# Closing the B2B Messaging ROI Gap How to connect your go-to-market story to revenue outcomes, to prove and improve impact. *Perspectives drawn from GTM leaders, marketing executives, and revenue practitioners. With a foreword from Elliott Rayner, Fractional Head of Storytelling at OWOW / Synopsis BV Author of "The Story Engine."* [www.troupe.ai](https://www.troupe.ai) ## Table of Contents - Foreword (by Elliott Rayner) - Introduction: The investment without a return signal - Questions answered with messaging success insights - 1: Getting to a shared definition of messaging success - 2: Why messaging has been so hard to measure - The revenue left on the table: Example calculations - 3: Why measurement approaches have fallen short - 4: A better framework: The main two components of messaging measurement - Adoption metrics - Outcome metrics - 5: What this unlocks for your team - For CEOs and CFOs - For marketing leaders - For product marketers - For revenue leaders - For sales enablement - 6. Closing: Visibility is the missing Ingredient - Troupe is designed for this ## Foreword Over the past few years, I have spent a lot of time inside B2B companies helping them build product narratives. The work is structured around a simple belief: that storytelling is not a creative gift but a discipline. Something that can be learned, built, and embedded across an organisation. And yet, for all the effort that goes into building the story, one question almost never gets asked: is it actually working? This is not a gap I noticed from the outside. I have sat in the rooms where messaging gets built. I have watched product teams spend weeks workshopping positioning, only to see Sales fall back on their own language within a month. I have watched Marketing produce a narrative they are genuinely proud of, then discover that the version reaching buyers bears little resemblance to what was intended. My own research suggests that average internal alignment on brand and product narrative across teams can be as low as 19%. Sales, Marketing, Product, and even Customer Success are often telling entirely different stories about the same product, to the same buyers, at the same time. That may be a creation problem. But it is certainly a measurement problem. Every go-to-market function operates under the same unspoken pressure: own a KPI or lose your seat at the table. Storytelling has thus far been exempt from that pressure. Not because people do not believe in it, but because no one had built the infrastructure to connect messaging to a number that actually matters. Without that connection, the work gets respected in theory and deprioritised in practice. Teams can sense that something is wrong with their story. They cannot see what, or where, or how much it is costing them. The question this ebook asks and answers is one that the field has avoided for too long. Not because people do not care about it, but because the infrastructure to answer it has not existed. You cannot measure something you cannot observe at scale, across channels, connected to outcomes. Until recently, that infrastructure was simply out of reach. The timing matters more than it might seem. AI has changed the economics of content production almost overnight. Building a message used to be hard. Now it is cheap, fast, and available to everyone. Differentiation no longer lives in who can produce the most content or automate more outreach and interactions. It lives in who has the clearest story, the most consistent delivery, and the ability to learn from what is resonating and adjust. Measurement is no longer a nice-to-have. It is the mechanism by which any organisation stays ahead. What I find valuable about the framework in this guide is that it separates two things that are often conflated: whether the story is being deployed, and whether it is working when it is. The distinction matters. A team that skips adoption and jumps straight to outcome data is measuring patterns in what happens, not in what they chose to do. Adoption metrics are what connect strategy to signal. Without them, you are optimising something you are not actually managing. The brands winning today are the ones with a clear and aligned narrative. Not the loudest, or the best-funded, but the ones who have approached storytelling consciously and treated it as infrastructure rather than output. In this guide, Troupe takes the next important step toward making that a reality. Your most important competitive asset may not be your product, but your product story. And your most important next hire may not be a CMO or a VP of Sales, but a Head of Storytelling. Read it carefully. The gap it describes is real. So is the cost of leaving it open. — [Elliott Rayner](https://www.linkedin.com/in/elliottrayner/), Fractional Head of Storytelling, Synopsis BV Author, The Story Engine ## Introduction: The investment without a return signal B2B companies invest heavily in their go-to-market story. Initiating messaging workshops, research, and "tiger teams." Often hiring outside consultants. Structuring a framework to organize the messaging content. Thinking through the content of launches, sales decks, and campaigns. Executing sales enablement training. The list is long, and the cumulative spend is significant. Yet most organizations have no reliable way to tell whether the messaging they craft