deal architect

Plex ML project - something every software vendor/systems integrator could emulate

Machine Learning is a hot topic. Every software vendor is talking about ML and AI in their products. Plex applied ML to something very different - an understanding of how their customers configure parameters in the Plex system. This conversation with Jerry Foster, CTO, describes the project.

It has always bothered me that after millions of ERP, CRM and other enterprise projects and upgrades, we cannot get predictability and savings from automation in these projects. I think every software vendor and SI can build on this exercise around other SaaS products to automate configuration and related templates. Another interesting area Jerry describes is how rethinking customizations and the UX can dramatically reduce configuration change effort.

 

When your competitors talk about machine learning, it’s typically in terms of customer data and different functional areas like accounts payable. Your project looked at implementation configurations, which is a unique approach. Can you share your perspective on Machine Learning?

Just like with any transformative technology, everyone likes to ask, "What is your machine learning strategy?" or "What's your IoT strategy?"  But you can really go down a rabbit trail because those technologies are so big. So we've always tried to ask “What problems are we going to solve for our customers?” which could even mean solving an internal challenge that ultimately helps our customers.

At Plex, we didn't want to just start building Machine Learning models and doing them willy-nilly. Instead, me and my team discussed areas where we could apply machine learning efforts in order to solve a problem while also gaining our own internal knowledge on what a ML project entails.

Can you share some background on how this specific Machine Learning initiative began?

Last year, our VP of Services told me that about 15% of incoming customer care tickets deal exclusively with the configuration setup of our system.

Because the Plex Manufacturing Cloud is a Software-as-a-Service product, everyone is using the same codebase, which means we use configuration settings (also known as customer settings) that determine how the software behaves and functions.

Settings have business flow and visual ramifications. For example, some of those settings determine actual workflow, which may be different for a food and beverage customer versus a discrete manufacturing customer.  While others are as simple as a setting that will show certain columns on the screen and, if it's turned off, they are no longer visible. 

Because the system is so full-featured, there are a large number of settings so it would be unreasonable to expect our customers or even our Customer Care team to fully understand all of the interdependencies of the settings and the unintended side effects from turning settings on and off.

Obviously, when engineers build these systems, they try to take that into account.  They will add a new setting and see how it works with all other similar settings.  But over time, you can't ascertain all of the effects of all of those settings.

Our customers are trying to figure out how to optimize their settings, so when I was pondering that stat – 15% of our tickets are related to configuration settings – I realized this would be a great place to build a machine learning model that could help us understand what the net effect is.

Our desire was to ask “How could a customer use this model to compare it to their settings?” and be able to say, "Here are the settings you should change in order to have the optimum configuration.”

Once you identified your objective, where did you start? What technologies did you use?

We put together a team of engineers and gave them this assumption that there is a data set that could be analyzed to determine whether or not a customer's settings are configured correctly.

The team came up with a framework and a set of tools, which included Azure Machine Learning Studio because, in our opinion, Microsoft has done a really good job providing the necessary tools to very quickly get up to speed on a machine learning presentation.  We also used Neo4j as our graphing database and a system called Notebook, which is a way to put together any Python scripts that you are using to augment your artificial intelligence project. 

From there, the team put these tools together and started building out their models.

Once you had the tools, what was the process like?

 There were actually three phases to the project.  The first is feature discovery, which is basically asking “What are the data sets that we need to test our assumption?”  The second phase was building the knowledge capture system, which actually gathers all of the data from the feature discovery.  Then, of course, the final phase is a building a recommendation system.

The first phase – feature discovery – was determining which data sets were the most important.  One thing I learned is that feature discovery is just as much art as it is science.  You really have to have people on your team that know the data.

We put together a set of data that we could use to test our thesis and we started to build some models around that data.  We found that once we had a set of features and a set of data points, it wasn't enough to determine what data points to use, we needed to establish just how much data to use as well.

For instance, if we were too restrictive in our data sets that we were using, the models couldn't make any inferences from that.  It was basically regurgitating what you put in.  But if we were too broad in the amount of data, data sets, and features that we were putting into the model, it just came up with all sorts of crazy inferences and dependencies that had no meaning.

We had to keep refining, which we is a big part of the machine learning process. We really had to learn to accept that there is an iterative nature to determining which data could best give us the results that we were looking for.

Once we had a set of data points and features that we felt were representative and returned some good initial results, we built a knowledge capture system using Neo4j, Excel spreadsheets, and a data factory from Microsoft. This system pulled in the data, adjusted it, and used the models that we had built in ML Framework to start analyzing the data. 

The next part was actually consolidating and analyzing the data using mainly Azure ML as well as some Power BI and Excel along with Neo4j for some of the visual representation.

Can you explain how this applied to the configuration setting problem you were trying to solve?

As I mentioned, our customers turn settings on and off in order to configure the system to meet their needs.

To make that process easier, Plex has a set of customer setting templates – 12 or 13 primary templates based on size, industry, facility count, etc. – that we use to categorize our customers, which then informs new customer implementations. When a new customer is brought into the fold, it’s more efficient to associate them with an appropriate template and copy the template settings to their instance.

