Shownotes
Running a Shopify business and want to learn how the By the Numbers app can take it to the next level?
Tune in with Kasim as he interviews Piam Kiarostami and Cyrus Sadaghiani, experts of the By the Numbers app, and find out what this amazing tool can do for you. Discover how it eliminates guessing games around ad spend success by tracking money movement between your business and clients giving you real results.
Furthermore, learn which KPIs businesses should pay attention to, the importance of diving deep into the factors that drive consumer behavior, who benefits from By the Numbers, and so much more.
With this helpful app, your Shopify business will no longer be flying blind when tracing ROI from campaigns or figuring out who’s buying what. Let By the Numbers give you an accurate understanding of what your business is really doing with comprehensive analytics.
Watch the video now and learn more about the power of data-driven insights with By the Numbers.
Mentioned links:
By the Numbers: https://www.bythenumbersapp.com/
Check out GoHighLevel.com:
https://www.gohighlevel.com/main-page...
*The link above is an affiliate link
0:00 How To Grow Your Shopify Business With By the Numbers
2:26 The critical KPIs every business should pay attention to
6:22 Looking into commercial factors that drive consumer behavior
12:27 Start by setting your goals
16:10 Connect consumer behavior and journey with your results
22:40 Who uses By the Numbers?
28:20 Learn more about By the Numbers
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Transcripts
Welcome to Daily Google News.
2
:It's Kasim.
3
:I'm here with my new best friends,
Payam and Cyrus Pym is the
4
:co-founder of By the Numbers.
5
:Cyrus was just brought on for operations
works on the front of the house.
6
:I think of you Cyrus's mother hen.
7
:The way that you guys introduced
it is Payam is like, he's the
8
:founder of The Visionary and
he needed co-founder somebody.
9
:Co-founder.
10
:Fred is the visionary founder,
engineer of the product guy.
11
:Correct me, dude.
12
:Whenever anybody introduces
me as the Founder Solutions,
13
:and I'm like, that's right.
14
:I never bring up John Marin because
he gets enough attention as it is.
15
:But I like that y'all have the
structure you have because very often
16
:I think, Payam, when left to your
own devices, and I'm speaking as
17
:an entrepreneur myself it's really
easy to dive deep into the weeds.
18
:And it sounds like Cyrus
is here to kind of help.
19
:Navigate the waters for this amazing
tool, which by the way, I'm not
20
:an affiliate, I'm not an investor.
21
:I have no reason to pimp buy the
numbers out other than I'm a believer.
22
:Y'all have created something
that allows, and I'm gonna do
23
:my best to articulate the value.
24
:And then, you guys correct
me, it allows e-commerce store
25
:owners specifically in Shopify.
26
:'cause it's only for Shopify, right?
27
:Shopify strollers to visualize their
numbers maybe more importantly, and
28
:articulate their numbers according
to key performance indicators that
29
:people don't often think about or use.
30
:How did I do there?
31
:You did great.
32
:How would you do it?
33
:I would say that our core philosophy
is to track the movement of money
34
:between you and your clients.
35
:That is where we come from.
36
:We believe in that sort of ground truth.
37
:We are Switzerland.
38
:We're neutral.
39
:We don't get into those games of
attribution where you try to figure
40
:out where your ad spend saw success.
41
:We give you the ground truth of what
your business is doing by tracking the
42
:dollars, and we do it by the numbers.
43
:somebody's watching this and they're
not going to buy, buy the numbers no
44
:matter what period full stop because they
don't want to spend the 13 a month they
45
:don't want to forego the monthly growth.
46
:We have more expensive one,
but you know what I mean?
47
:Like it's, you look at and you're like,
it's stupid not to get this damn thing.
48
:And I noticed you have a free version too.
49
:Yeah.
50
:What I'd like to do is I'd like
to leave our, viewers better off
51
:than we found them regardless of
whether or not they use our tool.
52
:Yeah.
53
:So, and I think the way that we do
that is if somebody were to take
54
:what it is that you're doing and
they were to do it from themselves.
55
:What are the KPIs that you think
e commerce store owners need
56
:to pay attention to, no matter
what, that they generally aren't?
57
:Where's the blind spots?
58
:There are a couple that
I think are so important.