and then ultimately deliver is driving revenue like it should. Not working in the vague sense of 'our brand feels strong' or 'we got more engagement on this content vs. the other version.' But working in terms of true impact on growth: is the story we intend and the way our teams are telling it moving deals forward? Is it helping us win? This topic of messaging effectiveness for B2B was raised with more than a dozen GTM leaders who were guests on Troupe's Said Differently podcast program starting in fall 2025 through spring 2026. Here's a blunt statement by one of those guests: > "I think [messaging] is everything. It might be second to great products as the leading predictor of revenue performance for a company." > — Jake Sorofman, fractional CMO; former CMO at Pendo and Visier | [View episode](https://www.youtube.com/watch?v=-2Ytp-1WK4w) That's a bold claim made by Jake — and an important one. Because if messaging is that consequential, the absence of any measurement system isn't just inconvenient. It's a strategic blind spot. Our team at Troupe (ourselves made up of GTM leaders across marketing, sales, and product) developed this guide for those ready to take a competitive leap-frog by closing that glaring gap. It covers why messaging has been so difficult to measure historically, what a better measurement framework looks like in practice, and how connecting story performance to pipeline data changes what's possible for marketing, sales, and revenue teams. The perspectives throughout this ebook are drawn from both the expertise behind the founding team at Troupe, feedback from our early customers, as well as from those podcast guests — all of which have had the first-hand experience of operating in the blind. ## 01 Getting to a shared definition of messaging success Before you can measure something, you need to agree on what you're measuring. And when it comes to messaging success, the definition historically has varied significantly depending on where you sit. When marketing talks about messaging effectiveness in terms of metrics such as open rates, click rates, number of shares, or number of MQLs, they are not convincing sales about outcomes that matter to sales: pipeline and sales cycle improvements. When sales shares with Marketing feedback about messaging that's primarily based on opinion or one-off anecdotes, that's not enough data to make a convincing case for change. But yet, as podcast guest Maureen West said, if sales is reluctant to use the planned or 'goal' messaging, the ROI will not materialize: > "Does the sales team agree with the message? Are they going to say it? If they don't like it, you are sunk in the water." > — Maureen West, Founder, Launch Actually; former VP of Marketing | [View episode](https://www.youtube.com/watch?v=BxPbQOjRBjE) When there's a shared understanding of how well messaging is actually performing for the business, then different roles across the GTM effort get better answers to these questions: | Role | Questions answered with Messaging Success insights | | --- | --- | | Product marketing | Are the messages being used? Are our messages advancing buyer interest? Is our story winning when we're in competition? | | Marketing leadership | Is messaging improving the ROI of our budget investments? Do we need to invest in improvements — and if so, where and how much? | | Sales ICs | Are these messages generating interest from the right prospects? Are they helping me move deals forward and compete effectively? | | Sales enablement | What messaging training should I prioritize? What feedback can I give marketing that's grounded in evidence, not anecdote? | | CRO and RevOps | Which messages are improving conversions, win rates, velocity, and deal size? How are we positioned competitively? | | CEO / CFO / Board | Is our GTM story driving growth and protecting market share? Or is an underperforming story increasing CAC and hurting returns? | Let's face it: At the end of the day, your messaging and the storytelling that delivers it should be outcome-focused. For nearly all B2B companies, those measures collectively will be about revenue and growth. Not "feels." The challenge is that most organizations have never had the infrastructure to isolate, quantify, and contextualize messaging success until recently. Thanks to advances in AI and machine learning used in solutions like Troupe, we can now connect the dots — something that podcast guest Chad Butz touches on during his episode about message-market fit: > "Who are we talking to? What are we saying? What's the signal to the noise? And how do we know whether we're telling the right story?" > — Chad Butz, Founder and Principal, Fielde; former VP Product Marketing, Dext | [View episode](https://www.youtube.com/watch?v=sqkLhmR4NFA) ## 02 Why messaging has been so hard to measure The measurement gap isn't the result of indifference. Most GTM leaders intuitively understand that their story matters. As Troupe's podcast host Jennifer Sikora has brought up in several episodes, often it's not necessarily the best product in a category that is the "winner" but the one that executes the best story. The messaging ROI gap exists because messaging operates in a fundamentally different environment than most other revenue inputs, and the structural challenges have made comprehensive measurement feel out of reach. We describe