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An Origin Story

An Origin Story

What exactly makes any data valuable?

In my previous blog, The Future of Data Protection, I started to look at what it is that makes data valuable. I think this is the best way to frame the future of data protection. In each application, we must know where the value in a piece of data lies if we are to protect it.

There are so many different things that might matter about a piece of data and thus make it valuable:

  • Authorship, including the authority or reputation of the author(s).
  • Evidence, references, peer review, repeatability and so on.   
  • In the case of identifiable (personal) data, the individual’s consent to have the data processed.
  • Details of the data collection process, ethics approval, or instrumentation as applicable.
  • The algorithms (including software version numbers) used in analytics or automated decisions.
  • Data processing system audits.
  • Sometimes the locality or jurisdiction where data has been held is important.
  • As data is added to, who were the contributors, and what were their affiliations?
  • The release of data to the public or specific users may need specific approvals.
  • What rights or conditions attach to released data as to further use or distribution?

A lot of this boils down to origin. Where did a given piece of data come from? 

This simple question is inherently difficult to answer for most data, because raw data of course is just ones and zeros, able to be copied ad infinitum for near zero cost.

But several interesting approaches are emerging for telling the story behind a piece of data; that is, conveying the origins of data. These are some of the first examples of the solutions category I call Data Protection Infostructure.

Proof of personhood

How can we tell human authors and artists from robots?  Or new bank account applicants from bots? The rise of Generative AI and synthetic identities has driven the need to know if we are dealing with a person or an automaton.

Identity crime is frequently perpetrated using stolen personal data. To fight this, we need to know not just the original source of identification data but also the source of each presentation.  In other words, what path did a piece of important data take to get to where it needs to be used?

A sub-category of Data Protection Infostructure is emerging around proof of personhood.

Delivering this sort of assurance in a commercially sustainable way is proving harder than it looks. Only recently, an especially promising start-up IDPartner Systems, led by digital identity veteran Rod Boothby was unexpectedly wound up.

Content Provenance

A conceptually elegant capability with plenty of technical precedents is to digitally sign important content at the source, to convey its provenance. That’s how code signing works.

The Coalition for Content Provenance and Authenticity (C2PA) is developing a set of PKI-based standards with which content creators can be endorsed and certified with individual signing keys. C2PA will be implemented within existing authority and reputation structures such as broadcast media licensing, journalist credentialing, academic publishing and peer review.

Similar proposals are in varying stages of development for watermarking generative AI outputs and digitally signing photographic images immediately after capture, within camera sensors.

Confidential Computing

The path taken by any important data today can be complicated.

For example, powerful AI-based image processing is now built into many smartphone cameras; the automatic manipulation of regular photographs can be controversial, even if it’s not intended to mislead.

And the importance of Big Data and AI in all sorts of customer management and decision support systems has led to strengthened consumer protections (most notably in the European Union’s AI Act) to provide algorithmic accountability and explainability.

So, data now flows through complex and increasingly automated supply chains. Signing important data “at the source” isn’t enough when it goes through so many perfectly legitimate processing stages before reaching a consumer or a decision maker. Data may be transformed by AI systems that have been shaped by vastly greater volumes of training data. Moreover, those AI models may be evolving in real time, so the state of an algorithm or software program might be just as important to a computation as the input data was.  

And we haven’t even touched on all the cryptographic key management needed for reliable signing and scalable verification.

For these reasons and more, there is an urgent need to safeguard data value chains in their entirety — from the rawest of raw data, before it leaves the silicon, through all processing and transformations. We are approaching a point in the growth of computing where every change to every piece of data needs to be accounted for.

Such a degree of control might seem fanciful but the Confidential Computing movement has the vision and, moreover, the key technology players that are needed to fundamentally harden every single link in the data supply ecosystem.  

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Enterprises leading with AI plan next genAI, agentic AI phases

Enterprises leading with AI plan next genAI, agentic AI phases

Enterprises that are leaders in artificial intelligence are evolving quickly and laying out plans for their next phases. The next phase of genAI deployments revolve around integrating AI into the business, optimizing processes, agility and scale. You should watch these companies closely for the road ahead.

When I think of leading AI companies, I'm usually looking for the buy side of the enterprise equation. These companies are deploying AI as well as integrating it into their business. For instance, Rocket spent years deploying its data platform and working with machine learning and AI models. When generative AI hit the enterprise, Rocket had all the DNA to move quickly and leverage the technology to deliver better customer experiences.

Intuit was in a similar position and saw its bets on data science and then AI work. It has an Investor Day coming up that'll feature a lot of strategy and AI. JPMorgan Chase is another player that has moved to the next phase of its genAI strategy. The upshot is that JPMorgan Chase has moved to integrating AI operations into its various business units.

JPMorgan Chase: Digital transformation, AI and data strategy sets up generative AI (download PDF) | JPMorgan Chase: Why we're the biggest tech spender in banking

With that backdrop, it's prudent to think of customer stories and case studies as living documents. Consider Rocket. Constellation Insights documented Rocket's use of AI and strategy (PDF) in May. Since then, the company has named a new Chief Technology Officer and outlined its next steps in its AI progression. Perhaps the biggest takeaway from Rocket is that there are no overnight AI successes. Rocket has spent $500 million over the last five years on Rocket Logic, its proprietary loan origination system that uses AI to streamline income verification, document processing and underwriting.

Simply put, AI isn't merely lift and shift. The work is never done.

Rocket CEO Varun Krishna said the company's "super stack" of technology is critical. "What makes our super stack special from a technology perspective? We have created a groundbreaking new architecture. It's data powered, humanity driven, and self-learning. This engine fuels every aspect of our ecosystem and we've spent years perfecting it. It's now driving efficiency, velocity and experience across the company," he said.

Heather Lovier, Chief Operating Officer at Rocket, said the technology stack has been applied to every process at Rocket. In underwriting, the company has " taken complex processes and the categories of income property asset, and credit and broken them down into hundreds of thousands of discrete tasks in order to apply automation and AI." That approach also expands into the experience layer. Lovier said it's a game of inches and continual improvement with AI. "We've been working on AI long before it was sexy," she said.

During Rocket's investor day, CTO Shawn Malhotra, who started in May after being Head of Engineering and Product Development at Thomson Reuters, laid out the plan. First, Malhotra outlined how Rocket's previous technology decisions left it in good shape for genAI. "AI is not new. It's been around for a while and it's been powerful for a while. Rocket delivered its first production AI models back in 2012," said Malhotra. "We now have more than 200 AI models in production adding real value for our business and our clients. It's important to remember that AI is not a what, it's a how that enables powerful outcomes. Those outcomes are what matter to our clients and our business."

For Rocket, those outcomes revolve around using AI to enhance the "entire homeownership journey" with more seamless processes that are efficient and personalized. This strategy means AI touches every customer touchpoint. Rocket works with Amazon Web Services and Anthropic to meld third-party models and proprietary systems. "We're always going to focus on our secret sauce and our proprietary AI models, but then we're going to deeply partner with the world's best to great large language models that are across domains," said Malhotra. "We're not going to just buy this from them. We're going to co-create."

