We're using Elasticsearch for event logging -- where "event" means analytics event, e.g. a page view -- and it's fantastic. The aggregation support is superb.
We initially used Influx, but it could not perform well at the time (0.8). Our events are also heavily label-based. Basically, we do ETL at the time of write, collecting multiple documents into one mega-event, which is a complex, nested JSON document. It may have perhaps 150-200 fields. A single event may be something like "clicked button X". By storing the original document, we can aggregate based on any field value, including text and scalar fields, without having to think about a schema or about planning ahead of time what fields should be indexed or not. ES handles the rest pretty well.
To do the same thing with Influx or Prometheus I suspect we'd have to reverse this and store the document as the labels, along with a single count (1) as the "metric". I don't know how well Influx etc. scale with number of unique label values, though I'd love to find out. The last time I read about this, I think they recommended not going overboard with them.
What's different with business analytics is that the end product is typically multidimensional rollup reports over large time windows (number of page views per customer per web property per month, comparing by 2015 vs 2016, for example), and it's almost all "group by count", sometimes "count distinct" or averages. Whereas "rate per second"-type metrics aren't used anywhere in our app, for example.