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Top 5 Learnings We’ve Discovered Working With The Best Companies For Customer Experience.

by Junior Hewitt on 25 Apr 2019

Chattermill is uniquely placed to identify key behaviours that enable companies to provide unrivalled customer experience across multiple industries.

Learnings

Here at Chattermill, we have the pleasure of working with market-leading companies such as Zappos, Skyscanner, Transferwise and many more. This being the case, we’re uniquely placed to identify key behaviours that enable companies to provide unrivalled customer experience across multiple industries.

So what’s the key to success?

1 - Customer Experience Analytics

Exceptional companies know that you can’t build great products without truly understanding the voice of the customer. It’s vital you have the technology and text analytics systems capable of analysing customer feedback at scale. If you’re not already, you should be collecting customer feedback and avoiding asking too many questions in your customer surveys.

Why? Customer feedback holds a considerable amount of customer opinion and sentiment on topics impacting their experience (find out the reason behind this here). The best way of learning is to ask customers to tell you what is essential in their own words. The difficulty with collecting customer feedback is that it’s unstructured data, and this type of data is hard to analyse. This makes having a Customer Experience analytics platform in place essential to make the customer feedback you collect actionable.

Thanks to advances in powerful text analytics, we can now unlock this data, helping you to understand everything relating to your customer’s experience and generate business insights across their journey.

However, picking the right tool is just as important.

When it comes to rapidly advancing technology, it’s key to partner with the right organisations which focus on applying cutting edge technology in order to get access to the latest developments.

Some of the companies mentioned above have the best engineering teams in their industries, but even the best teams can have so many projects and moving pieces internally meaning text analytics development can be left as an unprioritised side project as other pressing items make it onto the roadmap instead.

When a company does prioritise work on text analytics projects, a common pitfall I’ve seen is said project lacking any deliverables or real output objectives, wasting both time and money. The leading companies understand the importance of partnering with specialists who sit at the bleeding edge of CX technology to achieve their end goal.

Be confident of the accuracy of your analytics system.

Another pitfall some of our clients have experienced before partnering with Chattermill is relying on outdated rule based technology that simply isn’t up to scratch in the modern era of CX analytics.

A rule-based system finds it challenging to analyse sentiment in sentences accurately which usually means you’re not in touch with your customer’s emotions and how they feel about their experiences. Second, it’s impossible to create enough rules to capture the myriad of ways people discuss certain concepts.

A rule-based system will struggle to pick out the colloquialisms and abbreviations that customers use to describe and express their experiences with your brand. As a consequence, you can end up missing key insights and worse, make decisions off inaccurate data that doesn’t reflect the reality of what the customer wants. This is not only an expensive mistake in terms of sunk time, but could end up having a fatal effect on a business.

2 - Understand Segmentation

Once you have the ability to analyse customer feedback at scale through picking the right technology partner, you should be able to easily identify the key drivers behind customer sentiment. However, there is still much more to be considered.

The best companies know the importance of understanding how the different segments of their customer base think and behave. For example, Chattermill makes it easy to run a query to uncover which areas of the customer journey customers feel most negative or positive about. This process, however, is futile if you are not applying segmentation.

Take the chart above for instance; not only do these customer segments feel completely different about some very prominent parts of the customer journey, some of the drivers displayed in the chart above are more important to focus on than others, depending on what the objectives of your team are.

Additionally, segmentation adds a lot of context to data. As we see here in this chart, churn rate due to the negative customer service experience of Premium customers is arguably more of a problem than negative customer service experience of Freemium customers.

On the flip side, information is likely to be much more important to Freemium customers as opposed to Premium customers, with conversion to paid customers in mind here.

This brings me to my next point:

3 - Give access to all

Companies delivering the best modern-day CX care deeply about all employees having access to the Voice of the Customer.

At Chattermill, we’ve noticed that the more employees who have access to Voice of the Customer the higher the company-wide motivation to improve CX. This is because employees can access insights and can track and measure the fruits of their labour, specific to their role or function. We’ve noticed first-hand how Chattermill has helped build a customer centric culture across organisations.

Use of a customer analytics platform also acts as an excellent tool for the employee to brainstorm new ideas digging into customer feedback and actionable insights that can make a difference. Some of our most forward-thinking clients have granted access to Chattermill to their entire team so that anyone in the company can easily acquire visibility of the sentiment and text analysis we have applied to a customer verbatim.

Even in cases where companies which choose not to roll out company-wide access, dedicated junior team members are able to quickly and easily extract data insights without having to be a trained CX expert.

For example, when Chattermill pushes classified customer data back to a company’s data warehouse, an analyst with no prior knowledge of Chattermill can quickly run queries that displays the sentiment of a group of customers meeting a specific criteria. Given that this query would be run in their database, it means they can also include a plethora of personally identifiable variables within their queries (e.g. — “show me the 10 top most negative topics of customers based in London, aged 21–25 years of age who have spent over £200 in the last six months”).

4 - Find a single source of truth

If you’ve made these steps, you and your colleagues company-wide should now be able to easily see what customers think and feel about your brand, product and the customer experience. This is great, although potentially brings a new set of (solvable) problems around metric consistency — in other words, what metrics are you using to monitor customer sentiment?

Whether you decide to use NPS or Chattermill’s very own Net Sentiment, everyone across your company not least your customer-facing teams need to be aligned, using the same metric.

The reason for this is that in order to drive change and enhance customer experience it’s likely you’ll need to work collaboratively with other teams. You will need to be able to explain to the rest of the business the relative impact of solving a problem or making a change to the customer experience in a way that is understandable to multiple stakeholders. For your product managers and engineers it makes their prioritisation far easier and increases their impact on the success of the company if they are able to connect a task to the effect on the company’s NPS score.

