Category Archives: rants

Enterprise Software Companies Do Need Media Relations (Re-Post)

This post initially ran five years ago, but since the PR frenzy is back (as a result of the M&A frenzy), this needs a re-post!

In yesterday’s post, we insisted that Enterprise Software Companies DO NOT need Public Relations, because they do not. Why? Simple. They DO NOT sell to the public. They sell to big corporations. Big corporations are not the public.

Also, the messaging that you need to sell to a CFO is nothing like the message that you need to sell to an impulsive consumer. Good business is all about productivity, progress, and Return On Investment. Good public relations is all about feeling, connection, sexy, environmental responsibility, or anything else that happens to be the buzz of the day. Good enterprise relations is all about results. Public relations, like consumer advertising, is in constant flux. But the basics of good business never change.

However, the advertising channels through which business advertising have exploded, not only as a result of the rapid expansion of the ubiquity of the world wide web, but of social media as well. As a result, the complexity of media management has increased dramatically. The fundamentals haven’t changed, but the amount of work required to coordinate and manage the effort has. Not to mention the knowledge required to strategically place your advertising and messaging to stand out amidst the noise, which consists not only of a constant stream of advertising and messaging from your competitors but analysis, third party reviews, and random comments. It’s a media jungle, and unless you have a team of full time pros to manage it 24/7, you need help. Even if you do have a team, you probably need guidance.

A good Media Relations Team will help you:

  • Identify the Right Channels
    Which traditional print and online web publications are right for you?
    What are the right channels to advertise your coverage?
    Who are the right people at these outlets to reach out to?
  • Tailor the Message
    While you need to craft and own your message, you also need to recognize that different individuals at different publications who control different channels are interested in different parts of the message you have to deliver. To get your message heard, sometimes you have to focus in on the part that will get a crier’s attention.
  • Spread the Message
    Parts of your message have to spread through others, but thanks to the social media revolution, other parts have to be spread by your organization through social media channels. Managing these can be a full time job, and not the best use of your limited resources. This is best left to an expert.

In other words, you need help, but the help you need is not Public Relations. It’s Media Relations.

And if you really need someone to talk to in order to help you elicit your messaging in a collaborative fashion, hire a subject matter expert (SME) whom can also offer you project management, product development, or thought leadership consulting services. This will jump start those efforts as the subject matter expert will not only be fully familiar with your messaging, but with your modus operandi as well. As a result, there will be little to no learning curve for the SME when it’s time to start the project management, product development, or thought leadership creation. This will pay off in spades as you’ll get your project, product, and/or thought leadership done faster, hit the market faster, and see a significant return faster.

So when it comes to getting help, get the right help. Even if you don’t thank me for it.

Enterprise Software Companies DO NOT Need Public Relations! (Re-Post)

This post initially ran five years ago, but since the PR frenzy is back (as a result of the M&A frenzy), this needs a re-post!

Since we’re on the topic of what really grinds the doctor‘s gears, another thing that really grinds the doctor‘s gears is the incessant insistence by public relation companies that they need to be ingrained in all communication activities undertaken by an enterprise software company. To this I say, BullCrap!

Let’s start by defining what public relations is. As can easily be read on Wikipedia, public relations is the practice of managing the spread of information between an organization and the public. Let’s dwell on this. It’s the management of information flow between the organization and the public. Now let’s dwell on what enterprise software companies do. Enterprise software companies sell software made by their organization to their client organizations. Now let’s dwell on this. They move software from one organization to another organization. Not to the public. As a result, the accompanying information flow is between two organizations, not between the organization and the public. So where does public relations enter the mix?

Let’s dive into what modern Public relations organizations do, or at least try to sell perspective clients, to see if we can make any sense of this.

  • Audience TargetingWhile it’s important to sell to the right audience, enterprise software companies have a pretty good idea of who their audience is. It is companies with a potential need for their software that is their audience, and not only does marketing have a pretty good idea of what their audience is, it is their job to know what that audience is.
  • MessagingMessaging is of the utmost importance, especially with so many other vendors also hawking their wares, and in a world where many customers are looking for partners, or at least software providers who can offer a complete solution (software, services, and training), the messaging often has to be perfect. But this is why you have Marketing — this is their primary job.
  • Social Media MarketingSince many of the decision makers at a potential customer are on social media, this is an important channel in which to place your messaging. With so many social media networks (LinkedIn, Facebook, Twitter, etc.) and so many different individuals in the target organizations to target (employees, directors, C-Suite, etc.), this is a lot to manage, and secondary to the messaging and audience targeting responsibilities of Marketing. So it makes some sense to get some help here — but this help should come in the form of organizations that specialize in social media marketing for B2B organizations, not Public Relations firms that specialize in information flow to the public for B2C organizations.
  • Media RelationsThis is important for any organization that does business and needs to get its message out to the world, even if it is just the corporate sector. However, this relationship should be controlled by marketing, not some third party with a watered down message.

