We have been lying to ourselves since, well, basically since forever. We placed customer support agents into a padded room called the "contact center," handed them a ticketing system, and told them to keep the angry people away from the rest of the business. We tracked average handle times; we cheered when a routing algorithm saved a fraction of a second; and we pretended that managing an interaction was the same thing as solving a problem. Deflecting an issue was the holy grail.

That era is over. The walls of the contact center have been blown wide open, and the debris is currently raining down on the CRM and operations landscapes. The market is shifting from asking the question "who can capture the ticket best?" to "who can actually resolve the problem fastest?” Which is an entirely different category of question. And far more meaningful.

And as Cameron Marsh from Nucleus Research so accurately pointed out in our recent CRMKonvo, that is a much nastier, much more complex place to compete.

TL;DR

If you want to watch the full CRMKonvo, please go ahead here (optimized for smartphones) or here (optimized for tablets/computers).

Else, be my guest and continue to read.

Or feel free to do both …

The Illusion of the "Smart Ticket"

Let’s just be absolutely clear from the start: nobody wants a ticket. A ticket is simply a formalized receipt of failure. It is documented proof that a product broke, a service failed, or a user interface was too clunky to navigate.

For years, many vendors, including specialists like Zendesk, Freshworks, and others have built success around making those tickets prettier, easier to handle and pass around. And companies like Five9, Genesys, Verint and too many more to count happily managed the interaction center. But when Salesforce forcefully entered the CCaaS conversation with new voice and AI capabilities, they fired a warning shot across the bow of every standalone service vendor. Salesforce is betting that because they own the underlying customer data and the core business workflows, they should own the resolution. They are not selling a better queue; they are selling an operations layer.

The response from the service-first vendors is equally bold. They argue that because they capture the initial intent and handle the front-line friction, they are ideally positioned to resolve the issue before it ever touches a core system.

But the time-honored truth is that buyers do not really care about these architectural turf wars. Customers do not care if your platform uses a sophisticated LLM or a massive RAG implementation. They care about one thing: when something breaks, who fixes it? If your AI simply routes a ticket more efficiently to a human who still has to manually process a refund across three legacy systems, your AI is nothing more than expensive window dressing, or as Cameron aptly put it, “automation with a better branding”.

Smelly Data and the LLM Mirage

Which brings us to the most uncomfortable reality of the current enterprise software hype cycle: the data problem. Every vendor is currently parading their AI Agents and "AI Studios" around like magic wands that will instantly get things done while vaporizing operational costs.

But as we discussed on the show, almost every organization suffers from what we fondly call smelly data. They have decades of poorly categorized customer records, duplicate entries, and contradictory knowledge base articles. Pointing a state-of-the-art LLM at a mountain of unwashed data does not give you artificial intelligence; it gives you a very fast, very confident artificial idiot.

Garbage in equals garbage out. It is a cliché because it is true. The organizations that are actually seeing a return on their AI investments are not the ones buying the flashiest tools. They are the ones putting in the hard and unglamorous work of normalizing their data models, cleaning up the data and integrating their core systems. If you do not have a pristine data foundation, your generative AI will simply generate more work for your human employees to clean up while chasing your customers down rabbit holes.

The "Suite vs. Best-of-Breed" Debate Reignited

For the last decade or so, the enterprise software pendulum swung heavily toward "best-of-breed." We bought point solutions for every micro-problem and relied on brittle APIs to hold the Frankenstein monster together.

The trend slowed a few years and nowadays AI is starting to violently push the pendulum back toward the suite, or at least a platform. When you deploy an AI agent to resolve a customer issue, that agent needs instantaneous, read-and-write access to lots of data, often including billing, shipping, inventory, and customer history. Every single integration point is a potential point of failure. Every seam between different software vendors is a place where context gets lost and the AI starts to hallucinate.

As Cameron noted, the burden of proof has shifted. It is no longer up to the suite vendors to prove they are better. It is up to the best-of-breed vendors to prove that their necessary integrations will not slow down the AI or destroy the seamless execution of a workflow. If an integration creates lag or drops context, the ROI of the entire project collapses. The suite wins not because it has the best individual features, but because it has the fewest broken bridges while being good enough to do the job.

The True Predictor of Success: It Is Not the Tech

Here is the most interesting takeaway from Cameron and Nucleus Research evaluating dozens of successful AI implementations: the specific technology stack rarely predicts the success of the project. Whether a company chose Salesforce, Zendesk, or a bespoke solution, the common denominator of success was the human element.

The companies that succeed have a stellar relationship with their vendor's Customer Success Management team. They admit what they do not know, they ask for help with workflow design, and they partner with their vendors rather than treating them like mere order-takers. Conversely, the CSM teams admit where their software might not be the one to go for. It’s a two-way street. The technology is just the engine; the vendor relationship is the steering wheel. If you buy a Ferrari but refuse to talk to the mechanic, you are going to crash.

Reality Check: Three Imperatives for Enterprise CX Buyers

If you are a CIO, CXO, or IT leader currently evaluating AI for your service operations, you are swimming in an ocean of marketing fluff. Vendors are promising to cut your headcount in half while doubling your customer satisfaction. To avoid making a catastrophic and expensive mistake, here are three critical learnings you must apply to your buying cycle today.

Stop Buying "Deflection" and Start Buying "Resolution"

Deflection is a vanity metric. Often, a high deflection rate simply means you have made your IVR or chatbot so incredibly frustrating to use that the customer gave up and went to a competitor. Do not reward vendors for preventing customers from reaching you.

