Silicon Valley Research Group Blog

AI GTM Differentiation: Winning teams aren’t using more data-they’re using different data.

Alan Nazareli — Sun, 10 May 2026 22:54:20 GMT

Most teams talk about AI GTM differentiation but train their models on identical data: public firmographics, generic intent feeds, and the same third-party benchmarks. That leads to similar predictive scores, lookalike audiences, and plays—so your AI quietly drives you toward the exact same buyers with the same motions.

If every competitor is optimizing on the same signals, the outcome is convergence, not advantage. Research on AI-first buyer journeys shows that 89% of B2B buyers already use generative AI as a top source of information, arriving with shortlists and pre-baked preferences. If your models train in the same data as every rival, they rank and prioritize accounts the same way. The only durable edge comes from signals that are proprietary—captured from real buyers interacting with your brand, product, and category.

Examples of unique buyer signals your competitors don’t see

A buyer signal is any data point that reveals how a buyer thinks, evaluates, or decides. The differentiator is not just volume of signals, but uniqueness. The more proprietary the signal, the harder it is for competitors to copy your GTM.

The secret is what we call the Qualitative Edge, data from in-depth interviews with decision makers in your target markets. Here are some sample specs and typical topics that yield best data points for our client research projects:

Sample audiences:

Technical buyers (engineering, IT, architecture)
Economic buyers (CIO, CFO, line-of-business leaders)
Your competitors’ customers

Sample topics:

Why deals actually stall late in the cycle
The internal narratives buyers use to justify decisions
Hidden objections that never show up in CRM fields
The emotional and political dynamics behind “no decision” outcomes

For a B2B SaaS company, the above signals derived from in-depth qualitative research could be additionally enriched with additional "data arsenal" such as telemetry data, specific configurations used by top customers, feature sequences that correlate with renewal likelihood, or support ticket themes that show looming churn risk.

Building a first-party data engine that powers differentiated GTM

To turn unique signals into an advantage, you need a first-party data engine that systematically captures and normalizes them. This means treating every customer touchpoint—marketing, sales, product, and research—as a sensor feeding a shared buyer-intelligence layer.

Start by inventorying current sources: CRM fields, marketing automation events, product usage logs, customer research, and recorded calls. Then define a minimum set of standard IDs (account, contact, opportunity, workspace) so data from each system can be joined reliably. Many CMOs are being asked to “do more with less” as marketing budgets hover around 7.7% of revenue (Everworker), so focus on the few signals with clear line-of-sight to revenue.

Next, add the qualitative layer described above-this is truly your competitive edge, ensuring your AI GTM models significantly differentiated messaging and content than your competitors. One downside of AI driven GTM processes is that vendor messages in many categories are already converging. This qualitative layer separates you from your industry's perceived "sameness". The problem pre-AI was that this type and volume of unstructured data was not easy to synthesize and extract value from.

Turning buyer signals into AI scores, segments, and triggers

Raw signals are messy. To be useful, they must be transformed into features your AI models and scoring systems can understand. That starts with hypotheses tied to revenue outcomes: which behaviors predict expansion, which patterns foreshadow churn, which phrases from buyers correlate with pricing sensitivity.

Convert those hypotheses into labeled datasets by tagging historical opportunities and accounts. For example, label deals that closed in under 60 days versus those that slipped multiple quarters. Feed in features like number of champion interactions, presence of a specific integration requirement, or depth of product activation. Modern buyer-intelligence frameworks emphasize stacking these features into a “5-layer” view of the buyer, from identity through decision criteria (Oden).

From there, you can build AI-driven scores: deal health scores, expansion propensity, or pricing-risk scores. Each score then fuels segments and triggers that tell your GTM systems what to do next.

Orchestrating signal-based journeys across your GTM channels

Differentiation shows up when your signals drive actions across channels, not just dashboards. A signal-driven GTM connects your CRM, marketing automation, sales engagement, and product to respond in real time to buyer behavior.

For instance, when product telemetry detects a new team hitting an activation milestone, marketing can automatically launch a tailored education sequence, while sales receives a task to explore multi-team packaging. If call analysis surfaces a new objection pattern in late-stage deals, you can trigger enablement to insert specific proof points or customer stories into follow-up sequences.

