When the Revenue Report Lives in a Spreadsheet No One Is Maintaining

The site revenue report comes out of the BSS every month. Someone exports it, puts it in a shared folder, and regional managers pull the data they need. The problem is that nobody is tagging sites by cluster and sub-branch consistently, nobody is running the 2G/3G split analysis at the individual site level, and by the time the MoM growth report arrives at the branch level it has already been aggregated past the granularity that tells you which specific sites are declining.

A telecom revenue management tool that lives in a mobile database accessible to a field manager standing in front of a BTS tower in an outer district of East Java is a different thing from a desktop BI report. The RevSite template brings the site-level revenue picture into a queryable field database that works without a corporate network connection.

The Hierarchy That Makes Cluster Analysis Possible

The site identification structure — sitename, siteid, branch, subbranch, cluster, kabkec — is a six-level administrative hierarchy that mirrors the organizational structure of a large mobile operator in a geographically complex market. Branch is the top-level regional grouping. Subbranch is the mid-tier. Cluster groups adjacent sites for radio and revenue planning purposes. Kabkec (kabupaten/kecamatan — district/sub-district) ties the site to its administrative geography, which is the jurisdiction level that determines roaming settlements and certain regulatory reporting requirements.

Having this hierarchy in the database means filtering is possible at every level simultaneously. You can pull all sites in a specific cluster that show MoM revenue decline. You can pull all sites in a subbranch where the 2G/3G revenue distribution is outside the expected range. You can find sites in a kabkec that are categorized as a specific site type (catagorySite_M — the current month's category classification, which in Indonesian telecom practice typically reflects revenue tier: Gold, Silver, Bronze, or equivalent) and compare their performance against the cluster average.

Three Months of Revenue Visible in One Record

RevenueSite_M, RevenueSite_M-1, and RevenueSite_M-2 — current month, one month prior, two months prior — create a three-period revenue trend without requiring a time-series query. The MoM_Growth_Rev field stores the calculated growth rate between M and M-1, which means trend analysis at the site level is visible in the list view without opening individual records.

The 2G/3G revenue split fields do the frequency-level analysis that aggregate site revenue cannot. Rev_2G_M and Rev_3G_M give the absolute revenue by technology. Percent_2GRev_M and Percent_3GRev_M give the share. 2G3G_RevDistribution_M captures the ratio in a format that makes deviation from the cluster benchmark immediately visible.

Within the 3G breakdown, the template tracks revenue by frequency carrier: F1, F2, F3, and U900, each with a standard revenue figure and a 6-second revenue figure — the 6-second billing increment that was standard in Indonesian prepaid markets and that affects revenue recognition differently from full-minute billing. A site generating strong total 3G revenue but concentrated in U900 with weak F1 performance is a signal about coverage pattern and device mix that the aggregate number hides.

Latlong as a Field Operations Tool

The latlong GPS location field turns the site record into a navigable point. A regional manager reviewing cluster performance and finding three sites in negative MoM growth can open each record, check the coordinates, and see where they are in relation to each other — identifying whether the decline is geographically clustered (suggesting a network event or competitive incursion) or distributed (suggesting a broader market issue).

Frekwensi_Site records which frequency bands the site is actually running — not what the plan shows, but what is active. In Indonesian telecom markets where spectrum refarming was ongoing and U900 deployment was uneven across the island groups, knowing which frequencies are live at a specific site is the ground truth that the network inventory system may not have current.