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RevOps Books

Sources

Most business books assert. This one cites. Every volume ends with a consolidated References section, and this page collects all eight of them — 56 distinct works, 67 citations in total, with a link to each.

The series argues from structure rather than from anecdote, so where a claim is empirical it belongs to somebody else’s research and is named as such. Peer-reviewed work on pricing, customer lifetime value, forecasting and governance sits alongside the accounting standards that decide what may be recognised as revenue. A reader who wants to check a claim should not have to email the publisher to find out where it came from.

Volume 1 · 5 sources

Revenue Design: The Architecture of What You Sell

  1. Burkert, Michael, Franz Michael Ivens, Andreas Henneberg, and Philipp Schradi. 2017. “Organizing for Value Appropriation: Configurations and Performance Outcomes of Price Management.” Industrial Marketing Management 61: 194-209. doi.org/10.1016/j.indmarman.2016.06.007
  2. Ingenbleek, Paul T. M., Ruud T. Frambach, and Theo M. M. Verhallen. 2013. “Best Practices for New Product Pricing: Impact on Market Performance and Price Level under Different Conditions.” Journal of Product Innovation Management 30 (3): 560-573. doi.org/10.1111/jpim.12008
  3. Liozu, Stephan M., and Andreas Hinterhuber. 2013. “CEO Championing of Pricing, Pricing Capabilities and Firm Performance in Industrial Firms.” Industrial Marketing Management 42 (4): 633-643. doi.org/10.1016/j.indmarman.2012.09.003
  4. Stremersch, Stefan, and Gerard J. Tellis. 2002. “Strategic Bundling of Products and Prices: A New Synthesis for Marketing.” Journal of Marketing 66 (1): 55-72. doi.org/10.1509/jmkg.66.1.55.18455
  5. Xia, Lan, Kent B. Monroe, and Jennifer L. Cox. 2004. “The Price Is Unfair! A Conceptual Framework of Price Fairness Perceptions.” Journal of Marketing 68 (4): 1-15. doi.org/10.1509/jmkg.68.4.1.42733

Volume 2 · 7 sources

Go-To-Market: A Practical Method for Testing Market Entry

  1. Camuffo, A., Cordova, A., Gambardella, A., & Spina, C. (2020). A scientific approach to entrepreneurial decision making: Evidence from a randomized control trial. Management Science, 66(2), 564–586 doi.org/10.1287/mnsc.2018.3249
  2. Evanschitzky, H., Eisend, M., Calantone, R. J., & Jiang, Y. (2012). Success factors of product innovation: An updated meta-analysis. Journal of Product Innovation Management, 29(S1), 21–37 doi.org/10.1111/j.1540-5885.2012.00964.x
  3. Gordon, B. R., Zettelmeyer, F., Bhargava, N., & Chapsky, D. (2019). A comparison of approaches to advertising measurement: Evidence from big field experiments at Facebook. Marketing Science, 38(2), 193–225 doi.org/10.1287/mksc.2018.1135
  4. Langerak, F., Hultink, E. J., & Robben, H. S. J. (2004). The impact of market orientation, product advantage, and launch proficiency on new product performance and organizational performance. Journal of Product Innovation Management, 21(2), 79–94 doi.org/10.1111/j.0737-6782.2004.00059.x
  5. Lewis, R. A., & Rao, J. M. (2015). The unfavorable economics of measuring the returns to advertising. The Quarterly Journal of Economics, 130(4), 1941–1973 doi.org/10.1093/qje/qjv023
  6. Montoya-Weiss, M. M., & Calantone, R. (1994). Determinants of new product performance: A review and meta-analysis. Journal of Product Innovation Management, 11(5), 397–417 doi.org/10.1111/1540-5885.1150397
  7. Thomke, S. H. (1998). Managing experimentation in the design of new products. Management Science, 44(6), 743–762 doi.org/10.1287/mnsc.44.6.743