Research on AI-powered GTM playbooks shows that the most effective teams combine first-party data, AI workers, and real-time personalization to shift budget toward what actually moves pipeline (Everworker). The key is orchestration: every new signal should either update a score, change a segment, or fire a workflow.

A 90-day roadmap to stand up a differentiated AI GTM motion

In 90 days, you won’t build a perfect system—but you can set a clear path to signal-driven advantage. The priority is to move quickly from theory to a small number of working use cases that your team can feel in the field.

Days 1–30: define your top three revenue questions (e.g., “What predicts multi-year deals?”), inventory all available signals, and pick one or two segments (such as a key vertical) for your first experiment. Clean and join the relevant data, even if manually. Prepare to enrich with new data by adding your new qualitative edge layer by conducting or commissioning in-depth B2B buyer research. Start with a small pilot effort. Based on our work with recent clients, you will already see a difference in the output of your AI models and will be impressed how differentiated your messaging and content will already be from the rest of the pack!

Days 31–60: create one AI-powered score and one signal-based journey per experiment. For example, an expansion-propensity score plus a lifecycle program that surfaces plays for CSMs when that score spikes.

Days 61–90: measure impact on pipeline velocity, win rate, or expansion rate. Then standardize the data definitions, document the playbook, and decide which additional signals—research, product, or behavioral—you’ll layer on next.

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Alan Nazarelli is Founder & CEO of Silicon Valley Research Group. Based in San Jose, CA with offices in Seattle and New York, the company works with the world’s most innovative brands to provide timely and actionable market intelligence and strategic guidance to enable them to make well-informed decisions to positively impact revenues and profits and to achieve their growth targets. Connect with Al on Linked in

Monetization of AI Features in Cloud & SaaS

Alan Nazareli — Mon, 15 Sep 2025 23:56:53 GMT

In this week's post, we address a question that we expect is top-of-mind for every Cloud and SaaS executive-determining how to charge for newly developed AI features.

We are planning to announce a new service offering to address this in early October. In preparation for this, we have been surveying and interviewing our C-suite executive panel across industries on what they want to see from their Cloud and SaaS vendors. Here are some early voice-of-customer insights that may help with the pricing decisions:

Technology decision makers expect their software vendors to provide certain AI enhancements to their existing products for free. These fall into the category of ongoing product enhancements that they feel entitled to as part of their subscription agreements. Let's call these AI infusions. AI infusions use AI to facilitate performing simple tasks such as creating summaries, creating basic commands and prompts to initiate tasks, etc. These tasks are still primarily performed by the user, with AI as a facilitator. Using the JTBD (jobs to be done) framework popularized by Clay Christensen, author of "The Innovator's Dilemma", these tasks can easily be mapped out using the following rule that our panelists appeared to reach consensus on. If AI is merely enabling the user to save their time or cognitive load in performing the task, then it is merely an AI infusion and is expected at no charge as part of the product update cycle. A rule of thumb is that if the task's cognitive load is 70% borne by the user and 30% by AI, then no upcharge is expected for it. Likewise, if the time savings from the AI infusion are approximately 30%, no charge is expected, although some on our executive panel placed this threshold at 40%.
The second category is AI add-ons. Unlike the AI infusions described above, these add-ons bring a whole new capability to the product suite that the pre-AI product simply not capable of doing. Examples include a marketing automation suite that converted one form of content into another, such as converting this post into a landing page or social media ad. Using the percentage thresholds above, if the AI feature performed 70% or more of the cognitive load and/or time for the task, then it is a monetizable AI add-on.
When asked for their expectations on what additional charge for new AI add-ons would be acceptable, 30% emerged as the average. Remember this number is what they would prefer to pay and not what they do pay or have paid. On the latter question, many expressed dismay at being charged up to 50% for these AI add-ons, along with dissatisfaction with user experiences and return on the investments. It appears that software vendors have rushed to push out still half-baked AI add-ons to their customers. To this end, decision makers are also asking for better guidance on roll-out strategies that pinpoint which users in the total user pool need the add-ons to avoid overbuying. Effective end-user training is another gap they identified to derive better ROI that they could demonstrate to their leadership.

So here again with AI, our software and cloud clients are faced with age-old price optimization question: How much to charge so that it is attractive enough for customers to adopt without leaving money on the table?

