The news

During the Salesforce AI Day on June 12 as well as the Salesforce AI Industry Analyst Forum on June 20, Salesforce provided a lot of interesting information on how the company addresses the challenge – or should I say problem – of trust into artificial intelligence. Salesforce sees this gap caused by hallucinations, lack of context and data security as well as toxicity and bias. According to Salesforce, this gets compounded by the need for integrating external models into business software.

To address this problem, Salesforce has announced its AI Cloud that combines an “Einstein GPT Trust Layer”, Customer 360 and its CRM to offer AI-powered business processes that are built right into the system, based on an AI that can be trusted. The main vehicle is the Einstein GPT Trust Layer that takes care of

• secure data retrieval from business applications,

• dynamic grounding to reduce the risk of hallucinations and to increase response accuracy by automatically enriching prompts with relevant business-owned data,

• data masking, the anonymization of sensitive data to avoid its unintentional exposure of sensitive data to external tools,

• toxicity detection to make sure that generated content adheres to corporate policy, is free of unwanted words or images, and unbiased,

• creating and maintaining an audit trail,

• the external (or internal) AI not retaining, storing, any corporate information that gets sent to it via the request.

This trust layer sits in between the used AI models and the apps and the respective development environments. All requests to the models, along with their data, get routed through this layer, ensuring authorization protected retrieval of data, the grounding of prompts using it as well as data masking for anonymization. Responses by the models get routed through it as well. This enables an audit trail as well as toxicity detection. Models can be ones within Salesforce, ones developed and deployed by the customers in their infrastructure and third party models.

Figure 1 The Salesforce AI Cloud Architecture; source Salesforce

To round this off, Einstein Studio allows the building and deployment of models, their training using data within Salesforce and, at a later stage, the building of own models using a no-code environment.

The bigger picture

Although AI is not new, it is safe to say that generative AI is a game changer. OpenAI managed to get AI out of the realm of data scientists and into the hands of mere mortals. And most of us use business applications on a daily basis.

One of the most daunting problems of the use of AI is that there are a number of considerable risks involved with its usage. The one that is currently talked about most in the context of generative AI is the one of accuracy of responses to prompts, which is often referred to as hallucinations. This is not only problematic in consumer usage but even more so in business usage.

What comes on top in a business context is very much related to data privacy bias and profanity. Both have also been discussed in the consumer arena. Do you remember Microsoft’s infamous Tay bot? Or more recently of Samsung, Amazon, Apple, and other companies ordering their staff to not use ChatGPT et al.?

The management of all of these risks is of paramount importance to businesses, for regulatory reasons as well as for the need of protecting own intellectual property. No business can afford customer data and/or sensitive corporate data leak into external tools. This is doubly true in strongly regulated industries. But how to ensure this, when the models are not fully understood and when it is not even clear where and how data is stored? How to adhere in a GDPR-request to delete a customer’s data in this case? The management of these risks requires organizational, educational, and cultural measures in companies. These need to be supported or enforced with the help of technology. 

The obvious technical resolution for this is an AI security layer that I outline in my (upcoming, as of this writing) column article on CustomerThink as follows.

Figure 2 An AI security layer; source Thomas Wieberneit

This is of course simplified, not exactly trivial, but possible.

My analysis and point of view

One could, or rather should, say that trust and security are two of the most important assets in business. Customers need to trust that businesses do not collect an inordinate amount of data and that they furthermore use the data given by customers only for consented to purposes. In addition, they need to trust businesses that they keep their data safe. A multitude of regulations mandates this. Being trustworthy is even more important in times of AI as a service, when businesses cannot even tell anymore where customer data is stored, as it is learned by the AI and stored in a very decentralized manner – as part of an unknown number of parameters.

To enable this trustworthiness, what lies closer for a tier one platform vendor than ingraining an AI security layer directly into the own platform? The gateway to external services is already provided by the platform and can be reused by the AI security layer.

This is what Salesforce has done in an exemplary manner with the aptly named Einstein GPT Trust Layer. Kudos for this.

Figure 3 - How the Einstein GPT Trust Layer works; source Salesforce

In my opinion, the most interesting part is the zero-retention portion. Salesforce cannot guarantee on its own that external providers do not store any data. Whenever a prompt is sent to an external vendor, this data is leaving Salesforce’s systems boundaries. This means that external vendors assume temporary control of this data to provide their services. Masked or not, this data that can potentially be demasked, is handled by them.

To accommodate for this, Salesforce has established “zero-retention policies” with these vendors. According to information given during an analyst briefing, these policies ensure that the vendors won’t store any in-flight data, including inputs and outputs, nor won’t they use it for any purposes besides generating a response to the prompt.

This is quite an important statement that also indicates GDPR compliance, if “policy” can be translated to contract. On the other hand, this makes me curious how the refinement of prompts works in this case. Obviously, for highly security-oriented customers, this also suggests the preference of Salesforce or customer-owned models over external ones.

Overall, this is a great offering that addresses important concerns  of the C-suite.

The only qualm that I have is the price tag, which is quite steep, starting at currently $360,000 US. For sure, customers can derive good value out of it, this is not the problem. Where I see a challenge is that this is out of reach for most SMBs. I’d love to see an adaptation of this offering combined with offerings like Salesforce Easyor similar.

I wait to see when other vendors come forward with a comparable offering. Especially the other tier one but also the tier two vendors need to make a move now. Salesforce truly let the Genie out of the bottle and put them in a tight spot.

Kudos again!

The News

On September 14, 2022, Zendesk announced the release of its new customer sentiment and intent functionality: Intelligent Triage and Smart Assist. These new AI based solutions shall “enable businesses to triage customer support requests automatically and access valuable data at scale.

Intelligent Triage and Smart Assist are the next step in Zendesk’s vision to create accessible CX AI for companies of all sizes. The technology uses proprietary industry expertise and insights from trillions of customer data points and applies a vertical lens. This creates models custom to each business capable of identifying the intent, language and sentiment of each customer interaction.

This unique approach to applying machine learning creates more personalized and informed interactions to better serve customers. For example, specific inquiries, such as "I'm having problems with payment", can be automatically sent to an agent who is equipped to handle billing for a quicker resolution, while inquiries that include language written in all capital letters or in a sarcastic way will indicate a highly negative sentiment and be routed to the top of the queue.