Volume 3 · 5 sources

Marketing Operations: A Practical Method for Turning Demand Into Sales Work

  1. Berman, R. (2018). Beyond the last touch: Attribution in online advertising. Marketing Science, 37(5), 771–792 doi.org/10.1287/mksc.2018.1104
  2. Gordon, B. R., Zettelmeyer, F., Bhargava, N., & Chapsky, D. (2019). A comparison of approaches to advertising measurement: Evidence from big field experiments at Facebook. Marketing Science, 38(2), 193–225 doi.org/10.1287/mksc.2018.1135
  3. Le Meunier-FitzHugh, K., & Piercy, N. F. (2007). Does collaboration between sales and marketing affect business performance? Journal of Personal Selling & Sales Management, 27(3), 207–220 doi.org/10.2753/PSS0885-3134270301
  4. Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5–33 doi.org/10.1080/07421222.1996.11518099
  5. Wu, M., Andreev, P., & Benyoucef, M. (2024). The state of lead scoring models and their impact on sales performance. Information Technology and Management, 25, 69–98 doi.org/10.1007/s10799-023-00388-w

Volume 4 · 4 sources

Sales Operations: A Practical Guide to Pipeline, Forecasting, Pricing, and Handoffs

  1. Anderson, E., & Oliver, R. L. (1987). Perspectives on behavior-based versus outcome-based salesforce control systems. Journal of Marketing, 51(4), 76–88 doi.org/10.2307/1251249
  2. Fildes, R., & Goodwin, P. (2007). Against your better judgment? How organizations can improve their use of management judgment in forecasting. Interfaces, 37(6), 570–576 doi.org/10.1287/inte.1070.0309
  3. Lawrence, M., Goodwin, P., O’Connor, M., & Önkal, D. (2006). Judgmental forecasting: A review of progress over the last 25 years. International Journal of Forecasting, 22(3), 493–518 doi.org/10.1016/j.ijforecast.2006.03.007
  4. Oliver, R. L., & Anderson, E. (1995). Behavior- and outcome-based sales control systems: Evidence and consequences of pure-form and hybrid governance. Journal of Personal Selling & Sales Management, 15(4), 1–15 doi.org/10.1080/08853134.1995.10754033

Volume 5 · 14 sources

Customer Success Operations: A Practical Method for Onboarding, Retention, and Expansion

  1. Bolton, R. N. (1998). A dynamic model of the duration of the customer's relationship with a continuous service provider: The role of satisfaction. Marketing Science, 17(1), 45–65 doi.org/10.1287/mksc.17.1.45
  2. Gupta, S., Lehmann, D. R., & Stuart, J. A. (2004). Valuing customers. Journal of Marketing Research, 41(1), 7–18 doi.org/10.1509/jmkr.41.1.7.25084
  3. Gupta, S., & Zeithaml, V. A. (2006). Customer metrics and their impact on financial performance. Marketing Science, 25(6), 718–739 doi.org/10.1287/mksc.1060.0221
  4. Gustafsson, A., Johnson, M. D., & Roos, I. (2005). The effects of customer satisfaction, relationship commitment dimensions, and triggers on customer retention. Journal of Marketing, 69(4), 210–218 doi.org/10.1509/jmkg.2005.69.4.210
  5. Hilton, B., Hajihashemi, B., Henderson, C. M., & Palmatier, R. W. (2020). Customer Success Management: The next evolution in customer management practice? Industrial Marketing Management, 90, 360–369 doi.org/10.1016/j.indmarman.2020.08.001
  6. Hochstein, B., Rangarajan, D., Mehta, N., & Kocher, D. (2020). An industry/academic perspective on Customer Success Management. Journal of Service Research, 23(1), 3–7 doi.org/10.1177/1094670519896422
  7. Hochstein, B., Chaker, N. N., Rangarajan, D., Nagel, D., & Hartmann, N. N. (2021). Proactive value co-creation via structural ambidexterity: Customer Success Management and the modularization of frontline roles. Journal of Service Research, 24(4), 601–621 doi.org/10.1177/1094670521997565
  8. Hochstein, B., Voorhees, C. M., Pratt, A. B., Rangarajan, D., Nagel, D. M., & Mehrotra, V. (2023). Customer Success Management, customer health, and retention in B2B industries. International Journal of Research in Marketing, 40(4), 912–932 doi.org/10.1016/j.ijresmar.2023.09.002
  9. Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96 doi.org/10.1509/jm.15.0420
  10. Macdonald, E. K., Wilson, H., Martinez, V., & Toossi, A. (2011). Assessing value-in-use: A conceptual framework and exploratory study. Industrial Marketing Management, 40(5), 671–682 doi.org/10.1016/j.indmarman.2011.05.006
  11. Reinartz, W., Thomas, J. S., & Kumar, V. (2005). Balancing acquisition and retention resources to maximize customer profitability. Journal of Marketing, 69(1), 63–79 doi.org/10.1509/jmkg.69.1.63.55511
  12. Tuli, K. R., Kohli, A. K., & Bharadwaj, S. G. (2007). Rethinking customer solutions: From product bundles to relational processes. Journal of Marketing, 71(3), 1–17 doi.org/10.1509/jmkg.71.3.001
  13. Venkatesan, R., & Kumar, V. (2004). A customer lifetime value framework for customer selection and resource allocation strategy. Journal of Marketing, 68(4), 106–125 doi.org/10.1509/jmkg.68.4.106.42728
  14. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478 doi.org/10.2307/30036540