Instead, force vendors to prove their "fully resolved" rate. Demand to see how their AI handles a complex workflow end-to-end without a human ever touching it. If the AI can only handle password resets and order status checks, it is just a basic automation script wearing a tux. You are paying an "ego tax" for a label. Demand actual resolution.

Clean Your Pipes Before You Buy the Pump

Do not spend a single dollar on an AI agent if you have not invested time and resources to clean your data. The most sophisticated LLM in the world cannot resolve a billing dispute if your billing data is housed in an on-premise server from 2012 that only updates in batches every 24 hours and doesn’t even offer an API.

You need to establish a rigorous knowledge management practice. You need clean, structured data and clear workflow documentation. If you skip this step, your AI implementation will fail, your ROI will evaporate, and you will spend the next two years blaming the software for a problem that you created on your own.

Calculate the True "Human-in-the-Loop" Maintenance Cost

Vendors love to show ROI models based on how many tier 1 support agents you can eliminate. What they conveniently leave out of the spreadsheet is the cost of the highly skilled engineers, data scientists, and workflow managers you will have to hire to babysit the AI.

An AI system requires constant tuning, monitoring for hallucinations, management of drift, and edge-case management. You might eliminate fifty low-cost roles, but you will need to hire five very expensive experts to maintain the system. The need for humans in the loop further reduces the systems autonomy and hence “efficiency”. If your ROI calculation does not account for this shift in personnel costs, you are presenting a work of fiction to your board. Focus on the payback period, don’t forget about ongoing maintenance costs, and never buy a black box that your own team cannot audit.

The past weeks showed quite some interesting activity on the mergers and acquisitions and the partnership frontiers. NiCE acquired the German conversational AI rock star Cognigy for $955M and a short time later announced that the company enhanced its partnership with Salesforce. At nearly the same time, Genesys received an additional funding of $1.5 bn from Salesforce and ServiceNow. Salesforce acquired Waii and Bluebird. VC company Thoma Bravo acquired the still leading CCaaS vendor Verint, to name but a few of the more interesting, and perhaps consequential ones.

On top of all this, Avaya seems to have offered all employees a voluntary exit package.

What all this shows is that there is significant consolidation going on in the AI-assisted (or should I say, driven?) CCaaS market and that various players are battling to provide – or at least be perceived to provide – the most comprehensive and valuable platform while others fight for survival.

Yes, it’s nothing new, but can’t be repeated often enough. The CX market is and always was a high stakes platform game. The stakes got even higher with the advent, the promise and the necessary investments that generative AI and agentic AI require. While one can consider Salesforce’s acquisitions as tuck-ins that help rounding off its Agentforce platform, the other ones are a sign of something bigger going on in the CCaaS and customer service market segments. It is also notable that exactly these sectors get more and more referred to as CX market, whether this is a correct, or only good, attribution, or not. Hint: It isn’t.

Not unexpectedly, Salesforce is in the thick of all this, having a massive stake in Genesys and a strategic – at least for NiCE – partnership with NiCE. And regardless, of whether NiCE or Genesys turn out to be stronger, Salesforce wins.

For customers of the Salesforce ecosystem this, first of all, gives choice. For Salesforce, it gives access to a wide array of different customers and therefore the ability to fuel the data pipeline that Agentforce needs. With the different profiles of NiCE/Cognigy, customers need to establish for themselves whether they need the strengths on the customer service center side (NiCE/Cognigy) or the analytics and orchestration side (Genesys).

On the other hand, we have Verint getting into the fold of Thoma Bravo and being merged with WEM vendor Calabrio. This instantly creates a strong player that includes WEM and analytics capabilities. But looking into the Thoma Bravo portfolio, there is more, in particular Medallia. Adding Medallia’s capabilities adds improved journey management and especially a strong voice of the customer element that helps understanding and therefore improving customer sentiments at every single touch point. This would challenge CCaaS leaders like Genesys and NiCE by creating a comprehensive analytics story and a journey orchestration capability. With the help of UserTesting this gets even more pronounced as UserTesting’s technology allows the finding of a root cause of a problem identified using the Verint/Calabrio/Medallia stack. This combination can create something that one could call a total engagement and analytics platform that covers the capabilities of traditional customer service vendors, CCaaS vendors, and journey orchestration vendors. This has the potential to create a tectonic shift in these software categories, as they converge to form a kind of Gondwana of service solutions.

While this doesn’t necessarily create an immediate threat to the Zendesks and Freshworks of this world, it may severely limit their ability to enter into more complicated, multi-step service scenarios. On the other hand, this combination of Thoma Bravo capabilities needs to prove its ability to scale down to avoid being disrupted from below by vendors that have their strength in high volume ticketing scenarios.

I do not know whether this is what Thoma Bravo has in mind, but the scenario is quite appealing to me.

My analysis and point of view

But in any case, one thing is clear: The markets for conversational/agentic AI, CCaaS, UCaaS and customer service are converging while the markets are consolidating. And all of this seems to accelerate around two themes that NiCE and Genesys exemplify.

NiCE's is integrating vertically. The Cognigy acquisition is an integration move that aims at owning the full technology stack, from core CCaaS to best-in-class conversational AI. The goal is a seamless, single-vendor platform. The risks for customers are potential integration debt from the acquired asset, a less open and smaller ecosystem, and probably a premium licensing for these new capabilities after being locked in. The deepened Salesforce partnership is a pragmatic necessity to ensure relevance and access to the bigger ecosystem, not a fundamental strategy shift.

