Lithium, Jive-X, and Some Thoughts

Wednesday Lithium announced they had acquired the code base and customers from Jive-X (the external community product from Jive).

Let’s face it, this should be a non-event.

At least, that is what I thought at first.  After all, I wrote off Jive some time ago: great architecture, bad management, poor understanding of their target market – and the worse sin: they abandoned their user needs in favor of flashy, crappy “features” no one wanted.  I thought they had a potential way, way back when they first entered the external market; failed to deliver.

Lithium has had a good run, but very limited functionality and had invested a lot of time and effort into building a value proposition for marketing departments, and focused a tad too much on social for my taste (and most everyone else’s) with a product that trailed the one that Jive had re-architected for itself.

What good could possibly come from this merger?

Here is a good interview in CMSWire (words I never thought would come out together from my mouth, considering they are mostly a clickbait outfit for ads) with Rob Tarkoff that sums up pretty well the overall feeling in the market for this event.

You can search Twitter, LinkedIn, Facebook and every other Social Network for Pundits, Analysts, and even Users poo-pooing the effort.  If you don’t know where to start, go to my mentions column on Twitter – where everyone is posting comments about how horrible this is (sorry, Twitter changed their interface – don’t know how to point you there… yeah, I suck at social — but only because it sucks itself worse).

I even told them so.  When I got the notice from my contact at Lithium I told him: this is a non-event, my data shows that people are not interested in the product either of you are offering, the best you can hope for is what the market is saying: this is a customer purchase for renewals revenues, and you can transform the products into a cash cow and remain market leader for a niche that provides good profit.

Same things I read everywhere – usual stuff: “customer will never get value out of this”, “another cash-grab by a vendor”, etc.  Trust me, analysts — we are good at putting any merger and acquisition down because we have seen them all before.  And they all end up the same way – it is very, very hard to make an M&A work.  Automatic first take “analysis” has the same words – we see some synergy, but make sure you get a detailed roadmap, ask for assurances, you won’t get the value, find someone else if you are just getting started, etc.

Same old thing.

Except — once in a while, one of them is different.

I said for a long time, this was a good merger – not just Jive-X, but Jive and Lithium.  Ever since the destruction of GetSat at the hands of Sprinklr, there was no good platform to speak of for customers to use for communities (I like Vanilla Forums, not a client, but not there yet).  While Jive had a good platform, they lacked many things that Lithium had proven they could do – but without a good platform.

Then I got an email from my contact at Lithium, he offered me a chat with Rob Tarkoff (Lithium CEO) to clarify the strategy.

I like and I respect Rob.  I worked with Rob before, we did a strategy session a couple of years ago that was very interesting.  We had many conversations, he never tried to sell me a bill of goods – he was always upfront and respectful.  I like his approach to communities, and I like what he was trying to do before they got acquired (I always said if he was smart enough to keep Katy Keim as CMO, he could accomplish anything).

As with every other strategy session I have done with vendors, the thoughts and ideas leap the implementation, by miles — but the model was very, very interesting.

We had an interesting, frank conversation.  I started by telling him that I saw little I liked in the deal based on what I had heard: a cash grab was not in the best interest of the clients – or basically anyone.  I mentioned that my research was pointing to the fact that most CS practitioners were running (not walking, running) away from communities because of their experiences and their failed experiments.  I explained that while there is a lot of potential value in using external communities for customer service, no one since GetSat had gotten it right (please see my disclaimers for more on my relationships with all these vendors).

Then he explained to me the rationale for the deal (and the strategy) and — somehow, it makes sense.  No one will be entirely happy here: both Lithium and Jive customers will be missing some of the things they want to see in a community platform.  But here are five things that I see as the potential value here (key word: potential, execution is key).

