You’re being taken for a ride by companies who conflate vanity metrics with insights. Let’s end this practice today.

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What is an “actionable insight?” 

The word ‘actionable’ gets thrown around in software a lot! Let’s define actionable and talk about what it means to us. 

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Define: Actionable – “Capable of being acted on.” Simple enough. 

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So, ideally, an actionable insight should be a significant finding about your business upon which you can take immediate action. We use this interpretation as our ‘north star’ because we don’t consider information “actionable” if it requires lots of manual deciphering before it takes on meaning. 

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At Spiral, we’ve oriented our solution towards “specific issues,” meaning we present you with the distilled symptom of an issue, by listening to the way your customers are describing their problems and finding comprehensive patterns using our adaptive machine learning models. 

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Let’s take a look at an example. Imagine for a moment that you worked in Digital Product Management in Retail and needed to figure out what to work on for your customers – which automatic insight presentation would you deem more actionable? 

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Insight #1‍

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‍‍Insight #2

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I hope this seems like a bit of a leading question, because I know for sure that I’d prefer insight #2, as it spells out the issue in plain language and automatically detects anomalies indicated by metadata to help pinpoint the segment of affected customers. From here, the Product or Engineering team could immediately investigate and fix the bug. 

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Taking a look at the usefulness of insight #1, perhaps you could consider it a good starting point. But to turn it into something usable, it requires a manual ‘scavenger hunt’ to figure out why people suddenly feel negative sentiment towards the checkout experience. 

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At Spiral, we think like customer experience leaders – in fact, we were founded by former Amazon software developers that worked on discovering & fixing issues for the FireTV team – and have designed our insight engine to present you with immediately actionable issues ready for fixing, and insights that may be worth incorporating into your marketing or business development efforts.

Customer feedback isn’t all negative! 

Let’s take a look at a growth-focused consumer insight example, too. Imagine for a moment that you worked in Product or Loan Origination at a bank, which automatic insight presentation would you prefer? 

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Insight #1

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Insight #2

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Again, here I’d certainly take insight presentation #2, as this seems like a major upsell opportunity. Not only could this insight inspire some quick digital product adjustments, but it also might influence marketing campaigns and financial advisory consultations. 

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Because Spiral’s machine learning model is adaptive – and identifies unknown unknowns – we find opportunities like this for our clients every day.

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Why haven’t other companies done this before today?

A reasonable curiosity. And the answer is: a combination of awareness of the problem & the availability of technology. 

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In their careers as software developers, our founders faced the problem of having so much customer feedback that it was tough to figure out what to work on and in what order. After completing over 200 interviews with companies in many different industries and verticals, they quickly discovered that they weren’t alone in this problem. It affected many other customer-concerned professions, including software development engineers, customer service leaders, product managers, marketing leaders, and content creators – to some degree, everyone was trying to use data to figure out what to work on. Our founding team was uniquely qualified to found and grow a solution like Spiral, as they not only had extensive customer experience roadmap planning skills, but also had the core competency to adapt the latest Natural Language Processing (NLP) research into an application specifically designed to handle messy, imperfect customer feedback. 

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Regarding the readiness of technology to handle the problem at hand, let’s take a look at a quick timeline of machine learning and natural language processing:

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• The foundations of Machine Learning (ML) were established between the 1950s-1980s
• Topic modeling and GPU usage proliferated in the 2000s-2010s
• Deep neural networks took form in the 2010s
• But it wasn’t until 2018 when a handful of key research papers were published about Bidirectional Encoder Representations from Transformers (BERT), which made the modern text analytics insight engine – upon which Spiral was built – possible!
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The tl;dr – Spiral is in a class of its own because our research scientists are NLP experts who are solving the latest open research problems in order to give our customers cutting-edge access to text analytics insights. Other peer text analytics companies that started prior to 2018 are still leveraging legacy text models developed in the 1990s/2000s.

What results can I expect to see with Spiral’s approach? 

In its simplest form, Spiral will deliver to you a dynamic (real-time) list of specific issues and insights aggregated from your customer feedback. Certainly, there is more complexity and detail baked in, like customer segmentation analysis, trends viewable by day, week, month, year, and much more. Bottom line is: for the first time, you’ll have a tool that gives you 360-degree vision of what your customers are telling you. 

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Each time you log into Spiral, we present you with the full list of current customer issues, starting with the biggest issue at the top, so you can decide what to work on and in what order.

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How can I try Spiral? 

If you’re curious what Spiral’s turnkey insight solution can find in your customer feedback, let’s chat. Schedule a demo and before our first call, we’ll scan your company’s customer reviews and show you a personalized demo dashboard based on your public data.

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We can get started with as little as one feedback channel, or process all channels in your omnichannel support program. We work with live chat, support emails, phone calls, NPS, DSAT, CSAT, social media, reviews, SMS, surveys – or any channel you consider to be feedback. 

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Our customers come from spaces such as: financial services / banking / credit unions, fintech, connected hardware and software, insurance, retail, B2C SaaS, and managed marketplaces – and we’d love to hear from you. 

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Thanks for reading!