some of those obstacles and complexities below: ### The complexity of the modern sales cycle Enterprise B2B deals involve multiple stakeholders, multiple touchpoints, and weeks or even months of interaction before a decision is made. Measuring whether the story is working requires capturing and analyzing all of those touchpoints — not just one moment in time. ### The channel proliferation problem Messaging now travels through more channels than ever — sales calls, one-to-one emails, AI-generated outreach, AI chatbots, mass email campaigns, marketing and AI content, sales content, social media, and sales content. Stitching together a coherent picture has historically required more data analysis infrastructure than most teams are willing to build. ### Natural language doesn't behave like a keyword A sales rep who has deeply internalized your messaging won't say it the same way twice — and that's actually good. Great storytelling sounds human and contextual, not scripted. But this means you can't track message adoption the way you'd track a UTM parameter. Assessing whether the intended story is coming through requires an understanding of meaning, not matching strings. ### The stagnation of 'good enough' Perhaps the most insidious challenge is organizational. When metrics don't exist, the absence of measurement becomes normalized. Over time, teams accept that story performance isn't quantifiable, and the question stops getting asked and we keep settling for opinion instead. "Well, we're getting revenue so something is working." The problem isn't solved; it's just that nobody is looking at it, at least not in the right way. ### The tenuous revenue connection In a complex sales cycle, many factors contribute to a deal moving forward or not. It can be hard to isolate any one of those factors (attribution), so this isn't necessarily about direct causation but about measuring very high correlation that's statistically significant — meaning we're seeing patterns over healthy sample size numbers. So depending on the size of your business, for example: seeing that two closed-won deals made heavy use of Messages A+B+K isn't statistically significant; that's anecdotally interesting and you should continue to look for more like those in the future. But finding you won 20 deals out of 100 closed using that combination of messaging focus is data of meaningful interest. ### No ownership of metrics Product Marketing ideally should own messaging success, but it's also of critical shared interest across Marketing leadership, Revenue leadership (including RevOps and Sales Enablement), and of course the CEO and CFO. But Product Marketing hasn't had a solution to allow for proper measurement, as Eric Boduch co-founder of Pendo reflected during his episode on this topic: > "Product marketing can end up operating in a kind of dark art zone — subject to too much subjectivity, too much opinion, and not enough evidence." > — Eric Boduch, CEO/founder of 24 and Up; co-founder of Pendo | [View episode](https://www.youtube.com/watch?v=opPG7Bow4BY) This was also echoed by Scott Heimgartner during his episode on bridging the sales-marketing messaging gap: > "Product marketing is often a function that doesn't truly own a core metric — which is exactly why anchoring as close to revenue as possible matters so much." > — Scott Heimgartner, Senior Director of Product Marketing | [View episode](https://www.youtube.com/watch?v=opPG7Bow4BY) ### The revenue left on the table The cost of this gap isn't abstract. When messaging underperforms — or when improvements are delayed because teams lack data to act — the impact shows up directly in pipeline and revenue. Consider a company with $80M in open pipeline and an 18% average win rate. We'll look at two independent improvements, both plausibly achievable with better messaging performance, to illustrate the stakes. In this example case, using a solution like Troupe: - Identify high-converting messages in the early top-of-funnels stages that can be used more widely - Identify the messaging techniques used by your top-performing reps and train those at scale. As a result of applying both of those learnings, here's what you might influence in revenue that would have otherwise been left behind: | Scenario | Pipeline | Win rate | Revenue impact | | --- | --- | --- | --- | | TOFU conversion increases +15% | | 18% (unchanged) | +$2.2M new revenue | | Win rate lift improves +2pts | $80M (unchanged) | | +$1.6M new revenue | | Both combined | | | ~$4M new revenue | A 50x return on investment from a single platform (in this case, that return would be from Troupe) is not a stretch when the underlying lever is this powerful. The question isn't whether better messaging measurement is worth pursuing — it's why organizations have waited this long to pursue it. > "Marketing has always been responsible for relationships and revenue, and we have always struggled to attribute our effort to both of those things." > — Julie Bryce, Fractional CMO and Adviser; former CCO at TileDB | [View episode](https://www.youtube.com/watch?v=YiAMBhdXEfQ) ## 03 Why measurement approaches have fallen short It would be unfair to say GTM teams haven't attempted some version of message performance tracking. The problem isn't the intent; it's that the approaches available have been limited in coverage, lag in timing, are