59
:And I think the biggest one, which
you can kind of do yourself, LTV.
60
:You have to be thinking, look,
you go out, you do your ad spend
61
:for some cohort for some month.
62
:Now you have to get some data, you know,
you should wait a month, at least a month.
63
:Right.
64
:But then you should probably
get more data before, how good
65
:that particular campaign was.
66
:Here's the thing though.
67
:Every extra month you wait to get
data, yeah, the data gets better,
68
:but you're paying for it and you're
not doubling down on the winners.
69
:You're not dropping the losers.
70
:This is where predictive analysis
and things like LTV can come in.
71
:There are a lot of ways to do LTV.
72
:There's, you could do, the dead
simple LTV, if you just open up Excel,
73
:figure out the average tenure, figure
out their average order value, and
74
:figure out their purchase frequency.
75
:Multiply those together for a
cohort for a given month, and put
76
:that out and say, okay, that's
what my October cohort was doing.
77
:Average tenure.
78
:Average order value, purchase frequency.
79
:Yeah.
80
:So they're spending 50 on an order.
81
:They're ordering 1.
82
:2 times basically before
they churn out or 1.
83
:4 times.
84
:Kind of know what they're worth.
85
:That's your question for new
starts or even younger brands.
86
:If you're sub two years old or even
sub three years old, I don't know.
87
:How often somebody comes back.
88
:I don't know what my retention rate is.
89
:I don't know my ascension rate is.
90
:How do you, guess at that
without being stupid?
91
:That is where machine
learning came in for us.
92
:There's by the numbers do that.
93
:Yeah, you just feed it
every piece of data you have.
94
:And because we've seen so much,
we can pick up trends like the
95
:browser that you use when you make
an order has a fingerprint, if you're
96
:doing it from a bank or at work.
97
:Yeah.
98
:Like you're at your work console.
99
:That fingerprint is kind of unique and
machine learning just figures that out.
100
:It's like, oh, this person's
ordering during the day and they're
101
:doing it during their lunch break
and they're doing it from work.
102
:And we have a pattern.
103
:We have an established pattern for how
often people who do that and buy at a
104
:certain level at a certain time, come
back and it just sort of just comes
105
:out in the wash and it gives you a
slightly better guess, but still a guess.
106
:And the longer they're with
you, the better your guesses.
107
:Cause there's just so much metadata.
108
:You like, look, you have
everything from their, their
109
:billing location, their, currency.
110
:Are you an Australia paying in USD?
111
:That's a different kind of
person than an Australia paying.
112
:their frequency is going to be different.
113
:Their ascension is going to be different.
114
:They were everything's different.
115
:And like, I couldn't tell you how I
could just tell you that when you throw
116
:all those numbers into a matrix and you
do, you let the computer figure it out.
117
:It gives you back magic answers
that we can then verify are
118
:better than our guesses.
119
:They're better than our simple
modification because we look,
120
:we make our predictions for
the next month and we check.
121
:Magic answer is the right
way to say that too.
122
:Alright, LTV, that's a really good one.
123
:What's another one?
124
:What's another data point
that Cohort retention.
125
:Define cohort format.
126
:Absolutely.
127
:Groups of...
128
:Niche...
129
:Prospects based off of interest?
130
:Yeah, there's two ways to cohort.
131
:The most, the easiest way to
cohort is just based on when
132
:they made their first order.
133
:Do it by month, or week, or whatever.
134
:Okay.
135
:Other way to cohort, a little
more sophisticated, I'm going to
136
:make a cohort based on behavior.
137
:It might be the people who
use a discount code, or people
138
:whose first order is product X.
139
:those are two times.
140
:That's so fun.
141
:So I could see the difference in retention
by people that order the tires before
142
:the car and the car before the tires.
143
:Yeah.
144
:Now I start to see like, Oh, I
know my acquisition on the car is
145
:higher, but I should go after the
tires because they stay longer.
146
:Yeah.
147
:You can also look at commercial
factors that drive that, right?
148
:So if you're looking at, was it the
trial pack that drove a certain behavior?
149
:Or was it the discount
that drove the behavior?
150
:Or was it the deployment of a
campaign that drove the behavior too?