For this project, we wanted the algorithm to pretend we didn't have those templates and determine on its own how it would group our customers. We fed the data into the model and it begain to cluster our customers and match them with a particular customer setting template. Using a mechanism called similarity analysis, the model then made recommendations based on the delta between the customer and neighbors using their common template as a baseline. 

Can you show me how this played out in the model?

The image below is from Neo4j and it actually shows the clustering of customers matched to a particular template.  It first came up with its own clustering, and then it attempted to match those clusters with our templates. 

This next image shows the model’s clusters.  It identified 14 broad groups completely disconnected from our templates. 

Then what we told the model to do on this next screen was to try and match its clusters with our templates, and it actually grouped our templates into those four groups that you see highlighted right there: 0, 7, 9, and 11. 

So the model reclassified your templates?

Yes, the machine learning algorithm essentially said four of the groups it had defined (Group 0, 7, 9, and 11), could match up with one or more of Plex’s existing templates. 

What else does this tell you?

All in all, what you see here is basically six of its groups matched our templates. As you can see on this next image, the model matched two of its groupings – 3 and 8 – with a few templates that we don't typically use as much. The model also has two groupings, 2 and 6, that it couldn't match with any of our templates.

One of the main findings coming out of this is that we are basically missing two templates.  In other words, there is a set of customers that, when they onboard as a new customer, we're assigning them to a template that, although it may not be bad, is probably not the optimum template that they should be getting.

This final graph illustrates a finding that was consistent across the board : the number of times our customer settings were changed dropped off a cliff in about 2015.  That was a completely unexpected finding from the study.

 

This finding validated the work that we've been doing since 2014 to reduce our overall dependence on settings, including our new user interface, which is much less settings-dependent than Classic. It gave us confidence we were on the right path.

This is also really cool because it wasn't actually in our initial hypothesis. It wasn't what my team was trying to find or determine, and it was just an unintended side effect of the work they were doing.  Of course, one of the advantages when you start doing a project like this is you start looking at your data in detail and you come across insights that you had never expected.  This is something that the initial algorithms that we were putting together showed us that wasn't even part of our initial thesis.

Those were the two main results: identifying configuration setting templates that we had missed, and showing us that the work we had done in reducing the number of settings had paid off. 

What action will you take from here?

One of the follow-throughs from this, obviously, will be to look at the model’s groupings that do not identify with a Plex template and ask what are the characteristics of the customers in those groups?  Why did we miss those?  One of the main follow-ups that we're doing right now and in upcoming months is trying to determine which customers are in those groups and what are the characteristics of those groups so we can come up with a proper template for those customers.

The second action is to follow through with the recommendation system because that's really where the productization of this is going.  If I'm an existing customer, I want to know if my settings are optimum. I want to know, based on all the customers that are like me, which of my settings are different, and why do I have those settings different than all of the other customers who are like me?

We already have the "like me" part done.  Now, we actually have to productize that so we can provide a tool, and we want to provide that tool in two different contexts.  One is an internal tool for our support team so that when they get an incoming call that says, "Hey, I've got a problem," they can run this against the settings optimization tool and say, "You know what?  You might want to check these settings.  I've noticed that they're different from you, but all other customers like you have these settings set differently."  Then, of course, the next iteration or the second context would be a self-service tool that the customer could just go onto the Plex menu and analyze their settings. 

Were you happy with the results of this project?

Overall, I was very pleased with the outcome of the project and how the team worked.  It was exciting to see.  One of the interesting things about where machine learning has come from where it was five, ten years ago is that the tools are so strong that you don't need a team of data scientists.  You need a team of smart engineers and a couple people who know the data. 

Yes, a data scientist is obviously going to help, but to get 70% or 80% of the way, in my opinion, you don't really need a data scientist.  All the things we did here, there was no data scientist involved.  That, to me, was a real encouragement.

The last takeaway I'm taking from this is, once you start to realize what's behind machine learning, you actually start to use that as a mechanism to solve problems.  In other words, it's not just this machine learning project that the labs did.  It's how we can analyze any problem moving forward.

It's going to be a long journey, but it's very exciting.  I'm really excited to see where we can go with this.

Where have all the process visionaries gone?

A recent visit to an Amazon Fulfillment Center was eye-opening. They have completely redefined the old warehouse pick, pack, ship mindset. It's now stow, pick, pack, verify, and in their lingo, SLAM and "chaotic storage". Kiva robots and miles of conveyor belts bring work to employees not the other way round. Scanners, scales, sensors monitor and facilitate the worker - what size box to assemble, cut right length of packing tape, apply shipping labels etc. Read more about my visit here. 

I was recounting the visit to a software CEO. He said "Amazon is amazing". And I responded - "it's not just Amazon". I told him about companies I have worked with and written about that have dramatically rethought their processes with all kinds of modern technology - shop floors, cashierless stores, return logistics, transportation routing, payment processing and on and on. He asked me which vendors they work with. And I named a few then stopped and said "In the end, these customers are themselves driving the vision about the new process with a wide range of technologies. Rarely is there a single vendor or product".