disconnected from outcomes, provide varying answers (probabilistic), or any combination of those. | Approach | Coverage | Key limitation | | --- | --- | --- | | Listening to a sample of calls each week | 0-5% | Heavy selection bias. No pipeline connection. | | Reviewing a sample of sales emails | 0-10% | Point-in-time. Selection bias. No outcome link. | | Quarterly content and deck audits | 30-70% | Lags by at least 3 months. Doesn't cover conversations. | | Vanity metrics (opens, clicks, share of voice) | Varies | Measures attention, not story effectiveness or revenue influence. | | Ad hoc AI transcript reviews in an LLM | Up to 90% by batch | Point-in-time. Prompt-dependent. Inconsistent answers. No pipeline connection. | Let's take the last one listed in the table above, which is becoming more commonplace: Uploading call transcripts into an LLM for analysis. Without a proper solution, you will have challenges around consistency of responses every time you run this analysis. Are you comparing it to a 'source of truth' messaging guide? If so, the consistency in response risks even more variability. How often are you running this analysis? What is the scope? Who decides? Plus… The critical missing piece across all of these approaches is the same: none of them connects to pipeline and sales outcome data. A team can know that a message appeared in 40% of calls and still have no idea whether it helped or hurt conversion. Frequency without outcome data is just more noise. > "We spend tons of money on GTM, but we still can't measure story." > — Alison Murdock, CEO, 1021 Creative; Founder, Trusted CMO | [View episode](https://www.youtube.com/watch?v=YiAMBhdXEfQ) What's needed isn't just better coverage. It's a connected and predictable system — one that links the story you intend to tell, what's actually being said across every channel, and what happens to deals as a result. ## 04 A better framework: The main two components of messaging measurement Closing the messaging ROI gap requires a framework that understands what needs to be measured. At Troupe, we believe these fall into two main component categories. The first is about adoption: is the intended message actually being delivered? The second is about outcomes: when that message is delivered, what happens to the opportunity? Most measurement conversations skip straight to outcomes and neglect adoption. That's a diagnostic error. If your planned messaging isn't being adopted in the first place, outcome data isn't anchored to your plan. It's just showing you patterns in what's happening, without linking it to the strategy. Adoption is the necessary precursor. ### Component 1: Adoption metrics Adoption metrics answer the question: is the message reaching the market the way we intended? These are early indicators — they tell you whether the foundation is in place before you try to optimize for outcomes. Here are some metrics within this category: - **Time to adopt:** How quickly does a new message show up across content, calls, and emails after launch? Slow adoption signals a rollout or trust problem — whether in training, tooling, or team buy-in. Fast adoption that then fades signals the message isn't sticking and teams are trying then abandoning it. - **Frequency of use:** How often is the message appearing across touchpoints? A message that only surfaces in one channel or with one segment of the team is under-leveraged. Frequency across channels is a signal of true organizational internalization. - **Alignment score:** When the message does appear, how closely does it match the intended narrative? This goes beyond keyword matching — it assesses whether the meaning, emphasis, and framing are consistent with the messaging guide. High frequency with lower alignment often means teams have built their own version of the story. High alignment with low frequency means you have some who have adopted it well, but rollout remains an issue. - **Experiment signals:** Are there message variants appearing in the field that weren't in the playbook? Sometimes rogue messaging is a problem to correct. Other times it's a sign that a practitioner has found something that resonates. Surfacing those signals is one of the highest-value outputs a measurement system can deliver. - **Recommended updates:** Based on adoption patterns, what adjustments does the data suggest? If certain messages are consistently being skipped or modified, that's a signal worth acting on — either through enablement or through messaging iteration. AI can and should review the data outputs and make recommendations. > "You just need to be as consistent as possible, and the best way to do that is just to get alignment at the top." > — Katey Mokelke, Positioning Consultant, Firebrick | [View episode](https://www.youtube.com/watch?v=EuqU7CgoIc0) ### Component 2: Outcome metrics Once you know adoption is happening, outcome metrics answer the harder question: is the message working? These connect story performance to the revenue indicators that matter to the business. - **Sentiment:** How are buyers responding when specific messages are used? Positive sentiment signals (i.e. engagement, questions, affirmations) indicate the message is resonating. Objection patterns, silence, or topic changes suggest friction. Sentiment data is most valuable when correlated with deal stage and persona. - **Objections:** Which