Monthly Archives: July 2019

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.

Marketing Recognizes Procurement. Does That Mean We’re Sexy Now?

The World Federation of Advertisers, or the WFA, which is a global trade association for multi-national advertisers and national advertisers, quietly launched a Global Sourcing Board about six weeks ago to revolutionize the practice and perception of marketing procurement as per this press release.

According to the press release, the ambition is to reposition procurement as a driver of company growth rather than a seeker of savings, transforming cultures and approach within sourcing teams as well as perceptions across the wider marketing, finance and agency community.

Great ambition, and desperately needed, but how much change is going to be driven by a board of 12 people that meets only four times a year? No matter how big the companies they work for are?

At least we can say if a global marketing organization is recognizing procurement then we must be getting sexy. Too bad they’re not doing more to promote us.

You’re Understaffed. And You’re Not Alone. Now What? Part III (Updated)

Now that we’re in Part III, the doctor is going to tell you that even if you’re in the 2/3rds of Procurement Organizations that do not think you’re understaffed, you are. Even if you have enough headcount, chances are you do not have enough skills to tackle each category and project to the maximum potential as each staff member in your department is only human, and can only master a limited number of categories in a job where you are expected to be a jack-of-all-trades. The only question is are you slightly understaffed or significantly understaffed?

If you’re significantly understaffed, you’re going to have to augment externally as there’s no way you will be able to handle a large influx of internal staff, even if they are temporary and category/service experts, as they still have to be trained on your organizational procedures and policies, guided towards optimal outcomes for your organization, and managed.

If you’re moderately understaffed, it’s often a toss-up that comes down to your particular needs and the strength of the options provided to you.

If you’re slightly understaffed, you might just need one or two more resources internally to reach your potential, but you still might want to consider outsourcing if the appropriate talent is not available to you or it’s easier to get budget approval if you outsource a project to a services provider.

So, if you think outsourcing is a reasonable option, how do you make the decision?

First of all, you make sure that outsourcing is a viable option. The best way to start is to apply a sniff test and make sure that the proposed projects don’t suffer from the 10 ailments of outsourcing, as presented in a presentation by Andrew Downard (of AD Supply Chain Group) and Karl Manrodt (of Georgia Southern University) on Delivering Better Service, Lower Costs and Increasing Innovation Through Vested Outsourcing, and make sure there are no hidden gotchas waiting to jump out and bite you in the backside.

As per the presenters, and a co-author of Vested Outsourcing, you need to make sure that the proposed project is not:

  • Penny-Wise and Pound-Foolish
    and being considered for outsourcing just because outsourcing is expected to be cheaper
  • An Outsourcing Paradox waiting to happen
    because you expect that the provider will do what you tell them to which you incorrectly assume is the best thing to do
  • An Activity Trap
    where the provider is getting paid by the hour or transaction
  • The Next Junkyard Dog
    where you will assign the project to internal experts who will micro-manage the contract
  • The Result of The Honeymoon Effect
    where the provider is getting the work because they just went overboard on the last project
  • Sandbagging
    where the provider is penalized if they don’t deliver a contracted level of effort, but not incentivized for a better than average performance, so the provider will deliver minimalist results
  • a Zero-Sum Game
    where you don’t accept the provider’s preferred terms of engagement, assuming that what’s good for them is bad for you
  • Driving Blind
    as you don’t have any formal governance processes setup to monitor the performance of the relationship
  • Measurement Minutiae
    where you over-measure and under-incentivize the provider
  • Measurement-Free
    although you shouldn’t over-measure, you should measure the results of each project

If the potential Procurement project passes the sniff-test, then you can seriously consider the categories and/or projects for outsourcing, provided you have an appropriate provider with talented personnel. But is that enough to make a decision? We’ll address that in Part IV.

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.

Comprehensive Category Management: Are You Still Doing it Wrong?

As we said five years ago (and probably even earlier than that), spot buying individual categories at market lows or evening running reverse auctions at opportune times is NOT category management. And for that matter, neither is a strategic sourcing event that throws everything in the category into a strategic negotiation, especially if the category is metals and you are including the kitchen sink.

And you might be thinking that the doctor needs a psychiatrist because how could it not be category management if you are addressing the whole category? Category Management isn’t just about grouping all seemingly related items and running an event. Category management is about grouping items that have related characteristics that allow the items to be sourced effectively under the same strategy.

For example, while it might make theoretical sense to group printers, ink, and paper together —- because you use them together, from a sourcing point of view, ink and paper often go better with office supplies and printers with hardware. You can probably get them thrown in for free with a server purchase. But that’s just the start.

For example, if you source a lot of metal parts, you should probably start by grouping them by primary metal, since the price of steel, aluminum, etc. will largely dictate the price of those parts. Furthermore, it might even make sense to not only source all of the parts from the same supplier but even buy the metal on behalf of the supplier with your better negotiating power and/or credit rating.

But that’s just the start. Then you have to make sure the parts are (best) produced using similar processes, because giving a part to a supplier that is only easily produced by laser cutting when the supplier only has traditional machining / cutting is not going to be a good decision. Even though the volume will lower their cost of metal, the extra work will increase the cost per unit.

So sometimes you will need to group the category into sub-category by metal and production style and get bids separately and together (from any supplier that can offer both) and do a multi-level analysis to find out the best approach. (And this is yet a another reason that SI has been telling you since DAY ONE that you need an optimization-backed sourcing platform as this is the only way you can effectively analyze all the options.)

And sometimes you will have to ignore items with a large demand or core material component because they are cheaper when sourced as part of a different category buy as they can be produced by other suppliers or bundled for a larger volume-based discount.

For example, consider an organization-wide UPS replacement. They are technically a power transformer with a battery, but you wouldn’t source them from the manufacturer that manufactures custom transformers for your on-site renewable solar and wind farm since you’d source them from your hardware supplier who supplies you with the rest of your office electronics as they would be buying such units in bulk from a manufacturer who produces them in bulk and gives you a better deal.

Comprehensive category management is looking at a category from a holistic perspective and finding the right segmentation to get the best overall value through the right sourcing method at the right time.

It’s not just a one-time slice-and-dice, it’s a continual analysis of the category from a multi-dimensional and current market perspective to make sure each time an event is run, the right strategy is used across the right sub-category of products and services which are offered to the right prospective supply base.

And it requires up-front market analysis before the event as well as optimization-backed analysis during. So you need a good analytics platform, preferably with some automation that can constantly pull in market data, analyze it to current cost, plot and predict the trends, and provide the necessary market intelligence that can be compared to a best-practice knowledge base that will indicate the event type that has been the most historically successful under current conditions. (And in the spirit of our recent Applied Indirection series, this is not AI, this is RPA with parameterized suggestion look-up.)