Volume 6 · 8 sources

Financial Operations: Reconciling Contracts, Revenue, Billing, and Cash

  1. Baños-Caballero, S., García-Teruel, P. J., & Martínez-Solano, P. (2014). Working capital management, corporate performance, and financial constraints. Journal of Business Research, 67(3), 332–338 doi.org/10.1016/j.jbusres.2013.01.016
  2. Financial Accounting Standards Board. (2014). Accounting Standards Update No. 2014-09: Revenue from contracts with customers (Topic 606) fasb.org/page/document?pdf=ASU%202014-09_Section%20A.pdf
  3. Financial Accounting Standards Board. (2016). Accounting Standards Update No. 2016-13: Financial instruments—credit losses (Topic 326): Measurement of credit losses on financial instruments storage.fasb.org/ASU%202016-13.pdf
  4. Fildes, R., & Goodwin, P. (2007). Against your better judgment? How organizations can improve their use of management judgment in forecasting. Interfaces, 37(6), 570–576 doi.org/10.1287/inte.1070.0309
  5. International Accounting Standards Board. (2014). IFRS 15: Revenue from contracts with customers www.ifrs.org/issued-standards/list-of-standards/ifrs-15-revenue-from-contracts-with-customers/
  6. IFRS Foundation. (n.d.). IFRS 9: Financial instruments. Retrieved August 9, 2026, from www.ifrs.org/issued-standards/list-of-standards/ifrs-9-financial-instruments/
  7. Napier, C. J., & Stadler, C. (2020). The real effects of a new accounting standard: The case of IFRS 15 Revenue from Contracts with Customers. Accounting and Business Research, 50(5), 474–503 doi.org/10.1080/00014788.2020.1770933
  8. Prakash, R., & Sinha, N. (2013). Deferred revenues and the matching of revenues and expenses. Contemporary Accounting Research, 30(2), 517–548 doi.org/10.1111/j.1911-3846.2012.01164.x

Volume 7 · 12 sources

Revenue Operations: Aligning Revenue Decisions, Records, and Ownership

  1. Elmagarmid, A. K., Ipeirotis, P. G., & Verykios, V. S. (2007). Duplicate record detection: A survey. IEEE Transactions on Knowledge and Data Engineering, 19(1), 1–16 doi.org/10.1109/TKDE.2007.250581
  2. Financial Accounting Standards Board. (2014). Accounting Standards Update No. 2014-09: Revenue from contracts with customers (Topic 606) fasb.org/page/document?pdf=ASU%202014-09_Section%20A.pdf
  3. Fildes, R., & Goodwin, P. (2007). Against your better judgment? How organizations can improve their use of management judgment in forecasting. Interfaces, 37(6), 570–576 doi.org/10.1287/inte.1070.0309
  4. Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688 doi.org/10.1016/j.ijforecast.2006.03.001
  5. International Accounting Standards Board. (2014). IFRS 15: Revenue from contracts with customers www.ifrs.org/issued-standards/list-of-standards/ifrs-15-revenue-from-contracts-with-customers/
  6. Kahn, B. K., Strong, D. M., & Wang, R. Y. (2002). Information quality benchmarks: Product and service performance. Communications of the ACM, 45(4), 184–192 doi.org/10.1145/505248.506007
  7. Khatri, V., & Brown, C. V. (2010). Designing data governance. Communications of the ACM, 53(1), 148–152 doi.org/10.1145/1629175.1629210
  8. Peterson, R. M., Gordon, G. L., & Krishnan Palghat, V. (2015). When sales and marketing align: Impact on performance. Journal of Selling, 15(1), 29–43 www.wku.edu/jos/documents/issues/v15n1/15-01-03.pdf
  9. Rohaan, D., Topan, E., & Groothuis-Oudshoorn, C. G. M. (2022). Using supervised machine learning for B2B sales forecasting: A case study of spare parts sales forecasting at an after-sales service provider. Expert Systems with Applications, 188, 115925 doi.org/10.1016/j.eswa.2021.115925
  10. Schneider, J.-V., Alavi, S., Guba, J. H., Wieseke, J., & Schmitz, C. (2021). When do forecasts fail and when not? Contingencies affecting the accuracy of sales managers' forecast regarding the future business situation. Journal of Personal Selling & Sales Management, 41(3), 218–232 doi.org/10.1080/08853134.2020.1859941
  11. Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5–33 doi.org/10.1080/07421222.1996.11518099
  12. Weill, P., & Ross, J. W. (2004). IT governance: How top performers manage IT decision rights for superior results. Harvard Business School Press.