The new capabilities include:

• Instantly route and prioritize revenue drivers, ensuring agents are working on business-critical requests
• Analyze distribution of requests so businesses can better plan operations, collaborate across departments and identify improvement opportunities supported by data for more efficient CX operations
• Automatically guide agents on how to best resolve a customer’s issue in real-time, understand context, recommend solutions, and improve coaching and training with valuable insights
• Continuously boost accuracy as the AI solutions receive feedback on predictions and recommendations
• Detect sensitive information automatically to meet compliance and security needs or extract confidential data like names, addresses, phone numbers, usernames, and financial info for use in workflows”

The bigger picture

Customer support is a very difficult business. All too often agents are not trained too well and service departments suffer from high turnover. On the other hand, customers expect to solve issues faster and faster. This drives a demand for efficiency. This gets underpinned by the current economic climate in combination with a worker shortage (although this shortage might also be self-inflicted by businesses). 

In general, AI has become an important ingredient in customer service and, more generally, CX solutions. The main use cases are ticket routing and assisting in solution search. While there are a number of chatbot solutions that base on an underlying AI, their performance often is not good enough to be really successful. Another challenge is that implementations regularly take considerable time. Therefore, the pressure on customer service departments through case deflection does not decrease enough.

My analysis and pov

As Cristina Fonseca, VP Product of Zendesk and co-founder and CEO of Cleverly, the company that got acquired by Zendesk in 2021 and that is behind this feature set explained to me, Zendesk was behind the competition. What Zendesk now does is automating the mundane tasks, therefore effectively relieving agents from time consuming tasks. The second part is that the solution comes with pre-trained models that make it ready to run in very short times. Doing the hard work and analysing that 80 percent of inquiries have the same intent across industries has been the key for this. Still, the built-in feedback loop that allows for constant company specific readjustment of the model, is important as there normally is no one-size-fits-it-all solution and there is a high likelihood for service requests changing in nature over time. This is also true for the revenue drivers prioritization of requests, which is likely to be at least industry-, if not business model and therefore company specific. I am curious to see how this works across industries.

Making these features available as part of the suite and not as an add-on is a smart move as it doesn’t change the pricing for existing enterprise edition customers and potentially drives more customers to choose this edition.

With this solution Zendesk gains an edge, albeit probably of temporary nature. Clearly, the company should continue on this path to maintain it, maybe adding coaching capabilities that help agents and supervisors to train themselves and each other. Additionally, if the solution works as expected, why not also using it for case deflection. After all, from a system perspective, it does not really matter whether an agent is guided on how to best resolve an issue or whether the customer him-/herself is guided. From a company perspective, it does. Fonseca maintains that the market is not yet ready for it. This is probably right as many chatbot implementations are rather underwhelming. Still, coming from a working solution can change this. In addition, Zendesk should add an explainability capability, although Fonseca maintains that this is not a priority as there is too much turnover. Still, a good deal of productivity improvement and efficiency will come from achieving a stable workforce. With an explainability component Zendesk can achieve two goals: first it helps coaching the agents and second it will increase the acceptance of the system. Untrained agents will more easily accept the system recommendations, effectively treating them as prescriptions. In which case they better be accurate. The confidence score that is already provided is a first step into this direction. 

Everybody is talking of "creating customer experiences" - even I, although I am convinced that any experienced is created by the person on the receiving side of an engagement. 

But then, how to formulate this in only a few words?

Nevertheless, when it comes to engaging with customers or prospects, we are immediately also talking content, be it images, texts, videos, podcasts, 3D rendered objects, or whatever the future will bring us.

We are also talking about more than just marketing processes, but also service- or employee-facing processes. Which brings us into the realms of knowledge management, product information management, web content management, enterprise content management and enterprise search. Or the question about how the same content can be used in different scenarios? How can it be found, so that it can be made available? Does it need a platform? What type of platform?

Looking at individualised marketing campaigns that theoretically ask for individually tailored content. How much can this scale? Where is the limit? Spoiler here: Manpower to create the content ...

Still, how is a technology stack helping marketing, sales and service functions with prepared and personalised relevant information - that then also resonates?

What is the role of "headless" in this whole picture? Is headless a thing at all? Shouldn't be the content headless after all (which then solves the content problem across the metaverse - oops, now I used the buzzword)? 

Lot's of unanswered and very interesting and important questions!

This creates the perfect need to talk to someone who knows. So, we discussed with Dina Apostolou of Contentful how a content management system can be of help. And what else it does take to succeed.

Only so much here: Content is a core ingredient of a digital first business. 

Interested in more details? Check out this CRMKonvo! It is more than worthwhile!

It is the end of 2021 and I do not have anything better in mind than writing a last post for the year. So let's do it.

There have been some terms that were used more than others with some of them actually being quite hyped. Some of them for the right reasons, some of them for the wrong ones.

My favorites of these terms include in no particular order:

• Metaverse
• NFT (non-fungible token)
• RPA or robotic process automation
• Hyper personalization
• Hyper automation
• Customer Journey Orchestration
• Low code / No code
• Artificial intelligence / machine learning

Hyper personalization

I already did a short video that expresses my thoughts on hyperpersonalization. You cannot really avoid the term when browsing the web with anything related to CX in mind. Back in the day, what is now called hyper personalization was called one-to-one marketing. This was in the eighties. The problem was that we didn’t have the technology nor the computing power to technically implement a contextually relevant 1:1 approach at scale and in real time. That’s why we worked with segments. 

Now we have the technology and computing power to address individuals contextually relevant in real time, nearly regardless of the size of the audience. Technologically, this is quite amazing, and if used consciously is of great benefit for customers and prospects. 

But there is nothing “hyper” about it. It is still personalization, maybe individualization. 

My wish for 2022: Let’s do away with the hyperbole before we start to desperately look for the next hyperlative – or should I call it ultralative?

Robotic process automation

No doubt, RPA is an important technology. It provides a fast and easy – maybe too fast and too easy – entry into digitalization of processes.

Its origins lie in automated UI based testing, applied to the automation of workflows.

Using RPA or its derivative intelligent RPA helps companies to increase the efficiency of their processes, especially if they stretch across various applications that have poor or no interfaces or that haven’t been integrated.

This works very well for processes that rely on structured data and in the case of intelligent RPA increasingly also with unstructured data.

Why too fast and easy then?