GenAI is also about productivity. Rocket said that it has reduced closing times for refinancing and home equity loans by 30% to 45% faster than industry benchmarks. The company also uses AI to process more than 300,000 transcripts from client calls weekly to extract data points.

Malhotra said the next phase of AI for Rocket is about accelerating experiences in the homeownership journey. GenAI will be deployed more in marketing automation, customer interactions and servicing. Rocket also plans to double its use of AI in software development and customer-facing operations.

"We're helping our developers produce more code automatically in the last 30 days. We estimate that 27% of our code over a quarter was written by the AI. That's a good start, but soon, we're going to be doubling the number of developers who are using these tools," said Malhotra.

AI-powered chat across all digital platforms was another key theme. Malhotra said models will be improved to blend human empathy with models.

He outlined the following AI services that are being enhanced or rolled out.

Rocket Data Platform: Malhotra said that data platform needs continual improvement with new features such as ID resolution, more data ingestion and democratization of access with natural language.

Rocket Exchange: A platform with 122 proprietary models powered by 6TB of data that allows the company to accurately price mortgage-based securities in less than a minute.

Rocket Logic Assistant: A personal assistant for mortgage bankers, Rocket Logic Assistant transcribes calls, auto-completes mortgage applications, and extracts key data points. This helps bankers focus on relationship-building and customer service rather than manual tasks. Rocket plans to expand its capabilities and automate more of the mortgage process.

Pathfinder and Rocket Logic Synopsis: These tools use generative AI to assist customer service agents by reducing the time needed to resolve customer queries. This has led to a 68% reduction in the time to resolve client requests.

Rocket Navigator will be built out so non-technical team members can leverage AI and contribute to product development.

Rocket said it is increasingly focused on developing AI tools to improve the midfunnel experiences with a focus on personalization and long-tail engagement. These tools will deliver bespoke guidance, educational content, and tailored recommendations to potential homebuyers over time.

The company also hinted that it wants to develop AI-powered real estate tools to help homebuyers search for properties, get insights and automate more of the home-buying process beyond mortgages. This effort is likely to be built on data from Rocket Homes and other properties in its ecosystem.

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The Future of Data Protection

The Future of Data Protection

I recently released my latest Constellation ShortList™ for “Data Protection Infostructure” . In this blog post and the next, I drill into what these sorts of solutions are seeking to do.

“Data protection” in many parts of the world is simply synonymous with data privacy.  For instance, the General Data Protection Regulation (GDPR) is a data privacy regime; it is very specifically about limiting the flow of personal information, a special class of data.  Further, Europeans tend to refer to privacy regulators as Data Protection Authorities, and the conventional privacy compliance tool is the Data Protection Impact Assessment.  

So “Data Protection” in Europe has a narrow, even technical, meaning.

Now, regular readers will know I am a huge fan of regulating the collection, use and disclosure of personal information. A great many problems of the digital era, from Surveillance Capitalism to Deep Fakes can be tackled by more strenuous and creative application of regular privacy rules featured in most legal systems.

Nevertheless, there is more to data protection than privacy. Privacy by its nature is restrictive. I’d like to spark a broader discussion about what it is about data that needs protecting. We could begin by asking, What is it that makes data valuable?  

First let’s review how security professionals think about data.

Conventional wisdom in data security is that threats to information assets can be viewed in three different dimensions: Confidentiality, Integrity and Availability (or “C-I-A”). Different asset classes can be stronger to different degrees in any of these dimensions. For instance, patient information needs to be especially confidential but medical records also need to have high availability if they are to be useful at a point of care, and high integrity (error resistance) to keep patients safe.

On the other hand, historical employee records — often retained for legal reasons for seven years or more — might not need to be highly available, so archiving on magnetic tape or even paper is worthwhile to keep personal data away from hackers.

But the “C-I-A” perspective is missing so many of the richer dimensions that make data valuable.

Consider three current hot topics:

  1. Identity Theft is generally perpetrated by data thieves who acquire personal data and use it to impersonate their victims. The problem is that automated identification systems can’t tell if personal data is being presented by the individual concerned or by an imposter (see also my analysis of data breaches).
  2. Deep Fakes are images or audio that look or sound like real people but have actually been synthesised artificially (typically by Generative AI) instead of recording the real thing.
  3. And speaking of AI, there is increasing interest in the history of how models are trained. What sort of training data was used? Was it broad and deep enough to be free of bias? Were people in that data aware that it would be used to rain AIs?

Availability, Integrity and Confidentiality are not useful ways to think about safeguarding data in any of these cases. Think about how most LLMs today are rained on "public domain" data. No matter where you stand on the question of creators' intellectual property rights, we would all agree it's too late to make the artworks in question confidential. 

Instead of "C", "I" or "A", stakeholders across these and similar examples may want assurances that:

  • personal data submitted by a purported individual opening an account or applying for a job was really presented by that person
  • creative works used to train an AI model have been licensed for use
  • medical data used to train a diagnostic tool has been audited for bias and came from patients who gave informed consent
  • the science behind a diagnostic tool has been properly evaluated, and
  • software used to generate a particular result was version controlled and can be wound back to an earlier release if bugs are found.

From one digital use case to another, there will be different aspects or qualities of the data concerned that make the data fit for purpose — or in other words, valuable.

In my next blog, I will focus on one such dimension that’s missing from the traditional C-I-A picture: the origins of data.

 

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Constellation Energy, Microsoft ink nuclear power pact for AI data center

Constellation Energy, Microsoft ink nuclear power pact for AI data center

Constellation Energy said that it is restarting Three Mile Island (TMI) Unit 1 and will sell about 835 megawatts of power to Microsoft for AI workloads.

In a release, Constellation Energy said that the deal with Microsoft is its largest power purchase agreement. TMI Unit 1 is adjacent to TMI Unit 2, which shutdown in 1979 and is being decommissioned. TMI Unit 1 is an independent facility and hasn't been impacted by Unit 2. Constellation Energy bought TMI Unit 1 in 1999.

Nuclear power has seen a resurgence in interest as the electricity grid strains under data center workloads due to generative AI. In addition, technology giants are trying to find a way to power AI workloads and hit carbon neutral goals. Simply put, hyperscale cloud nuclear deals may become more commonplace. In January, Amazon Web Services acquired a data center attached to Talen Energy's nuclear plant. Talen Energy will sell power to AWS.

Generative AI driving interest in nuclear power for data centers

In recent weeks, the drumbeat behind nuclear power as a solution for AI data center needs has picked up. Oracle CTO Larry Ellison talked up nuclear-powered data centers on the company's earnings conference call. Meanwhile, OpenAI CEO Sam Altman is chairman of Oklo, which is touting mini-nuclear reactors that can scale with data centers.

Constellation Energy has advocated for co-locating data centers at nuclear power plants as a way to build out infrastructure for AI quickly.

For Constellation Energy, the Microsoft deal is big. Five years ago, the power company shut down Three Mile Island Unit 1 due to poor economics. In a statement, Joe Dominguez, CEO of Constellation Energy, said "powering industries critical to our nation’s global economic and technological competitiveness, including data centers, requires an abundance of energy that is carbon-free and reliable every hour of every day, and nuclear plants are the only energy sources that can consistently deliver on that promise."

