Your processes are fine. So what’s the problem?

80% of CEOs believe they deliver a superior customer experience. 8% of their customers agree.

Read that again.

That is not a measurement error. That is a $3 trillion blind spot sitting in the middle of most enterprise organizations, hiding in plain sight. And the frustrating part? The dashboards look great. Orders on time. Returns processed. Cycle times green across the board.

So why are customers leaving?

Well, your data has two completely different personalities. There is the data your systems produce. Clean (hopefully), structured, timestamped. The operational heartbeat of your business, flowing neatly into dashboards and reports. Logs on top of logs of measurable objective facts. And then there is the data your customers and employees produce. Survey responses that say “the delivery was a mess.” Warehouse notes that say “damaged item, repackaged.” Return reasons that say “not what I expected.” Post-purchase feedback that says “I had no idea where my order was.”

This second kind of data is everywhere. It is also almost entirely ignored, not because nobody cares, but because nobody has a good way to read it at scale and connect it to anything meaningful.

Let’s run through an example: here is what it can look like when it goes wrong.

An e-commerce retailer is hitting every operational target. Shipments leaving the warehouse on time, delivery rates solid, return processing within SLA. The ops team is happy. Then returns start creeping up. Support volume ticks higher. Customer satisfaction dips. And nobody can explain it from the process data alone.

Buried in thousands of delivery notes and post-purchase surveys is a pattern. “Address not found.” “Item arrived damaged.” “Repackaged at warehouse.” These phrases are appearing over and over, clustered around a specific fulfillment step, in a specific region, during a specific window. The operational data shows the symptom. The text data contains the diagnosis. But because the two have never been in the same room, the problem festers for a quarter before anyone figures it out.

That gap now has a fix.

SAP Signavio’s AI-assisted context analyzer does something that has not been possible before inside a process intelligence tool: it reads the unstructured signal and connects it directly to the process.

First off, AI-assisted context analyzer, sentiment analysis:  Instead of manually sifting through thousands of survey responses and support tickets, this capability automatically categorizes free-text feedback across the entire customer journey. Positive, negative, neutral, and the topics driving each. In seconds you can see that sentiment drops sharply at the fulfillment step, that “damaged item” is the top negative theme, and that it is disproportionately affecting one warehouse. No manual effort. No separate analytics project. Just a clear picture of how the process is actually being experienced.

The second capability takes it further. AI-assisted context analyzer, text to event matching automatically links that feedback to the specific process events where it originates.  It reads a delivery note that says “repackaged due to damage” and connects it to the exact process step: packaging at fulfillment. It shows you how often that pattern appears, which variants it affects, and what the downstream impact is on returns and support volume. You are no longer looking at frustrated customers in one tool and process deviations in another. You are looking at both, together, in one place.

No manual data mapping. No analyst bridging two spreadsheets. The connection is automatic. This changes what process improvement actually means.

For years, making processes better meant making them faster, more automated, more compliant. And all of that still matters. But speed and compliance do not equal a good experience. A process can be perfectly optimized and still leave customers confused, annoyed, or gone.

The organizations that win in the next few years will not just have efficient processes. They will have processes that they understand from both ends, what the data shows and what people actually feel when they live through them.

That is not a soft goal. It is a measurable, operational one. And it starts with finally reading the data you already have.

Want to know more? Read the detailed step-by-step tutorial! Alternatively, take a look at our click-through demo.  

Want to see it live? Reach out to us for a demo!

