Author Archives: Dave Kellogg

What is a Minimum Viable Product, Anyway? My Favorite MVP Analogy.

The concept of minimum viable product (MVP) has been popularized in the past decade thanks to the success of the wonderful book, The Lean Startup.  It’s thrown around so casually, and you hear it so often, that sometimes you wonder how — or even if — people define it.

In this post, I’ll describe how I think about MVPs, first using one real-life example and then using my favorite MVP analogy.

The concept of a minimum viable product is simple:

  • Every startup is basically a hypothesis (e.g., we think people will buy an X).
  • Instead of doing a big build-up during a lengthy stealth phase concluding in a triumphant (if often ill-fated) product unveiling, let’s build and ship something basic quickly — and start iterating.
  • By taking this lean approach we can test our hypothesis, learn, and iterate more quickly — and avoid tons of work and waste in the process.

The trick is, of course, those two pesky words, minimum and viable.  In my worldview:

  • Minimum means the least you can do to test your hypothesis.
  • Viable means the product actually does the thing it’s supposed to do, even in some very basic way.

I’ll use an old, but concrete, example of an MVP from my career at Business Objects.  It’s the late 1990s.  The Internet is transforming computing.  We sell a high-functionality query & reporting tool, capable of everything from ad hoc query to complex, highly-formatted reports to interactive multidimensional analysis.  That tool is a client/server Windows application and we need to figure out our web strategy.  We are highly constrained technologically, because it’s still the early days of the web browser (e.g., browsers had no print functionality) [1].

After much controversy, John Ball and the WebIntelligence team agreed on (what we’d now call) the following MVP:

  • A catalog of reports that users can open/browse
  • End-user ad hoc query
  • Production of very basic tabular reports
  • Semi-compatibility with our existing product [2]

But it would work in a browser without any plug-ins, web native.  No multi-block reports.  No pages.  No printing.  No interactive analysis.  No multidimensional analysis.  No charting.  No cross-tabs.  No headers, no footers.  Effectively, the world’s most basic reporting tool — but it let users run an ad hoc query over the web and produce a simple report.  That was the MVP.  That was the hypothesis — that people would want to buy that and evolve with us over time.

Because of that tightly focused MVP we were able to build the product quickly, position it clearly within the product line [3], and eventually use it as the basis for an entirely new line of business [4].

Now, let’s do the analogy.  Pretend for a moment we’re in a world where there are no four-wheel drive cars.  We have invented the four-wheel drive car.  We imagine numerous use-cases [5] and a big total available market (TAM).

What should be our MVP?  Meet the 1947 Jeep Willys [6] [7].

No roof.  No back seat.  In some cases, no windscreen.  No doors.  No air conditioning.  No entertainment system.  No navigation.  No cup holders.  No leather.  No cruise control.  No rearview camera.  No ABS.  No seatbelts.  No airbags.

No <all that shit that too many product managers say are requirements because they don’t understand what MVP means>.

Just the core:  a seat, a steering wheel, an engine, a transmission, a clutch, and four traction tires.

  • Is it missing all kinds of functionality?  Yes
  • In this case, would it even be legal to sell?  No.  Well, maybe off-road, but we’re in analogy-mode here.
  • But can it get you across a muddy field or down a muddy road?  YES.

And that’s the point.  It’s minimum because it’s missing all kinds of things we can easily imagine people wanting, later.  It’s viable because it does the one thing that no other car does.  So if you need to cross a muddy field or go down a muddy road, you’ll buy one.

As Steve Blank says:  “You’re selling the vision and delivering the minimum feature set to visionaries, not everyone” [8]. 

So next time you think someone is focused on jamming common but non-core attributes into an MVP, tell them they’re counting cupholders in a Willys and point them here.

# # #

Notes

[1] And printing is a pretty core requirement for a reporting application!

[2] This was key.  WebIntelligence could not even open a BusinessObjects report.  Instead, we opted for compatibility one layer deeper, at the semantic layer (that defined data objects and security) not the reporting layer.

[3] If you want all that power, use BusinessObjects.  If you want web native, use WebIntelligence.  And you can share semantic layer definitions and security.

[4] BI extranets.

[5] From military off-road applications to emergency off-road and/or slippery conditions to sand recreational to family vehicles on snow and many  more.

[6] Which in some ways literally was the MVP for Jeeps.

[7] Popularized by the Grateful Dead in Sugar Magnolia (“… jump like a Willys in four-wheel drive.”)

