It’s a question that doesn’t get a lot of attention – but it should! I mean, when you really look at it, there are far too many brands that fail to develop deeper relationships with consumers, content to make the occasional transaction every two to three years. They believe that the best technology or the best product wins. But it often doesn’t. They fail to understand the many other factors at play, including the role of frictionless user experiences. Far too many are “one-trick ponies” with little relevance in consumers’ lives. They lack a clear understanding of how to build a brand – how to build from strength – and underestimate the impact of brand loyalty and brand advocacy have on the cost of customer acquisition.

The U.S. Consumer Device Market: A Picture is Worth a Thousand Words

The brand map below captures the factors behind each brand’s performance.

Although Samsung users trust the Samsung brand more than Apple users trust the Apple brand, Apple has successfully developed deep relationships in a much more profound way than Samsung, enabling it to outperform Samsung in revenue. Each brand’s position correlates strongly with its share of the U.S. consumer device market, which was worth more than $600 billion in 2022.

However, the news is not all bad for Samsung, which far outperforms all other brands in depth of relationship and revenue. The large group of low trust, low relevance brands is vulnerable; they must expand their relevance or suffer the consequences of their shallow, transactional relationships.

Trust is Essential for Successful Relationships with Consumers

The level of trust in your brand determines its ceiling – its potential. Trusted brands get bought, used, and recommended more. They eventually end up with a higher proportion of loyal customers and are more profitable.

As important as trust is, relevance is essential for developing deeper relationships with consumers. A high level of trust that does not translate into greater relevance is unfulfilled potential – the problem that plagues Samsung.

Deepening the Relationship – Building Increased Relevance

The process of deepening the relationship comes from empathetically asking questions like: What needs are we currently meeting? How well are we meeting them? What are their pain points? How could we solve them? What things would they like to do that they cannot do very well or at all today?

Contrast that with the selfish mindset of the inwardly focused company (e.g., how can we convince consumers our offering is still relevant?) Rather than starting with the needs of the consumer, this approach starts with the company’s need to find nails to hit with its hammer.

This does not apply only to device markets. It applies to subscriptions and services too. Take the example of the market for wireless service, broadband internet, and entertainment services like audio and video streaming services. The right questions include:

What is changing about the way consumers communicate and entertain themselves?
What would deliver a better experience?” 

The answers require an understanding of the user journey, identification of pain points, and solutions that users find truly intuitive and helpful. It does not necessarily mean offering a bundle. Based on the needs and expectations of consumers today, it may very well mean offering excellent services, with great support, without a contract at prices that consumers consider a fair value (not cheap, but not exorbitant, and without built-in prices increases).

Charging a premium for a no contract relationship or forcing consumers to take on a two-year contracts to get the best price cuts against the grain, making consumers predisposed to change at the first chance to get the treatment they really want. Capturing new customers is much more costly than servicing them well and keeping them.

Two Kinds of Relevance

Relevance is all about a brand’s ability to meet consumers’ needs. There are two main kinds of relevance – market relevance and personal relevance. The more needs a brand meets, the greater its relevance. The more consumer spend that you compete for, the greater your market reach, and the greater your company’s market relevance. Brands with high market reach have a larger audience and the greatest market potential.

At the same time, not every brand is designed to play in the entire marketplace. Brands like TikTok and Tesla target specific subsets of consumers and drive dynamic and healthy businesses as a result. The more relationships consumers have with you, and the deeper those relationships, the greater your personal relevance to them. The deeper a consumer’s relationship with your brand, the more time or money they spend with you.

Trust That Does Not Translate into Relevance is Unfulfilled Potential: A surprising number of brands have a high degree of trust but fail to develop greater relevance, limiting their growth potential. There are many reasons for this.

Too Many Companies Focus Too Narrowly on One Set of Consumer Needs: Consumers use multiple form factors, moving easily from one device type to another, as they create content, shop online, or game. Since consumers own multiple form factors, brands that offer the form factors they own or intend to buy have much greater relevance than those who do not.

