The News

On September 12, 2024, Zoho released a new, AI-rich version of Zoho Analytics that brings self-service BI to any persona in business.

The release added more than 100 features and now offers powerful AI and ML capabilities. These enable diagnostic insights, predictive analytics, and automatic report and dashboard generation. “Additional advancements to Zoho Analytics include a custom ML model-building studio, seamless integration with OpenAI, and third-party BI platform extensions. The new version of Zoho Analytics has added power, intelligence, and flexibility to serve a broader range of businesses and users than competitors in the market.”

The Zoho BI and Analytics Platform now offers more than 500 connectors to other systems, including streaming analytics.

Overall, the new release offers new capabilities across four main categories:

• Data Management Hub: Zoho Analytics has expanded its data management capabilities, ensuring more accurate and applicable decision-making and deeper insights to accelerate business success
• BI Infusion with Generative AI: Zoho Analytics has introduced Generative AI capabilities across the BI platform to accelerate the adoption of insights for a broad spectrum of user personas.
• Data Science and Machine Learning (DSML): Zoho Analytics now features the DSML Studio, allowing users to simply and quickly build custom machine learning models, which, for example, analyze or predict customer churn. n of insights for a broad spectrum of user personas.
• Platform Extensibility: Zoho Analytics is more deeply extensible, allowing businesses to sync and standardize data stored across multiple tools and platforms for comprehensive analysis and insights. Zoho Analytics is a composable platform on which any analytical solution can be built.

Alun Rafique, CEO and Co-Founder of Market Dojo says about the new Zoho BI and Analytics Platform that "as developers, Zoho Analytics 6.0's new AutoML capabilities caught our attention immediately. Our team is currently working on a model to analyze and predict customer churn, among other projects. Zia Insights' diagnostic capabilities have been a game- changer as well, helping us understand the underlying causes of a spike or drop in sales or performance so we can take quick action. Again, as solutions providers, it's critical we understand what's working and what's not within our own company before helping other businesses and professionals. Zoho Analytics 6.0 continues to give us that insight and more. Ask Zia' is another useful feature. With a simple prompt, I'm able to get the right insights I need instantly. The new chart types are amazing, and I'm particularly impressed with the racing and sunburst charts."

The bigger picture

The Zoho BI and Analytics Platform has its origins in 2009 as one of the first self-service cloud BI platforms. It can be installed behind firewalls locally or can be deployed on cloud platforms such as AWS, Google Cloud, and Microsoft Azure. Additionally, it is available as an embedded platform that can be leveraged by other vendors. Originally, it was aimed at line-of-business users and in the meantime has evolved into a full-blown BI platform with around 17,000 customers, as of August 2024. Within the Zoho One suite, it is used by 70,000 business on a daily basis. This makes Zoho Analytics the second-most used app, after Zoho CRM, of the Zoho One suite.

The Zoho BI and Analytics Platform covers the complete analytics workflow from data preparation through visualization and exploration. It addresses challenges that a broad range of users, from data scientists and developers to line-of-business users, have.

This new Zoho Analytics release features more than 100+ updates, including new visualizations, enhanced dashboard building, audit and admin controls, revamped mobile apps, right-to-left (RTL) support, and more.

The new version of the Zoho BI and Analytics Platform shall address five key challenges, namely:

• Data velocity and diversity
• Data management and -governance
• Complex analytical needs
• Limited adoption of analytics caused by diverse user needs
• Rapid technology changes.

Zoho addresses these challenges using the four different categories listed above.

1. Deepening the data and integration management using a powerful data management hub to establish a strong data and data management foundation
2. Infusing AI, particularly generative AI, across the platform to accelerate BI adoption
3. Increasing the ability to build machine learning models for analytics workloads by making this capability available to a broader user audience
4. Enhancing the platform’s extensibility to serve more use cases.

My point of view and analysis

As my colleagues Brian Sommer and David Smith said in an article by Lynn Greiner on CIO.com, the Zoho BI and analytics platform has some key strengths, including the deep integration into the whole Zoho suite of applications while simultaneously offering “some 500 integrations to other firms’ software products and data”, which makes it a very good platform for generating insight from disparage sources. In combination with Zoho’s own and the ability to leverage external AI, this can lead to strongly improved business analyses and forecasts.

With the help of decision intelligence that answers the very important question “why” and recommendation, immediate value can get created for business users.

Lastly, the strong support of very different user personas helps in making powerful analyses available throughout the enterprise.

You can download and read my full analysis on the Zoho BI and Analytics Platform directly from the Zoho site.

Disclosure: My report “One Platform to Help them All – The Zoho Analytics Platform” was commissioned by Zoho. Zoho did not and did not try to influence the analysis part of the report in any way.

What the heck is customer experience, and who is responsible for it? These are two very good questions, for which I, myself, have some very distinct answers.

Let’s start with them, before I dive deeper into that topic with Praval Singh, VP Marketing for Customer Experience at Zoho, who – naturally – has some good answers, too.

You prefer the original? Of course, you can watch the complete conversation, too.

Here it goes.

