## Is your feature request related to a problem?

#4947 added a new query to the backend – time to convert. It works well, just one point is kind of rough: bin width and count.

## Describe the solution you'd like

Currently the number of bins is hardcoded. Ideally we should have two options:
- return a user-specified number of bins,
- otherwise infer a reasonable number of bins using some algorithm (auto option).

@alexkim205 [suggested](https://github.com/PostHog/posthog/issues/4855#issuecomment-872423877) the [Freedman-Diaconis rule](https://en.wikipedia.org/wiki/Freedman%E2%80%93Diaconis_rule) for determining bin width (and interval based on that), but unfortunately from my testing its formula would not be the most practical – it's great for keeping a pretty constant "smoothness" of the histogram esp. with normal distribution, but we face some additional constraints that make it less than ideal:
- we can't have histograms too dense visually in the UI, from my testing a practical limit is 60 bins (above that it gets too dense),
- our data generally doesn't follow much of a normal distribution.

Clamping may help though, or maybe a different formula may work better outright – I'll be testing this.

## Additional context

Issue pertaining to the experience of time to convert: #4855

#### *Thank you* for your feature request – we love each and every one!

ee/clickhouse/queries/funnels/test/test_funnel_time_to_convert.py
"funnel_window_days": 7,
                "events": [
                    {"id": "step one", "order": 0},
                    {"id": "step two", "order": 1},
                    {"id": "step three", "order": 2},
                ],
            }
        )
        funnel_trends = ClickhouseFunnelTimeToConvert(filter, self.team)
        results = funnel_trends.run()
        
        
        
        self.assertEqual(
            results,
            [
                (8280.0, 2),  
                (16560.0, 0),  
                (24840.0, 0),  
                (33120.0, 0),
                (41400.0, 0),
                (49680.0, 0),
                (57960.0, 0),
                (66240.0, 0),
                (74520.0, 0),
                (82800.0, 0),
                (91080.0, 1),  
            ],
        )