Now it’s no secret that the doctor does not like PR, for a host of reasons (chronicled in his Blogger Relations series), but this has nothing to do with his like of PR. This has to do with his dislike of many PR firms telling enterprise software companies that they need to be embedded in all of their communication processes and work with those companies in a collaborative and consultant manner for months and months to define their targeting, messaging, (social) media, and relations strategy and do all of the work that should be done, or at least managed, by Marketing at a very high cost to you. Not only are you shelling out 10’s of thousands of dollars for them to walk you through an exercise where you do all the work (because, let’s face it, they don’t have a clue what you’re selling, what’s unique about it, or how to uniquely position it), but you’re losing two, three, and sometimes even four quarters of momentum while you go through this drawn out exercise to get a message that your marketing team, possibly with the help of some subject matter experts, could figure out in a matter of weeks! It’s the oldest consulting trick in the book after making up a fad you don’t need — take your money to listen to you elicit what you need. (If you need to talk through your strategy to elicit your messaging, the doctor is certain a quack psychologist will be cheaper.)

So Fire That PR Firm and put your money where you need it:

  • Subject Matter Expert Consultingto help you figure out what is distinct about your solution and missing in your solution space
  • Thought Leadership and Expert Writing Servicesto help you get your message crystallized and down on (white) (e-)paper and in appropriate training materials for your clients
  • Social Media Campaign Managementto manage your messaging through social media and on-line channels

Just like you shouldn’t get taken in by companies selling infinite scrolling websites that you don’t need, you shouldn’t get taken in by companies selling your collaborative PR services that you don’t need either.

Big Data: Are You Still Doing it Wrong?

The only buzzword on par with big data is cloud. According to the converted, or should I say the diverted, better decision are made with better data, and the more data the merrier. This sounds good in theory, but most algorithms that predict demand, acquisition cost, projected sales prices, etc. are based on trends. But these days the average market life of a CPG product, especially in electronics or fashion, is six months or less, and the reality is that there just isn’t enough data to predict meaningful trends on. Moreover, in categories where the average lifespan is longer, you only need the data since the last supply/demand imbalance, global disruption, or global spike in demand as the data you need for the current trend before that is irrelevant … unless you are trying to predict a trend shift, in which case you need the data that falls an interval on each slide of the trend shift for the last n trends.

And if the price only changes weekly, you don’t need data daily. And if you are always buying from the same geography, dictated by the same market, you only need that market data. And if you are using “market data” but 90% of the market is buying through 6 GPOs, then you only need their data. In other words, you only need enough relevant data for accurate prediction. Which, in many cases, will just be a few hundred dat points, even if you have access to thousands (or tens of thousands or even hundreds of thousands).

In other words, big data does not mean good data, and the reality is that you rarely need big data.

But you know that AI doesn’t work without big data? Well, their are two fallacies here.

The first fallacy is that (real) AI exists. As I hoped would have been laid bare in our recent two-week series on Applied Indirection, the best that exists in our space is assisted intelligence (which does nothing without YOUR big brain behind it, and the most advanced technology out there is barely borderline augmented intelligence.

The second fallacy is that you need big data to get results from deep neural networks or other AI statistical or probabilistic machine learning technologies. You don’t … as long as you have selected the appropriate technology appropriately configured with a statistically relevant sample pool.

But here’s the kicker. You have to select the right technology, configure it right and give it the right training set … encoded the right way. Otherwise, it won’t learn anything and won’t do anything when applied. This requires a good understanding of what you’re dealing with, what you’re looking for, and how to process the data to extract, or at least bubble up, the most relevant features for the algorithms to work on. But if you don’t know how to do that, then, yes, you might need hundreds of thousands or millions of data elements and an oversized neural network or statistical classifier to identify all the potentially relevant features, analyze them in different ways, find the similarities that lead to the tightest, most differentiable clusters and adjust all the weights and settings to output that.

But then, as MIT recently published (E.g. MIT, Tech Review), and some of us have known for a long time, many of the nodes in that neural networks, calculations in the SVM, etc. are going to be of minimal, near zero, impact and up to 90% of the calculations are going to be pretty much unnecessary. [E.g. the doctor saw this when he was experimenting with neural networks in grad school over 20 years ago; but due to the lack of processing power (as well as before and after data sets to work on) then versus now it was a bit of trail and error to reduce network size]. In fact, as the MIT researchers found, you can remove most of these nodes, make minor adjustments to the other nodes and network, retrain the network, and get more or less equivalent results with a fraction of the calculations.

And if you can figure out precisely what those nodes are measuring and extract those features from the data before hand and create appropriately differentiated metadata fingerprints and feed those instead to a properly designed neural network or other multi-level classifier, not only can you get fantastic results with less calculation, but less data as well.