Earlier this week, Microsoft announced a partnership with BlackRock, Global Infrastructure Partners and MGX to invest in data centers and energy infrastructure to power AI.

Speaking on Constellation Energy's second quarter earnings call last month, Dominguez said:

"We're continuing to do well in our discussions and negotiations with data center companies. The simple fact is that data centers are coming and they're essential to America's national security and economic competitiveness. And it's absolutely critical that the U.S. not fall behind it. Time is of the essence. We simply cannot wait years for the data centers that are going to bring transformations."

Dominguez added that sustainability is also playing a role in nuclear and renewable energy demand:

"We're seeing more evidence of our customers, not just data center customers, but customers as a whole, evolving in their sustainability journeys from buying annual clean energy products to starting to match their hourly consumption with clean energy."

Bottom line: Nuclear power is likely to play a big role in the AI factory buildout.

 

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How Iron Mountain built its InSight DXP on MongoDB

How Iron Mountain built its InSight DXP on MongoDB

Iron Mountain recently announced that its InSight Digital Experience Platform (DXP) will use MongoDB Atlas and MongoDB Atlas Vector Search for document processing, workflow automation and information governance.

The company's InSight DXP is a product that highlights Iron Mountain's overall transformation. The company has been best known for its shredding and disposable of physical documents, but has expanded into digital services, document lifecycle management and even leasing data centers.

Iron Mountain's revenue for the first six months of 2024 was $3 billion, up 13% from a year ago. Storage rental revenue was $1.8 billion for that time period with services revenue of $1.2 billion.

We caught up with Adam Williams, Vice President of Global Platforms at Iron Mountain, to talk about use of MongoDB and scaling. Here's a look at some of the key takeaways.

The move from physical documents to digital transformation services. Williams said Iron Mountain has housed and digitized many physical assets including microfilm, microfiche and physical assets. "Customers were then asking us 'can you digitize those for us and management them as well?'" said Williams. "That's where we got into content management and repositories."

The decision to build instead of buy. Williams said that Iron Mountain initially used a bevy of vendors in enterprise content management and for content services platforms. "We work with large banks, large insurance companies and government agencies that have petabytes of data. For us to be able to store that data we need to have a very elastic and scalable database," said Williams. Williams said Iron Mountain decided to build on MongoDB's NoSQL and Atlas platform and consolidated a search vendor it was using.

"We were looking for the ability to do more at scale but without the overhead," said Williams, who noted that vector search was also critical. "We entered genAI, so we were able to take Mongo Atlas, search, the vectorization and then those SQL capabilities, and instead of using a patchwork of vendors, we're able to work with a single vendor. But more importantly, we only move the data once. We don't have to move the data three different times to three different places and then pay for it in three different places."

A multicloud architecture. Given Iron Mountain's footprint across the Fortune 500, many customers have data residency requirements. Iron Mountain supports AWS, Microsoft Azure and Google Cloud, but has stringent multicloud requirements that led to MongoDB instead of separate databases on each cloud, said Williams.

A platform to accommodate multiple customers. InSight DXP's front end and user experience is built by Iron Mountain. Williams explained:

"We have a modern user experience that we built. What's really needed is a user experience is customizable. At Iron Mountain, we deal with a lot of different industries. Our technology strategy with the platform that we built was actually designed out of frustration with all of the different industries that we work for. I found myself as a leader having to work with energy in the morning, healthcare in the afternoon, and then the next day I'm talking to financial services. You end up in this never-ending cycle where you can't be enough to please everybody. By going into platform approach, we're able to create customizable experiences for the different industries. With our updated user interface, we bring workflow, which we've built, and a connector strategy. We also bring in our data processing capability with the intelligent document processing that we've built."

Williams added that Insight DXP can pull in the data whether it's digitized by Iron Mountain or already digitized, transform, extract the metadata and move to workflows with information governance and management. GenAI can be used to gain insights from unstructured data.

The Insight DXP platform includes intelligence document processing with traditional AI and machine learning and internal models. Content management with the ability to absorb documents, edit and manage data and apply governance such as retention schedules and disposition of assets. Customers need audit ready compliance for all documents and data.

Business models. Iron Mountain charges a subscription for InSight DXP based on number of workflows, document times, number of users, overall size of assets and other metrics. Iron Mountain pays MongoDB based on consumption.

GenAI. Williams said Iron Mountain is building out its genAI product and the challenge is offering those services affordably. "We want to make sure we understand all of the finances behind genAI and we're getting better views from MongoDB on the costs by the different services and the different SKUs," said Williams. Security compliance with genAI is also critical for regulated industries and MongoDB has the controls to understand what data is being shared, he added. Iron Mountain is generally using Microsoft OpenAI since most customers are comfortable with it and there's a data privacy guarantee. 

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Amazon launches Project Amelia for third party sellers, powered by AWS

Amazon launches Project Amelia for third party sellers, powered by AWS

Amazon is rolling out a series of generative AI tools for third-party sellers built on Amazon Web Services' models and Amazon Bedrock.

The announcement of the tools highlights how Amazon can showcase AWS and its advertising capabilities with its commerce platform. Among the tools of note is Project Amelia, which serves as a personal selling expert.

Project Amelia, which is in beta and built on Amazon Bedrock, understands a seller's business and provides recommendations, insights and information. Amazon said Project Amelia will ultimately be able to recognize opportunities, diagnose issues, offer tips on how to grow revenue and optimize inventory and act as an agent to take action autonomously.

Project Amelia can:

  • Answer knowledge-based questions with personalized information and best practices.
  • Serve up sales data, traffic information and business metrics.
  • Resolve issues and take actions.

Other genAI tools being rolled out to Amazon sellers, typically small and midsized businesses, include:

Generative AI content and product listings. Amazon said it is upgrading capabilities with its genAI product listings so sellers can create multiple listings and workflows at the same time. For instance, a seller could upload a spreadsheet with listing details and Amazon genAI would create titles, bullet points and descriptions.

A+ Content, which gives brands on Amazon the ability to develop custom content, images, carousels and iterate. A+ Content is available today in the US with more countries rolling out by the end of the year.

Personalized product recommendations on the website and in Amazon's shopping app. Amazon uses genAI to tailor recommendations based on time, topic and season. Amazon said:

"By analyzing product attributes and customer shopping information, like preferences, search, browsing, and purchase history, we leverage a Large Language Model (LLM) to edit a product title to highlight features that we believe are most important to the customer and their current shopping activity. To ensure these titles accurately reflect what matters most to each individual, another LLM, known as an evaluator LLM, challenges and improves the results, assuring customers see the best possible product information."

GenAI video ads. Amazon is leveraging genAI and its ad unit to create video ads. Amazon launched Video Generator, which will give sellers Amazon's ad tools to create video based on a single product image for free.

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T-Mobile, OpenAI to launch custom AI-driven IntentCX system

T-Mobile, OpenAI to launch custom AI-driven IntentCX system

T-Mobile said it is custom building an "intent-driven AI-decisioning platform called IntentCX with OpenAI. The partnership with OpenAI was part of a broader AI push by T-Mobile.