The problem is that it is hard to improve processes with RPA. Instead, existing processes get entrenched even more. This is also why it helps in digitalizing processes and not in a company’s digital transformation, which involves the creation of new, different processes. Still, it is an important technology as it helps reducing friction in existing processes, helping in reducing manual work and speeding processes up considerably. As a consequence, both employee experience and customer experience can get improved considerably.

My wish for 2022: Let’s merge RPA into workflow automation because this is what it is.

Low code / no code

We have seen low- and no code for a few years now but it really popped into people’s minds in 2021. Related terms are “citizen developer” and “democratization of development”. 

Again, another important technology that improved a lot over the decades. 

Yes, it is available for decades. Look at all the abilities to customize that enterprise systems offer for a long time. Admittedly, it often looked ugly and was not that easy to understand, but still, it was there.

Low code and now code have a tremendous value in workflow automation and systems configuration. 

My wish for 2022: May there be sufficient governance to avoid the database hell that we experienced in the good ole Lotus Notes times (or have these been the bad ole days?).

Hyper automation

This term is worth a rant. Gartner defines hyper automation as “a business-driven, disciplined approach that organizations use to rapidly identify, vet and automate as many business and IT processes as possible. Hyperautomation involves the orchestrated use of multiple technologies, tools or platforms, including

• Artificial Intelligence (A)
• Machine Learning
• Event-driven software architecture
• Robotic process automation (RPA)
• Business process management (BPM and intelligent business process management suites (iBPMS)
• Integration platform as a service (iPaaS)
• Low-code/no-code tools
• Packaged software
• Other types of decision, process and task automation tools”

There are many more definitions like this, published by many pundits. 

Automation exists like, forever. It is one of the very reasons for the existence of software. And it always included the use of multiple technologies, tools and platforms. Just adding a bunch of shiny category names that can be used to the definition, doesn’t add any hyper to automation.  

My wish for 2022: Let’s cut the crap and name it as what it is: automation or process automation. Sorry for these sharp words but here we are inventing a term just for the sake of inventing a term. This doesn’t add any value besides making some pundits appear important and/or visionary (with the emphasis on appear).

Artificial intelligence (AI) and machine learning (ML)

There is no doubt that these two terms describe a set of technologies that are immensely powerful, given the availability of enough computing power. Machine learning is a subset of what artificial intelligence is. The technologies have entered a trajectory away from technology for technology’s sake towards being embedded into business process and outcome oriented. The technology itself is mutating from a kind of silver bullet to a mere helper. Of course, this is not always the case. There are still areas where AI is overrated, sometimes even dangerous. We are also seeing ethical problems and biased AI come up. Still, with the ongoing change the acceptance of AI and ML have increased a lot. 

My wish for 2022: May we see a continuation on this path along with an increasing focus on explainability and ethics. May we also continue to see “human augmented by machine” instead of “human replaced by machine”.

Metaverse

I have kept my three favorite ones till the end, the metaverse and, related to it, web 3.0, that is currently in everybody’s mouth being one of them. The preliminary peak of the hype was Facebook changing its corporate name to meta in October 2021.

Right now, nobody can credibly say what the metaverse will be, except that it will be a significant development towards communication and collaboration. But what is obvious is that metaverse will be another evolution of the platform play. It will be seen as a significant source of new business. The players are already lining up to achieve “dominance”. With the likely consequence that the “metaverse” will be broken down into a couple of walled gardens that are intended to further the garden owners’ agenda and lack interaction/integration. This breaks at least the first four of Tony Parisi’s seven rules of the metaverse.

So far, the metaverse reminds me of the gold rush at the Klondyke river. Many people rush there, most will return disillusioned (if at all) and a few (platform) players got incredibly rich.

My wish for 2022: Call me a dreamer but may there be a strong focus on above mentioned four rules, in particular openness and that no individual entity controlling it.

Non-fungible tokens

A non-fungible token (NFT) is a unique and non-interchangeable unit of data that is linked to a blockchain. It cannot be copied, substituted or subdivided. NFTs gained notoriety for being electronic identifiers that confirm the ownership of digital collectibles.

NFTs are closely related to the topics of blockchain, which is still trying to find a compelling use case, digital twins, and metaverse, which drives its hype. 

Given that, NFTs are yet another example of a solution in search of its problem. Or the other way round, it creates a totally new market for the exchange of digital assets.

The crux seems to be that everything seems to be around virtual, as opposed to physical assets. This means that, as long as no metaverse emerges, NFTs are actually not creating value but destroy it. Looking at sites like Decentraland where companies recently bought chunks of “real estate” for millions of dollars reminds me strangely of Second Life. But then, maybe I am just not creative enough to understand.

My wish for 2022: May we find a compelling use case for NFTs that creates a solution for an unsolved problem or provides a solution for a solved problem that is better than the existing ones.

Customer Journey Orchestration

I want to end this post on a positive note, that’s why I left customer journey orchestration to the end.

Customer journey orchestration is a technology that actually exists and that has some benefits. It helps businesses to think outside-in and to come to a mindset that their success is the consequence of making their customers successful. Implemented properly, it enables the identification of customers intents in real-time and to offer them touch points of their choice on their journey towards the solution to their challenge at hand (or job-to-be-done, if you will). It helps businesses to identify unnecessary friction in customer journeys and to remove it.

My wish for 2022: May we increase our understanding that automated processes like automated marketing campaigns are not customer journeys. 

The word is that it takes two to Tango. It turns out that sometimes it needs three! I will not spoil the excitement by naming who they are, though.

Why do we have silos in enterprise software? How to remove them? Do they even need to be removed? Does throwing more software at a software problem help? Aren't the problems a software problem after all? If more or different software helps, what type of software can or should be used? Or are corporate silos even a Wallstreet problem? Great questions and even better answers by Joshua Greenbaum in this #CRMKonvo.

This one got even bigger than I expected. But then, Josh thinks in big lines. And he has wits, a lot of wits. He did not disappoint, really!

Apart from a lot of knowledge, that is. Listen in to our CRMKonvo.

I can also highly recommend to read Josh's article "Death to all Silos, with Aphorisms". It is worthwhile, promised!

Customer service is in the middle of a transition. Not only is technology capable of doing far more than it was, say, ten years ago, but also are customers expecting far more. That has a consequence for businesses. Automation and conversational AI are one thing. These technologies already help companies serving their customers more effectively, efficiently, and the customers way. 