151
:So within that, you're also really looking
for a human being's behavior patterns
152
:and what the result on your business is.
153
:That's brilliant.
154
:And you called that cohort what?
155
:that was cohort retention.
156
:what we just walked into was segmentation.
157
:Okay, so let's talk
cohort retention first.
158
:How do I do cohort retention
without by the numbers?
159
:You just grab your, you divvy
up your customers by the first
160
:month, whichever month they
first made their first purchase.
161
:if you bought something today
for your first time, you
162
:are in this month's cohort.
163
:You do it next month, you're
in next month's cohort.
164
:Unless you're already in this month.
165
:Once you're in a cohort, you're
in that cohort forever, and
166
:then you just track them.
167
:And then you can compare month over
month retention rates, and you can
168
:do that instead of Google Sheets.
169
:Like, do they, how
often do they come back?
170
:And what this tells you, by the way,
is like, if you ran an interesting
171
:ad campaign in October, and you
got a set of clients that spends
172
:more money, you'll notice their
average order value is higher.
173
:You might notice their LTV is different.
174
:You might notice that they come back
more often, or they come back less
175
:often, but they spend more money.
176
:You just have to look at like the numbers.
177
:Well, it's so fun for a paid traffic
guy, because for me, it's always,
178
:cost per acquisition, obviously.
179
:And if my, seasonal, you have a
bunch of products that are seasonal.
180
:So seasonal products, if the cost
per acquisition is higher during
181
:a certain season, you think,
okay, that's not the season to
182
:maximize the value of our ad spend.
183
:So if I'm planning my annual budget
and my cost per acquisition is going to
184
:be higher, then I might move my budget
where it could be lower, but looking.
185
:Post click, post conversion, I see,
wait a minute, my cost per acquisition
186
:is 20 percent higher, but the cohort
value is three times because the
187
:retention is higher, the average
order value is higher, I'm actually
188
:making an inadequate decision.
189
:That would be a very difficult thing
to identify without doing the data
190
:analysis that you're talking about.
191
:Yeah, and you should just go look
at your last year's January cohort.
192
:January, February, traditionally, not
the best months for almost any store.
193
:Why?
194
:I don't know why, I
always see, I see dips.
195
:It's post holiday expend.
196
:so we're fatigued.
197
:We spent all the money.
198
:I'm poor.
199
:I don't want to do it again.
200
:It's a real thing.
201
:They're pairing.
202
:So people are paying their December
credit card bill in January and Feb.
203
:They're broke.
204
:That's like a very traditional.
205
:Spend and seasonality model for
lots of categories of products.
206
:Interesting.
207
:But if you go look at their
cohorts, for some people, people
208
:you pick up in January, February
are really good customers.
209
:if you can project, cause like you have
your historical data, you're two years
210
:old, you've seen two seasonal sales.
211
:You can get a sense of
what these people do.
212
:Then you can say, okay,
it is worth targeting it.
213
:If I can think 12 months instead of
thinking three months, and that's
214
:what the historical data is for.
215
:to move from cohort to segmentation,
segmentation is this idea of what you as
216
:a vendor know, what customer behaviors
matter to you, and you should slice up
217
:your customers based on that behavior.
218
:What if I don't, what are the behaviors
that you've seen are perfect question.
219
:Most impactful?
220
:Absolutely perfect.
221
:we obviously start with the standards.
222
:We're like people who use the
discount code, people who've
223
:never used the discount code.
224
:People who are in your top
percentile spent people with
225
:the highest average order value.
226
:Mm-hmm.
227
:. And then perfect segue.
228
:R f m analysis.
229
:what is R F M?
230
:Yeah.
231
:Recency frequency, monetization.
232
:We call it loyalty.
233
:So it's a graph.
234
:That doesn't change.
235
:It's just boxes you fall into.
236
:So have you ordered on the left
hand side, you think about how many
237
:times you've ordered in the last
month, along the bottom, you'll
238
:think about how much you spent, and
then there is recency, frequency,
239
:how recent your last purchase was.
240
:So here's your best customer.
241
:They've made four plus
orders in their lifetime.
242
:They've spent the highest amount,
and their last order was last week.
243
:those immediately get boxed best customer.
244
:You have loyal customers.
