AI for insights and analytics
A territory reports 80 percent reach over ten targets, but two are blocked and fourteen of twenty-eight calls went to accounts that cannot prescribe. AI analytics reads the call, access and prescription files, and answers the district manager: which calls could not have worked, and where has an access win not pulled through.
These are the questions pharma commercial teams ask. Each one maps to a measure our tools compute from your files. The AI model chooses the measure and explains the result; the arithmetic is done by our code, and every total is checked.
| The question | The measure behind it | What comes back |
|---|---|---|
| What is reach and frequency among accounts that can actually prescribe? | Reach and frequency, access-aware | Reach and frequency over accessible targets only, by territory, beside the unadjusted figures. |
| How many calls went to blocked accounts? | Calls on blocked accounts | Calls by territory on accounts with no access, with the accounts and the reps. |
| Where did an access win not turn into prescriptions? | Pull-through after access win | Prescriptions after a formulary or access change against the expected curve, by account. |
| Is the target list current? | Target list currency | Targets whose access, address or prescribing status changed since the list was set, by territory. |
| Which new prescribers activated after the first call? | New prescriber activation | Targets with a first prescription within the window after first call, by territory. |
| Which open-access accounts received no call? | Share within accessible accounts | Accessible targets uncalled in the cycle, by tier, with the potential. |
Every figure below was computed by a tool from the rows and checked before it was shown. The lines sum; the percentages match; each line opens to the rows that make it.
How many calls this cycle went to accounts that cannot prescribe?
450 calls this cycle went to blocked or unknown-access accounts across 3 districts, out of 2,900 calls made. Northeast district accounts for 210 of them, 47% of the wasted calls.
| Northeast district | 210 | 47% of the total |
| Southeast district | 150 | 33% of the total |
| Central district | 90 | 20% of the total |
| The 3 lines sum to | 450 | 0 unexplained |
Which northeast territories?
3 territories account for 185 of the 210 blocked calls in the Northeast district, 88% of them. In the largest, seven calls went to one blocked account while two open-access tier-one targets received none.
| Territory NE-04 | 84 | 7 calls on one blocked account; 2 tier-one targets uncalled |
| Territory NE-07 | 61 | target list 9 months old |
| Territory NE-02 | 40 | 3 targets changed access mid-cycle |
| These 3 are | 185 | 88% of Northeast district |
What does the district manager change before next cycle?
Refresh the target lists with current access, move the blocked calls to the uncalled tier-one accounts, and report access-aware reach beside the headline. The same question next cycle shows the shift.
| District manager, Northeast | Refresh target lists with current access status | Before next cycle |
| Reps NE-04 and NE-07 | Call the uncalled open-access tier-one targets | This cycle |
| Commercial operations | Report access-aware reach and frequency by territory | Each cycle |
Each measure has one formula, one source and one meaning. They are computed per territory and rep and in total, and every one carries an identity that must hold before it is shown.
| Measure | Formula | From | What it tells you |
|---|---|---|---|
| Reach and frequency, access-aware | Accessible targets called ÷ accessible targets; calls ÷ accessible targets called | Call log; dated access file | Coverage where a prescription is possible |
| Calls on blocked accounts | Calls made while the account was blocked ÷ all calls | Call log; dated access file | Effort that could not convert |
| Access-weighted territory potential | Category volume of accounts, weighted by access status, per territory | Prescription data; access file | Where reps should be |
| Pull-through after access win | Change in share in accounts covered by the win, months 1 to 6, against own prior trend | Prescription data; access file | Whether prescribing followed access |
| Call plan attainment by version | Planned calls completed ÷ planned, each against the plan version in force on its date | Call log; versioned plan | Attainment that can be reproduced |
| Sample to prescription relationship | Change in prescriptions against samples left, per territory, with lag | Sample log; prescription data | What the samples moved |
| Target list currency | Targets with status reviewed in the cycle ÷ targets; high prescribers not on the list | Target list; prescription data | Lists built on last year |
| New prescriber activation | Targets writing a first prescription in the period ÷ targets with none before | Prescription data | Whether calls create prescribers |
| Share within accessible accounts | Product prescriptions ÷ category prescriptions, in accessible accounts | Prescription data; access file | Performance where it could be achieved |
| Off-plan call rate | Calls on accounts not in the plan ÷ calls, with the outcome of those accounts | Call log; plan | Reps following judgement the plan lacks, or avoiding the plan |
Each measure is worked through, with the export it comes from and what to drop, in Commercial KPIs for pharma sales teams.
From your question and the measures declared for pharma, the model picks the one that answers it, and the period and comparison the question implies.
Deterministic code reads the rows, computes the measure, and checks the identities below. The same question on the same data gives the same answer, every time.
The AI model writes the sentence around the result, naming the territory or account behind it. It states no figure that is not in the result, and every figure links to its rows.
| Access | Targets = open + restricted + blocked + unknown, on any date |
| Calls | Calls = on plan + off plan; on accessible + on blocked + on unknown |
| Plan | Every call is assessed against exactly one plan version |
| Prescriptions | Account prescriptions sum to the territory total in the data source |
The exports pharma commercial teams already produce. Column names are mapped once and the mapping is reused. A file is the way in; scheduled delivery and connections to your systems come with the plan, and every source is listed here.
An answer is a list with an owner and a cadence, or it is a chart nobody works.
| Measures | Owner | Cadence |
|---|---|---|
| Access-aware reach and frequency; blocked calls | District managers | Each cycle, weekly within it |
| Pull-through | Commercial head with market access | Monthly after each win |
| Attainment by version; off-plan rate; list currency | Sales force effectiveness | Each cycle |
| Access-weighted potential; share in accessible accounts | Commercial head | Quarterly, and at realignment |
Reach and frequency computed only over accounts that can prescribe, from the access file you provide. A territory at 80 percent reach over ten targets is 71 percent over the seven accessible ones, and the difference is calls that could not have worked. Both figures are shown, and the blocked calls are listed.
Yes, when the prescription file and the access change dates are loaded. Prescriptions after an access win are compared with the expected curve you set, by account, and the accounts that have not moved are listed with the calls made since. The tool computes; the commercial head decides whether the field or market access owns the gap.
The tool needs prescriber-level call and prescription counts, not patient data. Prescriber identifiers can be replaced with codes before upload on Enterprise, and every file is read where you choose to run it. The model never receives the rows.
The call file by rep and account, the target list with tiers, the access file with status and change dates, and prescription data by prescriber and month. Sample records add the sample-to-prescription relationship.
Access status · Reach and frequency · Pull-through · List age · Territory potential · Coverage
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Bring a few thousand rows. The data map opens next, every column mapped once, and the first question is answered in minutes. Free, in your browser, no account.