 Your processes are fine. So what’s the problem?80% of CEOs believe they deliver a superior customer experience. 8% of their customers agree.Read that again.That is not a measurement error. That is a $3 trillion blind spot sitting in the middle of most enterprise organizations, hiding in plain sight. And the frustrating part? The dashboards look great. Orders on time. Returns processed. Cycle times green across the board.So why are customers leaving?Well, your data has two completely different personalities. There is the data your systems produce. Clean (hopefully), structured, timestamped. The operational heartbeat of your business, flowing neatly into dashboards and reports. Logs on top of logs of measurable objective facts. And then there is the data your customers and employees produce. Survey responses that say “the delivery was a mess.” Warehouse notes that say “damaged item, repackaged.” Return reasons that say “not what I expected.” Post-purchase feedback that says “I had no idea where my order was.”This second kind of data is everywhere. It is also almost entirely ignored, not because nobody cares, but because nobody has a good way to read it at scale and connect it to anything meaningful.Let’s run through an example: here is what it can look like when it goes wrong.An e-commerce retailer is hitting every operational target. Shipments leaving the warehouse on time, delivery rates solid, return processing within SLA. The ops team is happy. Then returns start creeping up. Support volume ticks higher. Customer satisfaction dips. And nobody can explain it from the process data alone.Buried in thousands of delivery notes and post-purchase surveys is a pattern. “Address not found.” “Item arrived damaged.” “Repackaged at warehouse.” These phrases are appearing over and over, clustered around a specific fulfillment step, in a specific region, during a specific window. The operational data shows the symptom. The text data contains the diagnosis. But because the two have never been in the same room, the problem festers for a quarter before anyone figures it out.That gap now has a fix.SAP Signavio’s AI-assisted context analyzer does something that has not been possible before inside a process intelligence tool: it reads the unstructured signal and connects it directly to the process.First off, AI-assisted context analyzer, sentiment analysis:  Instead of manually sifting through thousands of survey responses and support tickets, this capability automatically categorizes free-text feedback across the entire customer journey. Positive, negative, neutral, and the topics driving each. In seconds you can see that sentiment drops sharply at the fulfillment step, that “damaged item” is the top negative theme, and that it is disproportionately affecting one warehouse. No manual effort. No separate analytics project. Just a clear picture of how the process is actually being experienced.The second capability takes it further. AI-assisted context analyzer, text to event matching automatically links that feedback to the specific process events where it originates.  It reads a delivery note that says “repackaged due to damage” and connects it to the exact process step: packaging at fulfillment. It shows you how often that pattern appears, which variants it affects, and what the downstream impact is on returns and support volume. You are no longer looking at frustrated customers in one tool and process deviations in another. You are looking at both, together, in one place.No manual data mapping. No analyst bridging two spreadsheets. The connection is automatic. This changes what process improvement actually means.For years, making processes better meant making them faster, more automated, more compliant. And all of that still matters. But speed and compliance do not equal a good experience. A process can be perfectly optimized and still leave customers confused, annoyed, or gone.The organizations that win in the next few years will not just have efficient processes. They will have processes that they understand from both ends, what the data shows and what people actually feel when they live through them.That is not a soft goal. It is a measurable, operational one. And it starts with finally reading the data you already have.Want to know more? Read the detailed step-by-step tutorial! Alternatively, take a look at our click-through demo.  Want to see it live? Reach out to us for a demo! Read More Technology Blog Posts by SAP articles 

#SAPCHANNEL

Your processes are fine. So what’s the problem?

80% of CEOs believe they deliver a superior customer experience. 8% of their customers agree.

Read that again.

That is not a measurement error. That is a $3 trillion blind spot sitting in the middle of most enterprise organizations, hiding in plain sight. And the frustrating part? The dashboards look great. Orders on time. Returns processed. Cycle times green across the board.

So why are customers leaving?

Well, your data has two completely different personalities. There is the data your systems produce. Clean (hopefully), structured, timestamped. The operational heartbeat of your business, flowing neatly into dashboards and reports. Logs on top of logs of measurable objective facts. And then there is the data your customers and employees produce. Survey responses that say “the delivery was a mess.” Warehouse notes that say “damaged item, repackaged.” Return reasons that say “not what I expected.” Post-purchase feedback that says “I had no idea where my order was.”

This second kind of data is everywhere. It is also almost entirely ignored, not because nobody cares, but because nobody has a good way to read it at scale and connect it to anything meaningful.

Let’s run through an example: here is what it can look like when it goes wrong.

An e-commerce retailer is hitting every operational target. Shipments leaving the warehouse on time, delivery rates solid, return processing within SLA. The ops team is happy. Then returns start creeping up. Support volume ticks higher. Customer satisfaction dips. And nobody can explain it from the process data alone.

Buried in thousands of delivery notes and post-purchase surveys is a pattern. “Address not found.” “Item arrived damaged.” “Repackaged at warehouse.” These phrases are appearing over and over, clustered around a specific fulfillment step, in a specific region, during a specific window. The operational data shows the symptom. The text data contains the diagnosis. But because the two have never been in the same room, the problem festers for a quarter before anyone figures it out.

That gap now has a fix.

SAP Signavio’s AI-assisted context analyzer does something that has not been possible before inside a process intelligence tool: it reads the unstructured signal and connects it directly to the process.

First off, AI-assisted context analyzer, sentiment analysis:  Instead of manually sifting through thousands of survey responses and support tickets, this capability automatically categorizes free-text feedback across the entire customer journey. Positive, negative, neutral, and the topics driving each. In seconds you can see that sentiment drops sharply at the fulfillment step, that “damaged item” is the top negative theme, and that it is disproportionately affecting one warehouse. No manual effort. No separate analytics project. Just a clear picture of how the process is actually being experienced.