Great results come from great data that is smartly gathered, processed, and analyzed — not big data thrown into dumb algorithms where you hope for the best. So if you’re still pushing for bigger and bigger data to throw into bigger and bigger networks, you’re doing it wrong. That’s the wrong way to do it. And the only way you can call it AI is if you re-label AI to mean Anti-Intelligence.

AI: Applied Indirection in Contract (Lifecycle) Management

Continuing our expose of why you should think “Applied Indirection” and not “Any form of Intelligence” when you hear AI, because most solutions claiming to be AI are really just dumb systems with RPA (robotic process automation) and classic statistical models from the 90’s, we move onto Contract (Lifecycle) Management which, like analytics, is almost universally touted to have AI, even when there isn’t even a shred of anything that comes close.

This doesn’t meant that there aren’t vendors with true AI, especially when you classify it as Assisted Intelligence (and sometimes even Augmented Intelligence), in the space, just that, as the buzz-acronym reaches new heights, there will be many more vendors claiming AI than those that actually have AI and you will need to do your homework to find out which is which.

Example #1 of Applied Indirection in C(L)M: Auto-Renewal Detection & Management

Yes, evergreen contracts can be a big problem in Procurement when they have outsourced their usefulness, but detecting and managing these is not hard, and certainly doesn’t require any AI whatsoever. All you have to do is specify the contract as “evergreen” or “auto-renew” by checking a box and enter a notice-by date (to prevent an evergreen renewal” as well as the start date and end date and most contract management platforms can alert you in sufficient time to take action, escalate to your supervisor if you don’t, and kick-off a termination process at the push of a button.

For anything close to AI, you require a system that can detect when a contract is evergreen or auto-renewing when there isn’t a spelled out and easily identified auto-renewal clause that can be found with a simple reg-ex search. For example, when a crafty supplier buries an auto-renewal requirement in the liability section under the notices subsection titled “methods for delivering official notices” which starts off “Official notices shall be sent by X, Y, or Z, to A or B and only treated as an official notice upon proof of receipt. This includes a notice of non-renewal, as the contract will automatically renew 30 days prior to expiry otherwise.” Even a good lawyer might miss that in a fifty page contract when it’s snuck in on the third revision.

Example #2 of Applied Indirection in C(L)M: Off-Contract Purchasing

Maverick purchasing is a big problem. But it’s not one that you need AI to detect. If you encode all the products, services, and / or categories that should be bought on contract, it’s pretty easy to identify when a purchase for that product, service, or category is not bought from that supplier. And if the contract only applies to a region, it’s pretty easy to encode that too and it’s just a simple check.

And even if you have two or three suppliers in a multi-supplier contract for risk mitigation purposes, then it’s just a matter of making sure at least one of the supplier got the purchase, and if each supplier had a geographic area, that the right one for the area. Again, simple rule checks. No AI needed.

The key is to detect when something is off-contract when it is not specifically coded to a contract, either because it’s a new product, missing a category designation, required to hit a volume commitment, and so on. And while this can often be accomplished by identifying the closest product or service (using a document likeness statistic or weighted field match), sometimes advanced NLP may be employed for better results (and this would constitute weak AI).

Example #3 of Applied Indirection in C(L)M: Clause Suggestion

On the surface, this sounds pretty smart … point out clauses that should be in my contract to protect me. Under the hood, in most CLM systems that include authoring, it’s basically a set of templates that are used to specify what to look for in a contract type, with additions or subtractions for well defined industries that the provider serves. It’s basically a check list. And it’s about as dumb as it gets.

Can it be smarter? Of course, but the smarts are more around proper contract identification than clause selection. Because the clauses that should be included generally depend first and foremost on the type of contract, secondly on the product or service, and thirdly on the regulations that affect the products and services in the origin country, the destination countries, and any points in between. Then, identifying which regulations come into play and which types of clauses will be needed. This requires good NLP, probabilistic selection, and, preferably, adaptive learning that learns over time when Legal or Procurement chooses an alternate clause over a standard clause. A system should have assisted intelligence here to be useful, and augmented intelligence to be truly useful. But few do.

Note that SI is not saying that systems with the non-AI abilities discussed above are not valuable, as any system that automates tactical processes and minimizes non-strategic busy work is valuable. We are just saying you shouldn’t pay for what you’re not getting, or overpay for what you are. Buy what you need, and pay accordingly.

AI: Applied Indirection in Sourcing

As we said yesterday, hopefully we have made it clear by now that most of the time you hear AI you should think “Applied Indirection” and not “Any form of Intelligence” because most solutions claiming to be AI are really just dumb systems with RPA (robotic process automation) and classic statistical models from the 90’s (which were available in SAS in the 90’s as well, you just didn’t have enough memory on your PC to run all the data you wanted to run).