Genesys plays the ecosystem card. The capital injection from Salesforce and ServiceNow is probably a direct counter. It funds a horizontal integration strategy, betting that enterprises prefer a tightly integrated alliance of market leaders over a single-vendor suite, which is in line with both Salesforce’s and ServiceNow’s strategies. The risk is that these integrations remain shallow, and the "ecosystem" is more of a marketing concept than a technical reality. This largely depends on how the three parties build and execute their roadmaps.

Thoma Bravo’s portfolio around Verint can become a wildcard theme here. The merger of Verint and Calabrio would create a third force. This entity would not necessarily compete directly on the CCaaS infrastructure layer but would focus on the higher-margin levels, starting with WEM and interaction intelligence. Combined with the other portfolio companies, its power would come from either being a platform-agnostic integration hub—the "Switzerland of data" or becoming another vertically integrated solution.

With this convergence going on, customers will be able to create data informed and more seamless processes for their customers, simply because there will be deeper integration on a data platform level, not necessarily a software platform one.

What should customer executives look at now?

• NiCE/Cognigy customers face significant integration risks due to two leading technologies being combined. Customers should demand a detailed, time-bound integration plan and roadmap with technical milestones. They need to have a deep and hard look at how their current licensing models are changed by new ones and model their TCO inclusive of probable premium "Cognigy-powered" features.
• Genesys customers face a different execution risk. They require technical proof-of-concepts that demonstrate the depth and real-time nature of the Salesforce and ServiceNow integrations, respectively, along with a detailed integration roadmap. They should mandate that business value metrics be tied to the pricing and success of this "ecosystem."
• Verint and Calabrio customers face a similar integration risk plus an uncertainty about the detailed positioning and future of Verint. Customers need to demand a detailed integration roadmap with milestones and clarity about the final strategy – platform neutrality or vertical integration.
• Avaya customers are worst off – and have been for a while. Avaya is in survival mode. This situation requires immediate risk mitigation. Customers should work with a two-prongued strategy: Firstly, they need to secure existing SLAs and support commitments in writing. Secondly, they need to engage in a RFI/RFP process for at least two cloud-native CCaaS platforms. The objective is to have a fully vetted exit strategy that can be executed fast.

Some general recommendations

There is so much going on at a fast pace, that it pays off to reevaluate vendor roadmaps. An outdated understanding of your vendor's strategic position is a serious liability. The market of 18 months ago does no longer exist and the market of in 18 months will be very different from today’s.

As platforms consolidate, the risk of vendor lock-in increases. Customers should favor providers with a demonstrated commitment to an open, API-first architecture. There is no one-size-fits-it-all software platform for all businesses, nor will there be.

The current market will expose providers with weak financial fundamentals. Customers should review their vendor’s financial health and R&D investment as a default part of their due diligence.

Last, but not least, customers need to avoid AI-washing and require that any proposed AI solution be tied to a business case with measurable outcomes, such as reduced operational costs or improved containment rates. Additionally, contracts need to be tied to achieving these outcomes.

The news

On October 9, 2024, Zendesk held its AI Summit in New York’s Chelsea Industrial. The AI Summit is an event mainly for customers to inform themselves about what is new at Zendesk but also to network with each other. The event featured an interesting lineup of customer and partner speakers, headlined by New York Times bestselling author and podcast host Kara Swisher.

My estimate is that there have been more than 250 customer representatives in attendance who not only could listen to the speakers but also get in-depth demos of Zendesk’s updated offerings, following real-life use cases.

True to its name, the event centered around the use of AI, in particular bots, to increase not only efficiency, but also customer- and employee satisfaction. CEO Tom Eggememeier opened the event with an emphasis that Zendesk’s AI is built to support humans by stating that it “is designed for humans”, and Zendesk’s service solution is built to strengthen the human – AI partnership.

Kara Swisher talked about the promise and peril of AI, giving the audience some food for thought on the day after Geoffrey E. Hinton, the godfather of machine learning turned AI warner got co-awarded the 2024 Nobel Prize in Physics for his “foundational discoveries and inventions that enable machine learning with artificial neural networks”. While Swisher sees the value that the use of AI can bring, she, too, warned about the hurdles that still need to be overcome, namely the concentration of power that the technology creates and its immense hunger for energy. The tie into the Zendesk story is that customer service is a prime candidate for the use of AI, which turns information to insight while customer service in general has quite some room for improvement – and this is smack in Zendesk’s territory.

Some of the news revealed is that the functionalities that got announced at Zendesk Relate earlier this year are now in general availability. Zendesk announced them as a series of innovations, including AI-powered agents for omnichannel support, enhanced agent copilot, powerful voice, and an agent builder. The company reiterates its vision of having 80% of all service interactions automated. To achieve this, the company also delivers improved agent autonomy and an enhanced agent co-pilot. It adds that it wants to automate up to 50% of voice interactions by partnering with poly.ai.

The bigger picture

There is a continuing need for enabling a good/great customer experience and to engage correspondingly. In customer service scenarios, this makes it crucial to get responses right, fast. At the same time, customer service is a profession that still suffers from high employee churn, with the corresponding cost of re-hiring, re-training and low employee morale. As Swisher said, there is scope for improvement and a good way to achieve this is to focus on creating a positive “human-AI relationship” that helps human agents do what they can do best by helping them with what an AI can do best, which is delivering knowledge and insights as opposed to have humans research it.