Which is related to my last point:

5 - Find the monetary value behind your single source of truth

Once you have your single source of truth, you are in a fantastic place. You now know in fairly certain terms what needs to be done to give your customers the best experience possible. In addition to this you can explain to your colleagues in the product team and others what their exact impact will be on NPS if they prioritise fixing a specific problem.

Metrics such as NPS, CSAT and Net Sentiment are incredibly important and familiar to those of us who work in customer-facing or focused roles, but it’s worth remembering that may not be the case for people who don’t work in such functions. That said, everyone understands the cost of doing or not doing something.

The most successful companies which offer an expert level of customer experience know exactly what the difference of one NPS point means in terms of dollars, pounds or euros. Having this knowledge is incredibly powerful as it allows you to explain to your entire company how much money a particular issue is costing them. Without a doubt, this is the quickest and most effective way to drive change and enhance customer experience within your company.

In Conclusion

For your organisation to succeed in a world where Customer Experience is more important than ever, employees need to take advantage of the latest technology so they can delight customers and build great products.

Here’s a recap of the lessons your team can start capitalising on today:

  • Junior Hewitt

    Director of Customer Success at Chattermill

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Introducing the Premier League CX Table - Which Club Takes Home the Bragging Rights?

by Sam Frampton on 3 Apr 2019

Find out which Premier League club offers the best Customer Experience for their fans. In the article, you'll find how we used AI and Sentiment Analysis to find out which team comes out on top.

The Premier League

The screen in our hands is now a gateway to entertainment and sports content online and with one of the most exciting Premier League title races in recent memory in full swing, what better to keep track of your beloved team than a series of handy apps?

Sporting apps have never been more popular

According to Google, sports fans have been taking advantage of the new wave of apps to hit the marketplace. Google estimates that we spend on average nearly 50 minutes per day on our favourite sports apps!

(For some perspective, that’s roughly 26 hours per month and according to comScore’s Cross Platform Future in Focus report 30% of the time the average American adult spends on their smartphone every day.)

Now, it doesn’t take a business genius to recognise the tremendous opportunity for football clubs to capitalise on our passion to keep our football knowledge in check.

Analysing customer feedback can be a pretty big mess.

The tricky part, however, is figuring out what aspects of the customer experience football fans love and elements that are making them downright miserable.

Traditionally we’d turn to Customer Experience metrics (think NPS, CES and CSAT), and while essential to monitor, only give a quantitative view of what’s happening. Customer Experience metrics don’t reveal the why behind the score and provide insight into why a particular metric has dropped or improved over time. To get to why we need to listen to our customers and dig deep into their feedback.

When you’re a scrappy startup, every team member digs into the customer feedback with enthusiasm, pumped to find out what customers think about the product and the Customer Experience.

But this process isn’t feasible in the long run. Analysing customer feedback is hard when you have a mountain of feedback coming in regularly — some of it about bugs and other product issues, some of it about video quality — making it all actionable is a tall order.

To make matters worse when companies stop listening to customers, you can quickly lose sight of the customer. Before long your vision for the product or service offering you provide won’t reflect the reality of what customers want — leaving the door wide open for a competitor to build something better.

The challenge is understanding how to organise and prioritise the different types of customer feedback you’re collecting. How can clubs efficiently categorise their feedback from their fans and understand what fans are saying about them and how they feel about their app experience at scale?

Customer Experience Analytics: Understand what your customers think and feel

Enter AI-powered Customer Experience analytics. It’s now possible for a machine to understand human language accurately. It’s never been easier for you to analyse the written text of your customers and find out what customers hate and love about your brand.

A machine can now understand different contexts and meanings of words with the accuracy level of you, reading this text right now! That’s a big deal because we severely underestimate how complex the human language is.

Does the word ‘hot’ mean the weather is warm or that a dish is spicy? Consider the multiple ways we discuss the concept of price with our friends. We use a lot of colloquialisms when discussing certain products and their prices. ‘That cost an arm and a leg’, ‘daylight robbery!’, ‘inferior value for money’, the list can go on and on…

So how does this tie back to Premier League Football apps you may be wondering? Well, we know a thing or two about text analytics, and we like to experiment and have fun.

With the rise of sports apps and our appreciation for football in the office, we decided to put our knowledge to the test and analyse Premier League app data to find out which team is providing the best Customer Experience for its fans!

If you want to find out who comes out on top, keep reading!

Methodology

We decided to experiment by analysing all the open source data available on the Android and iOS app store. We gathered thousands of comments across the premier league clubs that have mobile apps and examined them using AI.

For the experiment, we used the metric Net Sentiment to rank how each app performed.

We calculate Net Sentiment by subtracting % negative mentions from % positive mentions across all themes and categories we discovered in the data.

For those of you who aren’t familiar with our terminology on themes and categories, our AI algorithm analyses customer feedback and tags each piece of feedback with one or more themes. Each theme is characterised as either a positive mention or a negative mention. We then group themes under umbrella categories.

The Results

Cool. Now back to the results. One thing for sure was how well teams perform on the pitch was not the most important correlating factor into how well the apps performed.

For any Tottenham fans reading right now, it’s maybe time to look away as St Totteringham’s day has come early…

So what’s separating the winners from the losers?

We can use Chattermill to slice and dice the data to get the full story on why some teams are delighting fans and why others teams seem to be scoring own goals.

Keen to find out more?

Then view our live dashboard here for a limited time period only.

Feel free to start a conversation and someone from Chattermill will be happy to answer your questions.

If you like what you see then to share the dashboard all you have to do is:

  • Copy the URL of the dashboard.
  • Share in any community, slack channel or email to any colleague.

P.S. If you want to see more industries analysed then leave drop us a message at hello@chattermill.io

  • Sam Frampton

    Growth Marketing at Chattermill - A.I for better customer experience

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Event Recap - The 2018 Q4 Experience Meetup co-hosted by Chattermill and Usabilla

by Sam Frampton on 3 Dec 2018

A recap of the Experience Meetup co-hosted with Usabilla on the 21st November 2018 at WeWork Provost & East.