The visual below which I shared in an earlier post is from a recent client use case (identity redacted). Note the dramatic flatlining of the curve in a couple of places within what customers considered to be acceptable pricing ranges. In these parts of the curve, the company can charge more at the end of the flatline without losing “share-of-preference” which is a proxy for expected market share in conjoint/discrete choice modeling.

Why you can’t ignore Psychometrics in the Age of AI-pt. 2

Alan Nazareli — Mon, 01 Sep 2025 18:11:34 GMT

In our last post, we introduced the concept of psychometrics and why it is ever important in the age of AI

Briefly, psychometrics is about the WHY behind underlying behaviors and takes into account factors such as personality, values, attitudes, lifestyles, the influence of peer groups, etc. in understanding customer purchase behaviors and decisions.

We dive deeper in today's post to explore the differences between more traditionally known methods of gathering and using demographics in market research versus psychometrics, how we gather data for each, and most importantly, when to use each-what problems in Marketing Research does it help solve.

The table below summarizes the differences between the two:

	Demographics	Psychometrics
Purpose	Defines customers based on measurable objective facts about who they are-age, gender, income, etc. that influence their purchase behaviors and decisions	Scientifically measures values, attitudes, lifestyles and personality characteristics that influence their purchase behaviors and decisions
Data sources	Quantitative data from surveys, product registrations forms, US Census Bureau data, etc.	Data from surveys and other sources that use previously validated psychological tests and scales
When to use	Determining market size Market segmentation	Message development Product positioning Customer Journey Mapping

Three additional considerations we should add here:

You will also hear the term Psychographics. While it also refers to attributes such as values, attitudes, lifestyles and personality, where it differs from Psychometrics is in the data collection methods used which are qualitative methods such as focus groups and individual in-depth interviews. While the results are not projectible to the population at large due to much smaller sample sizes, the value of this type of data when combined with quantitative data is immeasurable in helping to gain a deep understanding of customer motivations.
Ethnographic research, a technique borrowed from the field of Sociology and adopted for market research, involves spending large amounts of time observing a relatively small sample of customers. Here the objective is not just to understand factors that influence their purchase decisions, but to go beyond to uncover how your products integrate into their daily lives. Typically, this type of research is conducted by observing customers in their homes or places of work, i.e., interacting with your products in their natural environments.
Last, but not least, an increasingly popular trend, especially among digital products, is to combine these data sources with telemetry data. This type of data is generated from the digital footprints customers leave in their wake as they interact with a company's digital assets including websites, content, landing pages and, where relevant, the company's digital products. These data sources provide context for how customers actually interacted with your company's assets. Recently, the trend has been to combine telemetry data with the above-mentioned data sources to create even richer data sets to understand customers.

And with the power of AI, analysis and pattern recognition from such combined data sets has now become easier and ever more powerful!

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Why You Can't Ignore Psychometrics in the Age of AI

Alan Nazareli — Mon, 18 Aug 2025 19:56:45 GMT

This week's post focuses on an area that we find is increasingly overlooked or ignored these days with the advent of AI: Psychometrics.

One reason for this is that AI is highly focused on large data sets, which is turn leads to a focus on quantitatively measurable variables such as demographics that are easily projectable to populations at large.

Definitionally, psychometrics is about the WHY behind underlying behaviors and takes into account factors such as personality, values, attitudes, lifestyles, the influence of peer groups, etc.

As an example, let's take a financial services firm wanting to pitch a new investment opportunity to accredited potential investors. It would go about creating content assets such as webpages, landing pages, outbound emails and offers, highly assisted by AI of course these days. It may procure or build a list of potential investors to target with its enticing messages and offers

An area where psychometrics can boost campaign effectiveness is micro-segmenting its target list with psychometric variables. In this scenario, a highly valuable metric would be the risk tolerance profile of the list members.

Layering the list risk tolerance profiles would yield a list further segmented according to their risk tolerances and enable the company to create and pitch different messages and calls-to-action based on their respective profiles. For more risk-verse individuals, the company may design additional steps in the buy cycle to provide additional comfort levels without lengthening the cycle for those who don’t need additional assurances (a caveat here, of course, is doing this while adhering to financial industry regulations on messaging such as "blue sky" laws).