Looking at this from a customer angle, customers want faster and better service, i.e., higher quality of resolutions at a faster time to resolution.  

Achieving this requires the right tools. These tools need to help agents focus on the right cases, give them help for their own improvement and, most of all, keep the mundane off their backs.

From businesses, this requires an outside-in mindset that is not solely focusing on efficiency and productivity as its outcomes, but on becoming “better” in a business sense by looking at employee- and customer outcomes.

My analysis and point of view

Zendesk tells a very compelling customer service story by combining autonomous agents, copilots and a sophisticated QA solution that helps steering a customer service organization into the right direction – namely outcomes for customers, which includes agent coaching as opposed to merely monitoring them. The latter does create a climate that a customer service organization’s most valuable assets – the agents – perceive as negative. Where the story is a bit difficult for me is where CX is used as a synonym for customer service, but this is a different topic.

The audience of the AI summit clearly shows that this story resonates. The demo-booths were well attended, and customers asked lots of questions about how to optimally leverage the capabilities. In fact, those customers, who I had the chance to briefly talk to, looked beyond the usual narrative of increasing efficiency. Instead, they looked at how to provide better internal or external service in an environment that needs to manage with limited resources and personnel. Having said this, not every customer is there yet. Many are still too focused on cost reduction by headcount reduction. This is still the current reality that also AWS General Manager of AI and ML Tia White spoke out, while maintaining a more optimistic outlook about refocusing employees on higher value tasks.

On the other hand, as also a recent Salesforce study shows (free download, requires registration), customer service professionals experience that customer expectations are continuously on the rise; customers ask for more: more service quality, more personal touch, and more speed of resolution. This combination puts customer service organizations between a rock and a hard place. And this is where helping customer service agents with automation and (generative) AI helps. As Zendesk CTO Adrian McDermott and SVP Product and Solution Marketing Lisa Kant emphasized, the adoption of AI is both, a marathon and a sprint. To support this, it needs both, fast time to value through short implementation cycles with well running “pre-trained” models, the continuous improvement of these models with clean data, as well as the careful selection and implementation of meaningful business scenarios plus the analytics to identify them.

The sprint part is the fast implementation and the continuous model improvement that come out of the box.

The marathon part is staying on focus by staying use case aligned and defining relevant KPIs that measure success.

The chosen vendor software must enable a feedback loop that helps establishing this.

In my eyes, Zendesk delivers this for customer service scenarios, although some aspects of it like the prediction of a response perception by the customer or the (semi-) automated improvement of knowledge bases, require complementing software. So, buyers who are in the market for a new omni channel customer service solution should have a good look at Zendesk.

_{Disclosure: Zendesk has paid for most of the travel and accommodation cost associated with my attending the AI Summit. This came with no obligation whatsoever. All thoughts and opinions expressed are solely mine.}

Salesforce told us its agents were coming, and it was still a surprise when the agents arrived.

If you’ve been conscious for the past year, you know how much news (or noise) there has been about artificial intelligence. Salesforce billed Dreamforce24 as “the largest AI event in the world.” It caught me a bit off guard when I heard Marc Benioff say it, but it probably shouldn’t have; Salesforce has invested heavily in AI for the past few years, ever since it introduced Salesforce Einstein (now Tableau CRM) in 2016. Its latest effort is Agentforce, and the company is really leaning into it.

The topic of AI erputed in 2023 once ChatGPT was unleashed upon the general populace, and it’s only fair that a business would leverage the popularity of the topic. While 2024 seemed poised to be “the Year of the Copilot,” since a number of vendors introduced AI copilot apps—digital assistants that automate routine tasks—the fanfare was to be short-lived. Today, the talk is about agents, or agentic AI—AI that acts on its own to accomplish tasks and achieve goals, reaching into the knowledge stored in linked apps as necessary.

Marc Benioff declared, “Agents are the third wave of AI.” It’s hard to argue against this. AI agents have great potential to increase productivity and customer satisfaction, and mark a leap forward in AI capabilities. Benioff stated that one of the goals of this year’s Dreamforce was to get 1,000 customers to deploy Agentforce, a goal which I believe they achieved. It’s too soon for there to be any tangible results that anybody can talk about, so we’ll have to see how the reality lines up with the potential.

The canned demos of Agentforce made AI customer service look easy, with no code required to create and modify an interaction workflow. B2C customer service examples showed how conversational AI linked to business processes and live information make for a quick and satisfying experience. I have to wonder about the parts we didn’t see, such as how it works in a B2B context, but what we did see was compelling.

Salesforce attributes the power of its agents to the Salesforce platform, providing access to the existing security, business processes, and databases a customer would already possess. Platform will be more important than ever, if that’s the case. Other vendors are no doubt following suit.

There is still a question remaining about what effect agents will have on the workforce. On the one hand, there is the assertion that agents will free up existing employees to do more valuable work, possibly allowing employers to hire more people into affected roles. On the other hand, contact centers are infamously squeezed for the last penny of cost savings, and it seems likely that any gains in efficiency will be reflected in workforce reduction. Maybe not in every case, but I do not believe this issue has been sufficiently addressed. We'll need to keep an eye on this.

The News

On April 15 to April 18, 2024, Zendesk held its annual Relate event, including a half day analyst track on April 15. The event was attended by around 1,600 customers, partners and analysts.