Crowd Shot A recap of the Experience Meetup

Thank you to our attendees and speakers who attended the second edition of the Experience Meet Up co-hosted by Usabilla and Chattermill!

Were you unable to make the event? Don’t worry. We’ve put together our favourite highlights from our recent event.

At the Conference

On 21st November a hundred plus UX, Product Managers and CX leaders gathered in London at the WeWork in Provost & East for the largest event co-hosted by Chattermill and Usabilla to date. These one-day events are highly anticipated because they shine a light on the latest in customer-centric tech and best practices employed by cutting-edge companies to better understand their customers.

Key takeaways

Our very own Dave Ascott kicked off the night as moderator introducing the first speaker Mike Stevens, founder of Insight Platforms. Mike set the scene for the evening by discussing the evolution of CX, analytics and the research industry.

Mike began sharing his insights on where the CX industry is heading in the next few years. Mike had high expectations for the CX industry and had a great sense of optimism, pointing to how topical the sector is becoming due to the rising volume of customer feedback. He discussed how leading Customer Experience technology companies are empowering businesses to drive customer centricity across teams throughout organisations. Powerfully setting the tone for the rest of the evening by stating:

“For the CX tech ecosystem. We’ve arrived.”

We agree with Mike and the CX tech moment has truly arrived. Businesses now have access to a growing ecosystem of cutting-edge technology and products that you can plug into platforms to provide a holistic view of the customer journey to help bring to life excellent customer experiences.

The next speaker was Jenna Bolger from Bulb the UK’s biggest supplier of renewable energy and one of the fastest growing energy firms in Europe. Bulb has stolen unhappy customers from unloved incumbents with its excellent customer service, low prices and it’s commitment to providing a great CX for customers.

Jenna presented to the audience some of the strategies put in place by the UX team at Bulb helping redesign the future of the energy industry. One example mentioned by Jenna was the smart tariff three rates project. Peak, Off-peak, and Overnight tariffs designed to reflect the true cost of energy to their members, encouraging them to avoid electricity use at peak times and instead use it when it’s cheaper and in ready supply.

The customer could use the Bulb app to keep track of their energy use across the day. It encouraged them to avoid using energy at peak times by rewarding them with ‘leaves’ helping some customer to reduce their kWh by 33%. This is just but one example of how Bulb has placed razor-like focus on the customer experience and attempting to blow their customers socks off!

The final presentation was by Diana Walker from EE who is a leading expert on conversion rate optimisation. Diana introduced the audience to the concept of Golden Journeys. To put simply, it represents the most frictionless way for a user to get to A to B.

“A golden journey is the most effective and intuitive digital journey undertaken by a visitor on a given company’s digital estate to reach their intended destination to meet a given need.” - Diana Walker, EE

However, Diana mentioned it’s crucial to use both your left and right side of your brain and put your thinking cap on and use those insights to brainstorm some experiments put together an optimisation backlog. Once a backlog is crafted, you’ll need a methodology to prioritise A/B experiments and align them with your product roadmap for maximum long-term impact!

It was a fantastic event, with lots of great speakers and one that will go down as a huge success! We are already planning the next one, make sure to keep in touch to make sure you get your ticket!

Usabilla & Chattermill Solution Partnership

Collecting feedback via Usabilla gives you valuable insights regarding your customer’s journey. We understand that continued feedback may be challenging when it comes to analysis, especially to process data in a way that scales as data volumes grow.

To get the most out of your collected data, Usabilla partners with Chattermill. Chattermill helps you to gain a better understanding of the most important topics your customers talk about using AI to interpret theme and sentiment analysis across unstructured feedback.

With the Usabilla-Chattermill integration, we tailor each area of feedback to how your business runs and ensure that we go into as much detail as is required for your business to effectively gain the insight you need so you can make the right decisions.

If your company is interested in becoming more customer-centric, we would love to get in touch with you. You can also visit our blog to stay up to date with industry topics, company and product updates.

We hope to see you again soon!

  • Sam Frampton

    Growth Marketing at Chattermill - A.I for better customer experience

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How NLP, ML and Deep Learning Can Transform Your CX Strategy

by Sam Frampton on 5 Nov 2018

The post aims to give the reader a gentle overview of NLP, ML and Deep Learning and make the connection of how it can be applied in the context of customer experience and support.

Machine learning Image

Since the inception of computers, scientists have had a strong desire to make machines understand the human language. Communicating like a human is a big ask for a robot. We speak in colloquialisms and abbreviations. The same word can take on several different meanings based on subtle nuances in tone.

The pursuit of making the computer understand us is rife with challenges. Even in the context of a single language, there’s quite a bit of variation. While you could potentially feed a computer a digital dictionary loaded with every word known to humankind, you can’t necessarily account for every possible combination.

This led to an interdisciplinary solution that combined linguistics and computer science: Natural Language Processing (NLP). NLP has been around for a while, but as of late, has benefited from recent developments in Machine Learning and Deep Learning techniques. Machine Learning (ML) is a subfield within Artificial Intelligence that builds algorithms that enable computers to learn to perform tasks from data instead of being explicitly programmed.

There are plenty of applications that we know and use on a daily basis that are integrating recent advancements in ML, and NLP techniques into their product including Amazon’s Alexa, Google’s Search Engine and Microsoft Word’s spell check. Other applications serve to teach machines how to perform complex natural language-related such as machine translation, sentiment analysis, text classification and text automatic completion.

The post aims to give the reader a gentle overview of NLP, ML and Deep Learning. It will provide some initial concepts to invite the reader to continue investigating and make the connection of how it can be applied in the context of customer experience and support.

A Gentle Introduction to NLP, Machine Learning and Deep Learning.

