Totango (Odie) vs User Intuition: Research and Pricing

Odie is Totango’s customer intelligence offering. The research opportunity is the question a team still needs to ask customers. An account history may reveal a delayed rollout; an interview can investigate what the customer expected, what prevented progress and how that affected the renewal decision.

User Intuition’s churn research workflow helps the team compare those accounts across relevant customers. People who renewed despite a difficult rollout may explain a different tolerance or source of value. Understanding that difference can change the proposed retention action and the audience the team should investigate next.

In User Intuition’s study of 723 recently churned SaaS customers, the first stated reason matched the deeper explanation identified through interviews in 27.4% of cases. That finding supports exploring what “price” or “low usage” means in the customer’s actual experience before selecting a retention intervention.

User Intuition’s laddering approach is informed by Reynolds and Gutman’s laddering method, which connects product attributes to consequences and personal values. For churn research, “too expensive” may lead to unrealized value, an unfinished rollout or a loss of confidence in the relationship. Tracing those consequences helps a team distinguish a pricing problem from a delivery problem and choose the follow-up evidence needed to act.

A researcher can start with a reachable cohort of recent departures, renewals or reduced-usage customers. User Intuition supports your own participants and a 4M+ vetted panel, with interviews in 80+ languages. For a company-specific churn question, your own customer list supplies the relevant relationship and decision history.

Findings arrive as quality interviews finish, allowing the team to inspect explanations while the study continues. Concurrent interviews make it practical to hear from customers in different time zones and compare experiences across cohorts without arranging a moderator for every session.

User Intuition charges $30 per quality voice interview. Incentives for your own customers are separate; specialist recruiting requires a quote. A research team can therefore budget a focused churn investigation around the customers it needs to hear from, then fund another study when an important question remains unresolved.

Odie’s pricing scales with knowledge maintenance and access; its operating-system application has separate scope-based terms. Those purchases support an ongoing customer operation. User Intuition’s interview budget supports a defined investigation, such as determining whether early departures reflect onboarding expectations, implementation difficulty or an alternative that delivers value sooner.

In churn research, a customer who completes a session but only repeats “too expensive” may leave the team unable to choose a response. User Intuition checks whether the conversation develops substantive detail and addresses the brief before applying the platform fee. That protects the budget for interviews intended to explain a decision.

Customer operations software and commissioned interviews are different purchases. User Intuition ties its interview charge to automatic quality checks, then uses evidence review to assess the resulting accounts. A researcher can distinguish a well-developed customer explanation from a finding that still needs another cohort or a competing hypothesis investigated.

User Intuition’s evidence review is informed by Malterud et al.’s information power framework and Hennink et al.’s meaning-saturation research. These approaches connect sample adequacy to relevant information and distinguish identifying a theme from understanding it in depth. For retention decisions, User Intuition uses these principles to assess whether a proposed fix addresses the experience behind departure. It can recommend analyzing existing interviews for a competing explanation or recruiting a missing cohort, such as customers who renewed despite encountering the same problem.

A shopper study illustrates how this evidence review works. We interviewed 43 Walmart shoppers to understand purchase decisions, trip friction and return intent. Thirty-six said they would shop at Walmart again. User Intuition recommends recontacting shoppers after their next comparable trip and comparing those who returned with those who chose another store. The follow-up tests which experiences turn stated intent into a store choice. Explore the Walmart study, two recommended follow-ups and sample presentation.

Odie’s Customer Knowledge Engine combines team expertise with ongoing customer data analysis. A useful research question can arise when the operating view and the customer outcome diverge: for example, a seemingly healthy account that decides not to renew.

Through User Intuition’s agentic research workflow, an assistant can prepare interviews with that cohort and estimate the spend for your team to approve. The researcher can then inspect the supporting accounts, compare explanations and decide whether the next step is deeper analysis, another audience or a targeted change to the customer experience.

User Intuition builds synthetic participants from real, opted-in respondents, using their individual traits, habits and behaviors. It repeatedly compares synthetic responses with the same respondents’ real answers to calibrate the models. That grounding preserves differences in experience and priorities that generic LLM feedback tends to flatten.

This design is informed by Park et al. (2024). Their simulations of 1,052 people reproduced General Social Survey answers 85% as accurately as those people reproduced their own answers two weeks later. The study informs User Intuition’s emphasis on individual grounding and repeated calibration. For retention research, the useful output is a specific hypothesis about why a customer would renew, reconsider or leave, grounded in differences between individual experiences and priorities.