The partnership between OpenAI and T-Mobile is notable on a few fronts. First, the OpenAI deal with T-Mobile highlights the frenemy arrangement with Microsoft. In addition, the T-Mobile-OpenAI deal comes a day after Salesforce CEO Marc Benioff ranted against do-it-yourself approaches to generative and agentic AI.

Under the OpenAI and T-Mobile deal, the companies will combine the wireless carrier's data on intent, customers and sentiment in real time and couple it with OpenAI models. The two companies said they will continual to collaborate to develop AI services and tools as part of a multi-year improvement.

According to the companies, IntentCX will do the following:

  • Apply understanding and knowledge to every interaction.
  • Resolve issues and take proactive actions.
  • Maximize T-Mobile customer journeys and ultimately provide a blueprint that can be commercialized to other industries.
  • Personalize service with a combination of humans and digital agents.
  • Navigate multi-threaded conversations across languages with context.
  • Take action autonomously where needed.
  • Tap into OpenAI's latest models to improve engagement.

IntentCX will be trained in T-Mobile's customer care and team of experts business processes as well as billions of data points from customer interactions.

In a statement, T-Mobile CEO Mike Sievert said:

"IntentCX is much more than chatbots. Our customers leave millions of clues about how they want to be treated through their real experiences and interactions, and now we’ll use that deep data to supercharge our Care team as they work to perfect customer journeys."

For OpenAI CEO Sam Altman, the T-Mobile deal could pave the way for industry-specific platforms leveraging its models. T-Mobile said it is actively testing IntentCX with implementation on tap for 2025.

Along with the OpenAI deal, T-Mobile announced the following:

  • A technology partnership with Nvidia, Ericsson and Nokia to leverage AI in mobile networking infrastructure. The AI radio access network (RAN) will combine T-Mobile's 5G expertise with Nvidia AI Aerial platform and networking knowhow from Ericsson and Nokia. This effort will increase speeds and leverage AI to bolster gaming, video, social media and augmented reality.
  • A plan to enable AI customer experiences and grow market share. T-Mobile aims to reach 12 million 5G broadband customers by 2028 using excess capacity, a more than 50% increase from its previous target of 7 to 8 million customers by 2025. Service revenue growth is expected to have a compound annual growth rate of 5% between 2023 and 2027. AI and efficiencies are expected to boost adjusted Ebitda and cash flow. T-Mobile projected $18 billion to $19 billion in adjusted free cash flow in 2027.
  • T-Priority, a network slice for first responders.

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HubSpot launches Breeze AI agents, Breeze Intelligence for data enrichment

HubSpot launches Breeze AI agents, Breeze Intelligence for data enrichment

HubSpot launched Breeze, the company's AI that features copilots and agents, and Breeze Intelligence, which enriches data and identifies the best prospects.

The tandem of Breeze and Breeze Intelligence, outlined in HubSpot's Fall 2024 Spotlight, are designed to aid marketing, sales and service teams drive revenue.

Breeze includes Copilot, an AI work companion, four Breeze Agents focused on content, social media, prospecting and customer interactions, and more than 80 additional features. HubSpot also announced agent.ai, a marketplace for agents that has Agent Builder. HubSpot said agent.ai has more than 47,000 users and more than 1,700 builders.

With Breeze Intelligence, HubSpot is looking to solve multiple pain points. Breeze Intelligence includes data enrichment via more than 200 million buyers and company profiles and contacts in HubSpot Smart CRM. Buyer intent is designed to find the best prospects and form shortening tools to increase conversion by adding information Breeze Intelligence already knows.

"Our customers acquired a bunch of point solutions, and they are struggling to integrate all of those data points into actionable insights and to control the cost, and they're looking for much better ways to be able to get all of that information into a single platform," said HubSpot CEO Yamini Rangan on the company's second quarter earnings call. 

HubSpot also updated Marketing Hub and Content Hub with Content Remix for video, tools like Lead Scoring and Google Enhanced Conversions and tools to measure impact with Marketing Analytics Suite.

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Scotts Miracle-Gro Tests CX to Optimize for Extraordinary Growth | CR CX Convos

Scotts Miracle-Gro Tests CX to Optimize for Extraordinary Growth | CR CX Convos

Don't miss the latest CR #CX Convo with Constellation analyst Liz Miller! ?

Liz sits down with CX leaders Jessica Bailey and Hailey Schraer from The Scotts Miracle-Gro Company 🌱 to unpack how customer experience strategies and partnership with UserTesting drive omnichannel #revenue for the business. 📈

Topics include...

📌 UserTesting techniques (07:41)
📌 Championing the voice of the #customer (10:10)
📌 Balancing education and purchase focus (14:10)
📌 Importance of quick insights (18:45)
📌 Challenges with prioritization and balancing requests (25:22)
📌 Direct-to-consumer efforts and lessons learned (32:05)

For more info, read the full customer story by Larry Dignan ? https://www.constellationr.com/research/scotts-miracle-gro-how-cx-and-usertesting-drive-omnichannel-revenue
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Access the full video transcript: (Disclaimer: This transcript has not been edited and may contain errors)

Music. Hello, everyone. I am here with two amazing, amazing researchers, digital experience architects, CX visionaries. But you know what? More importantly, I am here with two people who know how to put in the work, and so I'm super excited to be joined by Jess and Haley from Scott's Miracle Grow. Should we just go ahead and jump right into it? Should we just go have some fun? Let's do it. I love it. I love it. I first. I want to get into it. Tell me about yourselves, like, what's your background? What's your role at Scott's Miracle Grow? What got you from, like, those first jobs, the job where you're at right now? And what do you do?

Yeah, I'll kick it off. I am coming up on my sixth year anniversary at Scots. Sort of started out pre covid Going into the office every day, but, yeah, it's been a really, really interesting journey. My background is a lot of user experience, more on the evaluative usability research side, but also information architecture design, and I had some really, really amazing experiences early in my career, working in the consulting space, working for big in house organizations. And I think I really found a love for being able to be in house and see the work all the way through, and and to see and well, and to see some of these really long timelines all the way through to and we have several projects that we've been working on that span multiple years. And it's such a blessing to be able to see that, as opposed to having to, you know, fall away after a little bit and hopefully come back, but it's been really great, and I've been able to bring on amazing people like Haley, who have made us just even better. I'll toss it to you. Love that?

Awesome. Yeah, I've been at the company for I just hit my five year in May, so I kind of owe my entire UX career to Jess. UX is where I started UX at Scots, and I've kind of been here ever since leading and building some sort of a team here so we can kind of build empathy across the organization with how our consumers use our digital products, use our physical products, etc. So excited to be here in chat a little bit more.

