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Innehåll tillhandahållet av Adrian Swinscoe. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av Adrian Swinscoe eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.player.fm/legal.
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The messy middle of customer service revisited - Interview with Michael Ramsey of ServiceNow

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Manage episode 371321138 series 3013800
Innehåll tillhandahållet av Adrian Swinscoe. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av Adrian Swinscoe eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.player.fm/legal.

Today’s interview is with Michael Ramsey, VP of Customer Workflow Products at ServiceNow, a cloud computing platform that help companies manage digital workflows for enterprise operations. Michael joins me today to revisit the idea of the messy middle of customer experience, what's happened since we last spoke about this around 2 1/2 years ago, what companies are doing to clean up their messy middles, the role AI and process mining plays in that, how generative AI is helping with this sort of endeavour and who’s doing it well.

This interview follows on from my recent interview – The pragmatic approach to transforming customer service – Interview with Mike Upton of First Tech Credit Union – and is number 472 in the series of interviews with authors and business leaders that are doing great things, providing valuable insights, helping businesses innovate and delivering great service and experience to both their customers and their employees.

NOTE: A big thank you goes out to the folks at TextExpander for sponsoring this episode of my podcast.

TextExpander is an auto-complete tool that allows your team to eliminate repetitive typing and stay on the same page with just a few keystrokes allowing you to delight more customers in less time.

Click here to find out more and to get a 20% discount for the first 12 months of TextExpander if you use the code: SWINSCOE

  continue reading

508 episoder

Artwork
iconDela
 
Manage episode 371321138 series 3013800
Innehåll tillhandahållet av Adrian Swinscoe. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av Adrian Swinscoe eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.player.fm/legal.

Today’s interview is with Michael Ramsey, VP of Customer Workflow Products at ServiceNow, a cloud computing platform that help companies manage digital workflows for enterprise operations. Michael joins me today to revisit the idea of the messy middle of customer experience, what's happened since we last spoke about this around 2 1/2 years ago, what companies are doing to clean up their messy middles, the role AI and process mining plays in that, how generative AI is helping with this sort of endeavour and who’s doing it well.

This interview follows on from my recent interview – The pragmatic approach to transforming customer service – Interview with Mike Upton of First Tech Credit Union – and is number 472 in the series of interviews with authors and business leaders that are doing great things, providing valuable insights, helping businesses innovate and delivering great service and experience to both their customers and their employees.

NOTE: A big thank you goes out to the folks at TextExpander for sponsoring this episode of my podcast.

TextExpander is an auto-complete tool that allows your team to eliminate repetitive typing and stay on the same page with just a few keystrokes allowing you to delight more customers in less time.

Click here to find out more and to get a 20% discount for the first 12 months of TextExpander if you use the code: SWINSCOE

  continue reading

508 episoder

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