It was about Zendesk’s strategy, which revolves around - no surprise here - AI to deliver better customer experiences. As part of this strategy, Zendesk also made clear how the past twelve month’s acquisitions of Klaus, Ultimate, and tymeshift get integrated into Zendesk’s customer service offerings, enriching and rounding them off. The company is betting big on AI, working on the assumption that interaction volumes between customers and companies are continuing to increase very fast. As a conclusion of this, service needs to become AI driven to accommodate this scale. Secondly, Zendesk sees AI as the technology underlying the necessary high degree of personalization. Together, this is estimated to increase the market size available to CX solutions that automate CX labor tremendously.

At the event, Zendesk had three key announcements. They were

• AI agents to improve self service solutions,
• a copilot that helps agents solve incoming tickets faster and provides insight to further optimize the service and
• a workforce engagement solution that helps improve the productivity of digital and human agents as well as the quality of conversations.

Behind all this lies the recognition that customer service is very much conversational.

Customers and partners that I talked with had a keen interest in learning more about AI use cases. Many of them had started to use AI but estimated themselves still in early stages. 

The bigger picture

The customer service software market has become very crowded with players ranging from service specialists to suite players, with classic call-center companies making inroads, too. Additionally, there is a raft of niche companies that are offering specialized add-ons to improve customer service solutions, from connecting them to workforce management or -engagement solutions, to QA solutions and many more. It is quite easy to see a kind of a consolidation going on here.

A good number of these solutions use advanced statistics or machine learning based technologies to increase the overall efficiency of customer service and/or to achieve a higher degree of automation. If not outright embedded into each other, solutions are regularly integrated using APIs, with customers often choosing to work with a small number of vendors in a kind of an ecosystem approach. With service centers being regularly trimmed to efficiency, companies usually follow one of two base strategies: they either look for efficiency, aka cutting cost, or for offering better service without adding cost. The latter is often combined with setting up the service center as a profit center, as opposed to a cost center.

The latter strategy was exemplified by a Zendesk Relate customer that built a continuous improvement strategy around its ticketing system. Three of the main KPIs that are measured and shall be maximized are customer satisfaction, employee satisfaction, and the ratio of automated responses. The company is achieving its goals using generative AI to solve issues, another AI to predict a customer satisfaction score. Human agents are involved if the AI cannot generate a response or if the predicted customer satisfaction for this response is not high enough. In this case, human agents are tasked with improving it. Quite the same happens with agent responses: The agents’ responses are evaluated for predicted customer satisfaction and sent back to the agents for improvement if the predicted customer satisfaction score is not high enough. This is combined with an automated coaching system that agents are encouraged to use to continuously improve their own productivity. The results of implementing this strategy - which admittedly is a bit more complex in terms of systems, metrics, and culture - are quite amazing.

My point of view and analysis

Let’s cover two topics here, the event itself and the content.

Starting with the event.

Looking at it through a customer lens, it was chock-full of information and, more importantly, the ability to talk to experts about the roadmap and to see working software in the exhibition hall. The customer presentations were very interesting.

Keynotes are always an interesting thing. Robert Richman very relevantly and excitingly talked about customer service requiring a service culture. Having Michelle Obama on the first day was certainly interesting. On the other hand, no one I talked to found much relation to Zendesk in her story. This is the eternal challenge that I have also seen at other events. While it is important to work with “luminaries”, it is sometimes difficult for the audience to relate their stories to the story that the vendor tells.

From an analyst perspective, I would have wished to have more access to company executives to dig deeper into information. Still, the ability to speak to customers and partners was valuable. 

Big kudos to the organizing team that mastered the challenge of creating an event that was interesting for customers, partners, and analysts.

Coming to the content.

As said in the beginning, these days every CX strategy involves AI, if it doesn’t outright revolve around it. This includes Zendesk. From a capability as well as a vision capability, Zendesk seems to be positioned better than many of its competitors in the customer service arena. And there are certainly a good number of competitors. Zendesk’s position is a result of a good product mix, the early creation of “ready-to-use” AI models to improve service efficiency as well as of interesting acquisitions. Of course, it is important to deliver this vision fast. These days, most advantages tend to be of temporary nature.

The good number of competitors is not only because the customer service market is crowded. It is also partly attributable to the fact that Zendesk claims to offer a “complete CX solution for the AI era”. This broadens the competitive field significantly, also into areas that Zendesk does not cover or does not cover with a strong product. CX is not limited to customer service.

On the other hand, Zendesk’s mission is to “power exceptional service for every person on the planet”. This focus on service processes somewhat reduces the ambiguity of the terms “CX” and “CX department”. As one of the execs said “we are great when it comes to tickets” - which is true. In short, the company maintains a focus on customer (and employee) service. It is important for customers to keep this in mind.

Having said that, Zendesk also offers a sales solution, Zendesk for sales. It was notably absent throughout the event; yet is important for the complete CX story. The same is true for the Sunshine platform, although in this case there still was a lot of talk about the platform, without naming it. This makes a lot of sense, both in terms of the need and also a need to make the fresh acquisitions part of this platform. 

Talking about these acquisitions, they are important add-ons to the core service functionality. This is especially true for the QA capabilities that come with Klaus and the AI engine that is delivered by Ultimate. Tymeshift as a WFM solution plays out its strengths increasingly with bigger growing service centers.