I love that you just said that you're building empathy across the organization for how people use your digital like, I love that statement. I kind of want to turn it into a t shirt. So I'm not gonna lie, if you somehow see that pop up someplace, just

make sure you send me one. Just send me Yeah, I'm just gonna send you one. There'll be, like, a whole like,

we'll Venmo each other, it's gonna be fine. Okay, so let's talk just a little bit about kind of what's going on and what are some of the big projects, what are some of the big initiatives when you start to think about experience, and especially when you start to think about digital experience, where are some of the directions, and where are some of the key areas that not only you want to see the organization start to go in, but what are those big projects you guys are working on right now that you can share? I don't want to give away the secret sauce, but some good stuff. Yeah, I

think a big thing for us is really honing out on education across all of our platforms. We need to kind of educate our consumers about what they need to do in their lawn, they need to do in their gardens, so that they feel kind of confident and comfortable tackling these projects, and the more confident and comfortable that they feel, the higher likelihood that they're going to purchase from us, so that they're going to revisit our sites, come back and learn more. And so I think that's that's always been a big push for us, and I think we just continue to find and drive ways to really push education, push all of the tips and tricks, so we can teach you how to be the best yarder and gardener that you can I

love, that I love, but that really brings up kind of a very specific need of being able to identify fairly well ahead of time, I would think that kind of earlier in the content and the experience development process, what the burning needs of education are like, what are those things that are top of mind? What are those things that people want to talk about? Like, you know, like me, I like, I want to talk about how to make the fat squirrel in my yard go on a diet. Like, I don't want them to stop eating. But, like, right? But so when we everyone has a different place that they want to start learning. So what are some of the things? What are the tools, the techniques that you guys are using to really, not only get an understanding of that, what maybe a little ahead of time, but then also mapping and under. Understanding if that's really what your audience wanted to experience. Like, is it like? I might sound as I think it's cool, but I might also be navel gazing.

Yeah, I think we're always looking at bringing a lot of cross functional input together. So I know folks on our team, even just last week, had this really amazing conversation with our sales our field sales team to understand like, hey, when you run into folks that when they're in the aisle of a garden center, you know, what are the questions that they're typically asking you, and how can we create a bridge for them? We have folks on the team that focus really deeply on weather and the impacts of weather on our category, and people shopping and trying to just participate in our category overall. And so how did they see weather trends impacting us? And what are some solutions that we can create there? Haley is highly involved, and runs all of our conversion rate optimization and AB testing. And so we're able to get some really clear answers on like, what kind of content is performing better and driving some of those key actions we're focusing on SEO, what are people searching for? The terms that we're looking at. So we really do try to bring a lot of different sources together to get at what are the burning questions that people have, and also, how can we help meet them in the middle? You know, we don't want to come in and be you know, we are the expert, and we know all overlords. Yeah, no, yeah, yeah. Like so your problem? Could your tomatoes be more could you have a bigger bounty or a bigger harvest? Yeah, maybe Squirrel. Squirrel is your big problem right now. So solve that one for you and make it more likely that you're going to put in another, you know, another seedling next year, and maybe two seedlings next year, because you really want to make it happen, then that's a success for us.

I love that. I love that, you know, listen, as a long term marketer, I feel like I should say recovering marketer. I think that's a recovering marketer. And, you know, been at agencies, you know, worked very closely with, you know, art departments, and I've been through that process of, right, like you, you're like, Oh, I gotta build a website, and I'm gonna go and do this, and it's gonna be amazing. And here, here's my, you know, here's my mood board, and here's all of my, like, you know, here's my site maps, and here's all of my research, all this. And then you go and build it, and you kind of forget that all of that research, all of that information that you poured your blood, sweat and tears in, even if you were looking at all the SEO research to make sure all of that foundational search was there, you kind of forget that the testing is to keep going. You know that you kind of have to constantly be working on that care and feeding. I know that you guys do a lot with user testing, so I'm curious. Can you share some of the examples of how you're using these testing tools, right, whether it is something like SEO, whether it is that research on the back end, whether it is AB testing, or whether it is these qualitative panels? Can you share a little bit about what you're doing, and then where that starts to slot in, when you start to think about kind of the life cycle of engagement for each of your brands.

Yeah, user testing has been a great partner for us, I think, for the past three, four years. At this point, we use them in every way that we possibly can. So we do a lot of like, post launch we do a lot of pre launch usability. We'll do some, like, moderated interviews, where we're getting really in depth and nitty gritty about how people are kind of shopping that aisle and what their triggers are for them to go out in their yard, their yards and gardens. I think most recently we we recently ran a study on a new kind of platform that we're looking at going out with and wanting to get some pre launch research with it, to understand what are the pain points that they're having, or what can we expect once we once we release this, or what are some hypothesis that we can pull from this? And then now we can take those and go in and do testing post launch. So now we can say, hey, we're lacking education here. How can we then tackle that post launch and come up with some different testing ideas and different designs where we can really pool and show that quant data of We saw this in qual. These are, this is what we heard from our consumers. And then now we can test against it and say, okay, hey, these two variations smashed it. We really got the consumers what they needed from these lenses. And so we use user testing a lot when it comes to triangulation, and starting to find like our big common themes amongst our participants, and then going out talking to our analytics team or doing AB testing, to really marry that quant, that qual together so we can come up and with the best insights we can

I. I love that. I'm gonna ask like a okay, because you said something, and now my brain went someplace, and so that's where the question's gonna go. But I think part of the problem a lot of times, or at least, I mean, I can remember being at those tables and it sometimes it can feel like the, you know, those data kids came in and told me my baby was ugly. I don't want to listen to them anymore, right? Like it feels like that sometimes. What are some of the things that you guys do like, really, to champion the real voice of the customer by triangulating this quant and the qual. You guys really are sitting at a very interesting intersection of where that true customer, user reaction is coming in. It's not you saying, Well, I think people didn't like this button. It's literally someone being like, I hated that button. You're like, whoa, like, there's the end. But you also get the reason behind that. You get the richness behind that. How you know, how are you guys finding the insights and the information that you're sharing and triangulating move like upstream, so that it's not just I didn't like that button, but it becomes really rich insight for even senior executives and business strategy to say maybe we need to make a different decision, because if education is pushing us here, maybe our product has to shift there too. Or maybe this is a different opportunity. Are you seeing what you guys do take a different stream than kind of just sitting in that digital optimization space.

Yeah, I think there's a few ways that we're seeing that happen. We have a team that is really,

like respectful, I guess,

of data, and not a lot of defensiveness at all. And so I don't know, we're very lucky, I think, but we've not really had a situation where it's like, well, you're just saying that because you don't like it, or that's your personal like, purple, yeah, you know the power of a highlight reel, right? The power of video, the power of having a video of eight people saying, I'm confused. I'm not sure what, like the the cycle on that would be, or I'm confused. I don't know how I would use that. Can't you can't really rebut that at all. So we have teams that are really, really oriented to improving and optimizing and being willing to be wrong and work in a really agile way of if you know, if we can optimize, if we can improve. And it's also the beauty of working in the digital space is that we're working on products that are constantly evolving. Much of the the physical products that we sell in stores and that we sell online, you know, they're always evolving too that time scale is a little bit longer, but our innovation pipeline is super rich, and we're really trying to just sort of help folks that maybe are a little bit more used to that longer time frame. Say, Hey, you know, when it comes to the digital experiences that we have. We're doing that same, you know, product life cycle. We're just sort of squeezing it down a little bit, and the R and D that you're doing to make sure that that product is super, super effective, and that it really does deliver on all of the promises. That's essentially what we're doing to just apply to a different, yeah, I love that. Jess, I'll emphasize