The realization sunk in that true process innovation is not really being bought "shrink wrapped". Companies which are taking a "systems approach" - adding sensors, robots, flexible workflow, RPA at front, center, back of any process - are getting stunningly better results.

Of course, plenty of SIs will continue to talk about "best practices" embodied in packaged software, and many customers will say they are satisfied with "good enough" processes.

Most software vendors have in the move to cloud computing hired good architects who have helped them think about multi-tenancy and platform technology. They have not invested similarly in business process experts, especially those unique to individual industries or geographies. And even with the underlying technology they have waited for consumer tech to innovate with UX, AI etc. A few of their "good enough processes" added together could translate to several basis points of margin lost every year.

Jeff Bezos, CEO of Amazon has been known to say "your margin is my opportunity". I used to think it was his way of targeting fat, dumb, high-margin competitors. After visiting his warehouse, I think he also means to target companies which are not focused enough on margins and instead waste them in process inefficiencies.   

Maverick idea: Shift some marketing dollars to product design

(Sir) Jony Ive recently left Apple to start his own design studio. On his heels, Marcus Engman with a similar role at Ikea is doing the same, and he has an interesting value proposition “I want to show there’s an alternative to marketing, which is actually design ..And if you work with design and communications in the right way, that would be the best kind of marketing, without buying media.”

He is definitely on to something. Look at all the money we have poured into modern marketing and the poor results

1. email marketing has led to all kinds of spam and phishing scandals
2. telemarketing has turned into nuisance calls, robocalls and scams
3. TV advertising has been abused to the point where the FCC does not even try to limit number of commercial minutes per hour anymore - other than for kid's programming
4. digital marketing is turning into a privacy nightmare

BTW, there is a naive view that GDPR and EU regulators will somehow make 4) more viable. To which I point to how poorly regulators have done trying to regulate 1, 2 and 3.

Nineteenth century retailer John Wanamaker is credited with saying “Half the money I spend on advertising is wasted; the trouble is I don't know which half.” We spend a lot more on advertising these days, and have much better tools, but the results are only marginally better.

The sad thing is enterprise vendors have for over a decade now missed the move to smart products, a trillion+ dollar market I covered in my 2012 book The New Technology Elite. Contract manufacturers like Flex and Foxconn and design firms like IDEO and frog were the big beneficiaries of a focus on smarter, better designed products and services.

Enterprise vendors brag about CRM, but the reality is most also missed out on the digital advertising market. Google, Facebook, now Amazon have mostly benefited.

Will they change their tune and encourage customers to try the balancing act Engman is preaching?

The "Glo-ver" phase in enterprise apps

I don't mean to pick on NetSuite but it is one of the oldest cloud apps vendors - born as it was in 1998. Over 21 years, you would think it would have a fuller global footprint. Especially since it had an opportunity for global subsidiary implementations in a two-tier ERP world.  Look at its site and see how few countries it supports functionally. Look closer and even there see how many functional areas are covered by partners. Some analysts say since Oracle's acquisition in 2016, its geographic reach has grown. Yes, likely in terms of sales reach, but in functional footprint - not sure it has.  

Same thing with Infor. For nearly a decade it has talked about emphasizing vertical functionality. But it has largely spray painted its financial, crm and hcm features with some industry terminology. It has not developed operational books of record - patient related functionality in healthcare, merchandising in retail etc. 

It's not just Netsuite or Infor. Most other cloud vendors are largely English speaking-centric and horizontal in functionality. Hey, cannot blame them - they are doing fine. Wall Street is generally happy with their performance.

In the meantime, however, the world has changed dramatically. Customers are not waiting and they are finding plenty of solutions to fill geographic and vertical niches. They are going to startups, SIs and are even filling the gaps themselves.

In the last few weeks I have profiled some of these extensions

• Mike Laven of CurrencyCloud covered fast moving trends in Fintech
• Larry Bridge of Cognizant talked about trends and solutions in healthcare 
• Uptake execs talked about assets and dealers in heavy industrial sectors 
• Raju Vegesna of Zoho talked about coverage in places like Japan, UAE and Nigeria which most vendors usually ignore 
• Mike Laven elaborated on how China and India are reshaping global financial markets 

Expect my coverage of global and vertical trends to accelerate. The applications industry is rapidly moving into the phase of "Glo-ver" extensions - global and vertical extensions to core horizontal functionality.

Cloud Applications - A long, winding road dotted with Bystanders

Frank Scavo's Computer Economics has a nice post about the the long, winding road - the slow customer march to cloud applications. Bill Kutik pointed to similar slow movement more specific to HCM cloud applications in a recent LinkedIn post.