245
:They do, they've done, they do like three
orders, and they consistently do it once
246
:a month, and they spend a lot of money.
247
:You have dormant customers.
248
:They made one order.
249
:They've never ordered again.
250
:And then you can just grab those
emails and each one of them, there's a
251
:different strategy to talking to them.
252
:Your best customers, they
don't respond to deals.
253
:They don't respond to discount codes.
254
:They respond to knowing
about what's coming next.
255
:If you give them a sneak
peek of a product, a new bag.
256
:You say, Hey, we're just giving
this first to our best customers.
257
:They respond to that.
258
:Your loyal customers and the ones kind
of under your, underneath that, you
259
:can activate them in different ways.
260
:You can give them newsletters, you
can give them, You can give some
261
:of the lower end ones discounts.
262
:You're dormant ones.
263
:You just spam like, sorry to say it,
but they're dormant for a reason.
264
:Oh, there's buy, you're on subscribe.
265
:You're talking to marketers here.
266
:You're probably on like, that is our jam.
267
:Yeah, you can activate them, but you
also don't worry about turning them off.
268
:you take more of a white glove approach
with the high end, and you take more of
269
:a spammy approach with the low end, and
as they change in groups, you change
270
:your strategy, which is why tools
like Klaviyo are so important, right?
271
:have you used Klaviyo?
272
:I'm obsessed with Klaviyo.
273
:Yeah.
274
:I'm excited for high level to
take Klaviyo over, by the way.
275
:Have you heard that they're doing that?
276
:No.
277
:No, I did not.
278
:High level is building out something
akin to a Klaviyo, not alternative, but a
279
:couple of those features, or at least they
were, I don't know if that's still on the
280
:roadmap, that'll be interesting to see.
281
:And if you're watching this and
you want to use high level, make
282
:sure you click on my affiliate link
in the description of this video.
283
:Nice.
284
:Go ahead.
285
:we integrate with Klaviyo, like Klaviyo
has these segments, but we could just
286
:take our segments, link it to theirs.
287
:We update it every time someone
moves in and out of a segment
288
:and Klaviyo knows what to do.
289
:And the automation just go ahead and
fire knows how to speak to people.
290
:How not to speak to people.
291
:Yeah.
292
:So analysis paralysis,
you give me all this data.
293
:I have all these
segments, all the cohorts.
294
:Great.
295
:I'm overwhelmed and I
don't want to do anything.
296
:Where's where's the
hierarchical structure?
297
:Where do you start?
298
:I love this question.
299
:You set up goals.
300
:This came to us.
301
:we're product driven company and our
customers come to us with problems
302
:and we do our best to solve them.
303
:This is not an uncommon problem.
304
:How do I get my team, say my sales team
to move when are they doing enough?
305
:When aren't they doing enough?
306
:when do I need to go and set a goal.
307
:And that's what goals are for.
308
:So find a way to set
sales goals for your team.
309
:We have predictive tools that
could tell you how much you're
310
:going to make in a month.
311
:And I recommend, depending on what the
emotions of your team are, you either
312
:set the goal below or above what you're
about to do anyways, to either give
313
:them an easy win when they need it, or
push them when they need to be pushed.
314
:you have analysis paralysis, but
sometimes a little positive kick, it
315
:goes a long way, which is why on our
sales go, we use a lot of emotes.
316
:You don't see emotes anywhere else
in our app, except in the sales goals
317
:page because that's to motivate.
318
:Like, you know, it's all rocket ship
when you make it, it's a sad face
319
:when you don't, it's a hurrah, you
can do it as you're getting there,
320
:because it is about that motivation.
321
:What are the secret KPIs that
you're seeing people base decisions
322
:off of that are counterintuitive?
323
:Like, what are the things that
the more sophisticated e commerce
324
:stores are paying attention to that
I wouldn't necessarily, it wouldn't
325
:occur to me as an early stage owner?
326
:Oh, that's so interesting.
327
:I don't know what's counterintuitive.
328
:I only know what people use.
329
:Well, I'm not trying to play gotcha.
330
:I'm just curious if there's
things on the periphery.
331
:I know that's really smart.
332
:You live in the corner.
333
:No, really smart.
334
:Cyrus, you got anything?