one of your points there too. Of like, highlight reels, we have shown that that like, sticks with our stakeholders the most. Like, there's nothing more important. And I think this is really how we've helped to, like, build empathy. Here is like, let's put this in front of user. We watch videos all day, so let's put this in front of our stakeholders, in front of our cross functional teammates and truly show them what these consumers and these participants are doing, so that they really have a chance to, like, put their put their feet in their shoes, and understand how they're experiencing it, rather than us just saying, hey, the button's ugly, like you can actually truly hear it. And it really does drive us and take us really far with our stakeholders. So that's

amazing. That's amazing, you know, and I think that it's so interesting in your business. I mean, so Scott's Miracle Grow. I think everyone thinks about it from a different entry point, right? Because the portfolio of brands is really expansive across the company. So you have a different mindset of buyer. You have a different skill level of buyer. You, you know, like you got me, like self professed urban farmer here in Los Angeles growing a tomato. I'm pretty proud of myself. But then you have, like, farmer, farmers, like you guys, have this really broad group of customers, but you also, I would imagine, that also translates into a very broad group of stakeholders internally, some of whom are looking for that transaction, right? Like, I need someone to buy that bag of soil. I need someone to be referred to a channel partner. Like, it's going to be very transactional. And Hayley, like you mentioned, you also have that stakeholder that's like, no, no, remember the education part? Like, remember how we lead people into the funnel? So those can often be in most organizations, a pretty. Significant push and pull when you're trying to figure out how to prioritize, whether it's testing, whether it's research, how quickly things get up. I think traditionally, in the world of research, you got to make choices, because things take a while. Can you kind of walk me through how you've developed best practices, whether it is how you prioritize the speed and scale at what you're able to work. But then also, how have you really been able to just, kind of, I guess, balance, like, balance everything. So now that you have the knowledge, how do you are you know? How are you able to then kind of make sure that, like, yeah, we're getting bags and carts, but we're also getting people to think about the next season?

Yeah, yeah. I think focusing on building confidence and motivation and having content that can get people excited to maybe start caring about their lawn for the first time in a while, or to take that very first step, that kind of upper funnel, I guess, like that kind of getting people to just start to care about something that maybe they've lost interest in, or they started a garden during covid, and they're, you know, maybe had one or two seasons of like, oh boy, but putting them back in, you know, if we can do that, then the rest of the pieces are much easier for a lot of our other partners that are in media and marketing and sales, like we've we've really primed them to say, like, you can hit a home run now, like, we've got folks in that right mindset. And so I think having that focus being on the upper funnel, we see that it's paying for things that come later down the road, and that that is the right place for the channels that we're focused on most often, that's the right role for it to play. I think that it can sometimes vary by brand. And there are certain in the portfolio that we have, there are certain brands that say we are exclusively here for education, if you want to compare products, because you're between two or three that have a similar benefit, that's why we're showing up for you in the digital space, or if you want to get inspired to take on a new project, that's why we're there. There are other brands and other sub brands where we see that purchase and that conversion being their top focus. And so we are having to shift a little bit to say, hey, you know you're really focused on moving people through the purchase path, getting that cart to move into checkout, getting that checkout to move into a purchase and so we do have to balance a little bit. And sometimes certain voices are louder than others when it comes to making that balance, we're We're a small team, and so we have choiceful and pretty strategic on how we take those on. But I think truly, in the last couple of years where we've tried to create efficiency in our martech stack and creating efficiencies with the platforms and tools that we're using, we're able to take an insight and apply it more broadly, a little bit easier than we have in the past. And so that rising tide can then benefit all of the brands, and the ways where it's appropriate to benefit all of the brands, and then note those that need, like a more specific focus or a more specific outcome, we can focus our attention really deeply there, while we've also brought everybody up to that same sort of baseline experience that we're able to identify love,

that how important is speed, when you start thinking about these tests, and when you start thinking about kind of how quickly you're able to like, let's look at, let's be honest, I remember back in the day this is, this is going to prove like, how old I am as a marketer. I worked in, you know, skincare, and I also worked in professional sports. And I can remember, like, remember good old, like, focus groups, right, where you were, like, let me get 20 people to sit over there in uncomfortable chairs with bad apple juice and maybe some of the butter cookies that came out of the tan, like, Jess, you come on. Like, we remember this, where you're like, oh, yeah,

I remember

projects. Yeah, I was

right, yeah. Like, can you? Can you? No, no, find the button. No, no, fine, not the real but no, you're not your shirt. But like, we remember those days, right? Like, I still have pain from those days. So how important is it now in this digital space? Well, we are expected to move so quickly, right? Everyone's like, Well, what do you mean? You can't just change it. Well, what do you mean? How important is speed and scale? Just one come before. Like, is it more? How do you start to look at those things? Yeah,

I think not to kind of poke out. These are. Testing again here, but a user thing super much helps us with that. Where our A our ability to test early and often and through the user platform, I think has been super helpful for us. I know we have stats somewhere in our case study of just like we've been able to grow exponentially and pull a lot of those consumer insights in much easier and much quicker, because we can test much faster. And I think that also just speaks to our consumer where we are not that like niche, very, very tiny, tiny business that has that very niche consumer. We want to talk to homeowners. We want to talk to those who are in their lawns and gardens, and so it's a little bit easier for us to then recruit for those folks, to be able to bring them in. So our definitely, our speed to insights has definitely increased. I know I can't speak to some of those kind of long days in a in a focus group room, but fun, Haley,

you don't want to. You don't ever want to. I

went to one, I went to one of my lifetime with Jess, and I will say, we work, we work much more efficiently and faster now. So

I did one on sunscreen, and it's like a day and a half I don't get back in my life just I'm just saying it was not good. Oh my gosh. Well, you know, there's two questions I want to leave us here and share with our audience, because I love these best practices, and I love the things that you guys are sharing, and I think that these are, I mean, you guys look at UX, you guys look at the digital experience, you look at this. But I think that so much of this can be actually applied, even in when we think about decision making for executives, and the velocity and the speed at which we need to make decisions about our businesses, we need to be able to bring in all the data, it bring in all the information, and to bring in that empathy, like you guys said, like, I love thinking about how you can apply that for a business leader and an executive, trying to move that concept of decision velocity forward. What are some of the things that you guys have noticed really make that difference? Like, are there any things that you would share with your colleagues and your peers across the CX ecosystem of if you could take this into your leaders three rungs up, you know, at the very top of the org, these are the types of things that we can and should be delivering specifically to accelerate decision making, anything that you guys would share?