NLP

Natural Language Processing is an area that sits at the intersection between Artificial Intelligence and Linguistics. It involves an intelligent analysis of unstructured data in the form of written language. Human communication is frustratingly vague at times; there are few rules, we all use colloquialisms, abbreviations, and don’t often bother to correct misspellings. These inconsistencies make a computer’s analysis of natural language difficult at best. If you have lots of data in the form of customer feedback and you want to get business insights from it automatically, then you would have to use NLP techniques.

NLP can be split into two different sets of approaches. The first approach is a rule-based approach and involves human-crafted or curated rule sets. Rules-based approaches look for linguistic terms such as “love”, and “hate”, “like” and “dislike”. The presence of positive and negative words defines whether a sentence is positive or negative. The second approach uses statistical techniques using machine learning for developing algorithmic models. With machine learning, data scientists, for example, can train an algorithm to understand sentiment based on large data sets, fine-tuning the model to predict sentiment in entirely new sentences.

(If you’d like to understand more about the topic then download our text analytics report here)

Machine Learning

Machine learning is a subset of artificial intelligence and is an inclusive term that contains a considerable number of approaches from the simple to the very complex. Machine Learning is a discipline responsible for giving computers the ability to learn without being explicitly programmed. Rather than a human telling the computer to follow a limited set of pre-defined rules, we ask it to look through the data, learn to perform specific tasks and get better at it over time.

In traditional software engineering, the developers give precise (sequential) instructions to machine (CPUs) how to execute a program/piece of code. For example, when you click the ‘Submit’ button after filling your details on a web form, all the program does is validating that the data you’ve filled is in the correct format, grouping it and sending it to a server to get a response from a piece of code running on that machine.

Different ML Paradigms

Supervised Learning

It is called supervised learning because it involves the process of algorithm learning from a training dataset and can be thought of as a teacher supervising the learning process. We know the correct answers, the algorithm iteratively makes predictions on the training data and is corrected by the teacher. Learning stops when the algorithm achieves an acceptable level of performance. Once the algorithm is trained it is then possible to make predictions about new observations.

Unsupervised Learning

In Unsupervised Learning, the algorithm only has input data but no corresponding output variables. The goal for unsupervised learning is to model the underlying structure or distribution in the data to learn more about the data. Unsupervised models are therefore mainly concerned with detecting patterns in the training data. They are called unsupervised because there is no teacher and pre-defined correct answers. The algorithms are left to their own devices to group observations into clusters or fitting a probability distribution over observations to detect improbable outliers.

Reinforcement Learning

Reinforcement Learning: The algorithm (also called the agent) interacts with the environment. That is, it observes the state of the environment, performs an action and then receives a reward/penalty. It starts off taking random actions, but over time figures out the optimal strategy. One of the most common examples of reinforcement learning is a machine learning to a play a computer game or in the case of Google’s Deepmind, an AI that has managed to learn how to walk, run, jump, and climb without any prior guidance.

Deep Learning

Deep learning is a subset of machine learning that uses neural network architectures inspired by the biological neural networks of the human brain. Neural networks are a specific set of algorithms that have revolutionized machine learning and are used in all ML paradigms.

They teach computers to do what comes naturally to humans: learn by example. It’s the technology behind driverless cars, enabling them to spot the difference between a pedestrian and a lamppost. Deep learning requires large amounts of data (which is no short supply considering we generate an estimated 2.6 quintillion bytes daily) and has come on leaps and bounds in recent years due to advancements in computing power, reducing the time it takes to train a deep learning neural networks.

Deep learning has now reached the point where it can outperform humans in a range of tasks like classifying images and it will continue to improve over time.

NLP, Machine Learning and Deep Learning Combined

As you can see from the Venn diagram above you can see how NLP is related to ML and deep learning neural networks. Deep learning is one of the techniques used in machine learning alongside other techniques such as regression and K means clustering. Machine learning is often used for NLP tasks that use techniques such as deep learning neural networks. Below you’ll see a table outlining how different deep learning algorithms are applied to NLP.

What Machine Learning NLP Advances Mean for CX Professionals

By capitalising on the advancements in NLP & ML, companies gain access to a whole new world of possibilities. Whether it’s through automatically reading and classifying new support tickets, getting ahead of bad press, or discovering new business insights from unstructured data—Machine Learning stands to be a powerful tool the customer experience professional can integrate into their workflow.

Below, we’ll look at four ways NLP, ML and Deep Learning are transforming CX.

Text Analytics for Business Insights

For any analytics platform, the objective is to allow the insights from data analysis to be as clear as possible. Advanced text analytics platforms should enable various sources of unstructured data to be viewed and measured using relevant scores to visualise sentiment and topic analysis across every part of the customer journey in real time.

Chattermill easily integrates with existing platforms used to collect and manage various aspects of customer experiences, such as CRM, Customer Support, App Store Reviews, Online Surveys and CEM systems to provide a combined view of all customer feedback across the entire customer journey.

Advanced text analytic platforms leverage deep learning neural networks to identify patterns that signify positive or negative sentiment in vast volumes of unstructured data. Here, machine learning and deep neural networks process unstructured text and classify it automatically in real time. The technology works to highlight fundamental topics affecting customer loyalty, such as product attributes, online experiences and customer support. Customer experience teams can interpret the bespoke insights to inform business decisions and prioritise resources to areas that can have the biggest impact on customer experience.

For example, NPS surveys allow customers to leave open ended feedback and often mention multiple categories that touch upon different business functions. Hidden within the feedback is customer sentiment about exactly how they feel and most often, customers talk about things that matter most to them in either a positive or negative manner.

Identifying themes and tagging feedback by sentiment is near impossible at scale when humans do the work. A single agent can only process 1,000 pieces of feedback a day consistently. For this reason, forward-thinking customer experience teams are embracing ML-powered text analytic tools built to handle massive unstructured data sets. Adding neural network algorithms that can pull accurate business insights at scale with human-level accuracy into their strategic planning.