335
:I was going to go back to the segmentation
for a second and just double down on that.
336
:And the reason I do that, and it sort
of dovetails into your question, is that
337
:There is data or sort of insight within
insight and it doesn't just come down
338
:to the numerical or by the numbers, but
it's actually understanding what drove
339
:those numbers and the behavior behind it.
340
:So if you're saying what are the
things on the periphery that you
341
:would want people to like really
know about or look about is.
342
:Understand the numbers implicitly, and
I'm going back to segmentation here, but
343
:then double down on not just understanding
the numbers, the behavior behind them.
344
:So why did that free trial
create this response?
345
:Like we've segmented the data,
we've seen that customer group, and
346
:we've seen the results behind it.
347
:I want to go back and actually
understand how that customer behaved in
348
:the why, not just the results, right?
349
:And I look at that as a balance
between lead and lag measures.
350
:Right now we have the ability to
look at all these lag measures
351
:within a business's performance and
we have the ability to forecast and
352
:we have the ability to set goals.
353
:But one thing that you really need
to think about as a person who's
354
:running e commerce business, you have
to think about what is the behavior
355
:that's driving these results, right?
356
:And some of those things can
be more difficult to measure.
357
:And yes, we can track clicks.
358
:Yes, there can be fingerprints.
359
:Within the value chain of
how a customer responds.
360
:But for me, I was a loyalty or a CRM
manager at an e commerce business,
361
:all of this data and all of these
visualizations and all of this way to
362
:look at it, to me, that's table stakes.
363
:If you're not doing this, like.
364
:There's a huge huge amount of money that
you're leaving on the table If you are
365
:doing all of this Then the next step
for me is looking at the behavior of
366
:the consumer and the consumer journey
that is driving those results And so
367
:if i'm able to do that I can calculate
seven different versions of LTV and
368
:KPIs that no one's ever heard of.
369
:But if you're not building that
connectivity and understanding of your
370
:customer's behavior, which actually
drives those KPIs, that's the gap for me.
371
:And the people that I see who do it
really, really, really well are naturally
372
:curious about that versus trying to
think about how do I just move this KPI
373
:by half a percentage point this month
so I look great to my manager, right?
374
:That's the difference maker for me.
375
:I could do it in three words.
376
:if you're saying it took me
like, no, you're brilliant.
377
:This is where I got it from.
378
:Like everything you said is right.
379
:There's one KPI for that.
380
:That's like that.
381
:There are many products
purchased together.
382
:So that's exactly kind of what I was
driving at, I know they're on the
383
:periphery, you don't know when you put
it in front and center, it gives you
384
:the option to look at something three
dimensionally and start to optimize.
385
:yeah, that's a great one.
386
:you get that.
387
:it's a little bit too perfect because it's
not a number, it's, great products right
388
:yeah there's a number they're like this
product was bought 114 times together.
389
:You want to know what are pairing?
390
:And then you'll know as a vendor, what
to do with that data, you know, as a
391
:marketer, if you see purchases drop.
392
:Very often you could blame interest,
let's say, maybe people are buying
393
:mechanical pencils and they're buying lead
refills and all of a sudden you realize,
394
:Oh, my lead refill sales are down.
395
:We need to add ad spend to
the lead refill campaign.
396
:And it's like, no, dude, you ran
out of pencils and those two things
397
:are always purchased together.
398
:You're looking at the wrong dataset.
399
:Exactly.
400
:Yeah.
401
:Those that's fun.
402
:So I did some consulting for
infusion soft a long time ago.
403
:They're a dumpster fire now.
404
:So I'm not endorsing them,
at the time they were cutting
405
:edge they found something that
I thought was so interesting.
406
:They brought in a guy, some SAS
consultant, he'd built who knows
407
:what, and did a billion dollar
exit, really brilliant human.
408
:And he did an analysis of all
their customers and he found that.
409
:The customers that
retained did two things.
410
:They uploaded their contact list
into the infusion soft CRM, and
411
:they sent their first email blast.
412
:And you think like that's the most
rudimentary set of whatever that
413
:I've ever heard, but the infusion
soft built their sales team so
414
:that you didn't get commissions.