Yeah, I think you talked about speed, and having that speed, we are obviously in a very seasonal business, right? Spring is our Christmas. Of a lot of other CPG and like online retail, like the holidays are, that's our spring, and I love it. Having an answer in spring for spring to impact spring is really important. And so that contrast from the olden days to now, where Haley has and can turn a study around in 24 hours, essentially. And I think that's where too I can take a very specific question that an executive has, a very specific or nuanced hypothesis that someone in leadership has, and we can run it, we can answer it, and we can get that insight back to them to inform it pretty quick, some of the things that we often are doing in the middle of that process that are sort of helping them massage their hypothesis too. Of like, well, you know, I think that people don't care about weather and how it impacts their seeding project, their grass seed project, you know. And so we're saying, Well, is it really about weather, or is it about temperature, or maybe about how long it's going to take to see results, or whether they have to water that much. And so we're trying to help understand, sort of the boundaries of the question that we're trying to solve or identify, at least, like the rough shape of the hypothesis, maybe that somebody has. And then we can come at it really iteratively, and say, we asked that question. Here's the response that we got. And we can ask two, three more questions too, to say, well, actually, hey, you know, they talked mostly about watering. Watering was their real concern. And so we can then spin it into another, spin it into another. There's a great way, I think, that we can help inform some of those leadership decisions that they're having to make, which are often so, so quick, in a way that we probably couldn't before.

I love that. That's I love that so. But then this leads to my net like almost second to last question. I'm going to count this as A, B to that last question I asked. Okay, just be honest, it's just between us girls. I mean, no one else is gonna see this, no one else gonna see this. Has the results of that, has the the ability to bring that research and that insight so quickly also kind of opened the floodgates, though, for folks across. Organization to be like, I too, would like you to test this where now all of a sudden everyone's like, I would like you to test blue versus dark blue, and you're like, that might not be a priority to date.

Oh yeah.

It's ironic to bring that up, because we just had an example of that this week, where, like, we are so dying to kind of get our research in front of people to share what we're finding and the insights that we have and build that empathy. But once you share it like it's not going to stop flooding in. And so I think we're right now trying to trying to figure out that balance a little bit too of and that's where like priority comes in, and seasonality, especially for us, comes into play, of how do we best balance these things within given seasons and within given priorities, because we want to help everyone. We want to bring all the insights and build as much empathy as we possibly can. But it definitely comes with, comes with the flood is coming in, and we got all the intake requests as possible. So everybody wants a new question. Okay, so

most important question I'm going to ask for you guys to share with our audience, like most important question, what should we be growing? Tomatoes, cucumbers, like, what like zucchini and I, we get along, but then when zucchini goes crazy and I now I got, like, 9000 bushels of zucchini and all the little thorns those who knew zucchini and cucumbers were actually painful. I did not know this when they were at just a whole foods. I did not know that I could get wounded by a vegetable. But there I am. Okay. So what are you guys growing? Come on,

I'm growing both tomatoes and cucumbers. There's this gorgeous, cute little variety of Bonnie Plants. It's called The Boston pickling cucumber, they're just little babies. And oh my goodness, do a refrigerator pickle at home. They're adorable. You always gotta, you gotta grow to the cuisine. You have to grow to what you like to cook, right? And so I love to cook Italian, and I love to cook like Mexican. And so

basil, you're like, three organo zone, like, you got a whole like, yeah, Herb era going on in the backyard,

yeah? But only plant what you're going to be excited to cook with. Otherwise, you're just that person that's shoving cucumbers on your neighbors and saying, Please, will you take this?

Yeah, looking alive. That was me.

I was like, I think I can grow bell peppers. I don't like bell peppers, yeah? So, like, I like them in one place, and that's a pizza, and that's it. Like, I don't and I only like one color. So it was just like, This is not Yeah, this is not good. I should not be doing this. Everyone got bell peppers. Like, I apologize now to everyone, pepper, but yeah, so. And then inside play, is it like, is there like, a favorite indoor like, do you guys have any indoor plant, like, preferences, or anything that you're telling us all go, like, Hey, y'all go out and get this, like, little fiddle thing.

I'm more of that indoor girly Jess. Jess takes care of the outdoors. She gets the basil. She makes me pesto. It's perfect. Love. I'm more of the indoor plant girly, where you unfortunately can't see any, but I'm a big window right in front of all the windows, but none behind me. Um, I love a good round tail snake plant. Let's see if I can drag her over real quick. I have one that is thriving. Yes, look at that guy. Yes. I'm a big, round tail snake or just normal snake. I don't discriminate. Love them all.

I'm the idea I got this guy because I read an article that sent me down a rat hole to a NASA study that showed how many allergen particulates this little guy can pull out of my air, and I'm like, every room will have one. Like they're everywhere in our house. It's ridiculous,

yeah, and they're also the easiest. So I'll

take it. I love it. I love it. Oh, my God. Thank you guys. Thank you guys so much. So many best practices, so many great stories. Congratulations, all the stuff you guys are doing.

Thank you. This has been great fun.

I'm gonna pause there, because that's where our editor is gonna go hack up the first 28 minutes of our conversation. I'm gonna ask you guys a couple more questions if you still have a little bit more time here. And I'm sorry, I think we may be going over. So I do apologize if you guys have to run. I totally understand, and I'm happy to send these over to you written. But if you don't mind, I'm going to ask just a couple more of these that Larry has sent over for the customer insights. That'd be okay, yeah, yeah, not terribly far over, but a bit yeah. I'm just going to ask you a couple ofthese important ones, and then maybe if there's a couple that Larry's like, no, I really need this, we might just email them back to you. But I think that the other stuff is awesome when it comes to metrics and how the team is being measured, how your kind of success as a group is being measured. What are your fight what are you finding that those business metrics for CX and for UX are, and how is that getting rolled up into other kind of business driving metrics you. Yeah, engagement is often what we're reporting on, and also brand perception and overall brand health.

We've gotpartners and siblings and cousins in our departments and organizations that are a part of email marketing, social media, and so all of those those channels are coming together to really drive like a personal relationship and engagement with our consumers. Awesome.

Love that. Love that.

So this is what proves that Larry listens to all the calls. Because here's the question, Has when what's interesting about Scott's is how CX directly aligns with the broader corporate strategy. Your CEO in the company's latest conference call cited market share gains and unit point of sale growth. How do you see your digital properties in terms of that direct sale? But also, how do your properties aid that in store buying process? Because, again, for a lot of organizations, they're run by two separate teams, but the insight that feeds the experience also comes from two separate teams. You guys seem very aligned across all of that.

Yeah, and I think when it comes to our websites, it kind of goes back to education and content. So if you are searching for something and you're in that aisle on what grass sheets should I use, or what fertilizer Should I put down now? And we intercept you on our own site. We'll educate the hell out of you, give you all the resources you need. And I don't care if you purchase from us or if we push you over to Home Depot or Lowe's or things to go buy on their websites or to go to their retail stores. It doesn't matter. And so I think that's really where we intercept and we grab them and build the confidence, educate as much as we can, and that's great. And if you go to somewhere else, that's also fine as well. So

I like that confidence seems to be a major currency for the that experience strategy for you guys that it's, you know, how are we making that exchange of content for confidence? And everyone can kind of bring that back into that virtuous cycle. I really like that. That's really well thought out. Okay, so Larry would like to know if you can share, how are those efforts to drive direct to consumer how's that going, and what are some of the lessons maybe, that you guys have learned through, whether it's user testing, whether it is through some of the other ways that you are testing and driving metrics. From that UX perspective, how are things going and where are you guys pushing and heading, let's say in the next year.