  1. Lithium customers have been mostly focused on social and marketing – and yes, there are customer service communities based on Lithium – but let’s face it, humongous forums are not only antiquated but also very inefficient for the purpose.  A new model for Customer Service was needed – and Jive has a platform that could deliver on that; my hope is to see a return to ad-hoc, integrated, freestyle, knowledge-powered communities a-la GetSat (but updated, and better).
  2. Jive customers had been abandoned some time ago by a very poor management team that was focused on building flashy apps for internal collaboration rather than focus on the communities. Their focus on flashy apps made them lose sight of the potential the platform could bring.  Very sad.
  3. There is a huge value prop in this deal, one that Rob mentioned in our call, the talent Jive had: leverage the Portland and Argentinian development teams that had re-architected and strengthened the Jive product and use their knowledge and passion to move the Lithium platform forward (this I see as a great point, and not just because there are Argentinians involved: Lithium needs more talent to move their dated product to a services-based platform for the next decade).
  4. While the plan is over the long run to move Jive users to the Lithium platform (and all M&A events are about this, in essence) the part that was most interesting was that Rob mentioned he did not intend to do so before he could figure out what was the value for all customers going forward.  Thus, for the next year or so there would be no roadmap other than studying the code bases, seek synergies, do all those nice-sounding words about finding value, and then in September of 2018, they will present a roadmap.
  5. We also talked about a way to overcome the reluctance from previously-burned customers to come back to communities, and how to leverage the lithium use cases and work with the technology work Jive did and find a new model to offer.   This was the most interesting part of the conversation, he gets the challenge – but is seeking the solution, and open to conversations with customers and prospects about it.  I’m a sucker for anyone open to conversations (ask my girlfriend – god bless her for putting up with me).

I get excited when I see potential in an M&A event.  Call me a romantic, if you may.  I see my role in this world as making bigger pies, finding the way for everyone to get more value.

I mentioned before I trust Rob and I like him, and I see something here that may — just MAY work.

Let me preface, as a disclaimer, that a part of the conversation was about working together again – which means there may be some who believe, and who don’t know me, that I will ben biased by the possibility to make some money.  If you do, your problem.  I have never written anything just for the purpose of making money and I stand by everything I ever wrote – even the punishing words I used for Lithium when we had a “fallout” back in the old days.

What can I say, I’m a sucker for maybe.  Just maybe…

disclaimers: all the vendors mentioned here have been or are customers.  I have had strategy sessions, content, and advisory work paid for by them, in addition to them paying for my attendance to their events and others.  I work with everyone that has potential in their plans, I choose not to work with people who don’t know what they are doing – I had canceled contracts with both Lithium and Jive in the past because they had no idea what they were doing; we fought about it, we discussed it intelligently, then I told them to take a hike, took my ball and walked away.  Nevertheless, I do see potential in this, and I think not a merger of the code base, not a rush to make money, but a methodical approach to building a new platform for communities that the market needs and no one is providing right now may just be the answer here.  I’ve been wrong before (twice divorced, among other things like driving a FIAT) — will see.

Article: Why Gaming is Good for Computers and AlphaGo is not AI by Itself

Title: 10 Breakthrough Technologies 2017: Reinforcement Learning


Source: surfin’

Why has reinforcement learning recently become so formidable? The key is combining it with deep learning, a technique that involves using a very large simulated neural network to recognize patterns in data

My Perspective on this: This is a good article not only for what it covers but a few things that I inferred from it:

  1. Why is it that the ultimates test for any AI technique, tool, or program is competing with humans? AlphaGo did not beat a human because of its superiority at playing Go – it beats humans because they are not used to playing machines (same with the poker players that lost to the machines earlier this year – how do you play poker without cues and body language?) The competition with humans only reinforces what we already know – computers are better at managing large series or combinations than humans — which is why I call that style of AI advanced analytics.  Fast processing is why we build computers – but still lacking many of the components that would threaten us.
  2. Of course, this does not take away from the power of reinforcement learning as an AI technique, but you have to consider it as one more tool – which is what the article infers.  The quote above tells you more about how combining techniques we see the value of the approach grow dramatically.  We tried for a while to use cognitive approaches, but without deep learning (marketing term for ML) in the mix, we could’ve never done it.  And this is done because of the availability of cheap, fast, easy-to-access computing power.
  3. If you add this technique combo to Generative Adversarial Networks (GAN) – another hot technique being used right now – you will start to see what are some of the ways (very advanced) in which we can train computers to do better.  This is the key point, IMO, from this article — takes a village to train a computer to pretend to be human.