415
:Until the person you sold uploaded their
contact list and sent off the very first
416
:email blast and then their retention
went from like averaging three months to
417
:14 months because now you actually have
their contact information you're sending.
418
:I've always thought that that was such a
brilliant analysis and it was so simple.
419
:It was bam inflection point.
420
:We're going to focus on this.
421
:I also feel like you can do that in ecom.
422
:I don't know how, and it's really
easy for me to be like, you guys
423
:look what they did over here.
424
:How do we carry this?
425
:But you've got the numbers in order
to be able to identify, because
426
:there are e commerce stores where
I just feel like, yeah, we do.
427
:We have a client that does
barbecue sauce and there's this.
428
:There's a difference between somebody
that comes in and buys a sample
429
:pack and somebody that comes in and
buys a sample pack and then buys the
430
:barbecue to us every single month.
431
:And I just feel like there's
ways to influence that a little.
432
:You're so on point.
433
:We just set this out in our latest drip
campaign, where we told people that
434
:we see a 5 percent increase in sales
for customers that use segmentation.
435
:If you use segmentation by the
numbers, your sales are going up.
436
:So I don't know, it's dead simple.
437
:All you're doing is saying, let me
split my customers based on behavior.
438
:I don't like you could be doing a
million different things with that
439
:data, but just the act of thinking
about your customers in different ways
440
:and saying, I just want to know about
customers whose first purchase was this.
441
:Yeah, it's relevancy right guys.
442
:Yeah, it's being specific, maintaining
continuity, congruence with their journey.
443
:it's relevant to me my background, I
come from a package good space, right?
444
:So, consumer package goods, alcohol, Bev,
cannabis, that's my background and sort
445
:of what I love and get very curious about
is behavior and the why's behind it, for
446
:me, the segmentation piece is key because
you're actually speaking to the customer
447
:in a more realistic way and something
that's more relevant and it's less spammy.
448
:Right.
449
:Like it's more relevant to how I behave
and how I think as a consumer versus,
450
:this is a generic message for everybody.
451
:And that's the difference.
452
:I feel like you get me as a brand,
I'm emotionally connected to you
453
:dependent on what you're selling.
454
:The consumer may be ingesting
this product into their bodies.
455
:They may be wearing these products.
456
:They may be slathering it all
over their skin as a cream.
457
:And that has a real, real emotional
connection with the consumer.
458
:And if the consumer thinks for a second
that the brand is talking to them, the way
459
:they talk to everybody, that's a huge gap.
460
:And that's a gap that you can actually
close by understanding people better,
461
:whatever method you want to get to
understanding, whether it's, understanding
462
:products bought together, understanding
what drove trial, understanding what
463
:drove a repeat or a higher acquisition.
464
:It just comes back to you understand me.
465
:I feel emotionally connected with you.
466
:And as nerdy as it is using the
data can actually help you get to
467
:that understanding of the consumer.
468
:I feel like you need a podcast.
469
:You got that like classic communicators
cadence that just desires to be heard.
470
:You're good at this dude.
471
:I like everything that you just said.
472
:I especially like the way that you
connected numbers to an empathic
473
:understanding of the human condition
as it relates to your products.
474
:Because I could say like, Oh, I sell
barbecue sauce who gives a shit.
475
:Or I could also say, I sell something that
you're about to put in your children's
476
:body, which is what you just said.
477
:Like that's a sacred trust.
478
:That's a big deal, like I am responsible
for something that you're going to
479
:ingest, and I should look at it that way.
480
:I really love that you went there
Payam, you said something that
481
:I just said your name, right?
482
:Yep.
483
:Absolutely.
484
:You said something that struck me,
and I don't know if you're allowed
485
:to share this or want to share this.
486
:It's a two part question.
487
:How many Shopify stores
used by the numbers?
488
:Part one.
489
:Part two.
490
:Are you and do you amalgamate all
that data so you can see trends
491
:and start to look at like, oh my
goodness, the, planets are shifting.
492
:That's exactly it.
493
:retrograde.
494
:Okay.
495
:We cannot ever share data
about any individual user.
496
:Right.
497
:We can amalgamate that user, throw it
into a matrix do multiplication over
498
:it, and then everyone can benefit.
499
:In the predictive tools.
500
:So some industries are sort of leading
indicators or lagging indicators of sales.