Yeah, direct to consumer, I think is so, so interesting. There are certain products for which direct to consumer, either through our own website or through our retailers websites. It feels so natural and such a wonderful fit. There are others where we have to do a little bit of educating on like, Yeah, this is something you can totally get it shipped to your house. And in fact, you want to subscribe to it, and we'll just send it to you every three months so you don't have to worry about it. So when you need to repot that plant and keep it from, you know, growing too far, yeah, that we can help make that really seamless and easier for you. But I think that also we're, we're helping drive to retailers and really smart ways helping, like Haley mentioned, intercepting folks and pushing them to retailers that are a good fit for them, and even if they've come to our site saying, Hey, you have a great store that's just down the road that can deliver this for You to do it today curbside, I think that. And you know, some of those buy online, pick up in store, kinds of solutions that are available are a really, really great match for our category, and with certain retailers being able to just add on those gardening supplies when you get your grocery order or when you're there to pick up the diapers and everything else that you need at the store. That just taking care of the weekends projects and getting all in one stop is a really great way for us to sort of show up for them.

So making it really convenient, making it easy, and giving that perception of ease when taking on those projects, I think is, is good, but yeah, growth and direct to consumer is it's going well. I think that we're leaning in. We have a really amazing omni channel and shopper marketing teams that are doing amazing things with some of these retailers and pushing the boundaries on a lot of stuff. I.

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Oracle's cloud, AI plans are a master class in co-opetition

Oracle's cloud, AI plans are a master class in co-opetition

Should Oracle's march toward fiscal 2029 revenue of $104 billion succeed it'll largely because it has managed multi-cloud deployments, co-opetition and triple wins for itself, partners and enterprises.

Oracle's CloudWorld news, led by a key partnership with Amazon Web Services (AWS) laid out a path that gives the database giant a path to expand its total market. Oracle and AWS--for all the cloud trash talk over the years--have multiple joint customers. Those customers really wanted the ability to use their AWS infrastructure and Oracle.

According to the Harvard Business Review, co-opetition arrangements usually start with a simple question: What happens if you don't pursue a cooperative opportunity? HBR: "If a cooperative opportunity is on the table, start by imagining what each party will do if it’s not taken. What alternative agreements might the other side make, and what alternatives might you pursue? If you don’t agree to the deal, will someone else take your place in it? In particular, will the status quo still be an option?"

Looking at AWS and Oracle through that lens makes the partnership a little less shocking. Oracle had already forged Oracle Database deals with Microsoft Azure and Google Cloud. That reality made a co-opetition arrangement between AWS and Oracle more likely. In addition, Oracle could theoretically lose a few cloud deals to AWS, but Oracle Cloud Infrastructure (OCI) is much smaller. If anything, AWS customers may try OCI due to the partnership. Meanwhile, Oracle sees its database business as its identity and a huge revenue stream.

Oracle CTO Larry Ellison explained during a Q&A on the company's Investor Day.

"I think the AWS deal increases the size of the market for us dramatically. I think it's interesting because we debated this a lot internally. What's going to happen when we partner with Microsoft, AWS and Google? Is this going to hurt our business and OCI? I think it's about the customer. Customers are using AWS and also like to use OCI. I'm going to have two cloud providers and one's going to be AWS and the other OCI. I think our OCI business has actually been strengthened. The goal is for us to be a leader in AI, if they can help us be a leader in AI, let's go work with them."

Clay Magouyrk, Executive Vice President, Oracle Cloud Infrastructure, noted: "I have conversations with customers now and they are incredibly encouraged by the fact that it doesn't matter which Cloud they choose. They can maintain their investment in the Oracle database. They can move that into the cloud and still get all of our best and greatest services. Everything that we have available inside OCI is now going to be available in our partner clouds."

Because Oracle missed the first cloud wave, it had to think differently for its database and applications. It focused on networking, file storage and autonomous databases. As a result, OCI turned out to be a handy platform for generative AI training. "It's nice having that late mover advantage sometimes," said Magouyrk.

Ellison also said Oracle had a little bit of luck in that it focused on database automation and innovation as competitors pulled back to chase cloud. Ellison said:

"We had made our name on the technology that we had pioneered relational database technology. I think most people thought we were going to lose that franchise because we only had it at OCI. We had a huge amount on-premises. There was a lot of skepticism whether all information would move to the cloud. In fact, no one else invested in the database during that period of time. Unless you have your data properly organized you can't use AI."

"There's no alternative to the original database. Microsoft SQL isn't a bad product and probably the second-best relational database out there. AWS is an incredible company, but they took open-source databases and moved them to their cloud. Google Cloud has BigQuery but it's not really a database."

Now that database business can play into gaining more OCI workloads, said Ellison.

Indeed, OCI is outlining illustrations that show better price performance vs. its partners, but as long as the AI workload pie is big enough the co-opetition arrangement is just swell. Co-opetition works great...until it doesn't.

Constellation Research analyst Holger Mueller said Oracle's success was built on a series of smart strategic moves. 

"What really delivered for Oracle was to build a cloud infrastructure for its database. It already had the hardware platform with Exadata - and now needed a flexible pod size that could scale to large data centers and the small government data center of a sovereign state of the size of San Marino. At CloudWorld, Oracle showed that the whole stack can run on three 'fridges' which is the smallest footprint of all cloud vendors. Next was to manage all these clouds--even at customer facilities the same way as Oracle Datacenters. That approach reduces Opex and gives CxOs peace of mind of a fully managed cloud stack on premises. Next the design point of a cloud for databases is very similar for a cloud for AI. And Oracle needs to be good at AI or the data from its RDBMS will flow into lakehouses and Oracle could be commoditized. Lastly, Ellison and Catz have spent on CAPEX like never before in Oracle history. And the small footprint works great to move the database to other clouds."

Larry Ellison is quite quotable

There were a bevy of quotes worth highlighting that didn't make the story. Here's a bit of Larry being Larry.

  • "I was just discussing with Safra (Catz) and every 10th sentence I say I feel like we're living in a science fiction movie. Then we get over it and go back and talk a little more about business."
  • "We'll be training robots to be nurses to do a variety of things in the hospital and at home."
  • "We're very involved with automating hospitals. We're connecting robotics in hospitals and connecting all of that data and machines on our IoT framework. We're good at IoT because we build applications."
  • "I went out to dinner with Jensen (Huang) and Elon (Musk) at Nobu in Palo Alto. I would describe the dinner as begging Jensen for GPUs. Please take our money. In fact, take more of it. You're not taking enough of it. It went well. The demand for GPUs and the desire to be first is a big deal."
  • "Another differentiator between us and the competition is that we do applications and infrastructure."
  • "Cerner will be another pillar for growth. You just haven't seen it yet. Healthcare is a multi-trillion-dollar industry and there are two giants: Epic and Cerner. We're used to competing with Microsoft, Amazon and Google. we're trying to automate the entire medical ecosystem with Cerner. If we can do it, we must do it. We have a moral obligation to do it. I can't believe that this industry that is so important and touches all of us doesn't have the very best technology."
  • "We could charge for a new AI module, but I think it misses the point. We're not trying to raise prices. We're trying to increase the volume of sales.
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