Re customer experience, I am with Paul Greenberg and Bruce Temkin, who some years ago defined customer experience. Paul defines customer experience as “how the customer feels about a company over time” while Temkin defines it as “the perception that customers have of their interactions with an organization”.

Either does it for me. It is the customer’s perception.

This makes it quite easy to answer the second question. Who is responsible?

Answer: The customer!

Why? Because the organization cannot control how I perceive my interactions because it simply doesn’t know enough about my current context, aka situation, at any given time. Organizations regularly do not know enough about my cultural background, my current situation, or my current mood. What they can do, is taking an educated guess, based upon whatever data and algorithm or AI they have at hand. What the organization can control to some extent, is their half of an engagement. This means that the best intended engagements can result in unintended and undesired perceptions.

Customer experience is a function of the customer’s experiences, the expectations towards a brand/product/company and the customer’s mood at the time of the experiences.

Hence also my cringing when I hear something in the lines of “delivering an experience”. An experience is nothing that can be wrapped up like a parcel and be delivered by UPS.

But that’s me. What is Praval’s take?

Praval is, of course, well aware that the term is pretty overused. He goes so far as to say that it even gets misused. He defines customer experience as “what would she talk about you when you're not in the room. That's what the experience is all about.” This experience is what gets established during interactions across a variety of touch points. Many of these touch points are digital. And this is where vendors like Zoho play their role although technology is only a part. Praval says that “the best of software cannot solve for good customer experience”. Additional important parts are people and process, “how you look at engaging with the customer, across all touchpoints sales, marketing, customer service.” Engagement is the crucial part.

But who is in charge then?

Within the company, definitely not a single person, as there are too many moving parts and “it takes a village” to bring a strategy, a framework, and possibly best practices to life. Touch points are very diverse. They range from digital ones “designed by a designer, built by a programmer or a developer” to physical ones like “the person who gets on a phone call or response to a chat to the salesperson who may or may not engage with a prospect. It's everybody put together”. According to Praval, engagement is a part of experience. Even the best software cannot solve the challenge that the customer having a good experience is.

So, we need to look at people, too. And this is where employee experience comes into the picture. Employee experience, though, is oftentimes looked at as disjoint from customer experience, as a separate set of systems and processes.

According to Praval, Zoho has learnt that employee experience and customer experience have a tight connection. He maintains that “we have learned talking to customers, setting these things up internally, we see great employee experience enables excellent customer experience. And with that, what I mean is if you empower your employees with the right set of tools, the right set of authority to sort of engage with customers take decisions on the fly. All that influences how you deliver the customer experience as a company.”

How can Zoho have learnt this, as opposed to many other business software vendors? Because of the breadth and width of Zoho’s integrated offerings.

“And I think we're in a position to say that confidently because we have employee experience platforms and customers for that. We have customer skills platform and customers for that. And they beautifully” into each other.

Of course, AI plays a role in this, not only since the advent of generative AI. “It is already happening” says Praval. There are well defined use cases, where AI already helps although they are not yet overly complex. “It could be letting a salesperson know when is the best time to call someone. So doing simple things straightforward is already in the works. It's happening; or helping a support representative with the right answer. It could be pulled from a knowledge base or could be intelligence based on the previous experience of the customer. But whatever it takes, AI can prompt the touch point, which is a support rep in this case, to have a better answer to a question that comes in”.

Praval sees lots of potential and many open questions, especially in the area of generative AI. It is about decisions, and decisions in context. “But what we're talking about the nuance of it, which is where the next gen of the AI would have to learn the pattern that we're talking about, and then not just have the insight but make inferences from it. And we'll have to see how accurate and how valuable that is. But I think we're talking about a fair few unknown unknowns in that path as of now. But yeah, it truly has it potential. And one thing that I strongly feel is, there's a lot of hype, obviously, about gen AI, and, and it works reasonably well in a consumer setting. I would say if you and I are doing some research, looking in for some answers there. But where gen AI lacks, at least today is having that business context.”

Part of the problem is poor integration. Looking forward, he sees this being mitigated by bringing all relevant signals into one system instead of only integrating different systems. “This whole idea of duct taping different products to to make something deliver, I think more and more companies are realizing that that phase is behind us. So, unification over integration is what delivers the best of context and brings in the best of context. And that in turn delivers the best of experiences.”

My point of view

Praval – and by extension Zoho – is touching some important functional topics here, even without going into the challenging areas of ethics and IP. With current systems, including the hyped generative AI “solutions”, the industry so far tackles only the low-hanging fruit. There are only few gen AI solutions that provide new capabilities beyond making existing ones more efficient.

This is partly because all AI solutions suffer from the same problem: data – or rather data quality. This is a result of the need for integrating far too many applications, which causes too much noise for AI to efficiently – even effectively – work. Unified systems – suites – are one way out of this dilemma. With an as clean as possible data base AI systems have a far better chance to identify underlying patterns and to with that not only present analyses but to also provide better next best actions and explain its “reasoning” or inference. In Zoho speak this is a decision intelligence that can provide value for more than just narrow use cases.