My recent book and my advisory work provide an additional perspective to their data. I identified a large group of what I called Bystanders in the SAP customer base. They have not taken advantage of its growing portfolio of new products - S/4, C/4, cloud properties, SCP, SAC, Qualtrics etc. They continue to stay with ECC, BOBJ, B1 and other 20-25 year old products. I did not call them laggards or anything else pejorative for reasons that will become clear as you read further. I estimated 60% of SAP's 400+K customers are in that group, especially those in Europe. If anything SAP is ahead of its on-prem peers. S/4 is maturing nicely and its coverage in functional and geographic areas is much wider. The competitors have even more Bystanders in their customer base.

There are three big gating factors for their lack of movement

Not enough functionality

I have heard from countless customers they do not see enough from individual vendors in the cloud, and they are afraid to add to their patchwork of already too many vendors. After 20 years of cloud applications, we have too little global or industry coverage. So, you have vendors like NetSuite brag they have users in 200+ countries and they support over 190 currencies, but their own site shows detailed functionality for less than 20 countries. You have vendors who claim to "do ERP" but have zero shop floor or warehouse or operational capabilities in the cloud. I got a nasty note from a vendor executive asking why I considered "back office" functionality "boring". I responded "I was trained as a CPA so boring is a strong term but I am amazed the industry talks so little about operational systems and vertical functionality compared to all the talk of hcm, crm and core accounting."

Too expensive and risky a migration

A number of customers have tested the waters and many have been shocked by the proposals they have seen from SIs. Many SIs are behaving like it's 1999. Just because the S/4 or Workday software is relatively new, it does not justify a premium in consultant rates. The functionality is not dramatically different. Also, SI delivery models have not evolved much. Just not enough automation in data conversion, testing and other phases of projects they do over and over at clients. Finally, there is their age old FUD of  "oh, there is so much change management". And yes, they still cannot show predictable outcomes. The muscle memory of the overruns and failures of the last wave of ERP and CRM remains with too many customers for them to sign up. I was actually pleased to catalog many case studies of ECC to S/4 migrations in my book which minimized the organizational change and the SI role - they stuck with SAP GUI, stayed on-prem etc.  They are postponing innovations, but frankly reducing exposure to the "change management" and other SI premiums.  

Cloud economics are just not as attractive

In wave 1 of the cloud, the economics were compelling. The cloud collapsed 4 contracts from on-prem world - the software, the hosting, the AMS and that of SIs who did frequent upgrades. Multi-tenancy and simplified contracting explained why it was so attractive to look at a Salesforce or a SuccessFactors relationship. But in the last decade, reality has set in. I have heard from a number of customers their incumbent infrastructure is fully amortized - the cloud only adds to that cost layer. I have heard from others who have sharpened their pencils, that buying their infrastructure is still cheaper than subscribing in the cloud when it is annualized. Then there is the expectation that comes from their outsourcing experiences - when you hand someone a 3 or 5 year contract you have the right to expect CMM Level 5 or Six Sigma continuous improvements. Instead, too many SaaS renewals are turning into unpleasant head-butting experiences - no improvements, just massive cost increases. 

Most customers I have talked to would love to modernize. Many, in fact are frustrated with the status quo. ABAP or Peoplecode are not exactly recruiting assets any more. Their significant customizations and technical debt are growing compliance and security risks. They realize they cannot leverage multi-tenancy, machine learning and other advances with their current set-up. But they are nervous. They want lots more operational functionality, and they want better economics and predictable results.

In the book, I called on SAP  to "tilt its bell curve" and jar the Bystanders into action. Other on-prem vendors need to do that even more in their customer bases. Pure cloud vendors continue to have the opportunity to replace incumbent vendors. But they need to tackle the three big issues I highlight above.

Just calling the Bystanders "laggards" or something else demeaning is to ignore the reality of the state of cloud applications.

Tim Cook - Book Review

I read the Leander Kahney book on Tim Cook on the long Emirates flights to and from India. In India, I had a conversation with an Apple consumer like me (not really that knowledgeable about the operations of the company). His point was India should require Apple make massive investments in return for access to their large market. I asked him if the US should expect the same of TCS, Infosys, other Indian outsourcing firms for similar access to their largest market? He ignored my question. Then we got into Apple needing to diversify production away from China given recent trade tensions. I asked him how India would improve its infrastructure to support Apple's finely tuned supply chain. Again, no good answers. Raise our amateur conversation a few levels of abstraction higher. It would highlight how Tim Cook is one of the most geopolitically influential executives in the world these days.

And if rumors of the Apple Car are true, Germany, Japan, S. Korea are just as interested in what he is going to do. So I thought it was odd that Leander's book does not look much at these global crosscurrents in his new book. Instead, I thought Kahney spends way too much time on race relations in Alabama where Cook was born and raised, and his sexual orientation. In that sense, I had the same reaction as I did to Walter Isaacson's book on Steve Jobs - bit too much focus on personal background and idiosyncrasies, less on the gears and timing belts of the Apple juggernaut.