So. what prevents organizations from doing so? The following is a list of obstacles:

Lack of awareness and education. Marketing research education of late has focused on measuring more concrete variables. As I stated in a recent post: “Quants rule the world today”
Another challenge is validating that survey instruments used are accurately measuring and portraying these variables and that scales and assessments used are valid. In a recent example from a pharmaceutical client wanting to assess risk tolerance for a new drug therapy among potential patients, creating and validating a risk tolerance profile survey instrument and scales that were relevant to the industry and audiences had to be developed, adding significant complexity and time to the completion of the survey. However, for more typical commercial use cases, there are enough measurement scales for values, attitudes and life-style factors developed and validated in the fields of Psychology and Sociology that can be easily adapted for market research application.
The assumption among many marketers is that psychometric factors do not play an important role with their target buyers, particularly in B2B scenarios. Yet, most sales training and literature are quick to highlight the importance of emotional factors in even the most objective buying scenarios. Ultimately, it's humans making high impact decisions for their organizations and getting a handle on the emotional and personality factors that come into play with individual buyers or groups of buyers can have a significant impact on sales win/loss rations.

The impact of ignoring psychometrics is that your audience profiling and persona development efforts will therefore not be as rich and robust as they could be, thereby impacting overall effectiveness and ROI from marketing investments. This is particularly wasteful in this day and age when marketing automation tools infused with powerful AI capabilities have become so feature rich and provide easy facilitation for the inclusion of psychometrics. To learn more, contact us at info@siliconvalleyrg.com.

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Strategies for strengthening brand performance in 2026

aln@siliconvalleyrg.com (Alan Nazarelli) — Tue, 05 Aug 2025 19:11:02 GMT

As we move towards the Fall when many companies will start planning their go-to-market strategies for the coming year, we thought it would be a good time to share some insights on strengthening brand performance.

For technology solutions being evaluated today, there is a heightened focus placed by buyers on capabilities and functionality, given how new and yet unproven so many AI-related capabilities still are. And many brands in the space being evaluated are new so there is no brand-building history for buyers to rely on.

According to the International Valuation Standards Council, an average of 18% of quoted enterprise value today is attributed to brand. This figure is corroborated from our conjoint studies where we test the impact of brand along with a list of product features to determine the relative impact of brand on the purchase decision and consistently see brand utility values of approximately 15% to 28%. This is a significant portion of valuation to leave on the table by not focusing enough on brand strengthening. For high growth tech companies seeking to raise equity this year, are you fine with undervaluing your company by 18%?

Here are some actions we use in our client work for you to consider for strengthening and quantifying your brand value for your market planning for the coming year:

Brand Measurement: Not to sound too cliched here, but what gets measured gets improved. We are surprised at the number of companies we encounter who are highly measurement driven, but do not measure their brand. Brand measurement gets ignored because it's an intangible metric compared to so many other KPIs that are being tracked today. Moreover, to be a valid metric, the brand value must be measured in the marketplace and not just with existing customers, which often requires external help from third parties who can also generate data about a brand standing among both directly and indirectly competing solutions.
A Focus on Brand Equity, and not just Awareness: Too often, companies focus on measuring brand awareness-what percent of people are aware of the brand? And comparing that with awareness of nearest top competitor brands. While useful in and of itself, brand equity measurement is s also required. The formula here is Brand Equity=Brand Awareness X Brand Stickiness where: Brand Stickiness is the length to which decision makers will go to resist pressure to switch brands, either due to corporate edicts from above, vendor consolidation and/or enticing offers from competitors to switch suppliers. This aspect can we measured and quantified though a series of survey questions asked of buyers and is of immeasurable value in decision making, especially around changes to pricing models of distribution strategies.
Brand Usage Affinity: Corporate users of your product(s) have significant sway with top decision makers when it comes to renewing contracts or resisting compelling competitive offers. To this end, measuring product utility and its impact on your brand power becomes another arrow in your quiver for asserting the value of your brand. This is typically measured by deconstructing all the features and benefits of the products(s), having them stacked ranked by importance or utility by end users, then having those end users rank how well your products perform on those attributes. In this manner, we can construct brand power maps for your product(s) against top competitors
Enterprise Worthiness: Last. but not least, for those selling to large enterprise, enterprise worthiness is something your large corporate decision-maker will want to evaluate your solutions on. Many sellers have used land-and-expand strategies to acquire large multi-division and global accounts. Departments or workgroups have often proven to be easier to pitch and sell to than central corporate entities and procurement groups. Such "rogue" or "shadow" installations (in the eyes of Central IT) are then propagated laterally to other workgroups and eventually floated for consideration upstream, at which point, various tests of enterprise worthiness of the platform are applied. For this reason, it is also important for large enterprise sellers to include enterprise worthiness perceptions and how these have progressed over time as the company garnered more company-wide implementations in their brand story. The picture above this post is a graphic from one of our studies for a business process automation platform (brand names redacted).