501
:And we could use that.
502
:So the more data we have, the
better our prediction is for the
503
:individual, if that makes sense.
504
:That makes perfect sense.
505
:Yeah, it's similar.
506
:Yeah, but we never surface anything
because it's just machine learning.
507
:it's a matrix of numbers.
508
:You don't even have to reason that as you
get bigger you get better because you have
509
:more clients to build into the mechanism.
510
:I didn't share with you numbers.
511
:I have no idea.
512
:Do we share numbers?
513
:I'm not sure if we share numbers.
514
:I don't care, but this guy's
in charge of this stuff.
515
:Don't if you're not comfortable with it.
516
:There's reasons.
517
:I think you should always be honest
about everything and hide nothing, Yeah,
518
:well, you're the by the numbers guy.
519
:So let me ask you this.
520
:Categorically speaking, I think
we could speak percentages.
521
:What are your users are generally
what like consumables, apparel,
522
:accessories, where do you live?
523
:Question.
524
:Full analysis to figure out
the, do you have a sense?
525
:You don't know your own segments.
526
:I don't know my own segments.
527
:Listen, talk to Cyrus about this.
528
:Your sales list by 5%.
529
:As soon as Cyrus's bag.
530
:What I was going to say was we
actually just spent a whole bunch
531
:of time doing some segmentation
to learn more about our customers.
532
:And this span.
533
:Of our customers is really interesting
in the sense that from a revenue
534
:generation perspective, it's like A
to Z, like it's really, really cool.
535
:We've got some customers in the 150,
000 range, and we've got customers in
536
:the a hundred million dollar range.
537
:Is that, that's monthly.
538
:annual revenue.
539
:So what's really cool about that
is the level of sophistication of
540
:who you're working with, right?
541
:you're all the way from a mom and pop
to, I've got five people working on
542
:my e commerce loyalty team, right?
543
:That's the span of our client base.
544
:And for me, I love that because
I get to help the sole proprietor
545
:and I get to help the, giant
company and everything in between.
546
:So.
547
:I could sit here for hours and talk
to you about the segmentation, but the
548
:revenue segmentation is super wide.
549
:The geographic segmentation is really
cool too, in the sense that we've got
550
:a lot of customers in North America,
we've got a ton of customers in
551
:Europe, and we even have customers
all the way in Australia as well.
552
:Do European customers suffer from the
GDPR issues as far as number tracking
553
:is concerned because you can't append
those numbers to individual prospects?
554
:Have you been able to hurdle that at all?
555
:Or do you just have a
We comply for everybody.
556
:we just decided that we should
do it their way for everybody.
557
:So the tool works across
the board no matter what?
558
:Okay.
559
:Yeah.
560
:Compliant.
561
:Yeah.
562
:Yeah.
563
:GDPR frustrates the shit out of me because
it feels impossible to comply with.
564
:it was not fun for Fred
is what I would say.
565
:Okay.
566
:Didn't hurt me.
567
:So there's geographic segmentation,
there's revenue segmentation, but
568
:when it comes to the product side, I
love going to our customers websites
569
:because it's links is to their
URLs and I go and click on it and.
570
:All I can say so far is the diversity
is extremely wide and there doesn't
571
:seem to be any sort of trend right now.
572
:The only trend that we're really
finding is the customers are linked
573
:through Individuals that we're
starting to start conversations with.
574
:And those are, people who work
at an agency level, like they
575
:love our product and they go
and tell their customer groups.
576
:We were speaking to an individual
and she shared with us that, she
577
:recently worked on these five different
customers and it turns out they all
578
:became customers of ours, right?
579
:So that was the only common
theme that we found, but.
580
:everything from fishing gear to
clothing and apparel to food based
581
:products to sports based products.
582
:The spectrum is super wide.
583
:And to me, that is a really exciting
opportunity as somebody coming into
584
:a business, because I'm not obsessed
about sort of tailoring something
585
:to a particular product vertical,
which makes my job very complicated.
586
:But what it signals to me on the
other side is saying, Hey, This
587
:stuff is important for everybody.
588
:I don't care what you sell.
589
:Like, you could sell, socks or
you could sell fishing rods.