Service processes will be automated more and more to achieve and maintain high levels of customer and employee satisfaction. This needs a solution that has the necessary intelligence. Zendesk is on a good way to get this job done. It already now solves parts of this problem and has many of the remaining parts of this solution at hand - and is working on combining them to a whole.

The news

On January 8, 2024, Zendesk announced the acquisition of Klaus, “the industry leading AI-powered quality management platform”.

With AI driving a rapid increase in customer service interactions it is necessary for customer service teams to become more efficient while maintaining their quality of service. This is accomplished by a combination of digital and human agents across an increasing number of channels. Ensuring good quality requires a QA solution that is capable of scoring 100 percent of customer interactions, which is what Klaus’s AI is capable of. In doing this, it “pinpoints conversations with positive or negative sentiment, identifies outliers, churn risk, escalations, and necessary follow-ups. According to Zendesk, most QA software does handle only one to two percent of all customer interactions.

With workforce enablement management capabilities, Klaus enables the identification of knowledge gaps and coaching opportunities with the goal of improving agent performance and productivity. The result is higher customer satisfaction.

According to Martin Kōiva, CEO and founder of Klaus, “Zendesk and Klaus share a vision of Ai-led, personalized CX with businesses fully anticipating and acting on their customers’ needs. QA software plays a critical role in this, ensuring consistency, assessing both human and digital agent performance and providing actionable insights for strategic planning. As part of Zendesk, we will continue to build and deliver thes crucial capabilities, but now at an even greater scale”.

The bigger picture

Customer service personnel works in a high-pressure environment with lots of turnover; even worse, as frontline workers, they are often the first ones to deal with customers who are already less than amused – equipped with tools that are not helpful, either. At the same time, partly due to the high attrition rates, agents often lack the training and experience to deal with difficult situations and do not have the contacts to find relevant information fast.

What they need is being able to concentrate on the “hard cases”, easy access to the relevant information, ideally without the need to use multiple browser tabs, good training and coaching, and empowerment.

In addition, the increasing need for automation, that is caused by a shortage of available personnel as well as increasing customer demands for availability, leads to an increased proliferation of chatbots. At the same time, customers do not trust into AI, as the last state of the connected customer (registration required) report by Salesforce found out.

My analysis and point of view

After the acquisition of Tymeshift last year and the provision of “ready-to-run” AI supported bots, Zendesk is continuing on its path to provide a very rounded customer service platform.

The emphasis on coaching customer service agents is great – and important – to see. Agents are already some of the most monitored and measured workers around, so they will be glad to see some help instead of just feeling surveilled. Especially, as there is so much attrition going on. Businesses that understand that a better trained and enabled team increases agent motivation and therefore in all likelihood the individual and team performance. This has a direct impact on customer satisfaction and with that on business outcomes. Zendesk now can support this better than before.

This is also something that was found in the recent Customer Experience Horizons report by Genesys. 70 percent of respondents will invest into AI-based coaching and training recommendations for their personnel with still almost 60 percent looking into implementing AI-based performance monitoring and evaluation solutions.

Being in times of AI, the ability to also analyze and especially improve the performance of digital agents becomes more and more important. With Klaus, Zendesk is now better able to identify customer service bots’ “knowledge deficiencies”. In my eyes, the next important step from here is to (semi-)automatically reduce these deficiencies, supported by generative AI. This, based on actionable analytics. Why is this important? Because the performance of both, human and digital agents, highly depends on a well-performing knowledge base.  I am interested in seeing how this topic evolves. Embedding Klaus in the fold, certainly should help taking the first steps by helping to find out what the relevant customer and organizational data is, and to initiate steps in improving the state of this data.

But again, looking at the needs of customer service agents, businesses that pursue helping their agents are now in an even better position when using Zendesk.

Rest in peace, Social Media!

Yes, I know, you have been pronounced dead numerous times already, and that as early as 2011 by the Sillicon Valley Watcher, if not earlier. You lived on. Still, now you really need to admit that you are a dead thing walking.

You had a short, yet exhilarating life. And you, admittedly, developed astonishingly fast and far from your humble beginnings in the early 1970s and the first bulletin board systems around 1980. These have been the glory days of FidoNet, CompuServe, or AOL. SixDegrees.com followed later. The early noughts gave us a flurry of messaging systems, LinkedIn and XING, not to forget the infamous 4chan.

Anno domini 2004 brought us Facebook, 2005 brought us YouTube, Twitter followed in 2006. Google attempted repeatedly to get the hang of you (Orkut, Google+, anyone?) and still has some messaging services up and running.

All of these platforms have in common that they started up with the claim, some of them even with the objective, to make the Internet more social, to foster user generated content and to, ultimately, shift the power balance from corporations to their customers. Who does not remember the war cry “the customer is in control”. This referred to the idea that the customer could get more information that is not controlled by brands, so that they can be better informed, instead of being forced to rely on corporate broadcasts.

This should have been achieved by giving customers a voice that is as strong as the corporate one, albeit without the (marketing) budget behind. In line with the definition of social, it enabled the tendency “to form cooperative and interdependent relationships with others” – mainly other people.

Your power skyrocketed with the advent of the iPhone in 2007 and the subsequent flurry of smartphones and other mobile devices. With mobile devices, you have fully become a ubiquitous set of “forms of electronic communication through which users create online communities to share information, ideas, personal messages, and other content (such as videos)”.

At the same time, the advent of mobile devices was your undoing – the begin of your end.