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Market Expectations for Outcomes-Based Pricing

Alan Nazareli — Mon, 21 Jul 2025 17:36:16 GMT

Pithy quote of the week: "The stock market is a device that moves money from the impatient to the patient" Cornelius Vanderbilt 1850

Outcomes-based pricing has arrived and expected to proliferate. This is something technology buyers have been asking for the last several years in answer to the magic wand question we ask at the end of our in-depth executive interviews.

The underlying enabler today is AI, of course, but interestingly, the problems technology buyers want to solve are quite different. It is worth noting these since they are still showing up in our research today and will provide sellers with insights into how to best position the value of their new agentic solutions. Here are the top five:

Weak ROI and value from technology solutions they have adopted. A constant refrain we hear is that ROI is either weak or difficult to prove to their executive leadership.
Underutilization of purchased assets-this is often cited as an underlying cause of weak ROI and are a consequence of non-intuitive user interface design leading to poor discoverability and adoption of features. Surprising to still be hearing this today, given how much emphasis organizations have placed on design in the past few years.
Features overlap between solutions
Poor or incomplete integration between solutions or integrations that break when products get updated
Last, but not least, technology buyers have simply overbought (or more accurately, have been oversold) solutions and are looking to consolidate.

Enter outcomes-based pricing or OaaS (Outcomes-as-a-Service). While it doesn't solve all of the five issues mentioned above, it has the potential to radically shift the burden of accountability from buyer to seller. The seller is now in the position of not just being able to deliver tools that create outcomes, but the outcomes themselves-agentic AI is doing the work.

Commensurate with this paradigm shift is the opportunity for technology purveyors to reconfigure their pricing models to align with value creation more closely for their customers. Early examples already emerging revolve around replacing user-based subscription pricing with some form of cost savings sharing. Expectations from buyers are to see incremental revenue contribution models for customer-facing apps such CRM and marketing automation, the chief current barriers cited being able to attribute contributions to particular activities or tools used.

Expect to also see buyers demand current consumption-based models in areas such as cloud and storage to evolve to outcomes-based. A major complaint over the last several years has been cost overruns and surprises in the monthly invoices from the likes of AWS. Whole departmental functions and new tools such as Cloud Zero, have sprung up to handle cloud cost optimization, resulting in additional overhead to organizations. While these overhead costs are more than offset by cloud cost savings, technology buyers are highly likely to welcome outcomes-based innovations in cloud pricing, offering opportunities for competitive advantage for those who seek to be disrupters.

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Barriers and Bridges to AI adoption in Marketing in 2025

aln@siliconvalleyrg.com (Alan Nazarelli) — Tue, 08 Jul 2025 19:37:09 GMT

Christopher Mims published a great article in the Wall Street Journal recently highlighting the barriers and bridges to AI adoption in Marketing using Clorox’s Hidden Valley Ranch division as a case study (link to article below). We have shared the dramatic visual he used to illustrate his point above. If a picture tells a thousand words, this is it! The image on the left was created by AI, the one on the right is after human refinement of the AI prompt.

Unpacking this further and cross-referencing it with our findings with C-suite in-depth interviews conducted so far this year, the following highlights emerge:

We have now crossed the 50% threshold for AI in tasks across most business functions (Marketing, HR, Finance, etc.) with some nuances, of course as shown below.
However, human interaction is still paramount in deploying AI. As the Wall St. Journal article also states approximately 30% human factor is necessary
As expected, service operations for customers and employees are more than 75% automated with AI, given their extensive deployment of chatbots and agents
Software development, currently at 30% to 40%, is the area where IT leaders expect most growth in the coming year and the most displacement of humans, very likely reducing offshored human workloads for US-based companies
While a lot has been published online about cost-savings realized by automation, much by AI vendors, we did not draw meaningful conclusions from our research on cost-savings. Many leaders are still focused on implementation challenges and are still working on amortizing their costs in adopting AI tools. At best, when probed about the subject, their responses were still best guesses.
We plan to conduct a C-suite study this Fall focusing completely on AI Economics and ROI, which will also identify the basis for these computations very much like we did for Cloud Economics a few years back. If you have questions you would like to see included, please contact me. (We will make a complementary copy of the study available to our Research Insights community.)