But this is not enough! The same technologies that allow this, allow it for every company. Which means that one needs to elevate the game. 

How this elevation can happen using unstructured data to improve the customers' service experiences by e.g. leveraging unstructured data to avoid escalations is something that Martin Schneider of SupportLogic has good and very interesting stories to tell about. He argues that systems must become more intelligent and that purpose built AI is the way ahead, of course fuelled by the demands and learnings of Pandemic times.

In a nutshell he says: For years, people wer not harnessing the insights inside the unstructured data inside support interactions. Today we have the power to do so - in a sense, finding the logic and the trends etc. in support interactions that help guide the business, all driven by these support interactions.

It is very worthwhile listening to him. 

And entertaining, too!

Enjoy!

The News

On November 11, 2021 Freshworks held its annual Freshworks Refresh event. This year, the event had a hybrid format with around 250 customers, partners and analysts participating on site while around 17k people have registered for online participation.

There was a social pre-event and an after-event for entertainment and networking purposes. 

The event itself was themed around “delight made easy”. Naturally, it had different agendas for customers and partners on one side and analysts on the other side.

The morning was dedicated to a 4-hour sequence of keynote sessions for everyone. The event was kicked off with a keynote by Neuroscientist, entrepreneur, and author David Eagleman, who spoke about the “Science of Delight”. The closing keynote was delivered by Amy Purdy, who shared her inspiring story of how she used creativity, a positive outlook and a never-give-up attitude to turn her life from nearly dying, finding herself with a double lower leg amputation and failing organs into becoming a 3 times Paralympic medalist. Between these two speakers, who set the scene, Freshworks offered product and customer information.

Freshworks CEO Girish Mathrubootham gave a product update that linked into Eagleman’s message and a distributed customer panel spoke about their experiences with Freshworks, how they implemented Freshworks solutions and how these help the respective businesses. Rounding this off, Freshworks awarded several prizes to customers who offer exceptional EX or CX and showcased the winners of an internal hackathon. The latter is relevant because these winning solutions made it into or will make it into Freshworks products.

The afternoon was filled with customer related information in the customer and partner track and product and strategy sessions for the analysts. The analyst track started with an open Q & A session with CEO Girish Mathrubootham, a session about the changing role of the CIO and continued with product updates and a distributed fireside chat with a customer. 

The bigger picture

Every significant player – and every player that wants to be looked at as significant – is positioning itself as a platform player and its platform as the foundation for the ability to generate a great customer- and/or user experience. This can be observed in offerings for all sizes of customers, from small businesses to enterprise. The definition of what a platform is and contains varies. So does the maturity of the various platforms. 

As I have repeatedly written before, having a great technology platform is not enough to enable experiences.

There is more to being able to create an individual experience than just a technology platform. Apart from an appropriate, customer-oriented strategy and a culture to match, that is.

The platform that is needed to enable experiences at scale for organizations consists of more than IaaS and PaaS. Software vendors that want to be successful platform players need to be able to deliver on four areas to succeed:

• (Technology) Platform 
• Ecosystem
• Insight
• Productivity

By extension, this means that smaller vendors that cannot or do not want to deliver on these four dimensions need to choose the platform or platforms they want to become a part of.

My PoV and analysis

This part I need to cut into two sections, one about the event itself and one about the content.

The event

First, kudos to the team who organized it. The event was well organized and structured. The Freshworks team around Alan Berkson and Shelbi Gomez was always available with a helping hand. I think the team can be sad that only 7 analysts came on site.

Plenty has been said in the past years about Las Vegas conference venues, so I do not need to add anything but that we certainly got our ten thousand steps. Seriously, the Mandalay Bay is a good choice.

The two external keynotes by David Eagleman and Amy Purdy have been highly engaging and in the case of David Eagleman also highly relevant to the event theme. The following keynote by Freshworks founder and CEO Girish Mathrubootham showed how ease of use and delight can be combined. Doing this, he established an emotional connection around delight and ease. Using this smart approach, these terms are now tied to the Freshworks brand.

Another kicker was the customer panel with representatives of DHL, GE Digital Aviation Software, Carrefour and TaylorMade who talked about which parts of the Freshworks software they use, how the implementations went (easy as) and how they use Freshworks to pursue the digital transformation of their respective businesses. In line with the nature of the overall event, it was made up in a hybrid way with one participant attending from a remote location.

From a brand perspective, these customers are at the upper end of Freshwork’s sweet spot in the market, but then this emphasizes on the company’s ability to scale up.

These customers, as well as the customer representatives, who accepted the prizes often spoke with strong positive emotion about Freshworks, rather than only pointing out rationally where and how the products help their business. This increased the tie between delight and ease on one side and Freshworks on the other side. 

Why is this important? Because every significant player is playing a platform game, which also reduces the differentiating factors. Consequently, it is important to be front of mind when it comes to positively loaded keywords. This worked well in quite some industries, think automobiles or smartphones, to name only two. Freshworks establishes itself as a likeable brand.

In an interesting twist, Freshworks not only presented several customer awards but also gave a glance at what I would call “employee-driven innovation”. Chief Product Officer Prakash Ramamurthy presented the winners of an internal hackathon. Their implementations did make it or will do make it into the products. 

The afternoon started with an open Q&A with Girish Mathrubootham who openly answered all questions that were fired at him, which is testament to authenticity. I didn’t fully understand the relevance of the subsequent CIO session, although it seemed to partly set the stage for the AIOps topic that came up a little later. It was followed by a fireside chat with a customer and various product roadmaps along with their respective Q&A rounds. Some of the sessions were based on more detailed video information that was made available to us before the event. It was often important to have this material reviewed before the event.

The event overall felt good. It was well structured, quite rich on information and offered many strong customer testimonials. I think that especially the customer success stories have been a strength of this event. More of them might have even strengthened it even further. In any case it was good to be able to freely speak with customers.

There was ample opportunity to network with Freshworks people, partners, and customers. As written above, from a session point of view I wonder how Amy Purdy fit into the message and what the sense of the CIO session was.

Being asked, Marshall Lager confirms that “very little of my time felt wasted, and I’m the first to admit it if I tune out of a presentation. I think they missed the mark by making Delight the theme, though, because the content didn’t really bear that concept out.” 