Perhaps there’s a fear that younger consumers, who’ll define the future market, are different. After all, to ensure your brand thrives going forward, it’s important to align with the needs of Millennials and Gen Z. The data is clear. These generations are also multi-device, with Gen Z especially driving the trend towards smartphones and PCs.

Merely Marking a Presence Does Not Establish Relevance: It’s not enough to mark your brand’s presence in a segment. Consumers must see sufficient acceptance by other users to merit their consideration. They need to see people they know using a product or service. They need to be able to ask someone they trust about their experience. Word of mouth is important. Two to three percent market or segment shares are insufficient.

Samsung, Google, and Microsoft are among the many brands guilty of “marking a presence” in certain device segments without playing to win. They do not spend enough money on marketing to generate sufficient awareness, sales, and a big enough user base to snowball into something bigger. Take the example of Samsung in the PC market. They have small single-digit share, yet their tiny user base is extremely satisfied demonstrating its upside potential. They are the only brand other than Apple to offer smartphones, tablets, and PCs giving it the opportunity to make a compelling counter case to Apple. But few consumers are aware of this fact.

Too Many Companies have a “White Space” Mentality: The statement “we need to find white space” is more often a reflection of a company’s desire to find a place in the market where there is less competition and higher profit margins than it is a desire to truly meet consumer needs. True white space opportunities are extremely rare and cannot be the core focus of building a brand.

Companies that are successful at finding white spaces are more often those that have a strong presence in the market and see a convergence of consumer needs that produces a white space opportunity. In other words, white space opportunities are born from an understanding of current needs, pain points, and an understanding of how to address those needs in a uniquely new way. When that is missing, the white space can best be described as a black hole.

Take Microsoft Duo as an example – a device that’s too big and unwieldy to be a phone (despite the company’s positioning to the contrary) and too small to be an effective tablet. A device whose killer use case was never apparent. With the same resources, Microsoft could have invested in the marketing necessary to make Surface a household name with universal awareness. Or, if it were willing to commit to the development of another form factor, it could have developed a true smartphone, especially given LG’s exit from smartphones.

Past Failures Lead Brands to Refuse to Embrace Obvious Opportunities: The reason is clear. Microsoft obviously thought, “We’ve tried making ‘traditional’ smartphones before. We’ve tried and failed terribly. We can’t do that again.”  Certainly, there is no clamor to bring back Windows Phones. Now let me ask you… does Microsoft’s past smartphone failure preclude it from developing a compelling, even premium Android smartphone now or in the future? Consumers have a high level of trust in Microsoft. The smartphone is not going away any time soon. A move to Android would be a powerful message. Superior functionality that allowed for smoothly going from a Microsoft smartphone to a Windows PC and an Android tablet would be welcome.

Apple’s Consumer Dominance: Deep Relationships

Now, I’ll be the first one to tell you – there is no reason for Apple to dominate the worldwide consumer tech marketplace as much as it does. In 2022, Apple accounted for 41% of worldwide smartphone, PC, and tablet revenue. The explanation is twofold. Apple has developed deep relationships with consumers and the other brands are largely conceding the market to them, focusing primarily on the enterprise market and unwilling to do the slow, hard work of building a consumer brand and business over time.

By far the most interesting part of Apple’s recent earnings call was the company’s spontaneous mention of the enterprise market and the increasing role of BYOD devices. Apple’s Consumer momentum results in “pull” for its devices, as small business owners and ITDMs are also consumers who bring their experiences to work with them each day. One way or another, it’s time to pay more attention to the consumer market. It may be the best way to slow the potential impact on the small business and enterprise markets.

Interested in learning more about IDC’s Future Consumer research and how it can help your business?

Customers are a critical component to the business.  Without them there is little reason to pursue the services or products that are developed, built, and sold by organizations. Customers bring in the revenue needed to maintain and refuel the business, as well as to help it grow and scale. 