And a decision support engine that works across a wide variety of use cases and that offers explainability is what is needed beyond the hype around generative AI.What t

During ZohoDay24, Zoho amongst other topics, gave some insight into how the company looks at AI. Raju Vegesna presented Zoho’s AI vision and progress. Additionally, I had the opportunity for a one on one with Zoho’s director of AI research, Ramprakash (Ram) Ramamoorthy. If you want to listen and watch the interview, you can do this here.

Both represented a vision that is refreshingly differentiated from the current hype with everyone and their dog talking like large language models, LLMs, are the everything one needs.

Well, let me tell you: They aren’t. But let me come to this point later.

In addition to not every language model being created equal, and typical for a hype, there is still too much talk about the technology itself, whereas in the words of Raju and Ram the best AI implementation is “when the customer doesn’t know they are using AI but finds value in the output”. This resonates very well with me, as one of my beliefs is that the customer shouldn’t care about the technology that is used to achieve the desired outcome, within some constraints like legality, ethics, and efficiency, of course.

Zoho is a technology vendor with a focus on business applications. So, Zoho quite quickly realized that consumer type AI that e.g., helps with spell checks, or nowadays research, suffers from two fundamental flaws: lacking privacy/security and accuracy when it comes to business applications. Both violate some of Zoho’s core tenets, namely their pursue of privacy and business applications that offer a lot of value to the customer. Take the example of improving one’s writing – for some years now, this is offered by Zoho Writer, instead of making the user copy and paste potentially sensitive information into an external tool with potentially questionable guardrails.

Similarly for the use of generative AI, or AI in general, in a business context. LLMs, as they are offered by vendors, regularly miss the business context. This means that their responses to a prompt are less than accurate – they are hallucinating. AI works best in context, in this instance in a business context – your business’s context. This requires a process called grounding, or RAG – retrieval augmented generation.

Or in plain words: It requires business data and analytics on it.

In Zohos words, this is contextual intelligence with decision intelligence adding on to it. Decision intelligences creates recommendations and/or actions based upon the findings. Raju Vegesna used a revenue and profit timeline to make this point. Business analytics shows a drop in profit. AI is perfectly able to identify this anomaly. But the real questions are why and what to do. The contextual intelligence correlates this drop to other factors, it gives a diagnosis, and the decision intelligence gives a recommendation on what can (or should?) be done, based on the diagnosis.

Which brings us back to the topic of large language models not being a silver bullet. Language models, especially multi modal ones, can increase the accuracy of e.g., the OCR of an expense application significantly. Ram and his team observed that, in the case of a receipt only having a merchant logo instead of its name, the accuracy of identifying the merchant “has gone up from somewhere {around] 75% to 98%” because of using a multi-modal models. Even a small language model already increases the OCR accuracy, e.g, by not confusing the cash given with the sales price. Scale this up. This increase in accuracy translates to a far higher efficiency of the expense process. And now look at the end-to-end expense process. It starts by taking a picture of the receipt and then extracts and infers different information. On submission the expense report is analyzed for anomalies and policy violations. This multi-step process can be efficiently handled by a variety of specialized models, from narrow models (OCR), small language models (text extraction and inference), mid-sized models (anomaly detection) to LLMs (policy violation. 

Another example is in the legal field, using electronic contract signature as an example. Again, a multi-step process is followed that leads from phishing detection via a narrow model, possibly translation via a small language model, summarization, and anomaly detection via a medium language model and finally the development of recommendations if the suggested document is not compatible with policies.

Similarly, a warranty process in customer service.

All these AI supported processes have two things in common: the user is oblivious to the use of AI and secondly, the orchestrated interaction of smaller models makes the process more resource efficient without endangering process efficiency. 

Ram maintains that “Even though these models are trained using energy intensive CPUs, we are able to run the inference on CPUs, and that that means I'm contributing positively to the enrollment. And again, I'm not passing on the GPU tax to my customer. So, find out models that work for different levels. I mean, it's okay, three to 5 million model is more than enough to identify the name of the merchant on a receipt. And a 50 billion model is enough to rephrase legal statements given its specialized in that domain, that domain and the context is what is helping us to lower the model size and thereby keep costs in check.”

In my opinion, Zoho is right in saying that AI models are going to be commoditized. Leveraging AI in a business application is already now a table stake.

But what about AI and ethics, especially when it comes to training models? After all, Zoho takes a strong position on privacy.

Ram extends this into the realm of training models. “We strongly believe that your data is your data. And it should work just for you, meaning you cannot use your data to train a third party's AI and in turn, you get some subsidized software. And that's not how it should work. Because that's your business secret, right? That's your secret sauce. And you don't want it to be taken over by some model. So, we have set clear policy privacy guidelines, where we have built foundational models that are independent of any of our customer data. And then they are fine tuned to individual customers.” He insists that “if I'm going to use the model that is trained on one company, for their competitor, who has just signed up for CRM, I'm basically selling their data. We don't do that. So, all of these privacy policies intact, and we also have a strong policy towards making our AI bias free.”

On top of this, Zoho makes sure that there is no PII used for learning. Drift is controlled via regular systems audits using split systems. 

Zoho promises to do it right, which is, as Ram says, the promise that every vendor should give and keep.