[8] Where I’ll define visionary as someone who has the problem we’re trying to solve and willing to use a new technology to solve it.  It’s a little easier to think of someone trying a next-generation database system as a “technology visionary” than the Army buying a Jeep, but it’s the same characteristic.  They need a currently unsolvable problem solved, and are willing to try unconventional solutions to do it.

Congratulations to Nuxeo on its Acquisition by Hyland

It feels like the just the other day when I met a passionate French entrepreneur in the bar on the 15th floor of the Hilton Times Square to discuss Nuxeo.  I remember being interested in the space, which I then viewed as next-generation content management (which, by the way, seemed extraordinarily in need of a next generation) and today what we’d call a content services platform (CSP) — in Nuxeo’s case, with a strong digital asset management angle.

I remember being impressed with the guy, Eric Barroca, as well.  If I could check my notebook from that evening, I’m sure I’d see written:  “smart, goes fast, no BS.”  Eric remains one of the few people who — when he interrupts me saying “got it” — that I’m quite sure that he does.

To me, Nuxeo is a tale of technology leadership combined with market focus, teamwork, and leadership.  All to produce a great result.

Congrats to Eric, the entire team, and the key folks I worked with most closely during my tenure on the board:  CMO/CPO Chris McGlaughlin, CFO James Colquhoun, and CTO Thierry Delprat.

Thanks to the board for having me, including Christian Resch and Nishi Somaiya from Goldman Sachs, Michael Elias from Kennet, and Steve King.  It’s been a true pleasure working with you.

My Two Appearances on the SaaShimi Podcast: Comprehensive SaaS Metrics Overview and Differences between PE and VC

The SaaShimi podcast just dropped the first two episodes of its second season and I’m back speaking with PNC Technology Finance banker Aznaur Midov, this time discussing some of the key difference between private equity (PE) and venture capital (VC) when it comes to philosophy, business model, portfolio company engagement, diligence,  and exit processes.  You can check out the entire podcast on the web here or this episode on Spotify or Apple podcasts.

I’ve also embedded it below:

Dave Kellogg on SaaShimi Discussing Differences between Private Equity and Venture Capital.

 

If you missed it and/or you’re otherwise interested, on my prior appearance we did a pretty darn comprehensive overview of SaaS metrics, available here on Apple podcasts and here on Spotify.

I’ve embedded this episode as well, below:

Dave Kellogg on SaaShimi with a Comprehensive Overview of SaaS Metrics.

 

Thanks Aznaur for having me.  I think he’s created a high quality, focused series on SaaS.

What Are The Units On Your Lead SaaS Metric — And What Does That Say About Your Culture

Quick:

  • How big is the Acme deal?  $250K.
  • What’s Joe’s forecast for the quarter?  $500K
  • What’s the number this year?  Duh.  $7,500K.

Awesome.  By the way:  $250K what?  $500K what?  $7,500K what?  ARR, ACV, bookings, TCV, new ARR, net new ARR, committed ARR, contracted ARR, terminal ARR, or something else?

Defining those terms isn’t the point of this post, so see note [1] below if interested.

The point is that these ambiguous, unitless conversations happen all the time in enterprise software companies.  This isn’t a post about confusion; the vast majority of the time, everyone understands exactly what is being said.  What those implicit units really tell you about is culture.

Since there can be only one lead metric, every company, typically silently, decides what it is.  And what you pick says a lot about what you’re focused on.

  • New ARR means you’re focused on sales adding water to the SaaS leaky bucket — regardless of whether it’s from new or existing customers.
  • Net New ARR means you’re focused the change in water level in the SaaS leaky bucket — balancing new sales and churn — and presumably means you hold AEs accountable for both sales and renewals within their patch.
  • New Logo ARR means you’re focused on new ARR from new customers.  That is, you’re focused on “lands” [2].
  • Bookings means you’re focused on cash [3], bringing in dollars regardless of whether they’re from subscription or services, or potentially something else [4].
  • TCV, which became a four-letter word after management teams too often conflated it with ARR, is probably still best avoided in polite company.  Use RPO for a similar, if not identical, concept.
  • Committed ARR usually means somebody important is a fan of Bessemer metrics, and means the company is (as with Net New ARR) focused on new ARR net of actual and projected churn.
  • Terminal ARR means you’re focused on the final-year ARR of multi-year contracts, implying you sign contracts with built-in expansion, not a bad idea in an NDR-focused world, I might add.
  • Contracted ARR can be a synonym for either committed or terminal ARR, so I’d refer to the appropriate bullet above as the case may be.