Unlike Isaacson, Kahney did not have appear to have direct access to the subject. However, from the citations he did have access to a handful of Apple executives, and with his long time blog The Cult of Mac he has a slew of contacts in the Apple ecosystem. I find when I write books, I have to take off my blogger hat and not focus so much on newsy, gossipy stuff and focus instead on complex issues that corporate executives are interested in. I don't think Kahney does enough of that. So much of the book is actually cut and paste of lots of historical reporting about Apple. I wish he had spent time in China crawling through Foxconn, Fedex and various component vendors that make the remarkable Apple supply chain work. How it is orchestrated with last minute design changes, how it is mostly kept secret etc. 

I do like that Kahney addresses the silly notion that Cook is not innovative. By launching Apple Pay and Watch he has bought Apple a toehold in massive emerging digital payment/commerce markets and wearables/healthcare markets. And if Car becomes a reality, he should do  the same in rapidly changing mobility markets. Just wish Kahney had explored those a lot more about how those products are bought to life, and how their complex supply chains are choreographed.

You will likely understand Tim Cook a bit better as a human after you read the book. I found it did not really add that much to my understanding of him as a business executive or that of Apple's operations.

Teflon SAP?

In the ASUG Executive Exchange on Monday morning at Sapphire I joked that it was fitting that we had yet another rumor floating - would Bradley Cooper show up with Lady Gaga at her concert later in the week? I cannot remember a Sapphire with so much rumor and speculation in the build up to the event. I was impressed how SAP executives handled much of it during the event. In one on ones, however, several of them were hurt and even angry about the coverage. Even more striking was how little customer execs had heard about or cared about the chatter.

Some of the topics about which there has been some wild speculation

Qualtrics

Yes, it was an expensive acquisition - a "high-beta" one. I am not a big fan of M&A in software. But in my new book, I point out several trillion dollar markets SAP, Oracle, IBM and others missed out on in the last couple of decades - digital advertising, smart products, cloud infrastructure etc. So I cannot fault Bill McDermott for betting a pile of chips on his view that the "experience economy" is going to be a massive one. Not only will it differentiate SAP's CX offering it is already opening up product design, employee engagement and other new opportunities for SAP. It also allows SAP to benchmark and reshape its own product ideation and customer relations. And it has brought a new breed of impressive, high energy execs into the SAP ranks.

HANA

There was some really irresponsible "HANA is dead" speculation. I told several journalists and analysts weeks ago I was baffled by the talk. So it was nice to hear Hasso Plattner after lowering expectations that his was an "old-style", "low-cost" keynote then proceed to announce “SAP HANA is too good a database just to lock it inside SAP enterprise applications,” Watch this keynote where along with Gerrit Kazmaier, he set ambitious new goals for HANA and for the broader portfolio of SAP's analytical offerings. 

Layoffs

There has also plenty of talk about layoffs including some of my colleagues asking for more "openness" from SAP on the topic. I was in Salt Lake City with several of the execs as they were being announced and I could see the hushed conversations. I chose not to pry given the legal and HR considerations that surround such moves. Besides, as I write in my book, SAP has to evolve to a much more cloud, open-source, machine learning savvy culture. So do its systems integrator partners. So do its customers. Continuing with legacy customizations is a growing security and compliance risk. Much worse, it is a recruiting liability when trying to attract younger technical talent. The transitions at SAP are just a start. The ripple effects will be felt throughout SAP Nation. The sooner we start planning for them the better, and we should not just hammer SAP for kicking them off.

Management changes

Nick Tzitzon, EVP of Marketing and Communications had told me "we have individuals in some cases who collectively have worked for SAP for 100 years, and who decided that it was time to try something else in their career. Does this not happen in every company?" Hasso Plattner was even more emphatic when he told a few of us that several customer executives have marveled at the smooth transition. He told us SAP is much "younger" now and he has never been more proud of the company. Watch this keynote and see the energy of execs like Christian Klein, Ryan Smith, Jen Morgan and others

Elliott investment

Geoff Scott, CEO of ASUG had to explain to an audience of customers who exactly Elliott is. That calm struck me as radically different from the panic I have heard from of my media colleagues. Yes, Elliott is a feared force as an activist investor but I am familiar with at least a few executives who have worked collaboratively with them. I liked Bill McDermott's comments during a Q&A “I’m very confident in our top- and bottom-line development,” McDermott said, and that he and the Elliott reps were “psyched” by SAP’s prospects. As I write in my book, there is a huge opportunity in "tilting the bell curve" - transitioning ECC and customizations, BW, S/1 to the new portfolio of S/4, C/4, SAC, SCP, Qualtrics, SuccessFactors, Ariba and many other products SAP now has to offer.

My book

Finally, I am bracing for potshots about my book. I had a chance to give my book to both Hasso Plattner and Bill McDermott. I warned them both it had many sections which would make them cringe. They were both gracious in their thanks - can I dare say they are mature enough not to expect a puff piece from me? But if hear some of my colleagues who have clearly not read the book, you would conclude it is an unqualified endorsement of SAP. I had a chance to sign several copies for SAP customer executives this week. Trust me they are not looking for fawning support of SAP when they talk to me.