Improve Support Experience

As your organisation grows, getting a deeper understanding of the issues that impact your customer is critical to a smooth scaling process and routing customers to the best person is vital. With hundreds of thousands of tickets surfacing daily across multiple countries for some support teams, customer support must ensure that agents are empowered to resolve them as accurately and quickly as possible. When an agent opens a ticket, the first thing they need to do is determine the issue type out of thousands of possibilities—no easy task!

Imagine you’re a customer support agent - you’ve got multiple customers writing to you and phoning you simultaneously. Your prerogative is to deliver answers they need quickly and accurately while providing a fantastic customer experience. Now layer on the need to track each inbound help request with contextual ‘topic tags’ for each ticket. Different agents monitor different cases, and finding the right pairing and routing to correct personnel can make or break the customer experience.

As your company scales, this model of customer support is broken because it asks too much of your agents. The goal of customer support teams is to facilitate the best end-to-end experience possible for users. Customer support teams can benefit from leveraging tools use machine learning and natural language processing (NLP) techniques. ML can use historical customer service data with keyword analysis to route incoming support tickets to the appropriate person.

For example, this technology can analyse tickets to learn why a customer needs support in the first place. From there, messages (tickets, social media message, even voicemails) are sent to the appropriate person. This means customers can bypass the frustrations associated with transferring calls to rep after rep. By making the life of the agent more simple, you help them focus on what truly matters: delivering amazing, personalised customer experiences.

The AI would automatically identify, tag, and route incoming tickets on your customer support platform based on their corresponding category or priority. This removes the need to manually evaluate each inquiry and make a call on who is the most appropriate contact. Helping customer support representatives improve their speed and accuracy, resulting in a better customer experience.

So far at Chattermill when working with clients we have seen some promising results! Ticket resolution times have reduced while delivering service with similar or higher levels of customer satisfaction. By empowering customer support agents to achieve quicker and more accurate solutions, Chattermills deep learning algorithms make support experience more enjoyable for customers.

Workforce Analytics & Voice of the Employee

Within the workplace, ML stands to play a critical role. Listening to the Voice of the Employee by systematically collecting, managing and acting on the employee feedback on a variety of valuable topics is essential. We’ve long been relying on gut feelings and corporate ideologies to make hiring decisions, but more and more, organisations are turning toward data to inform who is promoted and how to allocate rewards.

Leveraging advances in ML, NLP and Deep Learning and merging into existing approaches allows organisations to get a pulse on employee sentiment quickly. VoE programs typically use a variety of methods to collect and analyse employee feedback, including surveys like eNPS, exit interviews, performance evaluations, employee forums, social networks, or focus groups. As such, employers can act on their employee feedback data by integrating those channels into text analytics platforms and make changes that improve retention rates, increase engagement, and boost performance.

Social Media & Brand Monitoring

Every second, 3.3 million new posts appear on Facebook and almost half a million on Twitter. What if you wanted to keep track of all those times your brand was mentioned? Another benefit of Machine Learning and Natural Language Processing techniques is that it can help Customer Experience teams overcome information overload and analyse the massive pool of data from social media, where there stands to be a ton of information about your brand, product and support quality.

The idea is, you can extract, and filter data found on social media and feed into machine learning algorithms to identify business insights. Not only does this help determine things like press mentions or links to your site, but it also can help interpret how customers feel about your product/service.

NLP using sentiment analysis can help uncover messages that signify whether a customer is angry, satisfied, or needs help. It might also identify new leads, by picking up on messaging from people looking for a service like yours. For example, NLP could be used to pick out key phrases like “need accounting software.” And, if your company makes accounting software, you have an opportunity to present your solution.

In any case, as your company scales, a little help untangling the mess of social data—and adding machine learning allows teams to automate and scale feedback processing activities.

Final Thoughts

Market leaders have been aware of the advantages of using open-ended data for a while. However, previously it was only open to small businesses where a decision maker can single-handedly read and analyse all of the comments. Large companies have resorted to manual tagging where several agents would read a piece of feedback and categorise it.

Unfortunately, manual tagging systems are costly and hard to maintain. A single agent can only process 1,000 pieces of feedback a day consistently and require training and constant quality control. Adding new languages requires a whole new set of workers or adding an expensive translation step which adds a new layer of complexity for CX teams.

Tools that utilise recent advances in deep learning open up new possibilities and solve the complex challenges that come with scale. CX pros can now centralise the collection of feedback across multiple channels and analyse structured and unstructured feedback at scale with human-level accuracy powered by state of the art neural network algorithms. Gone are the days of sifting through customer feedback and manual tagging.

Advances in machine learning technology will be critical to the customer experience professional in the future as it enables them to pull detailed reports from datasets faster, with higher accuracy, that reveal trends and the potential to improve on the customer’s experience. CX pros can now be proactive rather than reactive and avoid customer problems through advanced text analytic capabilities. Our clients such as Uber, Transferwise and Spotify have identified critical business insights from unstructured feedback using our platform, allowing the organisations to understand sentiment at scale and make customer-centric decisions.

To find out how to integrate your customer feedback into advanced AI text analytic platforms, book a demo with Chattermill today.

Keep Learning
  • Sam Frampton

    Growth Marketing at Chattermill - A.I for better customer experience

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How To Improve Net Promoter Score?

by Sam Frampton on 16 Aug 2018

How to start the cycle of Improvement

Chapter 4 NPS Guide

There are no shortcuts with NPS. Improving this metric is tough - it requires internal buy in and you won’t see results overnight. Start the cycle of improvement today. If you’re not asking for feedback you’re in trouble, to understand what customers want - you need to start speaking a language they know - their own.