One more thing.  This article is part of a series that goes back some 15 years.  Take some time, or come back with time, and read some of the past years’ technologies and you will quickly see how we got to where we are — and maybe peek into where we are going.

Thoughts? Starting to go too deep? I can go much further 🙂 or just pull up a little… this is the last week I cover AI, moving on to platforms and ecosystems next.

follow-up reading:

Article: Why Singularity and Computers Won’t Take Over

title: The AI Cargo Cult – the Myth of a SuperHuman AI


source: surfin’

Problems need far more than just intelligence to be solved.

Intelligence is not a single dimension, so “smarter than humans” is a meaningless concept.

My Perspective: If you ever hear me talk about AI and SciFi you know that I am not a fan of the concept of singularity or computers taking over humans (as in the Terminator series).  There are certain things that computers cannot replicate (covered these in my article sharing these past few weeks as well) – intuition, innovation, imagination is my soundbite for the summary of these items.

In this article, I found new hope that Arnold won’t be back.  The author describes in detail both the points and counterpoints of AI taking over humanity.  In a great discussion, the article is a tad long – fair warning – and at times a little hard to digest, he looks at why computer intelligence and human intelligence are not comparable – and shouldn’t be.  I often say that computers would have to dumb down their behavior and operations to work like us, and Kevin makes a similar point – but goes further to say that even if they were able to match us, we would change to become a different intelligent being that would remain relevant – despite computers.

Another great point that the article makes, intelligence not being linear, is one that you should consider deeply when building analytics and AI programs: intelligence is just one piece of the solution, not the entire solution.  Being intelligent is not the same as being smart – and being smart (understanding how to apply intelligence) is what makes the difference between knowing what the answer is, and implementing the answer.

A final, unrelated point – I had a discussion yesterday afternoon with my oldest daughter about being omniscient and omnisapient — if you don’t know the difference, go ahead and google it (we did, it was fun).  If you don’t want to do either- check out my post earlier on this topic.

Do you think computers will become aware and take over?  Thoughts, and debates, welcome.

Article: A Deeper Explanation of Neural Networks (Easy to Understand, Though)

Title: Comment on a Medium Article

Source: Surfin’


My Perspective: Back in the 1980s I took a couple of classes in neural networks.  It was, at the time, the logical continuation of my interest in AI: we had little machine power, but we had C and Pascal (not to mention FORT and Assembler) and that was going to be enough.  The first course was a three months jaunt into building a neural network that could recognize any letter in in the alphabet by either hearing the sound or looking at a printout/drawing of it.  This is 1986-87.  It was fun, but it also introduced me to the complexity of a neural network and how we actually have to teach computers.

I found the above article while going through rabbit holes on AI a few months ago, and I really liked it because it does a great job of simplifying that first foray into neural networks (which we use, in different forms, for machine learning and “deep learning” (marketing name for machine learning)).  It covers what for me became the next long search: to understand cognition and learning (still don’t get the most advanced concepts, but it’s a fascinating hobby and science).

I am hoping that by reading it and mixing it with my previous link on ML, you begin to both see the potential and complexity behind ML (and realize that for most vendors today, it’s just another marketing term).

disclaimers: this sh— stuff is hard AF, but the easy part is to understand how and what cognition works.  when i say most vendors are hyping and marketing, i meant no a particular vendor — and of course, none of my clients  — but a generalized statement considering the complexity and, still, academic nature of true ML.  that first course i took? we barely made it past helping the machine learn both what a B was and how to recognize it.  today you can get that in an open source library, at worst.  ces’t comment la vie avance

Knowledge Summary: The Next Decade in Digital Transformation

A knowledge summary is a semi-long to a long post that synthesizes positions, concepts, and lessons learned around a topic.  They consist of a mix of primary research with ideas and frameworks I built based on conversations and working sessions.

This knowledge summary will focus on concepts you have to know to succumb to embrace digital transformation in the next decade.

Understanding Digital

I wrote about digital transformation first in 2013.  Then an update later in the year, and finally a call to end the talk in 2015.  And I wish, you don’t know how much, we could do that.

But we can’t.

The problem is that digital has become the new crutch word much like e-anything or i-anything became the crutch letter in the late 1990s.  And we have seen decent value arise from that use: iPhone and eCommerce are the two most recognized iconic words (and concepts).