Volume 8 · 12 sources

The Revenue Operations Playbook: A 30-Day Build Guide for Clear Handoffs, Reproducible Forecasts, and Decisions Teams Can Execute

  1. Adler, P. S., & Borys, B. (1996). Two types of bureaucracy: Enabling and coercive. Administrative Science Quarterly, 41(1), 61–89 doi.org/10.2307/2393986
  2. Benner, M. J., & Tushman, M. L. (2003). Exploitation, exploration, and process management: The productivity dilemma revisited. Academy of Management Review, 28(2), 238–256 doi.org/10.5465/amr.2003.9416096
  3. Carlile, P. R. (2002). A pragmatic view of knowledge and boundaries: Boundary objects in new product development. Organization Science, 13(4), 442–455 doi.org/10.1287/orsc.13.4.442.2953
  4. Elmagarmid, A. K., Ipeirotis, P. G., & Verykios, V. S. (2007). Duplicate record detection: A survey. IEEE Transactions on Knowledge and Data Engineering, 19(1), 1–16 doi.org/10.1109/TKDE.2007.250581
  5. Feldman, M. S., & Pentland, B. T. (2003). Reconceptualizing organizational routines as a source of flexibility and change. Administrative Science Quarterly, 48(1), 94–118 doi.org/10.2307/3556620
  6. Galbraith, J. R. (1974). Organization design: An information processing view. Interfaces, 4(3), 28–36 doi.org/10.1287/inte.4.3.28
  7. Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688 doi.org/10.1016/j.ijforecast.2006.03.001
  8. Kahn, B. K., Strong, D. M., & Wang, R. Y. (2002). Information quality benchmarks: Product and service performance. Communications of the ACM, 45(4), 184–192 doi.org/10.1145/505248.506007
  9. Khatri, V., & Brown, C. V. (2010). Designing data governance. Communications of the ACM, 53(1), 148–152 doi.org/10.1145/1629175.1629210
  10. Lawrence, M., Goodwin, P., O'Connor, M., & Önkal, D. (2006). Judgmental forecasting: A review of progress over the last 25 years. International Journal of Forecasting, 22(3), 493–518 doi.org/10.1016/j.ijforecast.2006.03.007
  11. Star, S. L., & Griesemer, J. R. (1989). Institutional ecology, “translations” and boundary objects: Amateurs and professionals in Berkeley's Museum of Vertebrate Zoology, 1907–39. Social Studies of Science, 19(3), 387–420 doi.org/10.1177/030631289019003001
  12. Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5–33 doi.org/10.1080/07421222.1996.11518099

Questions

Why publish the bibliography?
A claim a reader cannot check is a claim they have to take on trust. Listing every source, with a link, means they do not have to.
Are the citation styles inconsistent?
Yes. Revenue Design uses Chicago; the later volumes use APA. Each entry is reproduced exactly as its volume prints it rather than restyled here, so the page is grouped by volume and each block stays internally consistent.
Why do some entries have no link?
A DOI is shown wherever the source has one. Print books published before DOIs were routine do not, so those entries name the publisher instead.
Can I cite this page?
Yes. It is a reading list for the field as much as a record of what the series rests on.