Link to Wall St. Journal article: https://www.wsj.com/tech/ai/clorox-ai-hidden-valley-ranch-e997d3dc?st=eDXZ8c&reflink=desktopwebshare_permalink

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The Power of "If...then" questions when talking to C-Suite

aln@siliconvalleyrg.com (Alan Nazarelli) — Mon, 23 Jun 2025 19:48:41 GMT

One of the more challenging aspects of conducting C-suite research conversations is crafting the appropriate questions. Conversations with C-suite executive decision makers are inherently different than other roles within an organization. For both research interviewers and sellers, it is difficult to gauge how high-level the conversation needs to be.

In this post, we focus on the power of one type of question that we have found to be highly effective in engaging with C-suite decision makers, the "if...then" question. These come in a variety of flavors, and each yields a different type of insight on the thinking of the respective decision maker being interviewed.

Here are some variants to consider. While these have been highly effective for us our research efforts, they are also very effective in sales discovery sessions (we have used these to engage and do business with some of the world's leading tech brands!)

1. The technology adoption barrier-busting question. We start with the most powerful variant of the if...then question. When new technology solutions and presented and discussed with C-suite execs, it is difficult to discern their willingness and propensity to purchase. The rules of decorum and politeness make the respondent reticent to be completely honest with their intentions to purchase (or not purchase). The “if...then” question gets around this. The question is: "What conditions would need to be satisfied for you to feel adopting X would be beneficial to your organization?", remembering of course to probe for additional items. Their response to this question is in effect the "objections map" that the seller will need to overcome. k

2. The magic wand question. The magic wand question is designed to elicit their ideal state and highest aspirations and vision for their organization or line-of-business. In our experience, too many research and sales conversations focus on getting at pain points and not enough on the decision maker's aspirations. You've all heard the sales advice: “Sell the aspirin, not the vitamin.” Buyers, however, want both the aspirin and the vitamin. This question is best framed as sentence completion, "What would need to happen for you to feel satisfied that you have fulfilled your vision for X?"

3.The marketing message question. This is a question when asked directly yields surface answers that respondents don't give much thought to. Technology decision makers, in particular, claim to not pay much attention to marketing. A common refrain we hear from them is "we are above marketing" or "we don't care about marketing, but just about the technical specs". This, of course, does not mean they are above being influenced by your marketing messages. The question is: “if I heard that product X) could help me accomplish ______, then I would be compelled to take a further look"

Type 4: The people question. Last, but not least is the question that is not likely to emerge in the conversation unless specifically called out, the people and culture question. Executive decision makers are highly concerned with people issues within their organizations. Very often these don't surface naturally, especially when these individuals are asked to discuss or evaluate technology products. The question is: "How would implementing solution X impact your people?"

Type 5: The "future-scape" question. This question is best asked at the end of the discussion. This question is designed to uncover their future expectations “If you and I were to talk again [a year/18 months/3 years, etc.] from now, and you were looking back to today, how would your X (pick an aspect of the business that you have been discussing) look different from how it is today?”.

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Build your Brand Power Grid

aln@siliconvalleyrg.com (Alan Nazarelli) — Thu, 12 Jun 2025 21:31:50 GMT

In his seminal book, Differentiate or Die, Jack Trout makes distinctions between propositions that serve to differentiate a company and its offerings and those that don't. The following, he contends, are rarely differentiating ideas: quality, customer orientation, price and breadth of product line, despite the fact that so many market positioning attempts to differentiate on these propositions.

Successful differentiation, he goes on to say, is achievable along the following dimensions: being first, leadership, heritage, attribute ownership or market specialty.

I want to seize on one of these successful differentiation dimensions for this post: Attribute ownership. The good news for market and product leaders is that attribute ownership is measurable and quantifiable via market research methods. And if can be measured, it can be developed and harnessed to help companies win in the marketplace.