You have been truly social, in the sense of the word, as you enabled the formation of cooperative and interdependent relationships with each other.

But only for a short period.

Why?

Because, of course, the potential of reach created a caste of broadcasters, who were not interested in interdependent relationships or even interaction. Instead they were interested in reach and in creating their own brand identity … i.e. in broadcasting. Naturally, this led to some voices being louder than others. Also naturally, this led to brands becoming interested in these persons. The social media influencer was born. Of course, brands learned to leverage your power as well.

Poly-directional discussions with many equal participants got reduced to a unidirectional model with creators – broadcasters – addressing their audience. This audience has largely turned into consumers. The bullhorn that we knew from analogue times had returned.

Even worse, the platforms that called themselves social media platforms collected increasingly more data to help their paying customers – the brands – to target their customers – the social media users – even better than any time before.

The users have ultimately become the product. The excessive “use” of this “product” has led to an increasingly restrictive regulation, as the power balance between users, platforms, and brands is strongly against the users.

RIP Social Media!

The Social Media resurrection

To quote Martin Luther King, Jr., “I have a dream”. This dream is that social media resurrect, but in a better way, based more on consent and respect.

To be clear, it is by no means illegitimate for both, the social media platform, as well as brands, to be have profit objectives. The same holds true for influencers.

The secret sauce lies in setting this up in a way that maintains the user as a participant on eye-to-eye level.

The way to achieve this is making the platform instead of its users the product. The goal is to create a community again, a community that connects users with users, users with brands, and brands with brands, while equally serving everyone’s interest.

Communication between brands and users strictly happens based on explicit interest and consent expressed by the users. Longer term interests, like hobbies, affiliations, etc., are either made part of their profile or explicitly expressed during an interaction.

If users wants to interact with a brand, they subscribe to its communications, if they don’t – they don’t, and the brand does not have the ability to reach these users. Imagine this like tuning in to a radio station, or, to use a better image, joining an interest based chat room. Basically, relationships are consciously initiated by the users who also decide how much to share with which brand. Relationships can be ended by both parties.

This was also the core idea behind my former venture EpiKonic (the website is still up as a blog platform), a mobile social media and engagement platform. Some of these ideas are also part of Ephanti, a startup that I am currently working with. There are more ideas behind Ephanti, of course.

For the users, the advantages of this model are obvious. Relationships are initiated and built by them. These relationships are based on their expectations, needs and, last but not least, trust.

But what is in it for brands? After all, this approach turns the traditional social media norms and conventions upside down.

Quite simply: Precise targeting! As the user initiates the conversation and explicitly shares immediate and longer term interests, nearly all guesswork is taken out of the equation. The brand can offer journeys that the user can choose from and solutions with minimum own effort. The better the journeys and solutions are, the more trustworthy the brand becomes. The more trustworthy the brand is, the more likely it is that the user stays in contact and hires more solutions that the brand has to offer. In essence, the user becomes loyal – not captive. Ideally, the brand is even able to turn the user into an advocate, then using their own influence to convince other users.

This way, the brand can extend its business by looking at its own success as being a consequence of its customers’ successes.

The same holds true for influencers.

The platform becomes the trusted matchmaker, but more in a broker model. It needs to be fully trusted by all parties. To earn and maintain this trust, it enables the users to interact with each other, and to find brands based on their needs and interests. In addition, the platform makes sure that there is no surreptitious advertising.

The business model sounds almost the same as the one of current platforms. The service delivery is different.

The technology is there. The need and the opportunity, too.

Long live social media.

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. 

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 2, 2021, SugarCRM held an analyst summit to share what is going on at the company and to get some candid feedback of the participants. As usual for this type of event, there is quite some information that is still under NDA, so I will be able to cover some of it only in broad strokes rather than the detail that the matters deserve.

After a business update by CEO Craig Charlton, the event itself revolved around two themes:

• customer success stories, including customers describing how they are using SugarCRM to improve their own business by better serving their customers in an interview style format
• SugarCRM business development, technology, and its future trajectory. Naturally, this part is largely under NDA.

There were breakout sessions covering Sugar Sell, Sugar Service, and Sugar Market

As usual, and with the notable exception of the customer interviews, the event was slide-driven with giving the opportunity to ask questions at the end of the respective agenda items plus offering a brief Q&A with the executives.

Last, but not least, part of the event is a 1:1 session with a SugarCRM executive a few days after the summit.

After a short opening by Sarita Kincaid, Craig Charlton offered a business update, showing how SugarCRM developed in the past twelve months. The company is focusing keenly on the mid-market. Craig painted a bright picture that shows a very high customer retention combined with a good growth rate. Noteworthy are a steep increase of the recurring ARR. He quoted a nearly doubled number of new logos, combined with a more than doubled new/upsell ARR and an average deal size that went up by 160 plus per cent. 

The ratio of business outside vs. inside the US is increasing.

Another trend that he sees is a continued adoption of the SugarCRM cloud solutions with the US counting more than 90 per cent of new customers in the cloud, not surprisingly trailed by Europe and, more interestingly, Asia..

This success he attributed to the overall three-pronged strategy of investing into platform, product, and customers, with a strong focus on the mid-market. Focusing on the customer means for SugarCRM focusing on making them successful.

This strategy was supported by information about new wins and customer testimonials. Customers very much focused very much on integration, visibility of data/information via a holistic view and the overall flexibility of the application platform.