messages are generating the most resistance, and at which stages? Not all objections are bad — some indicate strong engagement. But patterns of the same objection appearing at the same stage across multiple reps signal a messaging gap that may be proactively addressed, whether through better framing, better enablement, or a genuine revision. - **Conversion influence:** Which messages correlate with movement between funnel stages? This is where messaging stops being an activity metric and becomes a revenue input. Knowing what specific messaging at what stage is moving more deals forward is exactly the kind of insight that helps bring focus to both marketing and sales efforts. - **Win rate lift:** When a message from your messaging guide is present in a deal, how does win rate compare to deals where it wasn't used? What about finding messages that are not in your plan? This is the highest-signal outcome metric, isolating messaging as a variable and connecting it directly to the most important sales outcome. And this isn't just about periodic measurement. This is about ongoing messaging performance monitoring, so you can detect new patterns as they emerge and flag deals that can benefit from a modified messaging strategy. Podcast guest Brett Queener advocates for continuous vs. ad hoc measurement: > "If we're listening and tracking the results in real time, we can figure out it's wrong instead of waiting through months of underperformance." > — Brett Queener, Managing Director, Bonfire Ventures; former EVP/GM at Salesforce | [View episode](https://www.youtube.com/watch?v=Bx9m237TiTE) Product marketing consultant Scott Heimgartner also talked in his podcast episode the importance of these metrics specifically for the Product Marketing function: > "If we can get as close to revenue in terms of what drives our objectives, product marketing can more clearly show impact and align with the rest of the go-to-market organization." > — Scott Heimgartner, Senior Director of Product Marketing | [View episode](https://www.youtube.com/watch?v=Tw05eHHMRDY) In summary: Together, these two layers create a complete feedback loop. Adoption metrics tell you whether the story is being deployed. Outcome metrics tell you whether it's creating impact. And the connection between them — knowing not just what's happening but why — is what makes optimization possible. ## 05 What this unlocks for your team When a connected go-to-market storytelling measurement system is in place — one that tracks adoption and outcomes across every channel, every stage, and every deal — the implications are different for each part of the organization. Here's what becomes possible. ### For CEOs and CFOs For executives responsible for overall growth and capital efficiency, the question is never just whether marketing is producing activity — it's whether that activity is producing returns. Your messaging story is the connective tissue running through virtually every GTM investment a company makes, which means the ROI of those investments is directly tied to how well your story performs. When messaging underperforms, it doesn't just hurt one marketing campaign or one channel. It introduces a drag across the entire marketing execution plan. Conversely, when messaging outperforms, you experience a benefit multiplier because the story it's built around is optimized. The same logic applies to the revenue side of the house. Sales hiring is one of the most significant investments a B2B company makes, and the ROI on that investment is substantially affected by how quickly new reps can get to productivity. Sales enablement programs, onboarding, ongoing training, and even new territory development all share a common dependency: they need effective messaging to build on. A measurement system that connects messaging performance to pipeline outcomes gives CEOs and CFOs something they've rarely had, which is visibility into story as a business variable and growth lever, not just a creative output. This matters especially in environments where growth efficiency is under scrutiny. The pressure to do more with existing resources puts a premium on understanding which inputs are actually driving output. ### For CMOs and marketing leadership The chronic tension between CMOs and CROs over whether messaging is driving pipeline becomes a data conversation instead of an opinion debate. Marketing leaders gain the ability to show which messages are converting, where adoption is lagging, and what the revenue impact of a messaging improvement would be. Budget decisions — whether to invest in a full messaging refresh, targeted enablement, or channel-specific content — can be grounded in evidence rather than intuition. For a new CMO or Head of Marketing stepping into a role, this is especially powerful. Instead of spending six months trying to understand what's working through anecdotal discovery, a measurement system provides an immediate diagnostic: what the team is saying, what's resonating, and where the gaps are. > "Why this, why you, why now — if you don't have top-to-mid funnel conversion, you have to interrogate whether the market cares, whether the category resonates, or whether the story is failing to carry buyers forward." > — Brett Queener, Managing Director, Bonfire Ventures; former EVP/GM of Salesforce | [View