Having been one of a few on-site analysts, what I missed was more possibility to interact with the remote colleagues. This may have been due to the tight schedule. As a caveat, I am aware that this is difficult to achieve.

All in all, I like the concept.

The messaging

Messaging is a difficult topic. Marshall Lager opines that the event tended to lean “into the message at the expense of substance.” I would agree to this as I sometimes had a feeling of questions being dodged by executives.

The topic of messaging is however, not only from a CRM (or CX) point of view, very important. During last year’s Refresh, this has been a major discussion point with the analysts, which was partly addressed.

According to Chief Product Officer Prakash Ramamurthy “CRM now does include support. Lots of investment is going into creating a unified solution. We have strong offerings in all three areas marketing, sales and service/support.”

Still, the Freshworks CRM suite message is slightly confusing. 

Freshworks announced a bundle named Freshstack, which consists of Freshsales, Freshmarketer and Freshdesk and that is dedicated to startups. Given this, it covers all functions of a CRM. Why not name it (Fresh)CRM? Especially, since some of the product slides refer to this combination of products as CRM. 

Freshsales and Freshmarketer, on the other hand, are grouped as the Freshsales Suite and marketed as an “All-in-one CRM”. This weakens the idea of the “One Platform for One Unified Business” and confuses the CRM (or CX) message. Again, CRM consists of marketing, sales, and service.

Still, this package acknowledges the trend towards fostering a closer collaboration of marketing and sales departments to achieve one common goal and not two separate ones although there is no talk about revenue operations, which is the next evolution of this trend.

The functional roadmap of the overall customer solutions suite appears sound; yet it shows that Freshsales and Freshmarketer are fairly young products.

Which is a fact that can be used as an advantage, as there is little legacy. Freshworks Head of Product for the Freshsales Suite, Peter Stadlinger is well aware of this fact. In the prerecorded part of the session he speaks of an “unfair advantage” that Freshworks has. It remains to be seen how the company uses this advantage.

Freshworks clearly positions itself as a platform vendor – like everyone else. Asked what beyond high usability the differentiators of the platform are, VP of Product Marketing, Neo, Tejas Bhandarkar maintains that “our platform strategy is based on 3 things: 

a) giving a unified experience to customers through unified customer experience, collaboration, multi-channel, federated search, etc., 

b) delivering insights built on data, enabling customers to compose their own analytics the way they want, and

c)  empowering developers to build simple as well as sophisticated apps easily.”

Prakash Ramamurthy extends that “easily customizable and extensible is what customers call out as a differentiator. The extended roadmap includes analytics.” This extension is important as several customers pointed at analytics when queried about where Freshworks could improve.

In addition to this, Freshworks recently partnered with UIflow, to enrich the Neo platform with low code abilities, while looking at Slack and Microsoft when it comes to productivity and collaboration apps.

So, looking at the components of a platform, Freshworks plays the ecosystem card in these areas.

Interestingly, Freshworks also does not talk much about AI and machine learning as part of the platform. These topics, e.g., in the form of the virtual assistant Freddy, mainly come up in the application layer. Freshworks wants to provide purpose-built features and not deliver AI as a technology. “Users do not care whether it is an AI or not”. Still, the foundation for these purpose-built features needs to lie in the platform, i.e., Neo, to allow for efficient development and deployment. I think that this topic deserves some more clarity.

However, Freshworks chose an interesting approach out of the dilemma that being a “platform vendor” is not a differentiator anymore. This approach is the use of emotion.

The message that permeated all communication is “we want your success; we are easy to deal with and we create our solutions for ease of use”. This was confirmed by customer testimonials in an impressive manner, even considering that customers who attend this type of event are rather positive and customers who get on stage are even more positive. With this, and with the themes of “delight” and “ease”, Freshworks managed to tie strong positive emotions to the brand. This is a path to differentiation that other vendors have not yet chosen to this extent, and this might give Freshworks an edge.

All this leads to the question how Freshworks gets into and grows in and with accounts. There are two main ways, organically and via the ecosystem, i.e., partners. Freshworks has partners with presences in more than fifty countries.

Although partners have not been that big a topic during Refresh2021, Freshworks has a network of more than 500 partners and a marketplace that lists 1,100+ applications. That certainly gives some scale and reach into various markets.

The company clearly positions itself for the mid-market. At the same time, it opens itself to enterprises that do not want or need too much customization but prefer functionality out of the box. Still, Freshworks wants to establish that its solutions do scale and are attractive for (divisions of) enterprises. This is in alignment with “delight made easy”.

A division that implements Freshworks then grows its implementation footprint in two ways, increasing usage across countries and rolling out additional solutions. Eventually, other divisions might implement Freshworks, too – a classical land-and-expand strategy. Asked whether the employee solutions and the customer solutions mutually enable this strategy, replies seem to suggest that land-and-expand works well between the customer solutions, e.g., a division starting with Freshdesk and then rolling out Freshsales or vice versa, starting with Freshsales and then moving on with Freshdesk; the employee solutions so far seem to play a smaller role here.

The last topic of interest is the one of industries. Freshworks does not offer industry solutions. Being asked when this will change and a vertical strategy be employed, Girish Mathrubootham answered that the company will remain horizontal to ensure that the market stays broad enough. While this is a sound answer, one might argue that addressing verticals does not reduce the size of the addressable market but adds ease to verticals that are specifically targeted. This, especially in times of industry clouds. It might become necessary to explain why Freshworks does not require industry solutions. This should be possible, also following the theme of ease. In any case it will be interesting to see how a pureplay horizontal strategy pans out.

Overall, I think that we see another interesting player emerge that can be a good and viable alternative to the bigger players in the CX market, given that customizing requirements are not too big and that there is an own local presence or a local partner.

Freshworks is a young company. The most mature products are on the customer service and ITSM sides. The sales, marketing and employee solutions are younger and, given that, still somewhat simpler than other solutions. On the other hand, there is an ambitious, young company behind them. Building and delivering on the existing messaging will be key.

The Clash of Titans seems to get an additional participant.

During the past weeks I had a couple of observations and conversations that lead me to thinking that sometimes software vendors underestimate the power that their machine learning based systems could have to improve the lives and experiences of employees and customers. 

