Is All Software Going to $0?
This episode might reference ProfitWell and ProfitWell Recur, which following the acquisition by Paddle is now Paddle Studios. Some information may be out of date.
Originally published: April 17th, 2019
To answer this question, we looked at over 2 million customers and their preferences around features and willingness to pay, and here’s what we found.
Similar to the price of computers and memory, software prices have declined substantially since the 1980s and especially since the 90s and early 2000s when the cloud started to go mainstream. Costs have come down, buyer sophistication has increased, and information has become more symmetrical between buyers and sellers.
Where’s the floor though? After all, understanding the value cycles when it comes to features and functionality is crucial to ensuring you can defend your price.
Well, software value is certainly declining.
But first, if you like this kind of content and want to learn more, subscribe to get in the know when we release new episodes.
When looking at just over 900,000 customer data points, you’ll notice that products and features have lost 70% of their value over the past five years, meaning that Salesforce integration that you used to sell for $100 per month is now only worth $30 and in a lot of individual cases is probably worth just throwing into the core product.
The loss of value is shocking, but should feel pretty intuitive. After all, the cycles of production and shipping features has increased substantially.
You used to be able to give someone a database with a simple user interface and people thought you were a god. Now if the product doesn’t have good design and great support, it doesn’t matter what your core functionality is because it’s not going to pass as acceptable.
While the value will likely not go truly to zero, the pragmatic question becomes how quickly do products move from a place of value to a place of not being so valuable.
To answer this part, we need to introduce you to a model we’ve been talking about for a bit, but is probably something you haven’t seen before.
When you’re speaking about value of any product, anything from a cup of coffee to a piece of software, there are two axes of value - the relative value of the features or attributes of that product and then the actual willingness to pay for that product.
So if we look at a cup of coffee and we survey a large group using some clean statistical methodologies, we likely will find out that something like taste is the most important feature to that group and things like country of origin aren’t important in the aggregate.
When we layer on the willingness to pay data, we may find that those individuals that care about taste are willing to pay more, those who care about temperature less, and those who care about country of origin, there’s not a lot of them, but they are willing to pay more.
Now if we have a feature where the value relative to other features is high and the willingness to pay of the group that cares about that feature is high, then we have a differentiable feature.
If we find a feature that is low value from a feature perspective, but has high willingness to pay, then we have an add-on.
Something that is high value, but low willingness to pay will be a core feature.
And finally, something low value and low willingness to pay will be trash.
I’m introducing this model, because we’ve been able to study millions of different customers in terms of preferences and further study how features flow from one quadrant to another.
Ten years ago the flow of features looked like this - moving from add-on to differentiable and a bit of core, as well as from differentiable to core.
The average time a feature took to move from one quadrant to another was at a pace of 8 to 10 years. Today when measuring that velocity, this is what the movement looks like:
Features are moving quicker and quicker toward not just core, but also not being valuable into the trash quadrant. The average movement time from one quadrant to the next is now as low as 2 years.
To be clear, this isn’t to say that the features you’re building aren’t valuable at all. However, given everything that’s happening in the market, the speed through which features are losing their premium value is extremely troubling given our approach to building product, which is typically devoid of much data or customer research as we’ve seen in previous data studies.
Ultimately, the speed of the market is out of our control with so many companies are out there creating value.
The secret now becomes owning your position in the market and working to find those pockets of value that move slowly between the quadrants, or at the very least getting to a point where you’re measuring the pulse of your customers to understand exactly where you need to optimize your product for customer value, which trickles into retention and growth.
Want to learn more? Check out our recent episode: Is every company destined for freemium? and subscribe to the show to get new episodes.
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You've got the questions,
and we have the data.
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This is the ProfitWell Report.
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Hey, Neil.
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This is Ryan Buckley,
the CEO of Mighty Signal.
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We've been thinking a lot about
pricing here, and I'm curious.
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Is the price of all
software going to zero?
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00:00:18,720 --> 00:00:22,000
Welcome back, everyone. Neil
here for the ProfitWell Report.
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Similar to the price of
computers and memory,
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software prices have declined
substantially since the
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nineteen eighties and
especially since the nineties
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00:00:29,365 --> 00:00:33,040
and the early two thousands when
the cloud started to go mainstream.
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00:00:33,040 --> 00:00:35,840
Costs have come down, buyer
sophistication has increased,
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00:00:35,840 --> 00:00:39,600
and information has become more
symmetrical between buyers and sellers.
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00:00:39,600 --> 00:00:41,145
Where's the floor though?
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00:00:41,145 --> 00:00:44,745
After all, understanding the value
cycles when it comes to features and
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functionality is crucial to
ensuring you can defend your price.
