AI for insights and analytics
A products-per-customer figure moves from 2.4 to 2.9 after duplicates are resolved and back to 2.3 after unused products are removed, and neither correction was visible in the headline. AI analytics reads the customer master and product files, resolves the view, and answers the segment head's questions with figures that say what they include.
These are the questions financial services firms 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 |
|---|---|---|
| How many customers do we really have, and how many products each? | Single customer view coverage | Customers after duplicate resolution, products per customer before and after, with the duplicates listed. |
| Which products were opened and never used? | Opened and unused | Products with no activity since opening, by product, channel and month, and the customers holding them. |
| Which segments hold fewer products than their norm? | Products held against segment norm | Products per customer against the segment median, with the gap in customers and value. |
| Who is showing signs of leaving? | Balance and activity attrition | Customers whose balances and activity fell against their own baseline, ranked by value and pace. |
| Which high-value customers have not been contacted? | Contact recency by value | Days since last contact by customer value band, with the untouched top band listed. |
| Which customers are moving between segments? | Segment migration | Customers whose value or activity moved them across a segment boundary, in both directions. |
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 products were opened and never used last year?
12,400 products opened last year have had no activity since, across 3 channels. Branch channel accounts for 6,400 of them, 52% of the total.
| Branch channel | 6,400 | 52% of the total |
| Digital channel | 4,100 | 33% of the total |
| Contact centre | 1,900 | 15% of the total |
| The 3 lines sum to | 12,400 | 0 unexplained |
Which branch products?
3 products account for 5,600 of the 6,400 unused openings in the Branch channel, 88% of it. The savings account is opened alongside a current account as a matter of routine and then never funded.
| Easy saver account | 3,100 | opened with a current account; 71% never funded |
| Overdraft facility | 1,600 | arranged and never drawn |
| Credit card | 900 | issued and never activated |
| These 3 are | 5,600 | 88% of Branch channel |
What should product and branch do about it?
Stop counting unused openings as sales, and start the funding conversation at opening. The tool gives the branch list, the product list and the customers, so both teams work from the same figures.
| Product head, savings | Change the opening flow to include a first deposit | This quarter |
| Branch network | Contact customers with an unfunded saver opened in the last 90 days | This month |
| Data lead | Report products per customer after duplicates and unused removed | Monthly |
Each measure has one formula, one source and one meaning. They are computed per segment and relationship owner and in total, and every one carries an identity that must hold before it is shown.
| Measure | Formula | From | What it tells you |
|---|---|---|---|
| Single customer view coverage | Accounts, and revenue, linked to a resolved customer ÷ total, by match method | Product systems; customer master | Whether any per-customer measure can be trusted |
| Products held against segment norm | Active products held ÷ median for the customer's segment | Holdings; segment file | Customers thinner than their peers |
| Opened and unused | Products with no funding or transaction within the window ÷ products opened, by product and origin | Product systems; transaction file | Cross-sell that did not happen |
| Tenure against depth | Customers by tenure band and products held; long tenure, low depth list | Customer master; holdings | Loyal customers never developed |
| Segment migration | Customers moving up or down a value band in the period, with the norm re-applied | Balances; revenue; segment rules | Customers outgrowing their service model |
| Primary relationship indicators | Salary or main income credited; share of outgoings through the account | Transaction file | Whether the firm is the main provider |
| Balance and activity attrition | Balances and transaction counts, last 3 months ÷ own prior year | Balance and transaction files | Customers leaving before they close anything |
| Contact recency by value | Revenue of customers contacted within cadence ÷ revenue of managed customers | CRM; revenue file | High-value customers nobody has spoken to |
| Repeat complaints and contacts | Customers with a second complaint or the same issue within 30 days ÷ customers who complained | Service log | Service failures that precede attrition |
| Revenue concentration | Top decile's share of revenue; largest relationships | Revenue file, by resolved customer | Dependence, visible only once customers are resolved |
Each measure is worked through, with the export it comes from and what to drop, in Customer KPIs for financial services firms.
From your question and the measures declared for financial services, 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 segment or account behind it. It states no figure that is not in the result, and every figure links to its rows.
| Customer view | Accounts = linked to a resolved customer + unlinked; revenue likewise |
| Holdings | Product revenue by customer sums to the revenue ledger |
| Opened and unused | Opened = active + unused + closed within window |
| Migration | Customers at start + new − lost = customers at end, per band, with moves netting to zero |
The exports financial services firms 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 |
|---|---|---|
| Single customer view coverage | Data or operations lead, with each product head | Monthly |
| Attrition signals; contact recency | Segment heads; relationship owners | Weekly to monthly |
| Products against norm; tenure and depth; opened and unused | Head of customer or distribution | Quarterly |
| Migration; concentration | Segment heads; finance | Quarterly |
By the rules you give it on the data map: matching identifiers, then names and addresses within the tolerance you set. Every merge is listed, so the operations lead can see which records became one customer and undo a match that is wrong. The products-per-customer figure is shown before and after.
Because the two figures answer different questions. Products held says what the book looks like; products used says what the selling achieved. Reporting only the first hides openings that were never funded, and reporting only the second hides the book. The tool shows both and the difference.
A fall in balances and in activity against the customer's own baseline, over a pace the segment head sets. It is a signal, not a prediction: the customer is listed with the figures, and the relationship owner decides what to do. No score is produced by a model.
The customer master, products held by customer, balances or activity by month, and the contact log. Names can be replaced with identifiers before anything is uploaded, and every file is matched to your column names once.
Customer master · Norm penetration · Segment migration · Contact recency · Tenure band · Identifier map
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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.