From various vendors in various lines of business, from process mining and automation via application performance monitoring to vendors of conversational AI and pretty much everything in between I hear something like the following:

“Our machine learning based system continuously analyses the process/interactions and detects anomalies. From there on it identifies the patterns and can make suggestions how these anomalies can be avoided or resolved.”

Of course, this is paraphrased, but you get the meaning.

Here are some examples.

A real life scenario that I once encountered is as follows: A global B2B e-commerce solution using synchronous pricing is set up in a template approach. It is using one single ERP system for pricing. This is a pretty common B2B scenario, as it is often not feasible to replicate all prices to the e-commerce system, due to the sheer amount of product – customer combinations that are possible.

In this scenario, adding a product to the shopping cart involves multiple calls from the e-commerce solution to the ERP system to establish the price.

The pilot country site is close to the country that hosts the ERP system. Implementation of the e-commerce solution happened in the country that hosts the ERP system for all e-commerce sites. Deployment into the target countries can happen only after the testing phase, which is clearly suboptimal.

Adding a product to the cart is reasonably performant for the pilot country.

The second country is on the other side of the world.

After deploying the site to this country, it immediately became clear that adding products to the cart is far too slow to be acceptable. 

Now, one could think that the development team should have seen the issue coming. It hadn’t. Most application performance management (APM) solutions wouldn’t have, either.

A simpler issue is the automated scoring of opportunities, which gives a value, a score, that represents a systematic estimation of the win likelihood of assessed opportunities. Or ticket routing based upon (bad) sentiment.

Another conversation was about chatbots. “Based on the past customer conversations, our AI will identify bots that should be built”.

Do not get me wrong, these abilities are already very valuable.

Just that they are too short of a jump. These are not enough!

Let me tell you why.

We deploy technology to get solutions for pertinent problems, aka challenges. The solution is not to inform someone that there is a problem but to either avoid it altogether or to actually solve it.

Building on the examples above, better solutions would look as follows.

An APM solution measures application performance to identify potential bottlenecks, ideally already before deployment, e.g., in a test phase. Most of them do that fine. Some of them even have predictive capabilities in a sense that they can warn before a critical situation arises. Even better, an APM system that predicts that excessive runtimes will arise for operations in given circumstances before a significant usability incident occurs would be incredibly useful. The scenario above was caused by a high number of calls between the systems in combination with a high latency due to the long distance routing. Situations like this one can be identified and therefore solved before they occur.

Scoring of opportunities already helps salespersons, now doubt. Still, additionally offering the actions that could be performed to improve the likelihood for successfully closing opportunities is far more helpful. Or in the case of the ticket routing based upon (bad) sentiment: suggesting which actions could be taken to improve the situation would help many agents.

In both cases some of the necessary activities could even be triggered and executed automatically.

Here are some examples that look even further and are partly already in place.

The best known one is probably delivered by Oracle. Oracle has enhanced their flagship RDBMS to something that the company calls the autonomous database. Oracle defines an autonomous database as “a cloud database that uses machine learning to automate database tuning, security, backups, updates, and other routine management tasks traditionally performed by DBAs. Unlike a conventional database, an autonomous database performs all these tasks and more without human intervention.”

In essence, Oracle delivered a database engine that uses available log- and meta data and a trained model to identify incidents to resolve them, freeing DBA time for more complex tasks.

Similarly, Ray Gerber, Chief Solutions Officer at Thunderhead envisions and works on an autonomous customer journey orchestration engine. This engine essentially discovers the actual journeys customers follow and optimizes them in real time to help the customers achieve their goals. This way, it also drives engagement and creates value for business. It bases on a constant analysis of customer journeys. With this capability, journeys that are offered to the individual customer journeys are (and stay) optimal – considering the business goals and objectives.

These are, admittedly, quite sophisticated solutions, but this is exactly the point. The needed technologies are often there and can be adapted and used.

The same principles can be applied to process mining and -automation. Process mining tools identify choke points in business processes. They also identify repetitive tasks in processes. They can see the data that is used, so they can start to auto-create the necessary workflows that take away tedious tasks from users.

The last example that I want to showcase, are chat bots. The existing bots with their capabilities are known. It is also known where these bots need to hand over the conversation to human agents because they reach the limits of their skills. Mostly, these human-to-human conversations are known, too, as they still happen in a chat environment. With that, solutions to further problems and the way to get there, are available. Even the conversations. 

So, a system that builds the corresponding bot, or at least a prototype, is conceivable and should be delivered. Agents will be grateful for yet another simple thing that is taken off their plates.

Is all this simple? No. A silver bullet? Of course not. Possible? Yes, with some constraints. Helpful for users and customers? Sure as!

So, I am calling on software companies to embrace what I would call autonomous automation and to go for it.

The News

On September 20, 2021 Oracle announced during an Oracle Live event named “The future of CRM” Oracle Fusion Marketing, which is not the same as Oracle Marketing.

According to Rob Tarkoff, EVP and GM Oracle Advertising and Customer Experience, Oracle Fusion Marketing is a layer that automatically executes account based marketing and sales campaigns. 

The product aims at enabling an end-to-end process from creating a marketing campaign to closing the sale, bridging the divide between marketing and sales. 

It does so by combining services that are delivered by three products:

• Unity, Oracle‘s Customer Data Platform
• Fusion Advertising, Oracle‘s digital advertisement platform
• Fusion Products & References, Oracle‘s recommendation platform

under one easy-to-use user interface that is modeled as a guided procedure. 

Oracle Fusion Marketing simplifies and accelerates the creation and execution of marketing by

• Building a target audience of known contacts: Marketers can select a product or service that is the focus of the campaign, and then select a list of known contacts from any CRM system
• Expanding your audience: From that audience, Fusion Marketing will automatically generate a highly targeted audience profile for use in online advertising to target people who are potentially relevant to your campaign – byt unknown to your contact database
• Identifying the best customer references: based on the focus of the campaign and specific industry of each customer, Fusion Marketing recommends the best reference stories to promote in the campaign
• Simplifying campaign configuration: Fusion Marketing provides a single user interface to assign all of the campaign assets required to run your campaign across email, website landing pages, and advertising channels
• Launching the campaign: the marketer can easily set up advertising budget, star and end dates and launch their campaign
• Monitoring results: a prebuilt dashboard that provides marketers with real time visibility into campaign performance.

