In short, as per Part 1, you

1. keep admitting to every mistake you are making and do something about it, then
2. continue by looking for cost-effective opportunities for improvement and pursue them and finally
3. never, ever, ever forget the timeless basics.

Today, we’ll continue by describing what you do when you identify, and admit to, the next mistake (mistake 7) we chronicled in our two part introduction to our “dead company walking” (Part 1 and Part 2) series (where we helped your potential customers identify problems that signify you are a SaaS supplier they should be walking away from). (You can find part 2, part 3, and part 4 here.)

7) Buzz and Sound Bites are More Important than Timeless Educational Content

The last few years have been a barrage of quick-hit sound-bite, buzzword, influencer, and rapid-fire quick-switch focal point campaigns (to see what sticks), and the doctor can tell you that your target customer is as fed up of it as he is. Especially since they don’t have a clue as to what the h3ll you’re talking about, what your solution does, how you differentiate from 20 other vendors spewing the same nonsense, or if you even offer core Procurement functionality (and the doctor is side-eyeing a couple of the fake-take vendors here who need to be clearer in their messaging; while they are all a great fix for those on monolithic suites with archaic interfaces and no organizational process visibility beyond Procurement, they don’t actually work on their own).

It’s critical to remember that:

It’s not the attention quantity, it’s the attention quality!

Ten thousand views of a clickbait LinkedIn sound bite that only results in 100 click throughs to your website and 10 registrations to your webinar is not only unproductive, it’s counter productive. You’re leaving a negative image of your company as one that doesn’t really care about customers as you’re wasting their time with unclear messaging and then presenting them with irrelevant information or SaaS. If those individuals ever have a problem that you would be perfect for, you’re not going to be top of their list, or a company they actively recommend to peers desperate for your solution.

In comparison a clear, Plain English, to the point description of a new functionality and the problem it solves might sound boring, and might only get 1,000 views, but what if 100 click through to your website and 50 register for today’s webinar. That’s 10X the initial click through rate (percentage wise) and 50X the initial registration rate (percentage wise). Think about that. Especially since there’s a good chance that half those fifty will have a problem similar to what you described in your messaging and half of those could be immediate sales targets.

Taglines are okay, but you need real content that resonates to the target’s needs.

Especially if they are clear and centric to your actual solution capabilities. For example, “Mid-Market Procurement for Hospitality and Service” is very good as it specifies the industries, market size, and core offering (and Procurement has basic requirements) and a mid-market customer in hospitality and service knows that it is a potential solution, and even if it’s not perfect for them, researching it won’t waste their time because they’ll learn something (regarding what they need, don’t need, why, and what a good solution should do).

On the other hand, “AI-powered supplier performance for margin multiplication” is utter bullcr@p as “AI-powered” doesn’t mean anything (as it is misused and abused by 6/7 vendors, and sometimes is simply “Applied Indirection” as there’s no real AI at all, not even of the artificially idiotic variety). “Supplier Performance” is vague … it has a few standard meanings … it could be simple measurements, it could be the creation and management of development plans, and it could even be risk or compliance mitigation (even though it shouldn’t be). And it’s been abused by sourcing, procurement, and supplier management vendors alike. And margin multiplication is among the most meaningless manure to be produced in the current cycle of buzzword madness. (Why do you think the doctor is insisting it’s time to start calling out the hogwash for what it is!)

People WILL read and listen when they are seriously evaluating you

the doctor knows we’re in a generation where no one wants to read anymore, where attention seconds are barely long enough for 15 seconds of fame, and everyone is overworked, underpaid, and just short on time when it comes to listening to the tsunami of messaging being targetted at them.

But here’s the reality the marketers desperate for an oversized share of your budget won’t tell you. When it comes to enterprise software, that doesn’t matter. No one commits $1M+ a year on a multi-year software purchase without doing their research and diligence. (They might not do it right, but they will do it.) (And, as per mistake 3, when you factor in maintenance, hardware & software updates, services, data feeds, integration fees, etc. most “six figure” SaaS licenses are usually pushed into the realm of seven figures from a TCO [Total Cost of Ownership] perspective.)

The situation is different when you get to an RFP and are among the final three. Unless it’s a fake RFP (where the buyer has already selected a solution being sold by his buddy Bob) being forced upon the buyer by public sector or corporate rules, the buyer, and key affected shareholders, will do their research. They will read your responses, and they will read any meaningful pieces of literature you put in front of them. As well as watch appropriate pre-recorded demos and webinars. Even if they don’t fully understand it (which is another problem), they will do their diligence because their jobs depend on it! (If they screw up, and their bosses decide it was a result of them not doing their best effort, they will be fired.)

Plus, if they are going to be stuck using whatever they buy day in and day out for the next 3, 5, 7, 10 years, they are going to want to make sure it does the everyday tasks well.