While your choice of lead metric certainly affects the calculations of other metrics (a bookings CAC or a terminal-ARR CAC) that’s not today’s point, either.  Today’s point is simple.  What you pick says a lot about you and what you want your organization focused on.

  • What number do you celebrate at the all hands meeting?
  • What number do you tell employees is “the number” for the year?

For example, in my opinion:

  • A strong sales culture should focus on New ARR.  Yes, the CFO and CEO care about Ending ARR and thus Net New ARR, but the job of sales is to fill the bucket.  Someone else typically worries about what leaks out.
  • A shareholder value culture would focus on Ending ARR, and ergo Net New ARR.  After all, the company’s value is typically a linear function of its Ending ARR (with slope determined by growth).
  • A strong land-and-expand culture might focus on Terminal ARR, thinking, regardless of precisely when they come in, we have contracts that converge to a given total ARR value over time [5].
  • Conversely, a strong land and expand-through-usage culture might focus on New Logo ARR (i.e., “land”), especially if the downstream, usage-based expansion is seen as somewhat automatic [6].
  • A cash-focused culture (and I hope you’re bootstrapped) would focus on bookings.  Think:  we eat what we kill.

This isn’t about a right or wrong answer [7].  It’s about a choice for your organization, and one that likely changes as you scale.  It’s about mindfulness in making a subtle choice that actually makes a big statement about what you value.

# # #

Notes
[1] For clarity’s sake, ARR is annual recurring revenue, the annual subscription value.  ACV is annual contract value which, while some treat as identical to ARR, others treat as first-year total contract value, i.e., first-year ARR plus year-one services.  Bookings is usually used as a proxy for cash and ergo would include any effects of multi-year prepayments, e.g., a two-year, prepaid, $100K/year ARR contract would be $200K in bookings.  TCV is total contract value which is typically the total (subscription) value of the contract, e.g., a 3-year deal with an ARR stream of $100K, $200K, $300K would have a $600K, regardless of when the cash payments occurred.  New ARR is new ARR from either new customers (often called New Logo ARR) or existing customers (often called Upsell ARR).  Net New ARR is new ARR minus churn ARR, e.g., if a regional manager starts with $10,000K in their region, adds $2,000K in new ARR and churns $500K, then net new ARR is $1,500K.  Committed ARR (as defined by Bessemer who defined the term) is “contracted, but not yet live ARR, plus live ARR netted against known projected ARR churn” (e.g., if a regional manager starts with $10,000K in their region, has signed contracts that start within an acceptable time period of $2,000K, takes $200K of expected churn in the period, and knows of $500K of new projected churn upcoming, then their ending committed is ARR is $11,500K.  (Why not $11,300K?  Because the $200K of expected churn was presumably already in the starting figure.)  Terminal ARR the ARR in the last year of the contract, e.g., say a contract has an ARR stream of $100K, $200K, $300K, the terminal ARR is $300K [1A].  Contracted ARR is for companies that have hybrid models (e.g., annual subscription plus usage fee) and includes only the contractually committed recurring revenues and not usage fees.

[1A] Note that it’s not yet clear to me how far Bessemer goes out with “contracted” ARR in their committed ARR definition, but I’m currently guessing they don’t mean three years.  Watch this space as I get clarification from them on this issue.

[2] In the sense of land-and-expand.

[3] On the assumptions that bookings is being used as a proxy for cash, which I recommend, but which is not always the case.

[4] e.g., non-recurring engineering; a bad thing to be focused on.

[5] Although if they all do so in different timeframes it becomes less meaningful.  Also unless the company has a track record of actually achieving the contractually committed growth figures, it becomes less credible.

[6] Which it never actually is in my experience, but it is a matter of degree.

[7] Though your investors will definitely like some of these choices better than others.

 

Why I’m Advising Bluecore

I first read The One to One Future by Don Peppers and Martha Rogers in 1997, four years after it was published.  As a marketer, the book made a big impression on me.  It was revolutionary stuff:  we should make the paradigm shift from mass marketing to individualized marketing.

When the book was published in 1993, newspaper ads were $75B/year, TV around $60B, the web browser was a mere three years old, and there were 623 total sites on the web.  There was effectively no web advertising market.  It was nine years before the Minority Report popularized a future vision of one-to-one advertising.  It was six years before Paco Underhill published Why We Buy revealing insights gleaned by manually tracking shoppers to understand in-store behavior [1].

Look at the subtitle: “Building Relationships One Customer at a Time.” You could use that in a webinar today.  The One to One Future was not just ahead of its time; it was so far ahead of its time that it could have equally been categorized under either “marketing” or “science fiction.”

