach is very good there. Now you said , sourcing the data from different platforms, from different apps. How does the integration work from your side?

Jonathan Halbrecht: , the integration is pretty simple. , most of these, apps, , have a, some sort of APIs that they expose the data for. It requires , the user to go in and connect us, or, in some cases, put in an API key or whatever it is. But, it's rather frictionless in that sense.

Once you put an integration or you sign in, , we then automatically starts pulling everything together. We already know. Based on, multiple stores, what type of data do we want to do? What kind of, models do we wanna build on the back end? , so the integration stuff is pretty seamless.

Once we've built that, , it's pretty easy. , and, frictionless from, the client side. , in some cases we come up with, recommendations. Some of the things you might want to change on your end. So, for example, we've noticed that. When we start downloading, , like Google Analytics data that some stores, , have not enabled, like enhanced e-commerce, right?

, it's just a checkbox that you tick, on Shopify and then on Google Analytics so that Shopify can start sending more like detailed events to Google Analytics. And yet it's something that, most people don't have, like a really good knowledge of Google Analytics. You've used it multiple times, but maybe you haven't set it up by yourself in the past.

it's just something, if nobody told you that before, something you never do and you're missing a lot of that data. When we notice it, and there we have some, opportunities to notice some of these things as we start, , downloading data. float and we alert and say, Hey, if you really wanna see some really, deeper insights and more data for yourself to explore, check that box, do that modification, and you should probably configure that one.

So we try to help our users with also how to get even better data, , for the analysis.

Claus Lauter: . , who's the perfect user from your site? Your perfect customer to use Blip ?

Jonathan Halbrecht: I'd say there's a lot. It depends on the size of the store. , we found that, , it's usually not for stores who just set up because they , don't have enough data yet to start, going through and getting more insights in.

Somebody who would just set up their store. It's probably not gonna benefit from that. , but once you start, getting your gears going and everything is set in place and, you start selling, maybe, 10 k a month , and start going up. You start accumulating data and you start having more and more probably platforms you start using, you're using, Facebook and using Clavio and you are using Google Analytics and the attentive and , there's a lot of things you're using out there you start.

Having data in silos, and you need to just like learn how to look at every, app and how to read their data. So that's when it starts to become, more complicated and more time consuming. And then this is where, our sweet spots, we start getting in, from when you.

Enough data for us to really, dive into, and then , we take stores. We have, , a broad range of stores , we work with usually until, like, you start having your own data science team who starts looking into the data ourselves. And even then we have cases where we just save them time, right?

So there's some things we can point out , and Direct their attention on , where they need to explore next. , but yeah, it's mostly not really, really when you start, but it's like once you start swimming in data and you need help with that, this is our sweet spot.

And then, We've worked with like founders and CEOs , who really want to understand their data and are really on top of all their processes. And we work with also, where it's like, eco managers and performance managers and marketing managers. Either they start having their own, subject manager experts and then their own tests and or things you want to look at.

We worked with agencies, so it's really broad. , it's mostly about, you just need to start having data , and start struggling with it. This is the perfect spot for

Claus Lauter: us. No, make total sense. You already answered my question as like, who's the person in the company? So that's answered. Yeah. When it comes to the store data and when we're talking a lot of data here, obviously data protection is a very sensitive topic in most companies.

How do you deal with that?

Jonathan Halbrecht: We keep each data for each store, , on their own. It's very, siloed. There's no crossing, there's no nothing like that. And we adhere to, you know, Shopify guidelines and obviously we encrypt our databases and everything like that. So everything in terms of data, you know, privacy and protection is, up to, whatever standards they are.

And, we've seen whether there are GDPR requests coming in to, , delete the records, anything like that, we see that we adhere to all. Policy itself. We keep with the standard , of the industry in terms , of, how we do that. And obviously we never cross, paths between stores.

If we have multiple stores and we see shoppers, shopping in multiple ones, it's all , very much like siloed. It's because , we wanna keep the, integrity , of each store.

Claus Lauter: Okay, so how long does it take to set it up how much time do people need to include in their day to get the set up, to get the training, to get through the whole process?

To get up and running? Yeah.

Jonathan Halbrecht: Yeah, it's a good point. , obviously, For smaller stores with less data, it's gonna take less time for, , larger stores. It's gonna take, more time to it, get everything in sync usually when you onboard, , we ask for, 48 hours.