How to start the cycle of improvement:

  1. Discover your baseline Net Promoter Score.
  2. Find patterns in the data.
  3. Get more data.
  4. Make an action plan based on the feedback - and act on it.
  5. Systemise throughout your company.
Discovering your baseline NPS score

It’s now time to launch an NPS survey. You can begin by sending out transactional surveys soon after an interaction with a customer, such as an email after a purchase, or after a self service experience on a website.

Some businesses choose to send out relational NPS survey on a rolling basis and like to stay up to date every day. Others launch one for a fixed time, close it, and launch a new one after they’ve taken an action based on the results of the previous survey. After collecting feedback you can begin to calculate your baseline NPS score.

Pay close attention to your responses and funnel detractors to customer support. If you have large volumes of feedback it’s possible to funnel feedback and complaints to teams automatically using AI!

Find Patterns

After analysing your results, you’ll learn what works for your customers and what doesn’t. If you’re a small business with less than 100 responses per survey, it’s possible to pinpoint themes and sentiments in the data and learn what does and doesn’t work for your customers.

If your company is receiving large volumes of feedback, it becomes an impossible task to learn what your customers are talking about and how they feel about their experiences at scale. It may be time to turn to advanced text analytic platforms to unlock data-driven insights from your feedback.

Using CX management technology to create bespoke theme structures and detect sentiment within feedback using machine learning to process data in real time. Advanced text analytic tools will highlight areas of the customer experience that demand the most attention. Making pinpointing customer pain points a quick and easy task.

Collect More Feedback

It’s essential to launch multiple feedback channels to maximise response rate to the NPS surveys. It would help if you offered surveys across both your desktop, mobile experiences, SMS messages and non-digital touch points. While you could create such tool in-house, we encourage firms to use collection tools that support feedback collection across a variety of user interfaces, such as solutions offered by Usabilla.

Don’t forget to get in touch with people who have churned and ask what made them churn, or why they switched to a competitor.

Always be sensitive to where you put your feedback channel. It shouldn’t interrupt the user’s experience, and it shouldn’t feel like a chore. Make it voluntary, short, and fun. Your data collection point is a touchpoint like any other and can make or break an impression of the brand.

Action Plan Based on Feedback

When you have enough feedback from all of your channels, it’s time to find out how to squash your bugs.

While teams spend much time looking at NPS detractors and how to address their pain points, it’s equally if not more so to spend time on promoters. Understanding what was different about their experiences to make them successful.

Finding correlations between specific customer experiences and NPS results (logins, delivery time, customer service, contact options) can help deduce what delights your customers and turn them into promoters. Then you can focus on optimising your customer experience and tracking NPS and Sentiment over time to get more of your customer base to this point. With the help of new advances in machine learning and text analytics, it is now quick and easy to chart specific themes and product features against NPS and Sentiment over time.

Systemise and Encourage Internal Buy In

Improving your customer experience is cyclical, not linear. That’s why the final stage is to systemize the process. Launch another NPS survey with the next cohort of customers. Take note of the improved score and report continuously in an easy and digestible way to share result company wide. Be sure to set up instant notifications of low scores so you can react in real time to a drop in NPS.

Remember to measure continuously over time to ensure a constant flow of data as it’s the only way to get the magical moments and optimise your customer experience.

You also want to the product, marketing, and customer support teams onboard because you never know where a good idea may come from. Converting unenthusiastic customers into loyal promoters will require a group effort and the best ideas often come from the unlikeliest of sources. It’s vital that everyone in your organisation understand what the Net Promoter Score is and why it’s important to improve it. Since enhancing your score means more promoters and happy customers.

Chattermill Solution

Collecting feedback gives you valuable insights regarding your customer’s journey. We understand that continued feedback may be challenging when it comes to analysis, especially to process data in a way that scales as data volumes grow.

To get the most out of your collected data, Chattermill helps you to get a better understanding of the most important topics your customers talk about using AI to interpret theme and sentiment analysis across unstructured feedback.

We tailor each area of feedback to how your business runs and ensure that we go into as much detail as is required for your business to effectively gain the insight you need so you can make the right decisions.

Contact hello@chattermill.io to find out more.

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  • Sam Frampton

    Growth Marketing at Chattermill - A.I for better customer experience

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The Ultimate Guide to Net Promoter Score | Chattermill

by Sam Frampton on 15 Oct 2018

What You Need to Know about the Net Promoter System

Chattermill Guide: Net Promoter Score: Guide, Tips and Case Study Introduction

All companies seek growth. And, growth that’s profitable, sustainable and organic occurs most often when a customer loves doing business with a company. A happy customer talks up a company to their friends, family and colleagues. A recommendation is one of the best indicators of loyalty because a customer is putting their reputation on the line.

Firms want customers to be happy; the challenge is how to know what customers are feeling and how to establish accountability for the customer experience. Companies need a simple method to measure and manage customer loyalty in real time.

In 2003, Frederick F. Reichheld introduced the concept of NPS in the seminal article in HBR ‘The One Number You Need to Grow‘. Following two years of research, measuring the link between survey responses of individual customers of a company and those individuals’ actual referral and purchase behaviour. Reichheld and his team found one question was correlated directly with differences in growth rates amongst competitors in multiple industries, “how likely is it that you would recommend [company X] to a friend or colleague?”.

It was a tipping point in the industry. A path to profitable growth lied in a company’s ability to get its loyal customers, in effect, to become their marketing department. Companies that enjoyed the highest rate of growth had the highest net promoter percentages among its competitors.

Reichheld found that a simple single question was able to show correlation between NPS and long term-growth. It has now become a key performance indicator (KPI) for many industry leaders to understand customer loyalty. When combined with an open ended question it provides and an actionable metric for enhacing your customer experience.

Indeed, other factors besides customer loyalty contribute to companies growth, economic or industry expansion, innovation and so on. While it would be a bold claim to say it guarantees growth, in general, it can’t be achieved without it.