So give them real, solid, educationally focussed content, and when they are truly ready to buy, they’ll eat it up and lick the virtual plate clean (and come to you begging for seconds).

And even if they’re not ready to buy now, if you repeatedly given them real, solid, educationally focussed content (in short, easily consumable, mini / single point white papers / webinars), they’ll build up a positive view of your company and offering and you’ll earn their respect and you will get called when the budget is approved.

This is fact. This is the same advice the doctor has been given companies since he started independently consulting with leading companies in this space in 2006 (and given away for free on SI since 2007), and every single company who took this approach that the doctor worked with before joining Spend Matters in 2016 either

1. had a successful exit on their terms (including all of SI’s sponsors and most of the doctor‘s original clients) or
2. had a successful raise on their terms and grew. (i.e. not a single one of these companies went out of business!)

And while it does’t work quickly web statistic wise (i.e. you’re not getting those thousand of eyeballs quickly, but as we just demonstrated, that doesn’t matter), it always works. Enterprise software is NOT consumer sales — people aren’t making high six, seven, and even eight figure purchases on sound bites and buzz.

But, at the end of the day, the reality is

All I got is an online blog, these words and the truth.
All I got is an online blog, the rest is up to you!

Stay tuned for Part 6!

In short, as per Part 1, you

1. keep admitting to every mistake you are making and do something about it, then
2. continue by looking for cost-effective opportunities for improvement and pursue them and finally
3. never, ever, ever forget the timeless basics.

Today, we’ll continue by describing what you do when you identify, and admit to, one of the next two mistakes (mistakes 5 & 6) we chronicled in our two part introduction to our “dead company walking” (Part 1 and Part 2) series (where we helped your potential customers identify problems that signify you are a SaaS supplier they should be walking away from). (You can find part 2 and part 3 here.)

5) An innovation burst is enough, especially if it is disruptive

A successful innovation burst is great as it can get you noticed, and as it’s very hard to get noticed in an overcrowded space (again, see the Mega Map), that’s a great start. But someone noticing you does not mean they’ll engage with you, and engagement does not mean they will buy from you.

Moreover, it’s never long before a one-trick pony loses the limelight as fast as he enters it. If you want to stay in the limelight, which wants to move on to the next story as soon as you’ve had your 15 seconds of fame, you need to keep innovating, or at least developing the core functionality necessary to flesh out the value message to the point the overall message is so compelling people want to hear it and repeat it.

The reality is that it is continued development, especially around core processes your technology is being designed to support, that will at least keep you on the periphery of the limelight, and that is where you need to be to not only attract enough potential customers, but convert them into customers who want, as we’ve been stating repeatedly, solid solutions to their problem and not shiny tech that looks cool but doesn’t do what they need it to do.

6) Too much investment, too soon, against an overly ambitious plan

This is one of the biggest threats to your success, especially since you will be pushed to scale up fast to support the rapid growth, that won’t come, or at least not early in your corporate development. Falling for this will burn the cash well before you are even close to break-even, and if you can’t raise additional funds fast when you run out, you’re dead.

The first thing to do when you raise too much money is to stop dead in your tracks, stop all external hiring and engagement, and step back and do the detailed market research described in our first mistake and figure out the MVP you’ll actually need to build a significant market share, and focus first on hiring the talent / or acquiring the third party tech, to get there as soon as possible.

Then you need to figure out what not only makes a good customer, but one that is easy to sell. It’s likely that the majority of these customers will need education to get them there. The next thing to do is hire the product people who can build these educational assets for marketing, sales, partners, and customers.

When you get close on the product and the marketing, then you start to ramp up marketing and high-performing sales (who can work without a lot of support and incomplete, but progressing monthly, assets) to start building the initial funnel when you are ready to go hard.

Then start building up your services teams with senior resources who can do multiple roles and initial implementations with little support.

And only once all the pieces start falling into place do you start scaling up.

And in the process, be sure to:

• review the marketing plan: cut the funding to anything not focussed on education and thought leadership in the early days
• review sales: cut the “leads” to those truly qualified with problems that match your solution; and definitely cut the spray and forget power washer lead blasting from 3^rd parties, you want well qualified leads only
• review the development plan: make sure it’s 90% steak and only 10% sizzle; sizzle doesn’t solve problems, or fill bellies, and that’s why customers want steak
• review the budget: anything not going to educational/thought leadership marketing, qualified solution-based lead generation, or solid development is extraneous and needs to be cut ASAP to ensure the money lasts until the solution is broad and deep enough to serve the intended market, command the expected price tag, and get the interest you need for steady, continued, growth

Stay tuned for Part 5!

In short, as per Part 1, you

1. keep admitting to every mistake you are making and do something about it, then
2. continue by looking for cost-effective opportunities for improvement and pursue them and finally
3. never, ever, ever forget the timeless basics.