So, why is it that only 12% of enterprises connect customer data between departments, using it to make the customer journey better?  In many cases organizations just don’t understand several factors which can enlighten the organization to improve the customer’s experience:

What data is available
Value of the data
Insights the data can bring

Data is everywhere and that includes customer data. In the digital world there are many channels the customer may use to purchase the goods and services that will be provided.  In fact, IDC finds over 30+ (and growing) different customer channels may be used to bring a seamless, dynamic, and personal customer journey, enabling the customer’s purchase capabilities.  

Each of these channels holds customer data or moments that matter.  Each moment that matters means the customer is at a decision point from research and investigation of product and services, to comparison, selection, purchase, delight of the purchase, and the experience throughout the process. This includes the customer service that is experienced throughout the journey, and after the purchase and aftermarket services. 

The data comes in many forms from structured to unstructured. In the Voice of the Customer (VOC) 3.0 it is inferred data. In our IDC article, Voice of the Customer Programs: Where are they today? More Importantly, where are they going?, we defined inferred data as information where the insights like customer intention, sentiment and emotion are inferred from observed customer data. In other words, it is looking at a customer’s actions and being able to understand their satisfaction from those actions, without needing to do anything like fill out a survey or post a review.

Inferred data can come from written or visual data where the analytical tools provide insights into the customer’s sentiment.  Sentiment is captured by looking at user behavior such as rage clicks. Rage clicks are the fast clicks on multiple selections of one part of a web page indicating a user is not getting a response when they think they should.  Analytics can quickly determine a rage click, and action can almost immediately be taken today, to engage the user. In addition, body language, verbal tone, facial changes, and eye motions, also try to determine sentiment.

VOC programs must be where the customers are, listening to direct, indirect and inferred customer feedback. These programs need to listen, analyze and act on the customer feedback available. Customers want to know that not only are brands listening, but also acting on their feedback. This builds customer trust and loyalty.

Listening to the data and then analyzing it allows the organization to act on the customer data.  Customer data platforms (CDPs), generative AI, and low code/no code options are quickly becoming part of the VOC programs as they help analyze the data.  As the data is analyzed, the organization uncovers insights that can be converted to a plan to act differently, bringing more delightful and positive customer experiences to the critical moments that matter. 

Ensure that support, customer success, sales and customer service agents learn how to understand VOC sentiment data and customer engagement history to use them to improve their engagements with customers.

The digital world is reshaping the way organizations build the customer’s journey, using technology to compete and gain competitive advantage, while conditioning the customer moments that matter positively.  Moving beyond the 88% of organizations that do NOT use customer data means a shift is required. Organizations must shift how they use customer data with innovative technology to listen, analyze, and act upon the information. 

It is clear, one cannot wait to embark into the customer’s digital world as the organization’s customers, revenue, growth, and scale depend upon this data.  IDC suggests organizations capture, listen, analyze, and act now to bring out better customer experiences. 

At IDC, we are seeing increased business focus on accelerating decision velocity – an interest that extends well beyond the traditional domain of IT executives. With greater frequency – and greater urgency – business leaders, including the C-suite, are now taking ownership of initiatives that address decision velocity.

In my recent presentation at IDC Directions 2023, I presented a video clip of a pit stop in a 2019 Formula race in Brazil to illustrate the impressive result of high-velocity decisions by highly trained professionals – four tires changed in under two seconds (yes, the Formula One driver – and his supporting Red Bull team – won that race). While most businesses must contend with less controlled, more volatile environments, Formula One offers an extreme, but nevertheless instructive example, demonstrating how these teams extract and analyze data from every race and apply those learnings to improve performance.

The Impact of C-Suite Turnover

In a recent IDC survey, when we asked organizations about impactful changes over the last few years, they cited a familiar set of challenges, including changing (remote and hybrid) work models, increased regulation, and higher than usual employee turnover. But, 30% of organizations also cited changes in the C-level executives.