It’s now widely accepted that a simple real-time feedback loop is essential for businesses to measure loyalty and using the Net Promoter Score (NPS) to measure customer loyalty has undoubtedly become industry best practise.

In our ultimate guide to NPS we cover the following key topics:

  1. What is the Net Promoter Score?
  2. Calculating your NPS
  3. Net Promoter Score: Types of Surveys
  4. Net Promoter Score: Analysis
  5. How Can I Improve my NPS?
What is Net Promoter Score?

Net Promoter Score (NPS) is a seemingly simple yet effective way for companies to track and measure promoters and detractors to produce a precise measure of their performance through its customer’s eyes.

NPS is built on the basis that every company can divide their customers into three distinct buckets - promoters, passives and detractors and customers are categorised based on their response to the standard NPS question - “How likely is it that you would recommend us to a friend?” on a scale of 0-10.

Once you’ve identified your Promoters, Passives and Detractors with your NPS survey, close the loop with personalised follow-up communications to each group.

The follow-up communication is critical. NPS is not about a number but about learning and improving. Understanding the drivers behind the score provides vital insights into why people are loving or hating your product. The second follow up question is what gives NPS its one-two punch, and allows you close the feedback loop.

The goal is to discover patterns inside the data. For promoters, just do more of what works. For detractors, do less of what’s causing customers to leave your product. For passive, just ‘ignore’ them for now. If you work on decreasing detractors and increasing promoters, you’ll address the passives over time. The primary goal is to identify pain points in your system that are creating repeatable results.

The cool thing about growing your NPS is that if you continue to improve it, you will have more and more people going around saying your product is excellent. But be warned, growing your NPS is hard and you won’t see results overnight. It requires a long-term effort. Very few companies have been able to systemise promoter growth. We’ll explain further on in the guide how other companies have achieved this feat.

Calculating Your Net Promoter Score

Your NPS is calculated by subtracting your percentage of detractors from your percentage of promoters. The best possible NPS is +100, and the worst is -100. Your passives are excluded from the calculation since their loyalty is relatively neutral.

Responses can be defined into three distinct clusters that represent different attitudes, sentiment and economic value.

Promoters (scored 9-10): Promoters are your biggest fans. They actively advocate your product on your behalf, bringing in the majority of referrals, and are far more likely than any group to remain customers. Their Customer Lifetime Value (CLV) is far greater than any others.

Passives (scored 7-8): Passives are satisfied for the time being but can defect at any time. Their referral rate is as much as 50% lower than promoters, and those referrals are of far less quality. Their CLV is also usually less than half that of promoters.

Detractors (scored 0-6): Detractors are unhappy customers and account for more than 80% of negative word-of-mouth opinion. They have the highest rates of churn and defection and harm your company’s reputation, putting off new customers.

The Net Promoter Score is a simple and straightforward metric that can be shared throughout the company with every function and team. You can also track by product, store, team, geography and more to focus on the goal of improving customer experience.

If you have more detractors than promoters the score will be negative and likewise positive for more promoters than detractors. Lower Net Promoter Scores can be indicators of bad customer experiences leading to potential losses of revenue, while higher Net Promoter Scores suggest a stronger performing business.

Net Promoter Score: Types of Surveys

Relational Surveys

A relational survey can be sent at any time, and it’s an excellent way to get a finger on the pulse of the relationship between your customers and your business. Relational surveys should be sent out often — a quarterly schedule is ideal.

However, while regular surveying keeps you up-to-date on customer feedback and loyalty, surveying your customers on such a consistent schedule risks “survey fatigue,” which can lower your overall response rate. It’s important to make sure your customers know you value their survey responses — some options are sending thank you emails or offering perks or discounts for customers who complete relational NPS surveys. It’s also important to only survey as often as you have time to analyse the feedback, respond to your customers and create an action plan based on their surveys (we’ll cover this in more detail in a later chapter).

Transactional Surveys

A transactional survey is sent after an event, such as soon after a new customer purchases your product or service, or after the resolution of a support ticket. The timing is a little easier with transactional surveys than with relational surveys — just make sure to send the survey before too much time has passed after the event that triggered it. Some good guidelines are:

While transactional surveys aren’t as likely to cause “survey fatigue”, they also give a narrower picture of your NPS, since they relate to specific events and not your business as a whole.

Irrespective of the type of survey you choose, if you can pass any additional data you have for your customers into your survey emails, such as the product they ordered, location and length of time as a customer, you can then begin to piece together the opinions of different segments of your customers to understand their similarities and differences too.

Timing and Survey Frequency

When thinking about how frequently to carry out NPS surveys, there are several key considerations.

The first is the size of your customer base. The smaller your customer base, the larger the sample you need to survey each time or even wait longer for more responses to achieve a high response rate, which constrains how frequently you can send out NPS surveys.

The second consideration to make depends on the user’s lifecycle stage with your product. If the customer has just started to use your product, then they need a chance to internalise the changes of a product or brand before they form a substantive opinion. Timing is critical, and setting up NPS surveys like a behavioural drip email series that triggers based on a user lifecycle means each user can respond to the NPS survey at comparable points in their experience. For instance, you can see data on how opinions on difference segments new users vs longtime promoters change after specific product updates.

Surveying the right number of customers

To get a clear picture of your NPS, you need a good sample of responses that represents your entire customer population. This can be tricky to get, but the bottom line is that the more responses you can get, the better your data will be.

It takes some complex math to get the exact number of responses will be representative of your business’ customer base, but a good rule of thumb is to assume that only 15 percent of the customers you send surveys to will actually respond, which means you need to send out a minimum of 1,700 surveys to get 250 responses, what’s generally considered a good sample for calculating NPS.

Net Promoter Score: Verbatim Analysis

If your goal is to build exceptional experiences for your customers, it’s essential you get critical stakeholders on the product, marketing, business ops, customer service onboard right from the beginning. Without this, the NPS survey analysis may get overlooked during the product development lifecycle. It’s also essential the information is easily accessible on dashboards across multiple teams to ensure company efforts are aligned on improving the rights areas.