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00:00:48,410 --> 00:00:49,850
So to answer this question,
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00:00:49,850 --> 00:00:52,730
we looked at over two million
customers and their preferences
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00:00:52,730 --> 00:00:54,810
around features and
willingness to pay.
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00:00:54,810 --> 00:00:56,010
Here's what we found.
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As to not bear the lead, software
value is certainly declining.
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When looking at just over nine
hundred thousand customer data points,
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you'll notice that products
and features have lost seventy
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percent of their value
over the past five years.
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Meaning that the Salesforce
integration that you used to
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sell for a hundred dollars per month
is now only worth thirty dollars.
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And and a lot of individual
cases probably worth just
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throwing into the core product.
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The loss of value is shocking,
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but should feel
pretty intuitive.
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After all, the
cycles of production
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database with a simple UI, and
people thought you were a god.
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Now if the product doesn't have
good design and great support,
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it doesn't really matter what
your core functionality is.
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It's not gonna pass as valuable.
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To answer this part,
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we need to introduce you to a
model we've been talking about
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00:01:42,504 --> 00:01:44,120
for a while.
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00:01:50,055 --> 00:01:51,175
To answer this part,
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00:01:51,175 --> 00:01:54,135
we need to introduce you to a model
we've been talking about for a while,
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00:01:54,135 --> 00:01:56,855
but it's probably something
you haven't seen before.
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When you're speaking about
the value of a product,
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00:01:59,240 --> 00:02:02,120
anything from a cup of coffee
to a piece of software,
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00:02:02,120 --> 00:02:04,120
there are two axes of value,
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00:02:04,120 --> 00:02:06,885
the relative value of the
features or attribute of that
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00:02:06,885 --> 00:02:10,245
product and then the actual
willingness to pay for that product.
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00:02:10,245 --> 00:02:12,645
So if we look at a cup of
coffee and we survey a large
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00:02:12,645 --> 00:02:15,265
group using some clean
statistical methodologies,
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00:02:15,380 --> 00:02:18,340
we'll likely find out that
something like taste is the
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most important
feature to that group,
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while things like country of origin
aren't that important in the aggregate.
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When we layer on
willingness to pay data,
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we may find that those
individuals that care about
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taste are willing to pay more,
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Those who care about
temperature less and those who
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care about country
of origin, well,
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there's not a lot of them, but
they are willing to pay more.
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Now, if we have a feature where
the value relative to other
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features is high and the
willingness to pay for that
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group that cares about
that feature is high,
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then we have a
differentiable feature.
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If you find a feature that
is low value from a feature
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perspective but has
high willingness to pay,
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then we have an add on.
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Something that is high value
but low willingness to pay will
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be a core feature,
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and then something low value and low
willingness to pay will be trash.
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We're introducing this model
because we've been able to
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00:03:06,965 --> 00:03:09,430
study millions of different
customers in terms of
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00:03:09,430 --> 00:03:12,630
preferences and further study
how features flow from one
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quadrant to another.
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Ten years ago, the flow
of features looked like this,
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moving from add on to
differentiable and a bit of
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core, as well as from
differentiable to core.
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The average time a feature took
to move from one quadrant to
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another was at a pace
of eight to ten years.
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Today, when measuring
that velocity,
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this is what the
movement looks like.
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Features are moving quicker and
quicker towards not just core,
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00:03:36,595 --> 00:03:39,955
but also not being valuable
into the trash quadrant.
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The average movement time from
one quadrant to the next is now
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as low as two years.
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To be clear, this isn't to say that
the features you're building aren't
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valuable at all.
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But given everything that's
happening in the market,
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the speed through which
features are losing their
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premium value is extremely
troubling given our approach to
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building product,
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which is typically devoid of
much data or customer research
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00:04:00,440 --> 00:04:02,680
as we've seen in
previous data studies.
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00:04:02,680 --> 00:04:06,360
Ultimately, the speed of the market
is out of our control given just how
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00:04:06,360 --> 00:04:08,845
many companies are out
there creating value.
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00:04:08,845 --> 00:04:12,205
The secret now becomes owning
your position in the market and
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00:04:12,205 --> 00:04:14,765
working to find those pockets
of value that move slowly
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00:04:14,765 --> 00:04:18,130
between the quadrants or at the
very least getting to a point
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00:04:18,130 --> 00:04:20,610
where you're measuring the
pulse of your customers to
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00:04:20,610 --> 00:04:23,170
understand exactly where you
need to optimize your product
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for customer value,
which, you know,
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00:04:25,295 --> 00:04:27,455
trickles into
retention and growth.
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Well, that's all for now.
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If you have a question,
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feel free to send me an email
or video anytime to neil at
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profit well dot com.
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And And if you get value from
today's episode or any others,
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we appreciate any and all
shares on Twitter and LinkedIn
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because that's how we
know to keep going.
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I will see you next week.
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This week's episode is
brought to you by binder,
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