Discover how Revology Analytics propels mid-market businesses to sustainable, profitable growth by building advanced, in-house Revenue Growth Analytics & Management (RGM) capabilities—fast.
Our senior expert-led, hands-on approach ensures you own the tools, insights, strategy and processes needed to thrive long-term.
We would love to hear from you.
Let’s chat!
Gain exclusive access to the latest insights from over 150 commercial leaders on the state of Revenue Growth Analytics in 2025, based on our expanded Revenue Growth Analytics Maturity Scorecard™.
Discover how Revology Analytics propels mid-market businesses to sustainable, profitable growth by building advanced, in-house Revenue Growth Analytics & Management (RGM) capabilities—fast.
Our senior expert-led, hands-on approach ensures you own the tools, insights, strategy and processes needed to thrive long-term.
We would love to hear from you.
Let’s chat!
Gain exclusive access to the latest insights from over 150 commercial leaders on the state of Revenue Growth Analytics in 2025, based on our expanded Revenue Growth Analytics Maturity Scorecard™.
Cross-sell and up-sell should fire when customer behavior crosses a threshold, not when the quarterly campaign calendar says so. Revology co-designs AI agents that monitor account signals continuously and surface next-best-action recommendations to the right rep, channel, or workflow. For mid-market companies ($100M–$2B), cross-sell propensity, up-sell readiness, and churn risk run from the same lifetime-value engine, so your team sees growth and retention in one view. Built inside your CRM and data stack. Owned by your team. Live in 90–120 days.
Churn, cross-sell, and up-sell should run from the same customer intelligence layer. Revology co-designs and builds AI agents that route next-best actions inside your CRM.
Instead of discovering churn after the customer is already gone, you’ll have early warning alerts for at-risk customers. For example, if a subscription customer’s usage drops dramatically or a loyalty shopper hasn’t visited in a while, the system will prompt a retention action (such as a personalized discount, loyalty reward, or a courtesy outreach) while there’s still time to change their mind. This proactive approach has been shown to significantly improve retention rates and reduce churn-related revenue loss.
y analyzing purchase history and behavioral data, the models can identify ideal cross-sell and up-sell opportunities that sales or marketing teams might miss. The system might suggest, for instance, that a customer who bought product X is likely to buy product Y within 3 months, and prompt a targeted offer. These data-driven cross-sell/up-sell offers tend to have a high success rate, boosting average order value and deepening the customer’s relationship with your brand.
Sales reps or customer success teams are armed with data-backed recommendations for each account, which means they spend less time guessing which product to pitch or which customers to focus on. The prioritization of leads (who is likely to churn, who is primed to buy more) helps allocate their time to the highest-impact activities. In short, your team can close more upsell deals and save at-risk accounts more efficiently by following the system’s guidance.
Because these churn and recommendation algorithms run on your existing CRM or business intelligence platform, they scale easily as your business grows. Whether you add new product lines, expand to new customer segments, or enter new regions, the models can be retrained or adjusted without having to rebuild the whole system from scratch. It’s a future-proof solution that grows with your customer base.
We implement psychological pricing enhancements in a methodical way to ensure they genuinely drive results and fit your brand
We work with you to identify key indicators of churn and cross-sell success specific to your business. This might include usage patterns (for a software product), contract renewal dates, customer service interactions, purchase frequency, etc. We define what outcomes to predict (e.g. churn in next 90 days, or propensity to buy a certain product) and how the end-users will consume these predictions (for example, as alerts in Salesforce or as a report for account managers).
Using your historical data, we develop churn propensity models and product recommendation engines with machine learning libraries (e.g. scikit-learn, XGBoost, or TensorFlow). We might use classification models for churn (producing a risk score for each customer) and collaborative filtering or association models for recommendations. We rigorously test the models against actual historical outcomes – measuring precision, recall, and overall predictive accuracy – and iterate until they meet high performance standards.
We deploy the final models directly into your operational systems so they seamlessly fit your workflow. For instance, if you use Salesforce, we can integrate the churn scores and next-best-action recommendations into the Salesforce interface (e.g. as fields or notifications for each account). Alternatively, the outputs can be surfaced in a BI dashboard or even via automated email alerts. The key is that your team doesn’t have to go to a separate tool – the insights appear where your users already spend their time.
You receive the complete codebase, model documentation, and an “operations playbook” so you can retrain or refine the models as your business and data evolve. We encourage full in-sourcing of this capability – we’ll often schedule a workshop to train your data science or analytics team on how the model works under the hood. This way, you can tweak features or thresholds, and incorporate new data sources over time, without needing external support. The models are yours to own and adapt for maximum autonomy.
(Many organizations have leveraged this approach to great effect. For example, a telecom provider used ML-based churn scores to target at-risk subscribers with special retention offers, achieving a 25% reduction in churn in a pilot group. In B2B SaaS, a client implemented an upsell recommendation model that pointed account managers to the most receptive customers for add-on services, resulting in a 15% increase in upsell revenue quarter-over-quarter. These outcomes illustrate the power of acting on predictive customer insights.)
This guide outlines six methods for estimating willingness to pay and the governance needed to translate these estimates into effective pricing decisions. Many companies set
Some companies are already using AI well. But most mid-market Manufacturers and Distributors are not. A lot of what’s out there is either theory or built for teams with data scientists and a big software budget, so plenty of commercial teams are stuck, knowing they should do more with AI but not knowing where to start.
How data brokers and identity graphs quietly set personalized prices: what the evidence proves, what it doesn’t, and how pricing leaders should govern the risk.
A Pricing & RGM Analytics Navigator Your Team Owns — Six Modules, One GL-Reconciled Foundation, and the Agent Layer That Follows Most CPG Pricing and
A pharma pricing analytics engine is a governed decision-support system that integrates transaction, contract, channel, market, macroeconomic, regulatory, and competitive data to improve price setting,
This manual is designed for CEOs, CFOs, CCOs, Pricing and RGM leaders, and commercial analytics teams at mid-market CPG manufacturers. It outlines six decision modules,
Revology Analytics® is the end-to-end Pricing & Revenue Growth Management advisory firm for mid-market companies: strategy and governance, AI-enabled analytics, and the adoption that turns both into profit.
Over 225 companies took our scorecard to improve their Revenue Growth Analytics & Management capabilities.
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