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Kandungan disediakan oleh Brian T. O’Neill from Designing for Analytics. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh Brian T. O’Neill from Designing for Analytics atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.
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143 - The (5) Top Reasons AI/ML and Analytics SAAS Product Leaders Come to Me For UI/UX Design Help

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Manage episode 418266467 series 2527129
Kandungan disediakan oleh Brian T. O’Neill from Designing for Analytics. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh Brian T. O’Neill from Designing for Analytics atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.

Welcome back! In today's solo episode, I share the top five struggles that enterprise SAAS leaders have in the analytics/insight/decision support space that most frequently leads them to think they have a UI/UX design problem that has to be addressed. A lot of today's episode will talk about "slow creep," unaddressed design problems that gradually build up over time and begin to impact both UX and your revenue negatively. I will also share 20 UI and UX design problems I often see (even if clients do not!) that, when left unaddressed, may create sales friction, adoption problems, churn, or unhappy end users. If you work at a software company or are directly monetizing an ML or analytical data product, this episode is for you!

Highlights/ Skip to

  • I discuss how specific UI/UX design problems can significantly impact business performance (02:51)
  • I discuss five common reasons why enterprise software leaders typically reach out for help (04:39)
  • The 20 common symptoms I've observed in client engagements that indicate the need for professional UI/UX intervention or training (13:22)
  • The dangers of adding too many features or customization and how it can overwhelm users (16:00)
  • The issues of integrating AI into user interfaces and UXs without proper design thinking (30:08)
  • I encourage listeners to apply the insights shared to improve their data products (48:02)
Quotes from Today’s Episode
  • “One of the problems with bad design is that some of it we can see and some of it we can't — unless you know what you're looking for." - Brian O’Neill (02:23)
  • “Design is usually not top of mind for an enterprise software product, especially one in the machine learning and analytics space. However, if you have human users, even enterprise ones, their tolerance for bad software is much lower today than in the past.” Brian O’Neill - (13:04)
  • “Early on when you're trying to get product market fit, you can't be everything for everyone. You need to be an A+ experience for the person you're trying to satisfy.” -Brian O’Neill (15:39)
  • “Often when I see customization, it is mostly used as a crutch for not making real product strategy and design decisions.” - Brian O’Neill (16:04)
  • "Customization of data and dashboard products may be more of a tax than a benefit. In the marketing copy, customization sounds like a benefit...until you actually go in and try to do it. It puts the mental effort to design a good solution on the user." - Brian O’Neill (16:26)
  • “We need to think strategically when implementing Gen AI or just AI in general into the product UX because it won’t automatically help drive sales or increase business value.” - Brian O’Neill (20:50)
  • “A lot of times our analytics and machine learning tools… are insight decision support products. They're supposed to be rooted in facts and data, but when it comes to designing these products, there's not a whole lot of data and facts that are actually informing the product design choices.” Brian O’Neill - (30:37)
  • “If your IP is that special, but also complex, it needs the proper UI/UX design treatment so that the value can be surfaced in such a way someone is willing to pay for it if not also find it indispensable and delightful.” - Brian O’Neill (45:02)
Links
  continue reading

113 episod

Artwork
iconKongsi
 
Manage episode 418266467 series 2527129
Kandungan disediakan oleh Brian T. O’Neill from Designing for Analytics. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh Brian T. O’Neill from Designing for Analytics atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.

Welcome back! In today's solo episode, I share the top five struggles that enterprise SAAS leaders have in the analytics/insight/decision support space that most frequently leads them to think they have a UI/UX design problem that has to be addressed. A lot of today's episode will talk about "slow creep," unaddressed design problems that gradually build up over time and begin to impact both UX and your revenue negatively. I will also share 20 UI and UX design problems I often see (even if clients do not!) that, when left unaddressed, may create sales friction, adoption problems, churn, or unhappy end users. If you work at a software company or are directly monetizing an ML or analytical data product, this episode is for you!

Highlights/ Skip to

  • I discuss how specific UI/UX design problems can significantly impact business performance (02:51)
  • I discuss five common reasons why enterprise software leaders typically reach out for help (04:39)
  • The 20 common symptoms I've observed in client engagements that indicate the need for professional UI/UX intervention or training (13:22)
  • The dangers of adding too many features or customization and how it can overwhelm users (16:00)
  • The issues of integrating AI into user interfaces and UXs without proper design thinking (30:08)
  • I encourage listeners to apply the insights shared to improve their data products (48:02)
Quotes from Today’s Episode
  • “One of the problems with bad design is that some of it we can see and some of it we can't — unless you know what you're looking for." - Brian O’Neill (02:23)
  • “Design is usually not top of mind for an enterprise software product, especially one in the machine learning and analytics space. However, if you have human users, even enterprise ones, their tolerance for bad software is much lower today than in the past.” Brian O’Neill - (13:04)
  • “Early on when you're trying to get product market fit, you can't be everything for everyone. You need to be an A+ experience for the person you're trying to satisfy.” -Brian O’Neill (15:39)
  • “Often when I see customization, it is mostly used as a crutch for not making real product strategy and design decisions.” - Brian O’Neill (16:04)
  • "Customization of data and dashboard products may be more of a tax than a benefit. In the marketing copy, customization sounds like a benefit...until you actually go in and try to do it. It puts the mental effort to design a good solution on the user." - Brian O’Neill (16:26)
  • “We need to think strategically when implementing Gen AI or just AI in general into the product UX because it won’t automatically help drive sales or increase business value.” - Brian O’Neill (20:50)
  • “A lot of times our analytics and machine learning tools… are insight decision support products. They're supposed to be rooted in facts and data, but when it comes to designing these products, there's not a whole lot of data and facts that are actually informing the product design choices.” Brian O’Neill - (30:37)
  • “If your IP is that special, but also complex, it needs the proper UI/UX design treatment so that the value can be surfaced in such a way someone is willing to pay for it if not also find it indispensable and delightful.” - Brian O’Neill (45:02)
Links
  continue reading

113 episod

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