The purpose of Digital is to replace all that has to do with computer-driven or computer-assisted.  We have had computers since the 1980s in the organization, we cannot say “computer-driven transformation” as that ship has already sailed;  we talk about digital as if it was a magical, mythical approach to changing things.

But it’s not.

At the core, this machine-driven revolution is different from the one 30+ years ago in what is focused on – data.  That’s it, as simple as it can get.  This transformation is about data.

It would be more appropriate to say it was about information (a combination of data, content, and knowledge that enables companies to solve problems better in context), but — as I said before… i-anything was already taken.

Seriously, it’s not about data only, it could never be.

Data is the representation of an event.  What happened to whom or which, when, how, and where – it’s the pieces of information you need to recreate something: a customer signed up for a newsletter (what), they received the newsletter (what, when), they opened three of the five links (what, when, which), they did a search for further information (what, how, when), they bought a product (what, how, when, where), we shipped it (what, when, where, how), they received it (what, when).

That’s all data – every piece of that.

Data does not mean much without the other two parts of information: content and knowledge.

Content is the static information we (or someone else, if we believe in communities) created that describes an entity (product, solution, use cases, manuals, etc.).  For example, the customer got a newsletter with content – information about products, services, discounts, coupons, etc. and acted on that.  The data about the newsletter being distributed and the action remains, but without the content, we would not know why the client acted and we couldn’t track specific actions they took.

And that brings the final piece: knowledge.  Knowledge, if you follow my writings, is wisdom – the applied, contextual, intent-driven use of content.

And that is where the reality of what data can do comes together — if you combine data about events with content related to that event and intent and context in the form of knowledge – what do you have? the right information, at the right time, in the right place personalized for each person, optimized for each situation, and outcome-focused.

In other ways, you have the reason your organization is trying to adopt a business transformation strategy.

Becoming Digital

Now you know what digital is and why it’s the source for business transformation this time around: even though data has been around from the beginning, it’s the leveraging of data mixed with the right content and knowledge, yielding information, that makes it the basis for this new evolution.

How do you use it?

There are two slides that I use to show my clients and people I talk to where / how / why information is to use it.  First, where does information come from?  Big Data — rather Big Noise.

Check it out…

The concept of Big Data is a misnomer: how can something be “data” or be anything if we don’t even know what it is.  If one trillion IoT devices generate measurements, which ones are truly data and which ones are noise? If there are 20 millions tweets in one week mentioning your company name, product name, or someone in the company directly by name, how do you know which ones to reply to directly and which ones to safely ignore? What about corrupt or misspelled or damaged “data”? it’s not data until you structure it, until then is noise – simple.

Although the first reaction is always to store everything and “later we will figure it out” the volumes are becoming too large for that, and the need to use the data (and the speed at which data usability and usefulness decay) increases dramatically every day.  Storing for future use is no longer possible these days.  The next step is to filter the noise and create a signal.  If a plane that uses Rolls Royce engines generates over a terabyte of data per flight, and there are thousands of flights daily worldwide – which piece of “data” should be stored? used? discarded?  Filters make that decision.

Once the filters take their turn and the noise is selected as valuable, then we structure the information.  This is the decision as to what it is: data, content, or knowledge?  This is the moment the noise becomes structured and ready to be acted on.  The concept of digital transformation being about acting on unstructured data is another misnomer – how can something be acted on if we don’t know what it is? where it came from? what is used for?  once we filter the noise (create a signal) then we figure out what it is that we have, structure it, and store it in the proper place (or not, but that’s another post) and act on it.

These actions, the analytics or workflows applied to them, generate an insight – a something we did not know.  These insights are what we use to make decisions, to act, or simply to inform – via dashboards and reports.  These signals are also the components of information that will be used to power algorithms and AI components – and that will become the new filters for Big Noise.  The cycle of optimization based on what we know and what we learned is then complete and we have both insights we didn’t before, and data (and content and knowledge) stored.

Very different from simply capturing everything to figure out later what to do, no?

Being Digital

This section is the second framework I use with clients and in conversations when talking about what it takes to become a digital enterprise.  I must confess, this is not a new model – I first introduced it in 2013 – but have been working and optimizing it since.  Thanks to all of you that helped me along the way.