No, Bradley Cooper did not show at the concert. But Lady Gaga was amazing on her own. I did not know much about her music prior to the concert but you have to admire her amazing pipes, incredible energy and her body of work. More importantly you have to acknowledge the sheer admiration she got from the audience. SAP is not everyone's cup of tea, but you have to similarly admire its wide body of work and the fact that it is the backbone for over 400K customers.     

I heard an executive say this week media attention on any topic lasts about 5 days. You wait that long and any PR crisis will blow over. That is not a compliment to our analyst/media side of the fence. We have to take the much longer customer view points when they invest in technology.

From my side, this week made me resolve to be even more careful in my reporting and to continue to validate my viewpoints with even more customer conversations.  

The Paradox of Selling Progress: a guest post by Mark Galloway

Mark Galloway and I go back to our respective Lawson and Gartner days. I caught up with him recently - he is Co-Founder & CEO of OppSource, developers of next-gen sales engagement tools. He told me of an interesting use case of his tools as he describes below.

After watching multiple waves of “transformative” technologies come and go in the enterprise over the past 30 years, none in my opinion have been as transformative as what we are seeing with the “Amazon effect”.  No, I am not talking about AWS or the cloud.  Rather, I am talking about the online consumer shopping experience that many of us interchangeably call “Amazon” regardless of whether we are actually shopping at Amazon or some other online retailer.  You ask, so what does Amazon have to do with transformation in the enterprise?  Let me tell you, a lot.  As consumers, we have taken our desired consumer experience for self-service and moment-of-interest engagement into every corner of the workplace.  The net result, we have created a new normal for a radically different B2B buying journey.  We don’t want to talk with salespeople until we absolutely have to, and in many cases, we don’t ever “have to”. 

On the other side of the commerce equation, sellers are having to quickly and radically change their sales coverage models to adapt to us modern buyers.  The days of press-fleshing sales calls are waning. The cost/per sales engagement for this traditional sales approach used with today’s modern buyer is no longer economically feasible.  As a result, companies everywhere in every industry are now grappling with how to transform their sales coverage models to grow more profitably. 

Many organizations have favored hiring young, smart, and high-energy “inside sellers” who come with almost no business, industry or product acumen.  Why hire on this profile?  Because they can force high velocity and high-volume outreach and engagement efforts on these young go-getters at a fraction of what they were paying their expensive, highly compensated field-based predecessors.  Yes, these new reps are sharp and yes, they are quick learners, but without any contextual understanding or experience, smart learning can only take them so far.  The paradox of progress for the modern buyer is that while they don’t want to talk with salespeople until they absolutely have to, when they do, they want to speak with someone who brings value and relevance to their unique buying situation.  The current hiring model for insides sales teams that many companies are using is out of alignment with the modern buyer’s sales experience expectations.

This is where artificial intelligence and machine learning can lend these young sales guns a hand.  Imagine as a young rep, being prompted with questions to ask, similar customer stories to tell, and appropriate product/solutions to position all while you are engaged real time with one of these modern buyers.  This is all being made possible by leveraging machine learning and AI to listen to conversations.  Using modern AI and machine learning models, sales reps can be provided these real-time prompts that will augment the missing business, industry, and product acumen that next generation sales reps will need. Working together, salesperson and machine will be able to satisfy the buyer’s expectations for a modern sales experience, one that comes as the highly personalized and relevant engagement they desire.

As an example, Lifetouch, the nation’s largest K-12 school photography studio used to rely on field sales people to drive around and call on schools in their local territory.  This model worked well in days gone by. However, the modern security measures employed by most schools today discourages unsolicited sales calls and in many cases are not even allowed.  Coupled with these security changes, today’s modern school administrator have enormous time constraints just keeping up with running their schools.  They simply don’t have time to meet spontaneously with sales people of any variety. These market changes required Lifetouch to rethink their sales coverage model. 

They chose to hire recent college grads as inside sellers who work collaboratively with strategically located field sales specialists.  Their job is to line up scheduled and qualified appointments with school principals and other picture day decision makers at schools.  But getting engaged with enough of these contacts to keep a field team of sales people busy with qualified meetings required a much higher volume and velocity of market outreach than what these inside sellers could do with just a phone and their CRM.  They needed a specialized system that provides touchplan automation, AI and machine learning to capture conversation data, and automatically setup callbacks, and appointments.  Working with our company and our OppSource Sales Engagement Platform, they saw the average revenue per rep driven by their inside sellers grow 4X over 24 months.  An incredible ROI by anyone’s standards.

This type of progress driven by AI and machine learning is coming quickly.  A recent McKinsey Institute study found that by 2020, as much as 80% of transactional sales related functions will be possible with Ai and machine learning. 

If your sales organization isn’t experimenting with AI and Machine learning to augment your evolving sales force, you may soon experience the paradox of progress.

You can reach Mark at mark DOT galloway AT oppsource DOT com

 

Sapphire 2019: A curtain raiser with Nick Tzitzon

As I prepare to drive to Orlando for the annual SAP conference I had a chance to talk to Nick Tzitzon, EVP of Marketing and Communications about what we can expect on the main stage.