The truth is behind the scenes is that we start seeing the data and start crunching. It's much sooner than that, but we wanna have safe space, for whatever happens. , sometimes, we keep on doing a lot of the data processing and downloading behind the scenes even after those 48 hours.

But we know we can start showing you value and we can start, floating these insights and monitoring your. key metrics, , starting that point in time? So there's no code we install, There's nothing, because we don't collect new data in a sense.

We, connect to whatever data you already have. So the onboarding itself is really easy. you log in, you put your name in, and then your email, and then you connect. Stores and whatever it is, you can finish , the onboarding in two minutes. , that's the average, what it timely takes, the majority of time takes for us to start bringing in and crunching it without the users, , involvement.

So for user, it's really, really fast.

Claus Lauter: So you have been in, , data driven companies for a very long time. Give me a gold nugget or some kind of, , challenge that you see or mistake that companies make all the time and where they can get better.

Jonathan Halbrecht: Yeah. , I see a lot of it actually.

There's a lot of things I mentioned that are very, low hanging fruits, , in terms of just tweaks and things you can set up and, You would've thought that, it's only true for smaller stores, but we see it , for larger source as well that have just not enabled it.

For example. Now I'll give you a couple examples, we looked at how many people are, , signed up for , getting marketing, materials , from the companies. And we look at the percentage, and actually you publish something about this on Twitter, but , what a percentage of your customers has signed up for getting marketing materials. And then, pop up and you enter and everybody's like, Hey, you know, sign up and get 10%, 15%, whatever it is. , that's one thing, that's one mechanism.

The other mechanism specifically on Shopify is that when they start getting to the checkout phase, there's like a small checkbox of, you know, hey, you know, get, sign me up to marketing materials and everything like that. And what we noticed. It's something that on your Shopify setting, for example, you can set it up to be by default, checked, checked, on versus checked off.

And, it's a very small tweak, but like we see a lot of stores that don't do that , and they're missing out on a lot of. Customers getting into their lists and, , we know that a lot of, their revenue is coming into the stores come from retention and from email campaigns and things like that.

And I think that's a missed opportunity. It's a really, low hanging fruit that, , stores can, notice and, do that really quickly. I'll give another example. Like discount codes, right? So we do a lot of the discount on analysis and see, a part of , one of the metrics that we look at and see, , how people are using it.

New buyers, old buyers , and returning buyers and, usage general. And what we noticed that, , When people are just setting up the store, , maybe , a discount code is something that is evolving with the age of the store, right?

But a lot of people, , either forget, or , they don't notice that they don't limit the usage goes of coupon goes. And it's not limited either by time. Or by, usage one per customer or by one order. And what we start seeing for some of those, , stores, there's an abuse, ?

There's an overage of specific coupons, for example, and we see that it's either floating out there in we tell me not deal spotter, like all these coupon codes , and you're losing a lot of revenue. Or we saw that, , people who, for example, they got the welcome code, like welcome 15.

They got 15% of their first order, but it was never limited to the first order. And they keep on using the same coupon for multiple orders and it's like you gave someone a discount for life for 15%. Now, if that's your strategy, that's fine, but I feel that, there's a lot of things like small things where you're leaking revenue and.

Small things for optimization there is very, very easy to fix. It's not huge insight looking to the data. It's just small things you need to notice, which we've seen in the data and obviously we float to some other

Claus Lauter: users. Yeah, very good tips in there. I have seen these things and I almost admit I have one of these things have happened to my own store, so very good tips there.

Where can people find out more about blip?

Jonathan Halbrecht: We , can find more about, blip, , either on our sites, , blip BLYP.AI listing page, , we have , a link we can share out. , we have a, LinkedIn accounts, social accounts, Twitter accounts, so we can.

Probably everywhere. , you can reach out to us directly. We're very friendly and we love talking to people, and users and, reach out, , email or intercom. I'd encourage everybody and then wants to maybe talk or have any kind of ideas or questions, just reach out. , we love talking to people.

Claus Lauter: Okay, I will put the links in the show notes. Then you're just one click away. Jonathan, I really enjoyed, , this chat today. I think there was a lot of good information in there and specifically with people who are not completely data driven, I think BLIP might be a good option to improve their business.

Thanks so much for your

Jonathan Halbrecht: time. Thank you for your time. I love it being here. Thank you. Bye. Bye.