A two-year churn intelligence system — from the Dec 2021 CEO executive report through the 2022 weekly dashboards — covering BAM mobile, Fiber home, postpaid, prepaid and business. Cohort analysis, competitive portout intelligence, retention funnel, A/B campaigns, and a churn prediction model that generated zero retentions and taught the team exactly why behavioral signals beat propensity scores.
The December 2021 executive report to the CEO established the baseline from which the 2022 weekly dashboard system was built. Three findings shaped the analytical priorities for the year ahead: a competitive advantage in portout that was being eroded, a retention funnel that exceeded contact effectiveness targets but revealed a critical model failure, and a growing volume of deactivation intents that the team was only partially converting.
WOM's portout was +8.8% vs the industry's +14.4% — already a competitive advantage. But Entel's consumer portout from WOM was growing at +38.7%, disproportionate to its share. This identified Entel as the priority competitor for plan-level retention intelligence, and $11K–$13K as the price band to defend.
The retention operation ran across four initiative types in W44–47: NPG (negotiation before portout), bajas (deactivation intents from stores and call center), handset renewals, and a churn prediction model. The funnel performance was healthy — until the model results came in. 14,292 clients scored, 2,185 contacted, 0 retentions. Not a targeting problem: the model was surfacing the wrong clients for the retention offer.
The 74.3% valid contact rate and 17.5% effectiveness both exceeded targets (10% target). But N2+ (second-inquiry) subscribers had 34.8% effectiveness — 2× higher than first-inquiry. This suggested that clients who called twice were more committed to leaving and thus more receptive to an offer. The funnel wasn't the problem. The model was.
This is the most important finding in the Dec 2021 report. A churn prediction model with 0% retention rate doesn't mean the model scored wrong — it means the intervention was wrong for that population. Clients predicted to churn in 30–60 days are in a different decision stage than clients who have already called to port. The right intervention for each stage is different. This insight directly shaped how the 2022 weekly dashboards prioritised behavioral signals over modelled propensity scores.
−7.57 lines per account (main line). No retention intervention. Business accounts losing lines naturally.
−2.65 lines per account. 434 lines retained in November. 65% reduction in line losses vs control — same pattern as the 2022 BAM data campaign but for the Business segment.
WOM Chile's Customer Engagement team needed more than a monthly churn number. They needed to know which customers were about to leave, why, and which intervention would stop them — segmented by product line, plan, tenure cohort, billing cycle and geography. A single monthly rate is a post-mortem. A weekly intelligence system is a prevention tool.
This case study covers the analytics infrastructure built in 2022 for two product lines: BAM mobile broadband and WOM Hogar fiber. Each had its own churn drivers, its own early-warning signals, and its own retention levers. The dashboards unified them into a single weekly cadence while preserving the product-specific granularity that made the insights actionable.
Monthly churn rate by product. No cohort view, no traffic correlation, no geographic segmentation. Retention decisions made reactively after subscribers had already left.
Weekly dashboards with daily churn tracking, cohort heatmaps, CAP-exhaustion-to-churn correlation, geographic breakdown, live A/B campaign performance, and actionable retention proposals with estimated impact in pp.
The pandemic effect is visible in the cohort data. High churn in 2020 (peak 7.2% Jun-Oct 2020) reflected subscriber activations during lockdown that lapsed as mobility returned. By May 2022 the pattern had stabilised — but the mix had shifted: 54% of churn was now concentrated in subscribers with 0–9 months tenure, a signal that early-life retention was the priority lever.
Portout and voluntary together represent 80%+ of churn — both are preventable. Arrears churn is a different problem (credit risk, not product fit). Separating these in the dashboard meant the retention and collections teams worked off different queues with different logic.
18% of the subscriber base hits their data cap every month. Of those, 70% are on the three highest-cap plans (100 GB, 150 GB, 200 GB) — which are also the three most deviated plans in churn rate. The subscribers most likely to churn voluntarily were also the ones running out of data. That's the retention lever: give them more data before they decide to leave.
Two opposing churn signals, both actionable: subscribers who use ALL their data (frustrated, need more) and subscribers who use NONE (disengaged, need reactivation). A single retention campaign can't address both — the dashboard surfaced both queues separately.
Early-life churn (0–3 months) and long-tenure churn (25+ months) peak simultaneously — two different problems. New subscribers who didn't find product-market fit, and long-tenured subscribers who stopped finding value. The middle cohort (7–12 months) is the most stable. Any retention program should prioritise the extremes, not the average.
Bundle customers (Fiber + TV) churn at 2× the rate of Naked customers. At first glance this seems counterintuitive — more services should mean more stickiness. The data suggests the opposite: Bundle customers have higher expectations, more points of failure, and are more likely to compare total cost against competitors. Naked churn (1.33–1.69%) was remarkably stable; Bundle churn (1.73–2.77%) showed much more variance.
40% of "I don't need it" fiber churn happens in the first 3 months. This isn't a pricing problem or a service quality problem — it's an onboarding problem. Subscribers who declare this reason made the wrong purchase decision, often during an aggressive commercial push. The data makes the case for better qualification at point of sale.
San Ramón (98%) and Cerro Navia (84%) show near-total incidence overlap with voluntary churn. When a network incident's incidence rate approaches 100% of the churning population in a zone, the churn is not "voluntary" in the commercial sense — it's a service quality failure. This allowed the infrastructure team to prioritise remediation zones by their predicted churn impact, not just by ticket volume.
The traffic analysis led to a direct retention proposal: proactively double the data allowance for subscribers who regularly exhaust their monthly cap. The target population was ~16,500 lines. Instead of rolling it out immediately, a three-variant A/B test was run across different bag sizes to find the most cost-effective dose.
The 70 GB bag was the most effective at 3.5% churn — but the delta from 35 GB (4.4%) to 70 GB (3.5%) is 0.9pp for double the data cost. The retention decision isn't "which bag works?" — it's "what's the cost per prevented churn at each tier?" The A/B result gave the Finance team the elasticity curve they needed to set policy. Estimated impact: −0.50pp overall monthly churn · ~14,850 subscribers retained out of 16,500 at risk.
"A churn rate is a fact. A churn rate with a tenure cohort, a traffic signal, a geographic breakdown and a tested intervention is a decision."
From revenue assurance and fraud detection to A/B campaign analytics and churn intelligence — consistent discipline: instrument before you need it, and make the output actionable.
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