If you think about the big picture, Vinnie, in terms of where the economy is and where our customers are, we approach SAPPHIRE with a really clear view of the challenges that we need to address and the opportunities we need to seize.

If I had to give you the top three headlines, number one is continuing to deliver on promise of the intelligent enterprise, which as you recall is something we launched last year. This was the pinnacle moment, if you will, in terms of what we always knew that the SAP HANA era would lead to for SAP. Our customers know it’s a big data world. They know that they need end-to-end connectivity in their business. They’re hungry for the exciting machine learning use cases and the new wave of industry best practices. You’ll see a lot more of this in Orlando.

Look for Bill McDermott to make the argument that we want our customers to focus on something of a cultural transformation. The idea here is “you need all people inside companies focused on people and opportunities outside of companies.” Demand chain integrates to supply chain in any truly digital business model.

This leads me to the second headline, which I think will surprise a lot of attendees in terms of the energy it creates in the SAP community. In looking openly at the same challenges facing our customers, we recognized there’s a big gap we needed to solve for. No company in our industry at scale, not SAP, not our competitors, could bring in real-time experience data to complement operational data. This goes directly to the culture change McDermott wants to hit. Nobody questions the operational heft of SAP. The question is – how can we help customers direct those systems to what their key stakeholders, customers and employees, actually feel. Experience Everything , Everyone is a Customer, Customer is Everywhere.

It's widely known that we went out to find a partner here. We found not only the market leader, but the pioneer of the experience management category with Qualtrics. SAPPHIRE will now be the largest event ever focused on experience management and helping businesses operate with greater agility in terms of knowing what's going on in their business, knowing why things are happening, being able to connect the two and personalize outcomes to a far greater degree. So while I think people have read the headlines about the Qualtrics deal, I don’t think there’s a full appreciation of the paradigm-shift that experience management on the Qualtrics XM platform will enable.

Number three, I think the consistent feedback you see, not just from reading about SAPPHIRE, but from reading about all vendor events in this industry, the customers do a better job talking about the solutions than the vendors do. Perhaps this a career-limiting admission for someone in the marketing and communications function, but I’m willing to take the risk. This year we made a concentrated effort to bring as many customers to Orlando as we can to let them tell the stories, to let them explain the experience they're having with SAP, why they chose SAP, why they're excited about the idea of experience management as the latest frontier for the intelligent enterprise.  You're going to see hundreds of customer-led sessions, and the dominant ‘red thread’ is that customer testimonials will be steering the conversation. 

Those would be, at a high level, the three things I think about.

In terms of product announcements, we're going to do a lot around SAP HANA. I can’t tell you how aggravating it is to read speculation in the media about our product strategy. Josh Greenbaum wrote a nice piece about this earlier this week. HANA remains—as Bill calls it--the intellectual foundation of SAP. You're going to see Hasso do what he does best as we blaze a new trail in the data management space with some announcements about SAP HANA, which is very exciting. 

You're also going to see some additional progress being made on the idea of combining experience management and intelligent enterprise capabilities. You have heard us talk about this as “X Data plus O Data.” You'll see some early releases, giving developers the tools to build on our platform and accentuate the capabilities of X plus O. There'll be exciting announcements on this front as well.

You have another breakout on C/4HANA?  Is Qualtrics going to be more in that? Is that its own sub-event within the big tent? 

Qualtrics is going to be everywhere, first of all. CX LIVE is the customer experience event focused on SAP C/4HANA and the five integrated clouds that run in that suite. Frankly, the interest has been so high since it was announced last year, we had to do a dedicated CX track at SAPPHIRE because we ran out of space. That’s why Alex Atzberger and his team are hosting CX LIVE as a separate but connected track in Orlando. I’m sure Alex will happily tell you that it's a bigger crowd than they anticipated, which is good news. I should also say that any registered SAPPHIRE attendee can also participate in CX Live. Bill will appear with Alex at CX LIVE.  Ryan Smith from Qualtrics will appear there. It’s going to be a fun event. 

What about all the drama over the last few weeks?  Will Bill touch on it? Are you going to let the media openly ask their questions?

Yeah, I think the media, influencers, analysts, can and should ask any questions they want.  Personally, I don't see the drama. People have been following us for years and have very high expectations for us. Bill tells us all the time – take the high interest in SAP as a compliment that people believe we’re a significant company with a lot riding on us. Having said that, I worry that much of what’s out there right now has largely been the result of speculation and not necessarily the result of facts. In the case of the executive departures, we have individuals in some cases who collectively have worked for SAP for 100 years, and who decided that it was time to try something else in their career. Does this not happen in every company?

And the other piece of those changes, we have a particularly strong bench of talent at the leadership level and at the engineering level. Names like Adaire Fox-Martin, Christian Klein, Jennifer Morgan, Juergen Mueller, Thomas Saueressig, Gerrit Kazmaier, Abdul Razack. I think these leaders and the quality work done by our engineers will be on display at SAPPHIRE. Maybe the only thing that will quiet the conspiracy theories is seeing a totally thriving SAP in action in Orlando. So I guess I’m happy it starts next week.

