Customer journey analytics: different maps for prepaid, postpaid and B2B

Three different worlds

Inside one telecom operator three structurally different customer types live in parallel, and their journeys hardly resemble each other.

A prepaid customer. Activates a SIM, tops up periodically, occasionally changes a tariff or adds a package. Contacts the operator rarely — a few times a year, often only for a top-up. The decision cycle is short and often emotional — disappointed by quality, saw a competitor offer, left.

A postpaid customer. Has a contract, monthly billing, more often an active subscription on digital services, more often uses the app. Contacts the operator regularly — at least a couple of times a month. The decision cycle is longer, the relationship more complex, churn rarer but more painful.

A B2B customer. A corporate contract, a fleet of employee SIMs, additional services (cloud, security, IoT, internet), regular billing reviews, an account manager. Dozens of contacts a month across channels — sales, billing, support, technical. Decisions are collective, slower, tied to budget cycles.

A universal journey map trying to describe all three is a compromise that does not work for any of them.

Where the prepaid journey usually breaks

Key moments — first activation, first top-up, recurring top-up cycles, the churn moment.

First activation. The customer gets the SIM and activates. What happens next is critical. If the customer immediately gets welcome content (how to use the package, what is included, what to do at the first issue), retention in the first 90 days is meaningfully higher. If nothing — the customer is left without guidance and leaves at the first problem.

First top-up. The customer tops up for the first time. A signal of active use. If at this moment the operator offers a relevant package (based on initial usage pattern), conversion to additional sales is high. Most operators do not use this moment.

Top-up cycle. The customer tops up regularly. Pattern changes — frequency up or down, volume change, switch from small frequent to one large — are signals. A deviation from the usual pattern should trigger an action — relevant offer, preventive outreach, something.

Churn moment. The customer stops topping up or sharply reduces activity. At most operators no outreach happens at this moment — the customer simply disappears from the active base. A missed opportunity for a last contact.

Where the postpaid journey is specific

The postpaid journey is longer and more layered. Key moments:

Onboarding — the first 30 days. A postpaid customer often signs up for several services at once. If they have not activated and used in the first 30 days what they signed up for, this signals the service is unneeded or unclear, and a downgrade or refusal will come within 90 days.

The first bill. The moment the customer receives their first bill. If it matches expectations — relationship building. If it is higher than expected or contains unclear positions — a moment of trust loss. Most postpaid customer-care complaints are connected to this first bill.

Package usage. If the customer consistently uses less than 30% of the package, that is an “overpaying” signal. A relevant downgrade offer could prevent churn (the customer will eventually realise the overpay and leave). If the customer uses more than 80% or exceeds — a relevant upgrade offer.

Annual review. Many postpaid contracts have renewal or annual review. A retention moment. Preparation for the annual review is a serious focus.

Where the B2B journey is fundamentally different

B2B is served through many touchpoints. Specifics:

A long decision cycle. From request to contract signing — 3-12 months. Many contacts in that period — sales, technical, financial, legal.

Multi-stakeholder. One B2B customer has 5-10 people involved with the operator. CFO, CIO, IT team, financial, procurement. Different needs and expectations.

Regular service review. Quarterly business reviews with the account manager are standard. Their absence is a signal of poor service.

Renewal as an event. Contract renewal is a separate sales event. Preparation takes 3-6 months. Without proper management, renewals often go to a competitor.

Issue management. B2B complaints have a different structure — they are often escalation chains, require SLAs, require formal responses. A different operations model from B2C support entirely.

What type-specific analytics means

Universal metrics — ARPU, churn, NPS — exist for all, but dashboards and triggers are structurally different.

Prepaid analytics. Focus on usage patterns and top-up dynamics. Trigger-based communications tied to balance level, expected top-up date, pattern changes. KPI — retention activity, recharge frequency, ARPU.

Postpaid analytics. Focus on usage within the package, on monthly billing patterns, on annual review preparation. Trigger-based communications tied to billing cycle events and usage anomalies. KPI — retention, cross-sell, average revenue per account.

B2B analytics. Focus on account health (is the contract being used, which services are under-used, which over-used), stakeholder engagement (regular touch with decision makers), renewal pipeline. KPI — account expansion, retention, NPS across stakeholder roles.

Each type needs its own dashboard, its own operating model, its own team. A universal analytics team serving all three specialises in none.

What is often done incorrectly

One customer journey map for all. As discussed — a compromise.

Cross-cutting KPIs like retention rate as one number. Without a split between prepaid, postpaid and B2B, one number deceives. A good number in one type can hide a problem in another.

Marketing campaigns that do not differentiate by type. A postpaid customer gets a campaign aimed at prepaid logic — they have other priorities.

Customer care treating everyone the same. A B2B incident through a ticket queue with a standard SLA is unacceptable for a corporate customer.

Data infrastructure that does not segment by type at the event level. Events flow into one stream without type metadata, and downstream analytics is hard.

What specialised analytics requires

Type segmentation at event level. Each customer event tagged with type. Simplifies downstream aggregation, dashboards, triggers.

Three operating teams. Not one — three separate teams each with its own specialty: prepaid commercial, postpaid commercial, B2B commercial. They run on a shared data foundation but with different priorities and tools.

Type-specific journey maps. Each type has its own explicitly described journey with key moments. Maps refreshed annually based on observed behaviour.

Type-specific dashboards. Not a universal dashboard but three. Each shows what matters for that type.

Type-specific triggers and offers. The same product can have different packaging for different types. And triggers on that packaging differ.

When specialised analytics is not a priority

If the organisation is in an acute phase of restoring basic data discipline (sources do not reconcile, master IDs do not work), specialisation on a broken foundation does not help.

If B2B is weak (under 5% of revenue), a separate B2B operating model may be excessive. A small dedicated team is enough.

If competition in prepaid or postpaid is weak, specialised optimisation gives a marginal result — the market does not require it.

If IT does not have capacity to build three separate dashboard infrastructures in parallel, a sequential approach is needed starting with the largest segment.

If teams cannot coexist with overlapping responsibilities, specialisation creates political friction.

Discussion points for the committee

What is the ARPU and retention distribution across prepaid/postpaid/B2B today? If unknown — diagnostic from there.

Which 3 moments in each type’s journey generate the most complaints or churn? That is the priority list per type.

Who owns customer journey as a function for each type? If one person owns all — that is the problem.

Is the organisation ready for a dedicated team on one type as a pilot?

What is the data foundation for type-specific analytics? Without event-level type metadata that is the first thing.

How SamaraliSoft can help

Customer Journey Analytics by Type — analysis of each type’s specific journey at the operator, identification of the top 5-10 moments per type where value is created or lost, design of type-specific dashboards and triggers, organisational design of dedicated teams, and a 90-120 day pilot on one type.

Related reading

• /en/insights/telecom-subscriber-intelligence-operating-model/ — operating model
• /en/insights/telecom-micro-segments/ — micro-segmentation
• /en/insights/telecom-nba/ — NBA as context
• /en/use-cases/telecom-churn-war-room-mnp/ — retention in the MNP era

Sources

• Digital Government Portal — digital infrastructure UZ
• UzDaily — Central Asia telecom market
• Ministry of Digital Technologies — telecom in UZ
• MNP.UZ — number portability