Why is that important?

Well, turnover in the C-Suite is often a leading indicator of new, impactful projects and initiatives, as new executives ask questions, address problems, and propose solutions. New projects, particularly ones that involve data analytics and artificial intelligence, will typically kick off within six to 12 months of a major change in the C-Suite. And, as these projects are implemented, business leaders must contend with the three familiar Vs of data – velocity, variety, and volume.

There’s more data than ever before… it’s faster, and it’s more varied. Data is now increasingly distributed geographically, across hybrid and multicloud environments, creating additional complexity. The only way to thrive in these highly volatile and uncertain environments is to improve decision velocity. How organizations do that requires an understanding of what it means to make big decisions and what the decision-making process looks like.

In the 1970s, Colonel John Boyd, USAF, defined one of the best-known decision processes, the OODA loop, which was used by fighter pilots in its earliest iterations (Colonel Boyd later became a consultant to many of the largest organizations in the U.S.). While the OODA loop can become quite complex as additional feedback loops are added, the fundamental model relies on four steps – observe, orient, decide, and act. The goal is to move through the loop as quickly as possible, but there is still a need for control. In fact, that’s how we define decision velocity – the delicate balance between speed and control

Some organizations understand this delicate balance; many don’t and don’t invest accordingly. We found that digitally mature organizations assign importance to both speed and control with twice the frequency of less mature organizations.

We can see the impact on economic output over time, as data dense products grew to comprise two-thirds ($17.3 trillion) of U.S. GDP in 2022. In contrast, low data density products and services grew by only by one trillion since 2006, suggesting that data and decision velocity will be the key differentiating factors for achieving desired societal goals, such as addressing issues of aging populations, food supplies, and energy usage.

And, we have made progress. In 2002, $290 billion was spent on big data and analytics – that is, data warehouses, links, data integration and machine learning and other related tools and supporting infrastructure.

Are we seeing a strong return on that investment? I would argue that we’re not quite there, yet. Challenges persist, as many organizations still struggle with data silos, data quality, data analysis, and ultimately getting data to the right decision makers.

There are also technical challenges. Today 77% of organizations say that data intelligence is a challenge, which translates to a lack of data lineage and an inability to understand where data is and who has access to it. IDC analysts Stewart Bond and Phil Goodwin discussed these challenges, data logistics, and the need for a unified control plane in greater detail in their respective IDC Directions 2023 presentations.

Only 26% of streaming data is analyzed in real time before it makes its way to a repository, such as a data lake. In keeping with the Formula One theme, this is analogous to driving around a track at 200 mph only to make a pit stop that takes two hours – rather than two seconds – to change the tires.

We see this happen time and time again… and the real impact takes the form of data waste. 34% of surveyed executives have indicated that they often don’t get around to using the data they receive, obviating all the investment in data capture, analysis, cleansing and presentation. And, then there’s the issue of data decay. Half of 1,000 organizations surveyed globally indicated that their data loses value within hours; 75% of respondents said it loses value within days. If organizations don’t have the required decision velocity to act, they are simply wasting the data.

Decision Types

In addition to control, organizations must factor in three decision types – situational, scenario, and portfolio. IDC’s research shows that 13% of use cases require situational decisions to be made in seconds. Examples include financial services companies assessing and blocking potentially fraudulent credit card transactions. Just about two-thirds of use cases revolve around scenario decisions, which can be made within a few hours. For a financial services company, a scenario decision may involve the review and approval (or rejection) of a loan application. Portfolio decisions – 22% of use cases – require the most time. Examples include hiring a new chief risk officer or making an M&A decision.