The second capability takes it further. AI-assisted context analyzer, text to event matching automatically links that feedback to the specific process events where it originates.  It reads a delivery note that says “repackaged due to damage” and connects it to the exact process step: packaging at fulfillment. It shows you how often that pattern appears, which variants it affects, and what the downstream impact is on returns and support volume. You are no longer looking at frustrated customers in one tool and process deviations in another. You are looking at both, together, in one place.

No manual data mapping. No analyst bridging two spreadsheets. The connection is automatic. This changes what process improvement actually means.

For years, making processes better meant making them faster, more automated, more compliant. And all of that still matters. But speed and compliance do not equal a good experience. A process can be perfectly optimized and still leave customers confused, annoyed, or gone.

The organizations that win in the next few years will not just have efficient processes. They will have processes that they understand from both ends, what the data shows and what people actually feel when they live through them.

That is not a soft goal. It is a measurable, operational one. And it starts with finally reading the data you already have.

Want to know more? Read the detailed step-by-step tutorial! Alternatively, take a look at our click-through demo.  

Want to see it live? Reach out to us for a demo!

 Your processes are fine. So what’s the problem?80% of CEOs believe they deliver a superior customer experience. 8% of their customers agree.Read that again.That is not a measurement error. That is a $3 trillion blind spot sitting in the middle of most enterprise organizations, hiding in plain sight. And the frustrating part? The dashboards look great. Orders on time. Returns processed. Cycle times green across the board.So why are customers leaving?Well, your data has two completely different personalities. There is the data your systems produce. Clean (hopefully), structured, timestamped. The operational heartbeat of your business, flowing neatly into dashboards and reports. Logs on top of logs of measurable objective facts. And then there is the data your customers and employees produce. Survey responses that say “the delivery was a mess.” Warehouse notes that say “damaged item, repackaged.” Return reasons that say “not what I expected.” Post-purchase feedback that says “I had no idea where my order was.”This second kind of data is everywhere. It is also almost entirely ignored, not because nobody cares, but because nobody has a good way to read it at scale and connect it to anything meaningful.Let’s run through an example: here is what it can look like when it goes wrong.An e-commerce retailer is hitting every operational target. Shipments leaving the warehouse on time, delivery rates solid, return processing within SLA. The ops team is happy. Then returns start creeping up. Support volume ticks higher. Customer satisfaction dips. And nobody can explain it from the process data alone.Buried in thousands of delivery notes and post-purchase surveys is a pattern. “Address not found.” “Item arrived damaged.” “Repackaged at warehouse.” These phrases are appearing over and over, clustered around a specific fulfillment step, in a specific region, during a specific window. The operational data shows the symptom. The text data contains the diagnosis. But because the two have never been in the same room, the problem festers for a quarter before anyone figures it out.That gap now has a fix.SAP Signavio’s AI-assisted context analyzer does something that has not been possible before inside a process intelligence tool: it reads the unstructured signal and connects it directly to the process.First off, AI-assisted context analyzer, sentiment analysis:  Instead of manually sifting through thousands of survey responses and support tickets, this capability automatically categorizes free-text feedback across the entire customer journey. Positive, negative, neutral, and the topics driving each. In seconds you can see that sentiment drops sharply at the fulfillment step, that “damaged item” is the top negative theme, and that it is disproportionately affecting one warehouse. No manual effort. No separate analytics project. Just a clear picture of how the process is actually being experienced.The second capability takes it further. AI-assisted context analyzer, text to event matching automatically links that feedback to the specific process events where it originates.  It reads a delivery note that says “repackaged due to damage” and connects it to the exact process step: packaging at fulfillment. It shows you how often that pattern appears, which variants it affects, and what the downstream impact is on returns and support volume. You are no longer looking at frustrated customers in one tool and process deviations in another. You are looking at both, together, in one place.No manual data mapping. No analyst bridging two spreadsheets. The connection is automatic. This changes what process improvement actually means.For years, making processes better meant making them faster, more automated, more compliant. And all of that still matters. But speed and compliance do not equal a good experience. A process can be perfectly optimized and still leave customers confused, annoyed, or gone.The organizations that win in the next few years will not just have efficient processes. They will have processes that they understand from both ends, what the data shows and what people actually feel when they live through them.That is not a soft goal. It is a measurable, operational one. And it starts with finally reading the data you already have.Want to know more? Read the detailed step-by-step tutorial! Alternatively, take a look at our click-through demo.  Want to see it live? Reach out to us for a demo! Read More Technology Blog Posts by SAP articles 

#SAPCHANNEL