Today, we’ll continue by describing what you do when you identify, and admit to, one of the next two mistakes (mistakes 3 & 4) we chronicled in our two part introduction to our “dead company walking” (Part 1 and Part 2) series (where we helped your potential customers identify problems that signify you are a SaaS supplier they should be walking away from). (You can find Part 2 here.)

3) Shiny New Tech is More Important than Tried and True Methodology

Tried and true ALWAYS trumps shiny new tech in enterprise software. Especially when the total cost of ownership after factoring in license fees, maintenance, hardware & software updates, services, data feeds, integration fees, etc. usually push a solution into the realm of seven figures (and eight figures for large enterprises). Companies want a return, and shiny doesn’t generate a return. So focus on algorithms, processes, and approaches that are guaranteed to yield solutions. Do this by:

i) As THE REVELATOR would say, take an agent-based approach and focus in on what the solution should do

It’s supposed to be people-process-technology (or, in the doctor‘s words, talent-transformation-technology since you should first look for opportunities to improve your processes before investing in technology), not technology-forced workflow-prisoner! When you focus on the tech first and forget the process it is supposed to support and the people who have to use it, you’re never, ever, ever going to get it right. And believing that an over-hyped unproven technology will eventually show emergent behaviour (that it has zero chance of doing because of the underlying technology) and “evolve” to solve the problem is just stupid.

Real tech encodes real solutions identified by real human intelligence that have been implemented, repeatedly verified, tested in real world operating conditions, and understood to the point that the success can be repeated predictably. Sometimes it’s traditional AI, sometimes it’s RPA, sometimes it’s workflow, and sometimes it’s a simple calculation. It all depends on the problem and the people-process combo that can be applied to solve that problem. Tech is only transformative when it supports the business. You don’t lead with tech, you follow.

ii) Then identify the best tech options for each step

Once you have fully documented the problem(s) the people need to solve, the processes they can support, and the solutions that will work acceptably (maybe not perfectly, but it’s rare that a solution is perfect — plus, as we’ve repeatedly indicated, most enterprise users would cry tears of joys if they found something that just worked), identify all the potential tech options at your disposal, from open source to out-of-the-box from third parties to custom development.

Then, for each option, evaluate:

• it’s cost
• the relative return (for the customer and how that will translate into sales)

And identify the options with the best balance. You’ll be surprised at how often it’s not the new shiny. The best hotness is the old busted hotness.

iii) Select tried-and-true when all things are equal (and save)

Finally, when all things are equal, go for the most stable, tried-and-true, methodology — don’t do experimental new “AI” development if a sold optimization or analytics-based solution already exists.

4) Over Reliance on Third Party Tech is a Sustainable Business, Especially if its Gen-AI!

Reliance on third party tech, especially that which has not been proven to be on stable ground in the market, is not a good business plan. What do you do if the provider goes bankrupt, or, realizes they are on the path to bankruptcy and turns the tech off? If you have nothing else to pivot to, you go bankrupt too. Not a good scenario!

Even if you are building on tried-and-true stable tech (with a large install base), unless your business plan is to be acquired by the tech you’re building on and you actually have a chance of making that happen (you came from the provider, have good connections, etc.), your options are very, very limited if you don’t succeed.

You need to focus on a solution to a real problem that companies will pay to address first. And while it can be based 100% on third party tech to start, that shouldn’t be the game plan. The plan should be to acquire it, build something better in house, or at least find three or four options that will serve the same function until you can acquire or build appropriate tech. (The exception being a product maintained for you by a third party that is based on open source that will always be there for you to take ownership of and then assign to a new team.)

Once you have a solution, and know what tech you need, do the cost benefit analysis of:

• licensing someone else’s tech
• acquiring the third party tech
• building on, and contributing to, open source
• building your own

for each aspect of the solution, as well as the points at which one option becomes better.

In addition, even when you select a solution, always keep your eyes out for an alternative that has been demonstrated to be more reliable, efficient, or cost effective. When a problem has been newly identified, some companies will just throw anything at it to see what appears to work, without doing a proper analysis and designing a proper solution from the ground up. Eventually, some smart minds powered by Human Intelligence (HI!) will come up with a better solution. Once that becomes economical, you’ll want to switch to that if you don’t have an equivalent solution in house, while keeping back-up options open.

And definitely don’t (over) rely on third party Gen-AI — even the biggest companies can only afford to bleed for so long. There’s nothing Gen-AI can do that traditional tech can’t. It’s best uses are as an interface layer for (very) low TQ (Technology Quotient) folk who are being forced to use tech but just don’t get it (as a more natural language chatbot interface) or as a massive document store search and summarization solution (when traditional semantic / neural networks would just be overloaded). That’s it. And in all of these cases, it needs an underlying application that actually does the work and manages the data.

Stay tuned for Part 4!