While each decision type requires organizations to maximize decision velocity, the approaches across decision types can differ significantly. As Dr. Hannah Fry, Professor of Mathematics of Cities at the Centre for Advanced Spatial Analysis at University College London has discussed, sometimes organizations need to be data-driven; other times they need to be data-informed. It’s critical to understand the difference because many low-level situational decisions can be data-driven and fully automated. On the other hand, it’s highly unlikely that we’re going to see a ‘ChatCEO’ capability to automate executive-level portfolio decisions any time soon.

We don’t see enough of this nuanced view, yet, but organizations have been making progress toward improved decision velocity – 62% say that automation across the decision-making workflow has increased and 64% reported that metadata is growing faster than raw data, suggesting that data is being organized and structured in ways that make it available for consumption.

So… we’re moving in the right direction.

Just about 40% of organizations are prioritizing budgets for streaming data analysis as they invest in decision velocity. But, organizations need to do more to keep the flywheel of innovation in motion. They need to invest in new technologies and assess new categories of opportunities, including decision intelligence, a category of solutions that empower organizations with greater situational awareness while helping them recognize alternatives, assess risks, and perform simulations and to provide decision makers with better recommendations. And, despite calls for more self-service capabilities, what organizations really want is just the opposite – full service. They want data that they can use with their tools and applications when needed.

A third category is knowledge networks, a new generation of knowledge management tools that are becoming critical to improving decision velocity. As part of this focus on knowledge networks, organizations need to address the issue of humans learning from each other, especially in an environments characterized by high employee turnover. We’re seeing a lot of progress in this space, but a lot more needs to be done.

Finally, organizations must continue to invest in enterprise digital twin technology, bringing it from traditional design and product engineering, where it has been successfully deployed, to the business domain. Easier said than done, but this should be the goal, as organizations leverage decision velocity to improve overall enterprise intelligence.

Interested in learning more about IDC’s Future of Intelligence research? Download our eBook, Building Enterprise Intelligence: A Framework.

This blog assumes fundamental understanding of generative AI concepts, terms, and technology.

Over the past few months, generative AI has taken the technology world by storm. As per CB Insights, 2022 was a record year for investment in generative AI start-ups, with equity funding topping $2.6 billion across 110 deals. Whether it is content creation with Jasper.ai, image creation with Midjourney, or text processing with Azure OpenAI services, there is a generative AI foundation model to boost various aspects of your business.

How Data is Processed

Whether you decide to train your proprietary foundation model or fine tune and prompt tune an open source/commercial foundation model, or a domain specific foundation model as an ISV solution, it is critical that you take the necessary steps to mitigate potential data security and privacy risks.

Some of the commonly asked questions are:

Can we provide sensitive information to Large Language Models (LLM) either through fine-tuning or prompt augmentation?
Will LLMs reveal my information?

A common concern is that an LLM might ‘learn’ from your prompts and offer that information to others who query for related things or be used to further train the LLM. Or the data that you share via queries is stored online and may be hacked, leaked, or more likely, accidentally made publicly accessible.

It is critical that you take the necessary steps to mitigate potential data security and privacy risks.

Types of Data

Let us first delve into the types of data a generative AI service or application provider may process if you decide to fine tune and prompt tune an existing foundation model, using the example reference diagram below:

Checklist for Mitigating Risk with Generative AI

Now that you have a baseline understanding of how your data is processed, you need to review how your data is retained and what custom controls are available to you. Basically, you need to understand how your data sharing as part of fine-tuning or prompt augmentation is managed. To aid with this, here is the checklist of questions you need to ask the provider and ensure adherence to your corporate policies to mitigate your data security and privacy risks:

Does the provider support your ability to opt-in/opt out of including your data for training their model?

Subject to your use case and for proprietary data, make sure you opt out or, at a minimum, that the training data provided by you is only used to fine-tune your model and not used by the provider to train or improve any of their models.

Can you delete your training and validation data and your fine-tuned models?

Make sure you can.

Does the provider process the data [prompts, completions, and generated result] to train, retain or improve their models?