But since we want to make it abundantly clear that most of the “AI”, even in our space, is not “AI” at all, we are going to continue to take the major areas of SPT (Strategic Procurement Technology) and highlight some areas where AI is commonly claimed, but rarely found, continuing with Sourcing.

This doesn’t meant that there aren’t vendors with true AI, especially when you classify it as Assisted Intelligence (and sometimes even Augmented Intelligence), in the space, just that, as the buzz-acronym reaches new heights, there will be many more vendors claiming AI than those that actually have AI and you will need to do your homework to find out which is which.

Example #1 of Applied Indirection in Sourcing: Automated Auctions

Some of the shiny new sourcing platforms are really slick and can run an automated auction and do everything from the time you do a product/service selection all the way to final award recommendation. Now, I’m sure you’re thinking such a platform must be at least on the order of augmented intelligence, approaching cognitive, to do all this, but the reality is that you can do all this with simple RPA and a rules-driven workflow. Supplier selection? Just select the past suppliers and pre-approved suppliers from the last sourcing event. RFIs? Use the template with the standard terms and conditions. Ceiling prices? Use current price or current market price if the price has been relatively flat for the past year or falling. Floor prices? Pull in the should-cost model, marked up by a fair margin, if it is available, or use the lowest of the lowest paid historical price and lowest market advertised minus the typical savings percentage in the category. Minimum Decrement? 1%, rounded to three significant digits. Pre-populated bids? Current prices or last bids or advertised market price. Duration? Standard auction duration for the category. And so on. It’s literally just a set of rules, with tolerances, and RPA.

There’s only AI if the platform can run a sophisticated market and category analysis on internal, external, and automatically identified market data, identify a prime set of products in a category for an automated auction, determine the appropriate contract length (and projected demand) to result, and basically take a bunch of the analysis off of your plate for non-strategic / low-value, regular, purchases.

Example #2 of Applied Indirection in Sourcing: RFX Auto-Fill

One of the most time-consuming parts of the Sourcing process is waiting for the suppliers to fill out the RFIs with not just the bids but all of the other information you require. However, there’s no reason that a lot of this information can’t come from their profile, their catalog, and previous RFI responses (where you asked the same questions).

It doesn’t take AI to encode meta-data mappings between profile fields and standard RFI fields, catalog fields and standard RFI fields, and re-use of the same question across RFIs (and previous responses) to pre-fill the majority of an RFI and simply require the supplier to confirm and enter new, additional, or changed data (and, of course, check the boxes that say they accept the T’s & C’s, verify the data, and confirm their bid).

Unless the platform can seek out additional data on the supplier’s web-site, third party directories, third party audit sites, and process semi-structured and un-structured data using natural language processing and identify and extract new data and new information relevant to the RFI (and the sourcing event), it likely doesn’t have anything close to AI (even in AI’s weakest form).

Example #3 of Applied Indirection in Sourcing: Outlier Identification

Here’s another capability that doesn’t require anything close to AI. Statistical algorithms to identify outliers have existed for decades and decades. Many even pre-date modern computers. Run a simple mean/modal comparison, do a simple clustering maybe even use a regression. Easy-Peasy. Even Kitty can do it!

The trick is to identify outliers that aren’t easily identifiable mathematical outliers. A bid in range that is not sustainable for a supplier because the bids were supposed to be landed cost bids (and included shipping as the buyer wasn’t taking possession until the goods hit the warehouse) and one supplier’s bids clearly didn’t, when you think about it, is the type of outlier we want the system to detect for us.

If four of the suppliers are near-shore, and the cost of shipping for them on a unit basis is typically 10% of unit cost, but a fifth supplier is offshore, and the cost of shipping for that supplier by unit is typically 30% of unit cost, even if the bid looks okay, it might not be. For example, if the bids for all the nearshore suppliers are between $100 and $120, their actual unit prices are about $90 to $110. If the off-shore supplier, with a shipping cost around $30, comes in at $100, a mathematical outlier algorithm won’t detect that it’s underlying unit bid is roughly $20 less than the lowest bid, and if the driving costs are expensive raw materials, and the should cost model for that supplier indicates a production cost of $80 (even though their production and overhead costs are significantly less), then a bid of $100 is unsustainable and an outlier (or will not be honoured as the supplier misunderstood and expected to bill shipping separately). If the platform truly has assisted intelligence, it should detect this, even when a human doesn’t. (While you always want the lowest cost, you want the lowest sustainable cost — it doesn’t do you any good if the supplier goes bankrupt halfway through the contract and you have to scramble to find another.)

Note that SI is not saying that systems with the non-AI abilities discussed above are not valuable, as any system that automates tactical processes and minimizes non-strategic busy work is valuable. We are just saying you shouldn’t pay for what you’re not getting, or overpay for what you are. Buy what you need, and pay accordingly.