After campaigns are launched, Fusion Marketing automates the generation and qualification of leads:

• Personalized campaign microsites: emails and ads take people to an automatically generated „microsite“ landing page for each campaign, which includes customer references personalized to match the industry and interest of the customer
• AI-powered lead qualification: Fusion Marketing monitors engagement across all campaign channels including emails, online ads, and microsite visits. Because interest at the company level is what matters, Fusion Marketing will qualify account level interest by aggregating engagement frompeople at the same company. This AI algorithm automatically generates qualified opportunities for salespeople when it detects sufficient engagement.
• Deliver qualified opportunities to any CRM system: Wen identified, qualified opportunities are delivered to any CRM system.

According to Oracle, Fusion Marketing helps marketers and sellers focus on what they do best: building relationships and closing deals.

The Bigger Picture

As I have often written and said, sales representatives do not love their system. One of the main reasons for this is that salesforce automation systems and CRM systems are often targeted rather at the management than the actual workforce. They are good at collecting data and reporting, but not so good at helping sales representatives – and marketers, for that purpose – getting their job done easily. The result is widespread dissatisfaction and poor adoption. Phil Winters spoke about an adoption level of 25 per cent in a recent CRMKonvo.

In the words of Larry Ellison: „The sales automation system has value. It keeps track of your opportunities, it allows you to communicate with management, and say „I expect these deals to close in the quarter“. It is really a management tool for individual salesprople and their bosses to make estimates; how many deals will close in the quarter and figure out what revenue will look like in the quarter.

Now, that has value! But the sales system does not increase sales. It doesn‘t automate selling. It autmates sales, forecasting and improves communication. But what we want to do is actually build systems that help you sell more“. He continues „We realized that we couldn‘t just do a second generation sales automation system. To help sell more, we had to also include marketing to generate, and most importantly, qualify leads. The question is being able to figure out which of those leads are high quality.

Which of those leads do you want to turn over to salespeople, and then get them engaged? So, we thought, we had a two step process. We had to autmate lead generation and qualification – a new second generation marketing system. And then, as soon as we got that don, build a second generation sales automation system that would maek the salespeople more productive and help them sell more“.

What needs to change is that the systems are available where the users are, in a physical and digital way, that they work like the users work and give them relevant information as and when needed and finally, that the systems are enabled to interact with the users in natural ways, which includes language, text and voice.

Two specific problems facing marketers and sales representatives are that it is often hard to set up multi channel campaigns, especially when they include advertisements in the public web and walled gardens like Facebook or in the Apple ecosystem and then to hand over well qualified leads that are generated by the marketing department to the sales department. Who hasn‘t heard of the eternal fight between marketing and sales, one side claiming that it doesn‘t get any leads and the other side complaining that leads are just not taken up.

Oracle Fusion Marketing sets out to solve both challenges.

My PoV and Analysis

Let me start with that the event’s naming raised quite some expectations that led me to state during the CRM Playaz Watch Party to the event that I haven’t seen much that I haven’t heard from other vendors, too. I stand to this statement, especially when it comes to integrating or aligning marketing and sales departments. Every suite vendor that is worth a grain of salt can either move a qualified lead out of the marketing subsystem into the sales force automation system and/or directly create an opportunity there (which is something that the sales reps often like to do themselves). An agreement about what constitutes a qualified obviously needs to be achieved before. Most of the vendors can also fairly easily create campaign templates basing on existing objectives, target groups and marketing assets.

However, there are three main differentiators that I can see. 

• As shown in the demo of a campaign creation process as used at Oracle itself, the user interface to create this campaign is extremely sleek, also thanks to the new Redwood UI.
• The ability to combine traditional campaigns with web advertising out of one single technology stack, which Oracle can do using its Advertising line of products.
• The  tight link into the, again own, enterprise content management system.

As I do see it, there are only two companies that could deliver close to this value proposition using an own stack, and these are, Microsoft and Salesforce, maybe Adobe as a third one. Still, Oracle has clearly an edge.

The other suite vendors are either not there at all, or do rely on partners for parts of this process.

Still, building a campaign in a few steps while automatically identifying relevant content for targeted accounts to serve on landing pages and in personalized emails is quite a feat. So far, I have only seen this based upon rules. Oracle seems to have done a great job at this and I would love to have a peek behind the scenes.

I believe in technology and am less sceptic about the power of AI to qualify leads etc. based on existing interactions than esteemed colleague Jon Reed expressed in his article; therefore I am quite convinced that Oracle Fusion Marketing is able to find and deliver the relevant high quality leads to be created as opportunities in a sales force automation system of whatever couleur. This ability is of great help to salespeople. As I like the emphasis on openness, I would have loved to see both, what it takes to create an opportunity in an Oracle as well as a non-Oracle system.

Will there be false positives or negatives when creating these opportunities based on an AI?

Of course!

Is there a very good chance that the system is better than the average salesperson?

Yes!

This ability, however, is nothing that sets Oracle apart from the tier one competition in my eyes. This competition in the case of marketing consists of Adobe, Microsoft, Salesforce and SAP. All of these have the ability to create either qualified leads or opportunities in sales force automation systems. And these are only the tier ones. The difference will be in the degree of effort that is necessary to do so.

The business objectives that are covered by Fusion Marketing are currently quite limited. There are only few business objectives and the need to supply a target group to the system. This is due to it being a very young product and is ok for now! There is no reason to assume that these objectives will not be expanded fast, by either Oracle itself or by partners. I think that it would be a good idea to have the system also identify target groups based upon business objectives, similar to or better than what e.g. Emarsys is already capable of in the B2C arena. Building on that, it would be great to see a system that is able to build or suggest campaigns based upon identified customer needs. This would tie neatly into customer journey analytics and orchestration and build a story of outside-in capabilities.

The process that Oracle demonstrated to create a campaign towards an objective is amazingly simple. It can be used by any person, including non marketing personnel. This means that there can be quite some upside in offering it to salespersons or sales teams. Marketing as a service. The objectives are there, the collateral is there, why not enable salespeople to create campaigns targeted to their objectives, based on predefined templates? 

Having said all this, there are a few things that I would like to see as neither the announcement nor the demo covered them.

Assuming that a good number of Fusion Marketing customers also owns an e-commerce site that is based on Oracle technology it would be great to see how Fusion Marketing integrates to e-commerce, especially Oracle technology.