Subject to your use case and for proprietary data, make sure you opt out, or by default the output data is not used by the provider to train or improve any of their models.
Do not submit any private and/or proprietary data to any public LLM as prompts.

Are the prompts and completions data stored temporarily? If “yes”, how long is the data stored for?

Make sure it is stored securely in the same region you operate from and is logically isolated with your subscription and API credentials.
Make sure it is no longer than “N” number of days that is aligned with your corporate policies.
Make sure it is encrypted, at best, by providers’ managed keys.

Is the data shared with partners?

Many providers anonymize the data while sharing it with partners. Make sure you are fine with this specific to your use case, this may not be enough for your corporation.

Who has access to it from the provider?

Make sure only the authorized employees have access to it.

How is the data used by the provider?

It may be used for debugging purposes in the event of a failure and/or investigating patterns of abuse or misuse.
The content filtering models are run on both the prompt inputs as well as the generated completions.

Can you opt out of content filtering and logging?

If your use case involves the processing of sensitive, highly confidential, or legally regulated input data but the likelihood of harmful outputs and/or misuse is low, check with your provider if you can opt out of content filtering and logging.
Once the provider approves the opt out, make sure they do not store any prompts and completions associated with the approved subscription for which abuse monitoring is configured off. In this case, because no prompts and completions are stored at rest, no provider employees have access to your data, even for limited time.

Does the provider log model usage and support traceability for your compliance needs?

Make sure they do.

If your use case necessitates the creation of a proprietary model, which means you train your own model, you could either train the model in-house or partner with the model provider that supports training a new proprietary model. If partnering with the model provider, in addition to opting for VPC for training and hosting the model, make sure you follow the above noted checklist where relevant and ensure adherence to your corporate policies to mitigate your data privacy and security risks.

You can subscribe to IDC’s Artificial Intelligence Software and Strategies Research and hear more on generative AI during our latest webinar.

The Exciting Year Ahead for IDC Spending Guide

IDC’s Spending Guides, standard-bearers for extensive and exhaustive technology market intelligence, are undergoing major updates starting in July 2023 and continuing into 2024. The updates will bring deeper analysis capabilities to market through enhanced visibility into sectors, sub-sectors, and industries—increasing the total number of industries covered from 20 to 28. The update also includes a planned size segmentation refresh which will add additional granularity of insight.

IDC and Spending Guide: Years of Proven Success for Market Intelligence

For decades, IDC has been the tech industry’s standard for consistent, benchmarkable market intelligence in six continents. Designed around a global overarching technology taxonomy that includes detailed segmentation by geography, industry, size, use case, and other metrics, IDC’s market intelligence is one of four integrated “intelligence” domains IDC maintains for the tech world.

Since 2016, IDC’s Spending Guides have delivered this market intelligence in a collection of over 25 market forecast products which detail planned technology spending across a variety of different market scenarios. Created leveraging a combination of demand side and supply side data and validated by our in-country industry and technology analysts, IDC provides the most accurate forecasts available. This intelligence enables tech vendors, investors, and partners, as well as others that serve and observe the tech world, to deeply analyze the size, trajectory, and growth opportunities over time in highly specific markets and segments.

Industry Enhancements Coming July 2023

Starting in July 2023, IDC Spending Guides will become even more powerful with the restructuring of industry coverage within the IDC taxonomy.

Starting with the Enterprise and SMB by Industry and Software and Public Cloud Services Spending Guides, IDC will update industry coverage across the entire product line, adding clarity to segmentation-scope and introducing coverage in eight new industries. At the same time, this update maintains the fundamental methodologies of IDC’s existing taxonomy, keeping companies whole, SIC based, and aggregating sub-industries with similar IT needs or along the same value chain. This major undertaking will eventually expand to all Spending Guide products, including our 3rd platform use case-based guides, with the goal end-date of 2025 H2.