590
:This data, however you choose to get
it, however you choose to package
591
:it, however you want to visualize
this data, just go and do it because
592
:it'll help drive your business.
593
:Hmm.
594
:Where do people find you guys?
595
:If I want to go try Buy the
Numbers, where do I do that?
596
:Buythenumbersapp.
597
:com or on the Shopify store.
598
:Look for Buy the Numbers.
599
:And if somebody wanted to start
using it, how hard is it to set up?
600
:It's one click.
601
:You don't need a credit card.
602
:It's free for two weeks.
603
:that's it.
604
:Only for Solutions 8 YouTube
channel though, right?
605
:Absolutely.
606
:You have to mention Solutions 8.
607
:Mention this podcast.
608
:That's right.
609
:So here we are, buythenumbersapp.
610
:com.
611
:Y'all, John sings this
app's praises beyond belief.
612
:That's how Payam and I met, actually.
613
:He came onto the Perpetual Traffic
podcast and started pimping it out.
614
:Did y'all get any lift from that?
615
:We did.
616
:That's how I heard about you.
617
:I had multiple customers reach out to
me and say they heard it through your
618
:app, your podcast was really exciting.
619
:Also for a small company like us, where
it's just like two, three people, it makes
620
:such a difference to hear stuff like that.
621
:Yeah.
622
:It's really emotionally uplifting.
623
:I don't know how else to put it.
624
:Thank you.
625
:You're doing great work in a great way.
626
:I love your product first paradigm.
627
:Nobody does that anymore.
628
:You know what I mean?
629
:Like nobody, especially in the software
space, dear God just been cannibalized and
630
:it's sad because it's been cannibalized
by the smartest people in the world.
631
:You know, like when something's
been cannibalized by dummies, like
632
:real estate, it's like, all right,
I'm going to go beat the dummies.
633
:But when something's been cannibalized
and it's like the most finances
634
:this way too, it's just like, oh,
the smartest people in the world.
635
:are taking this and they're
ruining it and it's hard to fight.
636
:So when you see some guys that fit in
that moniker, like you're obviously part
637
:of the consortium, there's the smartest
guys in the world, but you just chose to
638
:do it kind of in a slightly different way.
639
:I feel that way about Sam Altman.
640
:I think the way they built open
AI and chat GPT is, is absurd
641
:from a monetization perspective.
642
:It makes no damn sense whatsoever,
There was the right way to do that.
643
:By the way, do you use it?
644
:I use it every day.
645
:I'm obsessed with it.
646
:I'm obsessed with Sam.
647
:I'd cuddle with that guy.
648
:If he allowed me.
649
:Dude, big spoon, little spoon.
650
:I just think he's great.
651
:Yeah.
652
:But it's because it's not how
are we gonna ROI instantly.
653
:It's, hey, I think I'm doing something
that's gonna have a massive impact
654
:on people's lives and I should
probably do that the right way.
655
:And you guys seem to be approaching
this in a very similar fashion.
656
:And if you're watching this, we
obsess over this all the time.
657
:If you don't know your numbers,
This is the easy button.
658
:Not an affiliate.
659
:I don't have an affiliate link.
660
:I should at some point, I'm going to get
one, but right now today, just go to buy
661
:the numbers, install it into Shopify.
662
:you've got 14 days.
663
:If you don't make one actionable decision
based off of the data, then jump ship.
664
:But if you don't make one actual
decision based off of the data,
665
:you're doing something wrong.
666
:So really appreciate y'all being
here and hope we have you back.
667
:There's strategic
partnerships in the future.
668
:Last words to you guys.
669
:What are the closing
comments for people watching?
670
:I don't go for it.
671
:Sure.
672
:Thank you to everyone
who uses our product.
673
:Honestly, we are, like
you said, product driven.
674
:We care about you as a customer.
675
:So reach out to me.
676
:I answer and read every single email
that sent you're not talking to a
677
:faceless customer service person.
678
:You're talking to co founders.
679
:Tell me what you use it for.
680
:Tell me what you wish
you could use it for.
681
:That's how we've gone to where we are.
682
:I love that, man.
683
:That's great.
684
:Like, comment, subscribe.
685
:You know how YouTube works.
686
:I'll see y'all tomorrow.
687
:Peace!