The demo is based upon existing target groups and existing e-mail templates; it did not cover the actual segmentation process, be it based upon personas or on static target groups, nor the creation of the email- and microsite-templates. I‘d love to see how this is done and what products it needs. 

Because this is where the the effort is hidden that helps Marketing Fusion be so simple.

In summary, I applaud Oracle to what it announced here. I am a sceptic and would love to see the answer to some of the questions and doubts that I have. 

I also see that, marketing buzz or not, the competion now needs an answer to the challenge that Fusion Marketing is.

On June 15, 2021, the CRMKonvos crew had the chance to chat with Andreas Schuster, Customer Success Director for SugarCRM in Europe, about the company’s evolving vision and goals. He did not disappoint.

Schuster has filled a variety of roles over the years, both in the software industry and outside of it, and has developed an appreciation of what well-administered CRM can do for a business and its customers. “I have been able to get to know CRM in the industry and in sales, and I keep getting to know it again and again,” Schuster said. “I never get bored watching companies actually supporting their sales management with software, but also just the way they work together with the customer and with the customer. And it is always exciting.”

One important thing that Schuster believes, though, is that CRM is more than technology; it is behavior and culture which technology can enable to be better. “CRM is not so much a technical tool; it really is an approach,” Schuster said. “It's a strategic sales approach, and there's a lot that goes into it now, but it's still the same.” The technology is an important starting point though, especially with larger and more complex businesses. Schuster added: “I keep coming back to this: the software that's used has to work great, it has to be intuitive, it has to look good. But taking this CRM approach is first of all a strategic thing in a company. You have to want it. You have to prepare for it. And you also have to set an example for your company from the point of view of the executives. Those who do this are very successful.”

CRM Language

It sounds like Mr. Schuster is speaking CRMKonvos’ favorite language—the language of customer and employee experience alongside raw quarterly numbers. The conversation moved on to other topics, such as the oft-mentioned 360 degree view of the customer. Does it exist? Can it exist? “It's always situational,” Schuster said, noting that a great number of companies mention the 360 degree view in their product claims or mission statements. “It is of course an approximation of such an ideal case, but 180 degrees would already be a super advance for companies that have no CRM approach at all.”

If one is to talk about a view, one must also talk about visibility, and that’s something that rarely remains consistent, especially in companies with multiple divisions and diverse stakeholders. “In CRM cultures, it's exciting to see how collaboration changes in companies where everyone has access to customer data, at least in the sales area, but also across departments,” Schuster said. “This means that by sharing access, we also have a joint opportunity. And then we can ultimately work together on reliability and team success in the interests of the customer.”

Unfortunately, the intent often gets lost. “The first thing that companies have done is limit the visibility. So the Rhineland sales channel is only allowed to see Rhineland customers and the branch in France is only allowed to see their own customers, and then again horizontally. So one distribution channel is only allowed to see its products, the other only the others. I take two steps forward and one step back.” This sort of self-limiting approach to consistent, broad-based information access is running out of time and space. “Today's customers are also global, they also look at me globally,” Schuster added.

Regarding another common term, the customer journey, there seemed to be agreement that sales and marketing tools needed to evolve from something that supports the hunter-gatherer-farmer model that has been used so often, into more of a tour guide image. “Customers rarely stay forever, but they should have a good time while they are with you, and if you lose customers … they should remember you well because you always see each other twice in life,” Schuster said. Thank-you emails, offers of favorable contract extensions, and similar things will leave a good memory, showing gratitude that they were a customer rather than punishing them for leaving. “A customer who is won over today should be won over sustainably. They should be accompanied, not won over and discarded. These are customers who will be lost again.”

The Platform and Beyond

Still, Sugar sells technology which businesses use to succeed, and the company is always moving forward in that area. Schuster went into some detail about Sugar’s direction and goals, including the company’s new motto, “Let the Platform Do the Work.” It’s more than the automation every vendor has been building for decades; CRM is starting to use artificial intelligence to provide support and guidance so workers can be both more effective and more efficient with their time. “Artificial Intelligence, most people think that's something difficult. But if you look at how simple the application possibilities are—say I look at my leads in Sugar. They are listed for me, one to ten, these are the most probable, these are the most improbable. I can plan a whole week around that.” Schuster adds that using software to do the grunt work to fulfill a request such as “How should I plan my week?” can take tremendous stress off of a person, so that they start each day energized and focused on what they need to do.

To hammer the point home, Schuster turned the focus to smaller businesses—consultants, like us. “You guys are essentially a one-man show. You live by correctly assessing sales opportunities. Is there someone who will actually become a customer, or do I potentially spend days feeding that sales opportunity and ultimately nothing comes of it,” he observed. “SugarCRM now has a feature, it's even called Predict. That means, if I create a sales opportunity or a step before, I want to qualify a lead, then the system already tells me: ‘So from this industry you had a lot of leads in the past, but you didn't win any of them. That means you can try again, but save your resources, be economical and realistic.’ With another lead possibly, ‘From this industry you have won a lot of customers in the past. That means, put more effort into it.’ That's what the system does today. You used to have to build reports, combine them, export them. That was a real science. Today, the system does that by itself.”

If platform is the word of the day, then ecosystem is one of the runners-up. The shift of major vendors to become platform providers has meant greatly increased focus on partnerships—something Sugar has always done very well. One of the reasons for this ties back to the idea of a good customer journey. “SugarCRM is an ecosystem. We offer services ourselves. We have a system there, a network of channel partners. But no matter who it is, we basically offer customers this customer journey functionality as well, because we simply believe it brings something to win customers sustainably,” Schuster said  “It prevents escalation. Escalations and re-acquisition measures are many times more costly and damaging for a company than customer acquisition. It really costs resources, it leaves a bad mood. Why should I win a customer only to fight for him next year? It doesn't make sense.”

To close, we asked Schuster what three wishes he would like to grant to a potential customer so that he has a successful CRM project. “What would I want to give him? Definitely an open ear, because you have to listen first in order to be able to advise. Then all the experience I've gathered that I can throw into the ring, into the pot, that I can offer. You can't avoid experience. You just get experience. It can be good or bad, but you can always help others with it. And the third thing I can give him is this: If he decides to become a customer, to stick with it, not to take for granted that it all just works now.”

This article has actually been written by my colleague Marshall Lager and is published with his permission