Once you get past the awesomely-designed majestic use of colors and boxes, you end up with six components you can focus on: four yellow boxes, and two blue boxes.  I know, mind-blowing.

The yellow boxes represent infrastructure, the purview of IT and Architects in your organization.  As your organization becomes more entrenched in the cloud (which by now is commoditized, so I am assuming you are either already there or on your way there) they will notice more and more a need to structure their approach.  The yellow boxes separate the different pieces they need to focus on:

  1. cloud infrastructure, the core components used to run and interconnect everything
  2. legacy access, because I am still proud of the code I wrote in the 1980s in COBOL and to be fair – a large number of you still use it and need it to run your organizations
  3. interface connectivity, because in the age or mobile and IoT there is no longer a requirement but actually a necessity to be device-independent — this is not mobile-first, this is mobile-also
  4. and to accommodate the hype of the day – AI (or advanced analytics, if you read my writings on the topic) with three specific outcomes: optimization, personalization, and automation.

I wrote about this framework before, the link is above and there is an update to it here as well, which is why I am just summarizing it here.  If you need more details read those posts, or contact me.

Once you configure your distributed computing architecture (AKA cloud) to operate in a model similar or comparable to the above, you will end up with two blue boxes – which is where the magic behind digital business transformation happens.

The information blue box is what we were discussing above in the previous section: how to find the right data, content, and knowledge to create the necessary information for every transaction.  The information will come from anywhere: devices, legacy data or applications, or AI and analytics engines.  The role of the information layer is to make sure that all information is considered, and the best selected, when crafting the response to an interaction.

The experience layer is the one where most organizations would love to have control – but they can’t.  The concept of building experiences for customers is archaic and, frankly, dumb.  This is not about understanding customers journeys or planning for them – or anything like that.  This is about understanding that stakeholders (notice it says customers as well employees, partners, and public) will interact with the system on their own terms, according to their expectations, via any channel, at any time, in any way they see fit.  Today they may have more time, tomorrow less – and they would appreciate a quick summary today, and more information tomorrow.  To accommodate these shifting expectations, each experience will be built ad-hoc by the stakeholder – provided they have access to all systems, information, and rules that apply.

This is the essence of being digital: building an infrastructure that allows any stakeholder to interact with any part of your organization, at any time, anyway they want, for anything they need.  if you can do that, you are “living la vida digital”

To get there, that’s what you need to do over the next decade; this is not a simple system purchase and deployment.  It requires extensive changes in all layers of the organization: people, process, and technology.  It also requires new thinking in governance and metrics.  and that — that takes a decade or so to complete.

Your move.

disclaimer: first things first, thanks to Jon Reed for the idea of using crossed-out text.  Since Sameer Patel does not like my parenthetical digressions, I am testing some minor ones using crossed-out text to see if it works.  if it does, all credit belongs to Jon who uses them to great effect (far greater than I could ever do) at Diginomica. I would reluctantly, but understandably, relinquish the use if he asks.  second, I also have to give credit to the kind folks at OpenText who helped me with the first chart a bit during a consulting session in December.  the other people, many, who contributed over time are also very kind and i am profoundly thankful – but they were more motivation and inspiration, OpenText tweaked the slide for me and let me use it.  third, no vendors are mentioned – but y’all know that i work mostly with them, so there’s a chance that some of this stuff shows up in something they do / use and i will gladly take all the credit for that.  ideas are mine, originally and follow-through, so feel free to yell at me for anything i got wrong.  if it works, not me – if it didn’t, me.  comment box is below.  thanks for reading.

Article: Memory is the Barrier Between Computers and Humans

Title: Understanding the Four Types of #AI

Source: Twitter, via Vala Afshar


We need to do more than teach machines to learn. We need to overcome the boundaries that define the four different types of artificial intelligence, the barriers that separate machines from us – and us from them.

My Impressions: I often marvel at the attempts from Hollywood to portray AI as a foregone conclusion of using computers, and how simple it is to do things if you are a computer.  My favorite depiction continues to be the Star Trek computer – if you ever worked in AI you know how far we are from something like that… but I digress.