Why?

  • It turns out, as with science fiction, that it’s easier to envision something than to build it. Remember, “they promised us flying cars and we got 140 characters.” [2]
  • Building individualized marketing systems required layers and layers of underpinnings that were simply not in place. You can’t do good personalization without a clean, real-time, 360-degree view of your customer.  Clean means a big effort into data quality and data profiling and typically either master data management or a customer data platform [3].  Real-time means real-time data integration [4].  360-degrees means pulling relevant data from virtually all of your systems.  Self-driving cars don’t work on cow paths.  Building those layers of requisite infrastructure has taken decades.
  • Marketing’s focus on the perfect offer was flawed. Say I found an offer with an 90% chance that you’d respond affirmatively.  Perfect, right?  But it was for a product that was out of stock.  The perfect offer has to be for the right product, in the customer’s preferred size or color, and available to sell.  We can’t just find the set called {great offers}.  We needed to intersect it with the set called {in stock and need to sell}.  This made a hard problem harder by pulling inventory and the supply chain into the equation.
  • Marketers got trapped in a vicious downward cycle of communications. Email click rates have nearly been cut in half over the past decade.  Marketing’s solution?  Send more emails to make up the difference.  Email vendors, who typically price by the email, were only too happy to accommodate.  That, however, is a short-term mentality.  More bad email with lower open and click rates isn’t the solution.  The same holds for ads and promotions.  Marketing needs to get out of this race to the bottom.  We need to focus on quality, not quantity.  And pay vendors for performance delivered, not communications sent, while we’re at it.
  • Finally, the retail industry needed to shift mentality from store-first to digital-first. Roots, as they say, run deep and retailers have long, deep roots in physical stores.  Bricks-and-mortar supposedly changed to clicks-and-mortar, but really, it was mortar-and-clicks the whole time.  The industry never really changed to digital-first from store-first.  Until Covid-19, that is.  While this meme, popularized in Forbes, was intended for many industries, it could have been custom made for retail [5].

So where does Bluecore fit in?

  • Bluecore is a multi-channel personalization platform. They’re building what marketers in the past dreamed of, but couldn’t build, because the infrastructure wasn’t there.  Now it can be built, and they’re building it.
  • Bluecore is an AI/ML company focused on retail analytics and personalization. I’ve blogged before that AI/ML is best applied to specific problems and not general ones, and this is a great example.  They are a closed-loop, retailed-focused application that gets smarter every day and with each new customer.  If you believed in the increasing returns of marketing leadership in technology markets before AI/ML [6], you should believe in them twice as much after.
  • Bluecore’s personalization understands both customer and product – and intersects them. Across a catalog of more than 250M products and SKUs, Bluecore can match customers and products at a 1-1 level.  It automates what would have been the work of a team of in-house data scientists.
  • Bluecore is paid for performance, not volume. They back up their performance claims with a pricing model based not on volume but on success.  This is a great example of superior technology enabling disruptive business model innovation.

Why am I advising Bluecore?  Three reasons:

  • As a true, blue marketer this stuff genuinely interests me. I love working with marketing companies on marketing problems.
  • It’s always about the team. I’ve loved working with Fayez Mohamood (founder/CEO) and Sherene Hilal (SVP of Marketing).  As a bonus, former Salesforce teammate Scott Beechuk is an investor and on the board.  I like working with people who like working with me and appreciate my inimitable (I inadvertently almost typed inimical) style when it comes to feedback.
  • The momentum and market opportunity. Bluecore’s a highly successful company, having raised over $100M in VC with top-tier investors, and they are pursuing transformational change in a $4T market.  The last 100 years in retail were all about stores, the next 100 will be about retailers meeting customers wherever they are.  And that’s what Bluecore does.

# # #

Notes
[1] And why, to this day, you can find still baskets strewn throughout many retail shops as opposed to only at the entrance.  His work was kind of a manual predecessor to systems like RetailNext, whose founder I got to know through mutual investments in a prior life from StarVest.

[2]  Peter Thiel at Yale.

[3] Which weren’t to be invented for about 20 years

[4] The data warehouse was invented in 1992, with the publication of Bill Inmon’s Building the Data Warehouse.   Ralph Kimball would invent the star schema 4 years after that.

[5] Apologies to frequent readers for using this meme again – but I just love it!

[6] Tech buyers, and particularly IT buyers, tend to face high opportunity costs and high switching costs and are ergo generally risk averse.  This drives increasing returns for early market leaders.  Think:  no one ever got fired for buying IBM.