The chain was real. The view of it was not.
Amplify sells life and annuity policies online, backed by a calling team and multiple carrier partners. Its data lived where a distributor's data always lives: ad platforms, a CRM, carrier feeds, commission statements, and a cloud warehouse.
Dashboards answered what happened. Why took a request and a wait. And the full picture, from a marketing dollar to the premium that actually stays on the books, was something each leader assembled by hand for their own corner of the business, every single week.
Connected within an hour. Speaking Amplify's language the same day.
Klaris connected to Amplify's warehouse with read only access. No rip and replace, no migration project, no new dashboard to learn.
Then it learned the company's own definitions: what counts as a qualified lead, what a carrier application submission means, how inforce is defined, which carriers and channels matter. From that point on, every answer and every report spoke Amplify's language.
The first useful answers arrived the same hour the connection was made.
The CMO cuts the funnel any way she needs.
Amplify's marketing leader runs her own analysis now. When she needed application conversion by individual campaign, a view her dashboard was never built to show, she asked for it in plain English and had it in seconds. When the answer raised new questions, she kept going: ten follow ups in one sitting, no analyst in the loop, no ticket, no wait.
- Down to the page level. Affiliate sources that drive a quarter of monthly spend, broken out by the individual pages delivering the leads.
- Her own forecasts. Next month's campaign plan, built by her, against her targets, in her definitions.
- The Monday tracker, automated. The weekly performance sheet she shares with her agency now assembles itself, fresh data waiting when her week starts.
The CFO watches the week like for like.
For Amplify's CFO, Klaris is the place he goes for analysis he can trust: quick pulls, double checks, and deep dives in seconds, with the query behind every answer open for inspection.
The view he values most is one no dashboard ever gave him: this week compared to the same point in prior weeks, with maturation factored out, so a real problem shows up in days instead of after the month closes. He projects where the month lands on submissions and placements while there is still time to act on it.
When a number breaks pattern, the question is not what happened. It is where is the problem, and where do I dive in.
His weekly executive narrative, the report that goes to the CEO, now follows the same path: the comparisons run, the root cause checklist works in order, and the write up arrives ready to share.
The CEO asks the hard questions herself.
Persistency, the measure of what actually stays on the books, is the number a distribution CEO lives with. Amplify's CEO interrogates it directly: year over year from inforce, by cohort, with her own definitions, refining the math line by line in plain English until it is exactly right. No queue, no translation loss, no waiting.
The lapse model that beat a specialist.
Keeping a policy on the books is worth more than placing the next one. Klaris built a lapse prediction model on Amplify's own data, ranking the in force book by risk with the premium at stake, so retention effort goes where it matters most, before the lapse, not after.
In a live evaluation, the Klaris model outperformed a vendor with more than 10 years in predictive analytics.