I often find myself having the debate on whether that model will become reality, whether the singularity is possible, and how we get there.  While most people get hung on feelings and emotions as the differentiating factor between us and them, I favor what I call the three I’s (feelings and emotions can be recognized, measured, and replicated — the key is to identify the few variables that affect them – like sarcasm, but we can talk about that some other time).

The three I’s are Intuition, Innovation, and Imagination; they are not measurable or easy to replicate (yet).  As I often use as an example – Monsieur Fleming would’ve never found penicillin with the search parameters her was using if he was a computer.  It’s a fungus growing on food — not sure how to tell a computer to test that (especially since they would not bring their lunch, where the mold would grow on).

This article goes one further examining how the real barrier between computers and people is memories – and how they work.  If you think about it, the most complex process we have as humans is memory, not emotions.  What, how, where, and why something is stored, recalled, blocked, used, or discarded is far more complex that crying when you see a commercial with puppies (not that I do that, but I’ve been told some of you do).

Read this article so you can see what are the major challenges we have to focus on, not for advanced analytics only (although the ability to hold a conversation that is coherent requires memory) but for machine learning.

Good stuff…

Article: Differentiating Between #AI and #ML

Title: The Dark Secret at the Heart of AI


Source: MIT Technology Review, via Google Alerts

“If it can’t do better than us at explaining what it’s doing,” he says, “then don’t trust it.”

My Impressions: I am not going to say that machine learning is misunderstood, but the fact that most everyone uses AI and ML interchangeably says that for me.

Understanding machine learning, called deep learning by astute marketers, takes a lot of knowledge in the more complex concepts of AI (neural networks, cognition, etc.).  We are not talking about what most companies are using today (advanced analytics) and calling AI – but about totally trusting the machine to make decisions without a good understanding of what it is doing.  All those Sci-Fi films you saw that we use to base our (incorrect) understanding of AI are focused on ML, not the ability to process data, find patterns, and predict outcomes (which is what we do for AI, mostly, today).

This article will help you understand why Stephen Hawkins, Elon Musk, Bill Gates and other luminaries are warning about what AI can become (they mean ML; don’t want to overemphasize the difference – but it’s critical).  It will also show you how my take on this, we are in control – regardless of what we tell computers to do, is valid if we do the right things going forward.

Good read, plan 10-12 minutes for it.

Article: A (Very Clever and Well Done) Critique of #AI


source: Technically Sentient Newsletter (subscribe here)

“computer programs that use statistics and lots of data.”

my impressions: this is a very contrarian view of AI as a solution.  The author makes some good points, leaves some things that we know out of the discussion (likely to strengthen their own point), but ends up being a great summary of why AI is not the panacea we y’all some of you think it may be.  Good reading if you are considering embarking on a decision on AI.  The quote above encapsulates my thinking on AI as it’s being hawked at enterprises: it’s nothing more than advanced analytics as presented — and there’s lots more to be done, if someone wants to take the time to do it right (will save you the suspense, nah… not likely to happen).

If you want to follow up on the concept of advanced analytics and machine-learning (what you really think of when you think AI), drop me a note.

Opinion: My Quick Take on Jive’s Acquisition

Monday Jive Software (see disclaimer below, we have history) announced it entered a definitive agreement to be acquired by ESW Capital, LLC – a private equity firm.  Details for the deal can be found here (I link to a couple of interesting writeups throughout, people I like and ask the right questions).

There are three things that come to mind with this deal.