Vinnie, I'm normally, as you know, somebody who is pretty sensitive to what's going on across the company, and I feel like the company is going in with a full ahead steam with a pretty clear sense of what it has to accomplish.  I just don't see a lot of this so-called drama.

While I understand there is some sensational stuff in the media, we put more credibility in the facts.

If there’s any learning I have from all this, it’s that we need to do a better job over-communicating about everything. We know what our customers expect, we know what our shareholders expect, we feel motivated and excited about the future. That’s the story I personally need to do a better job getting out into the influence channels.

Any other big announcements?  Any other directional stuff?

You’ll see a re-invigoration on the industries focus from us. There's a lot of really good content we're going to have at Sapphire on the industry side, a lot of good road mapping and best practices. 

You're going to see a lot on the ecosystem. I think our partners are fired up for SAPPHIRE.  You're going to see some more detail in terms of how our partnerships with cloud infrastructure providers have matured. These are topics that we know the SAP customer base is interested in, so they will be well featured at the event. 

I also think you're going to see an increasingly good balance between the technology topics and the business topics. This is something that all vendors struggle with. We, like many, have an audience that comes from multiple perspectives, but what we really like about the intelligent enterprise, particularly now in the context of the experience economy with experience management from Qualtrics, this is a conversation that bridges the gap between business and technology.  It completely unites the value chain on what needs to be done to keep companies successful or, in cases where they're struggling, gets them back on the right track. SAP is talking end-to-end now in multiple respects and we're excited for SAPPHIRE, if for no other reason, because it's our best opportunity of the year to show people just how complete and broad our portfolio is and how complete our strategy is in terms of giving them the tools that they need to be successful.

Plus, we're going to be distributing copies of exciting, new, future best-selling books. In case you have any recommendations in that regard.

Well I know at least one person - the publisher of SAP Nation 3.0 - will enjoy that drama :)

Thanks for the sneak preview.

Digital Transformations: Too little, too late?

I find Frank Scavo a great sounding board.  He has a way of simplifying things and pushing back when things are buzzword-y. I have quoted him in several books. He has introduced me to case studies I have profiled in my books and blogs.

I liked his recent post on Digital Transformations, especially the graph on left.

But it reminded me of 2 things.

In The New Polymath, I had invoked Doblin's (a design strategy firm) 10 types of innovations. You could take Scavo's graph and nuance it some more.

However, the key point I want to make is I started to write that book in 2009. It was inspired by innovative people, products and places I had been cataloging on the New Florence blog since 2004.

In 2012, in The New Technology Elite I had bunches of examples of companies making smart products - taking analog products of the past and embedding sensors, software, satellite and mobile support. They had started designing those products years before.

In 2014, I helped Karl-Heinz Streibich wrote The Digital Enterprise. It cataloged a number of companies rethinking their supply chains, their business models. They had been doing it for years.

In 2016, in Silicon Collar I cataloged companies in over 50 work settings changing their business processes with all kinds of automation - robotics, drones, wearables, 3D printing etc etc. They had been doing so for years,

In my latest book, SAP Nation 3.0, I point out the countless missed opportunities at some of our favorite vendors. Markets don't wait. Startups, corporations as in healthtech and healthcare, SIs have been developing solutions they should have 

What's my point?

If you are only now starting to think about digital transformation, you likely are years behind many of your competitors and your peers.

Here is something even more sobering. The solutions you are likely looking at also missed out on working with those pioneering customers. Their SI partners will try to convince you the solutions embody "best practices". No, they don't - the best practices belong to those pioneers.

Which is why I was pleased to see Brian Sommer write his new book Digital with Impact. In my review, I wrote:

"Brian Sommer and I go back a long way and we have consulted with clients on several projects he describes in the book. We are both worried that today's "digital' project is becoming the "re-engineering" project of the 90s. It starts off with the right goals, then many companies just implement off the shelf solutions justifying them or being told by their consultants they embody "best practices in a box". End result in 90s was massively expensive ERP and CRM projects and failures. Companies risk making the same mistake again with similarly poor results. This is where I like Brian's 4 step framework - spend plenty of time looking for digital opportunities (not just move your back office to the cloud) and build your own jigsaw of solutions given the plethora of cloud apps, automation technology, developer platforms and other tools available today. I also like his emphasis about thinking about in terms of a circle. Rinse, repeat - don't view it as a project which is one and done. Finally, he has a slew of exercises you can use to encourage lots of back and forth with colleagues.."

Frank is right when he concludes "each digital journey is different, because every company is different." In addition, as I said at the end of my review of Brian's book

"Its important you scream and debate. Opportunities like this come only every couple of decades - you want to do far better than most companies did with the "re-engineering" wave in the 90s."

You are likely late. But in addition, please do not do too little with your digital transformation project.