This combined with ease of use and quick time to value.

Technology-wise chief product officer and CTO Rich Green emphasized upon the value of the underlying time aware (data) streaming model and an AI that works with data augmentation using external sources via the acquisition of node.io about a year ago.

The bigger picture

Every CRM player that addresses the mid-market and above sends two messages. The first one is that they provide a platform and the second one is that they are CX players, not mere CRM players. All of them are also increasingly deploying machine learning based solutions to achieve this. 

For companies, it is quite important to get a fast time to value and not have month- or yearlong projects before a system can go live.

Users increasingly demand a consumerization of business applications, especially those that are implementing customer facing processes – i.e., CRM systems. In essence, what they want is an easy and simple to use application.

For them a CRM system, to get value out of it, needs to exhibit three basic tenets:

• it needs to be there where they are, physically or digitally
• it needs to show the users what is important in the current context and to make solid suggestions or even take actions.
• it needs to speak with them, their way.

Be where I am

Their CRM system is but one of many applications people are using across devices, laptops, tablets, and smart phones. By their very nature, CRM systems are tools that ingest, aggregate and enrich data, so it can be used for a purpose. The result is actionable information that needs to be provided to its users. This way, e.g. interactions with contacts are logged without any explicit intervention or are pulled from various sources to be then also visualized via different applications or communications tools.

Furthering this thought, the system must offer is services across channels, be available within social media tools, productivity tools and especially within the used email client. The latter part being fairly easy as probably 90 per cent of all business users use either Outlook or GMail. Still, what this means is that the system must embed itself and be available in the tool of choice, either by means of a chatbot or by offering a user interface that becomes a part of the social media tool or mail client and that from there, intelligently using the users context, provides relevant information.

Speak with me

The mobile aspect is a given, too. No CRM system that is worth a grain of salt comes without mobile capabilities, as an app or at least via responsive user interfaces. What they are lacking, though, is the ability of being used in a hands off fashion, e.g. using voice as the interaction channel. How about getting a call from your friendly CRM system after a customer visit, asking for a visit summary while you are driving to your next appointment or creating a report via voice commands? 

Voice as an interaction channel will become an increasingly important interface to CRM systems, be it initiated by the user “hey CRM” or by the system itself as in the example above.

Show me what is important and make solid suggestions

Last, but not least, the system needs to be able to show the users what is important for them at any given time. This includes AI-supported lead- and opportunity scoring, the creation of target groups and campaigns based upon interaction quality, frequency, change thereof, deal size, and sentiment, business objectives, to name a few criteria. This It also includes the ability to suggest next actions that provide the best chance to further nurture a lead or opportunity, or to resolve a service case. On top of this, the system might unearth opportunities for the sales representative, which provides tremendous value.

My PoV and analysis

A bit more than two years ago I asked whether SugarCRM is getting its mojo back. At the end of 2021 the company clearly shows that the answer to this question is a yes. 

Thanks to a strict focus and several smart acquisitions, the most important one to me being node.io, Sugar has re-established itself as a formidable competitor in the CRM market. The now embedded AI engine named Sugar Predict makes the AI a ubiquitously available helper and not a geeky and not necessarily trustworthy technology. This, and its being available to everyone (not only management), some explanation capabilities, should increase user acceptance. The system “speaking” the user’s language by for example classifying an opportunity as “more likely” instead of giving it a pseudo-accurate likelihood of 85 per cent is the icing on the cake.

Generally, making sure that the platform does the work for the user by reducing busy work, making what still needs to be done simple and by surfacing relevant information in context is key. It looks like SugarCRM has really found a way to not only claim this but to also deliver. 

With the acquisitions of W-Systems and Loaded Technologies, the company has increased its ability to deliver professional services and to enable partners.

It is a smart move to focus on serving the upper mid-market segment, although there is some competition in this area. The enterprise market is just too saturated with the big four although SugarCRM should be able to successfully compete in some areas. The solution does scale, so customers do not need to be worried to outgrow it.

SugarExchange, the Sugar app marketplace, is fairly young and lists about 360 apps at the time of this writing, which is a fairly small number. Increasing the number of available partner solutions would surely help increasing SugarCRM’s reach even further. SugarCRM has a number of loyal implementation and technology partners.  However, compared to the other vendors, the size of the ecosystem is fairly small. According to the company, it has more than 300 ISVs, 111 reseller partners, 9 solution partners and 2 distribution partners.

I am not a financial analyst, still it appears to me that SugarCRM’s growth figures are in the range of other companies, which indicates that SugarCRM, being a smaller player in the market, does not outgrow it yet. But then, the pivot happened less than three years ago.

And, as said, I am not a financial analyst.

All in all, what I see is a company that has established a core platform with a solid solution set on top of it, that has a sound strategy and a strong team to implement the strategy. And it has an investor with the funds to support important acquisitions. This is important, as there are still some gaps to close – but then, who doesn’t have gaps?

What is more, the team is emphasizing on an outside-in mindset: Make your customer successful in making their customers succeed. There are only few companies who work this way. 

Together with SugarCRM’s Clint Oram and fellow analysts Kate Leggett, Liz Miller and Michael Fauscette, theCRMKonvos team discussed first impressions of where SugarCRM is going. It is worthwhile watching the discussion (shameless plug: Please subscribe to our channel. We deliver interesting content on a weekly base).

Kudos all around. SugarCRM has gained considerable traction and visibility. I am curious to see what comes next.