Spending Guide: The Year in Review

IDC spent a year researching existing industry taxonomies. In this time, we conducted a study of economic standards, client inquiries, tech vendor industry taxonomies, and competitive taxonomies. The conclusion was that a restructuring of the industry segmentation within IDC’s taxonomy was necessary to keep clients informed to the degree expected in the modern world. The resulting update, which is summarized in the figure below, brings more granular industry scope and enhanced industry definition to IDC’s taxonomy.

Figure 1: IDC’s new industry segmentation

While this update impacts all industries to varying degrees, the benefits will be felt most notably in the following industries: Discrete Manufacturing, Process Manufacturing, Professional Services, Wholesale, Personal and Consumer Services, Resource Industries, Retail, Transportation, and Insurance.

Why Updating Industry Coverage Matters

With more thoughtful categorization and granular segmentation, Spending Guide users are better equipped to identify, analyze, validate, and action questions and insights specific to the industries they serve.

From a usability perspective, a more narrowly defined industry scope means less “noise” in the data, increasing the ability to find relevant information while decreasing the time it takes to do so. With less time spent in analysis, there is more time for execution. Analysts and strategists can also track trends across more industries, validating assumptions while understanding nuances of buyer behavior and if / how these change in adjacent segments. Executives and their teams can better inform strategic decisions and justify tactical actions, such as acquisitions, partner selection, resource allocation, territory and campaign planning, and more at granular industry levels.

For example, a leading technology provider targets small to medium size DX technology vendors with specific industry focuses for acquisition. Once acquisition targets are identified by the team, they need a financial case for the acquisition to present to the executive team and the board, thereafter. Using the Worldwide Digital Transformations Spending Guide, the company aligns the offerings of target vendors to specific DX use cases across industries of note. From this vantage, they can narrow down the high-value targets and make their own internal assessment on technology fit and support. With enhanced industry granularity, the acquiring organization can further streamline their process and reduce the time to prioritize high-value targets against those less aligned.

Deploy Spending Guide Industry Insights With Other IDC Solutions to Maximize Impact

When used in combination with other IDC Data & Analytics products, the industry update makes it so Spending Guide can be used to further investigate global assumptions, insights, and trends in granular industry scenarios, including those characterized by different geographic, technological, size, use case, and other segmentations. For instance, total available and serviceable addressable market figures identified IDC Black Book can be refined and clarified at the industry-level in the Spending Guides, helping prioritize product development and acquisition plans. Conversely, Spending Guides can serve as an effective starting point for market sizing that is further validated at global levels in Black Book.

Looking at it from another perspective, industry-related assumptions, insights, and trends identified in Spending Guides can be further refined and actioned with the data in other IDC data products for different purposes, like:

Tracker®, which provides vendor performance metrics in over 150 markets, driving competitive intelligence and helping to position products and align go-to-market plans.
Channel Partner Ecosystem, which provides profiles and performance details on technology partners, helping articulate partner go-to-market strategies.
Wallet and Services Contracts Database, which provide segmentation of individual company budgets and services contracts (including contract value and renewal dates) for technology buyers and vendors, helping craft strategic marketing initiatives and pinpoint sales activity timing.

What’s Next on the Spending Guide Product Roadmap?

IDC’s Spending Guide products and the Data & Analytics team are dedicated to continuous improvement as part of ongoing operations. While the magnitude of the industry expansion for Spending Guides is evident, IDC remains committed to other areas of improvement in the next 12 months, including the granularity of size segmentation in the taxonomy.

With a target release of January 2024, IDC will introduce new company size details to the Enterprise and SMB ICT Spending Guide taxonomy. The change will result in nine ranges of company size across five size segments which are further categorized and grouped. See the figure below for full details on this size segment update.

Figure 2: IDC’s upcoming size segment refresh

There is a lot of reason to be excited about the product enhancements coming out of IDC Spending Guide in the year ahead. To learn more about the solution, market intelligence generally, or to request a demo please visit the Spending Guide section of IDC’s Data & Analytics Solutions page.