episode](https://www.youtube.com/watch?v=Bx9m237TiTE) ### For product marketing leaders (PMM) Product marketing has long suffered from a measurement problem. The function creates (or has the potential to create) significant strategic value but has historically struggled to prove it in hard metrics, much less in revenue terms. A connected messaging system changes that. PMMs can show which messages they developed or helped to shape are being adopted, which are converting, and what adjustments the data suggests. The role moves from being judged on deliverable and task output to being measured on impact. > "If you can bring them something new to the table that is also strategic and directional in nature, executives and boards love it and appreciate it." > — Ali Hanyaloglu, VP of Product Marketing, Showpad | [View Episode](https://www.youtube.com/watch?v=O3Uj4IKweD4) That kind of rapid iteration — based on real signal rather than retrospective survey — becomes a competitive advantage. PMMs who can show that they moved the messaging, measured the response, and improved win rates are operating at an entirely different level of strategic influence. ### For revenue leadership Revenue leaders get the ability to understand what their top performers are actually doing differently, not just in terms of activity metrics, but in terms of how they're telling the story. If your best reps are consistently using a specific value prop at the right moment in the sales cycle, that's a playbook waiting to be replicated across the team. Enablement decisions stop being based on guesswork and start being driven by performance data. ### For sales enablement and revenue operations Enablement teams gain a precise view of where messaging gaps exist, which reps need specific coaching, and which objections are surfacing most often at each stage. Training investments can be targeted rather than broad. And the feedback loop with marketing becomes concrete: instead of qualitative impressions from the field, enablement and RevOps can share data-backed patterns that support meaningful messaging iterations. ## 06 Closing: visibility is the missing ingredient Across every conversation that informed this guide, one theme kept resurfacing in different forms: B2B messaging is too important to remain mostly invisible. It influences win rates, conversion velocity, and deal size. It determines whether marketing and sales align or pull against each other. It shapes whether product marketing earns strategic influence or stays stuck defending its existence. It governs whether launches land, whether content resonates, and whether the story that leaves headquarters is the same story that reaches buyers. And it drives overall go-to-market ROI up or down. Messaging often gets put on the shelf once documented, which isn't ideal. Messaging should be adaptive but based on real data and context. As Jake Sorofman also noted during his episode: > "Set it and forget it is dangerous, because you don't have data to validate and optimize against." > — Jake Sorofman, fractional CMO; former CMO at Pendo and Visier | [View episode](https://www.youtube.com/watch?v=-2Ytp-1WK4w) The two-layer framework in this guide — adoption and outcome metrics — connected to pipeline data gives GTM teams the infrastructure to move messaging from a creative exercise to a managed, measurable growth lever. That shift is what makes the messaging ROI gap closeable. ### Troupe as a Solution Built for This Troupe is the messaging attribution and success platform and the first to market to close this gap. It connects your intended messaging to what's actually being said across calls, emails, and content — and shows how it impacts revenue. ![Troupe messaging-to-revenue framework diagram](assets/troupe-messaging-framework-diagram.png) Troupe automatically ingests assets and interactions from your existing tools, analyzes them against your messaging guide, measures adoption and outcome metrics across the full funnel, and surfaces recommendations for where to refine, retrain, or scale up story performance. Here are two specific examples of the types of insights Troupe's AI produced for actual customers, with identifying messaging redacted to preserve their anonymity: As one can see from those measurements surfaced by Troupe, each of those are highly actionable for both marketing and sales functions. One can immediately start to think of how such insights would influence aspects including sales retraining; content prioritization; campaign themes; demo talk tracks; and perhaps even product strategy. For GTM teams looking to maximize the impact of their story, Troupe is how you find out whether it's working — and where it needs to get better. Learn more at [www.troupe.ai](https://www.troupe.ai) ### About the perspectives in this guide The practitioner quotes throughout this guide are drawn from Said Differently: The Show About B2B Storytelling, a podcast program produced by Troupe and hosted by Jennifer Sikora. Contributors include Jake Sorofman, Eric Boduch, Scott Heimgartner, Julie Bryce, Alison Murdock, Ali Hanyaloglu, Brett Queener, Chad Butz, Maureen West, Katey Mokelke, and others. The full episode catalog is available at [troupe.ai](https://www.troupe.ai/podcast) and on all major podcast platforms. We also thank [Elliott Rayner](https://www.linkedin.com/in/elliottrayner/) for his foreword to this ebook.