We have been helping clients create attribute ownership for some time now and would like to share this concept with the rest of our community.

Let me begin by saying what attribute ownership is not. It is not category ownership. It could be said that Amazon owns the category of online shopping, but online shopping is not an attribute.

An attribute is one of the ways your customer or user dimensionalizes or defines the act of consuming your offering. It could be a feature or benefit, although not exclusively, because it could also be an emotion evoked in the act of purchasing and/or consuming your product. And yes, for those of you business marketers reading this, it also applies to your B2B offerings. For the decision maker, its dimensions such as safety/risk alleviation (the old adage: no one got fired for choosing IBM comes to mind here). For end users, it includes design aspects that promote task execution, satisfaction, tactile look and feel, etc.

So how does one go about measuring attribute ownership as a first step in creating a well-crafted strategy to develop and harness it for competitive advantage?

To do this, we developed our Brand Power Grid™. Most brand tracking studies measure brand awareness, viewing it as a proxy for brand success. True, brand awareness is important as a KPI worth tracking over time to determine if your investments in brand are paying off. We contend, however, that brand awareness is not enough. Because in order for a brand-aware prospect or shopper to select your brand over others, they go through a mental process, checking your brand and what it specifically offers them against attributes that they are seeking. These attributes could be global requirements that align with their purchasing values or practices (as in: I always choose quality or durability over price) or specific to this particular intended purchase (as in: I usually choose quality over price, but in this case, I am looking for the best price). So the best way to measure attribute ownership is to represent a select list of attributes associated with the consumption of your product category to potential customers, create a choice-modeling experiment around these and have customer insights survey respondents rank them both in importance to them and in association with your brand. Add to this exercise, your top three direct competitors to the mix and you have a fully developed brand power index.

When the data is collected, the questions then become:

Which attributes are most tied to your offering?
In which ones do you lead both with the customer and among your competitors, i.e., which ones do you own or dominate in?
Is this the category you want to own? Is it the one that you want to or can invest in developing? As you can imagine, in some cases, the category you own or dominate may not be the one that aligns with your long-term objectives or your corporate umbrella brand.
If it makes sense based on the above to own a different category, how clear of competitive entrenchment is that category based on the data? Can you realistically lay claim to outright ownership? Referring again to Jack Trout, only one brand can occupy a category in the customers mind.

Then comes the hard (internal) work of internalizing the data and making trade-offs and making data-driven investment decisions. And if you lead in none of the attributes measured, which ones make sense to invest in and develop, given your company's long-term objectives and resources? Because if you lead in none, your offering is not substantially differentiated enough, and you are most likely leaving significant revenue and profits on the table.

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Innovation Research Framework-Update for 2025

aln@siliconvalleyrg.com (Alan Nazarelli) — Wed, 11 Jun 2025 17:15:52 GMT

In this week's post, we share our Innovation Research Framework updated for 2025. This is our seven-step framework that we have used extensively in our client work. It is designed to develop, process and prioritize innovations for monetization. As with any such process, it needs to be customized to each organization's unique situation, but the essential steps are featured, and at a minimum, it will serve as a checklist to ensure that new innovation ideas have been fully market vetted. We update it each year. I have also included a link to download a complimentary template for our clients and community members. It is in PowerPoint format so you can customize it for your organizations unique needs. Some highlights for this year:

As the infographic illustrates, Product/Market fit is first established in Stage 2, Concept Validation. However, it is an iterative process and needs to continue through stages 3 to 7. Product/Market fit is most difficult to establish at strategic inflection points as we find ourselves in currently with AI as our recent client work with disruptive new solutions is demonstrating.
For software offerings, monetization opportunties for AI-powered additions and enhancements are tested in stage 5 are tested. This is proving to be a critical and controversial topic with decision makers who are balking at pricing. SBI research indicates that 20% of software companies are monetization their AI-powered features as an add-on. However, only 20% of new customers purchase the AI add-ons, underscoring the need for validation and price sensitivity analysis as well as testing of limited-time price offers for these add-ons. SBI also reports relatively low usage of only 38% for AI-powered add-ons, indicating increased churn risk down the road at renewal time.

Besides changing the market landscape, AI is also disrupting the frameworks and processes we use to deliver research insights to our client work, including this one. Stay tuned, we will provide updates to this framework as they become available

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