  1. As David F Carr says here, no surprise that it was acquired.  After several quarters of poor performance (celebrating a single digit growth for the year in 2016 was the lowest part of it IMO), the inability to sell the company to someone in the industry that could do something useful with the product, and a demoralizing campaign that broke the spirit of most employees I talked to – management finally managed to do something they have been trying to do for over 2 years.  Make no mistake, in spite of $462 million seemingly being a lot of money – it is nothing compared to what some others were prepared to offer in past years (I cannot offer data, I am compromised by NDAs – but I have no reason to lie at this point) but were unable to close during due diligence.  What is my point? I am glad that this management team is not going to be calling the shots anymore.  An utter lack of vision, a focus on menial details that provided no value, and no purpose highlighted the last two years of the company’s performance.
  2. The product people rock.  It is well accepted and well known in the industry that the two things that clearly worked at Jive lately were product and professional services.  Back in the day when they migrated from on-premises to cloud (between versions 3.0 and 6.0), they showed the direction the product was aiming for.  The last two years, although compromised by the release of inane little apps that added no value, was a continuation of that – ending in the announcement at JiveWorld17 of their migration to AWS by the end of the year (among other improvements that should be celebrated).  Well done, and as long as Aurea (the new overlords) don’t compromise the integrity of that move it assures customers that Jive will be around for a while – with better performance, and better solutions to come.  I see this move as beneficial for the product, as long as their Head of Product, Ofer Ben-David – a brilliant technologist, remains with the new company.
  3. Communities are the future.  I have been supporting Jive since the days of the migration to cloud as the logical choice to offer the ad-hoc community platform that all enterprises need to have going forward.  Despite the best efforts from Lithium to catch up (and the great run lately of converting many Jive clients to Lithium clients – driven by the management team actions at Jive as well as the clear leadership of the Lithium management team) I still believe Jive’s product is better (if you take out some of the bells and whistles added the last two years for no reason) and Aurea, if they leave the product alone, will greatly benefit from it.  The model that Jive can deploy for communities (both internal and external) now that they reside on AWS requires some evangelism but it’s a clear winner.  Time will tell if the new overlords leave them alone, if Ofer Ben-David stays, and if the current product is not dismantled for parts (as it happens in many PE acquisitions; if the course stays, Jive has a good chance of powering the future of communities.

Of course, there are many issues remaining, and Derek Du Preez does a good job over at Diginomica positioning those questions, but I am hopeful that this deal will let the product shine for its goodness and remove the bad people from the decision-making of where the product should go.

What are your thoughts?

disclaimer: Jive was a client and a partner for a long time.  I started working with them in 2009 and remember the revolving door that saw 4 CMOS within a year, the run to the IPO, the beginning of the battle with Lithium, and many other things.  our partnership (and client relationship) ended under the current regime when I was asked to not attend their conference last year – unless I signed a “code of conduct” that essentially said I was to ask no questions, and “behaved as stipulated”.  you can imagine my response, there are few words I can publish here from that conversation.  you can say i am bitter, you can say i am resentful, and you can say that i am writing this because of that.  you can also say that the easter bunny exists, and santa delivers all christmas presents within 24 hours every christmas eve — none of them are real.  i stand by my reputation and those that know me will attest to it.  i had a similar run-in with Lithium back in the day, and remain to this day friendly with the company and continue to provide advice to them – while maintaining the tough stance towards their products and positions (see above) as well as recognizing their prowess.  there are many others.  take this as you may – i stand behind my opinions and the only way i will change that is if Aurea does the right thing and stands by the current product.  i hope this was useful.  welcome your comments.

Research Project: Customer Service Going Forward – Please Help

It’s that time again – time to give me your ideas and plans for Customer Service, in exchange for a consolidated view of the market.

This is my sixth year doing this research project on adoption and trends for customer service and this is the most exciting so far.  We are entering the era of cloud-based, micro-services focused, platform-built solutions for customer service and I want to find out what you are doing.

Take this short survey, 15-20 minutes at most – likely less.  Let me know what you and your company are working on, what you’re planning, and what looks interesting.  Last year we highlighted the fast rise of chat and chatbots – this year we are focusing on the coming age of platforms and ecosystems, mobile, and the far faster decline of social.

What says you?  Take the survey, let me know.  I will publish the results via this blog in summarized form and send you the final report when we are done.

Thanks for your help, once again.

PS – here is a little hint of what I am working on for research and where I need your help — this is a model I crafted (with m excellent design skills) on what the platform model looks like for CS.  More on the final report, but I need your help with data to get there.  Thanks.

disclaimer: as with all my research projects, all data remains mine and no one else will ever see it.  your contribution is secure and confidential, and I will never reveal any PII about you or your company.  all results are aggregated, and all analysis is done in private without sharing data with anyone else.  while this effort is sponsored by (a client) I make all decisions on what to ask, how, and I keep all the data.  they help me pay the bills – and put the kids through therapy…

the blog!

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