Secondly, while your NPS score is useful for maintaining a constant finger on the pulse of how your customers feel about your business or service, analysing the open-ended questions included on your survey gives you the most actionable part of the NPS survey.

To get to the ‘why’ of your score the categorisation of the open-ended verbatim comments from promoters and detractors into themes and tag comment sentiment is necessary.

Themes

A theme is simply feedback around a recurring topic; for example, shipping times, support tickets or prices. You may already have an idea of some themes just from reading through the feedback you’ve already received. Or, as you begin to tag your feedback, you’ll likely start to see themes naturally emerging. One potential difficulty with tracking themes is how many different ways there are to give feedback around one theme;

for example, if your theme is “shipping times,” you may see feedback like “fast shipping,” “lightning quick” and “arrived on time.” There are so many different ways for people to refer to the same theme, which can make theme tagging labour intensive.

Sentiment

Another way to organize analytics is by the sentiment of the customer. Sentiment is more closely related to NPS because, broadly, it means assigning a metric to a piece of feedback that details how positive or negative that feedback is.

Approaches to text analytics

There are a number of ways to analyse your customers feedback.

Rule-based approaches

Rule-based analytics rely on sets of rules to determine which keywords found in customer feedback are positive and which are negative, and then determine a cumulative score for the feedback based on how many positive and negative words were found. Since rule-based analytics can function only within the boundaries of their existing rules, they work well when the scope of what they’re analyzing is narrow.

Excel Macro Approaches

It’s possible to use Excel macros to collect rule-based analytics. Keywords can have values attached that denote how positive or negative they are, and then Excel will assign an average value to each piece of feedback depending on the number of positive and negative keywords found. This method is limited because keywords must be manually entered and assigned value.

AI Approaches

Natural Language Processing, which is an application of artificial and machine learning to unstructured data is quickly becoming a need to have for customer experience teams. Supplementing structured feedback from NPS with text analytics is the most advanced way to measure customer feedback.

Many customers provide a response to the NPS follow-up question, “Care to tell us why you’ve given us this score?” Promoters, for example, want organisations to continue to do well, but they often ask for specific changes. Acting upon unstructured data has been painful in the past and has involved teams crawling through feedback on a 1:1 basis. It’s inconceivable for a large company to ask every customer for feedback and to read each one. If you try to do it yourself, you will struggle to notice an emerging theme and fail as soon as your volumes get into 100’s.

Fortunately, modern technology allows you to break the trade-off between quality and quantity of insight. AI-powered text analytics tools can analyse large-scale responses, surfaces key themes, and reveal what is truly significant to your business. With machine learning, you can understand every single comment and provide you with an aggregated view of what drives your feedback in real time.

In today’s world, an NPS score alone cannot provide the full picture of the customer experience. Natural language understanding is a critical component to customer feedback mechanisms like NPS. It enables organisations to utilise large amounts of feedback real-time to develop a complete understanding of customer’s needs.

How Can I Improve my NPS?

Here’s how you should start the cycle of improvement:

  1. Discover your baseline Net Promoter Score
  2. Find patterns in the data
  3. Get more data
  4. Make an action plan based on the feed - and act on it
  5. Systemise

Discover your baseline NPS score

It’s now time to launch an NPS survey. There are lots of great surveys tools out there to capture customer feedback. You can begin by sending out transactional surveys soon after an interaction with a customer, such as an email after a purchase, or after a self service experience on a website.

Some businesses choose to send out relational NPS survey on a rolling basis and like to stay up to date every day. Others launch one for a fixed time, close it, and launch a new one after they’ve taken an action based on the results of the previous survey.

Pay close attention to your responses and funnel detractors to customer support. If you have large volumes of feedback it’s possible to funnel feedback and complaints to teams automatically using AI!

Find Patterns

After analysing your results, you’ll learn what works for your customers and what doesn’t. If you’re a small business with less than 100 responses per survey, it’s possible to pinpoint themes and sentiments in the data and learn what does and doesn’t work for your customers.

If your company is receiving large volumes of feedback, it becomes an impossible task to learn what your customers are talking about and how they feel about their experiences at scale. It may be time to turn to AI platforms like Chattermill to unlock data-driven insights from your feedback.

Using CX management technology to create bespoke theme structures and detect sentiment within feedback using machine learning to process data in real time. Chattermill will pinpoint and prioritise aras of the customer experience that demand the most attention. Making pinpointing customer pain points a quick and easy task.

Get more data

It’s important to launch multiple feedback channels to maximise response rate to the NPS surveys. You should offer surveys across both your desktop, mobile experiences, SMS messages and non-digital touch points. While you could create a such tool inhouse, we encourage firms to use the NPS services that support collection across a variety of user interfaces, such as the one offered by Usabilla.

Don’t forget to get in touch with people who have churned and ask what made them churn, or why they switched to a competitor.

Always be sensitive to where you put your feedback channel. It shouldn’t interupt the users experience, and it shouldn’t feel like a chore. Make it voluntart, short, and fun. Your data collection point is a touchpoint like any other, and can makeor break an impression of the brand.

Action Plan Based on Feedback

When you have enough feedback from all of your channels, it’s time to find out how to squash your bugs.

While teams spend a lot of time looking at NPS detractors and how to address their pain points, it’s equally if not more so to spend time on promoters. Understanding what was different about their experiences to make them successful.

Finding correlations between specific customer experiences and NPS results (logins, delivery time, customer service, contact options) can help deduce what delights your customers and turn them into promoters. Then you can focus on optimising your customer experience and tracking NPS and Sentiment over time to get more of your customer base to this point. With the help of Chattermill’s customer experience management technology, it is quick and easy to chart specific themes and product features against NPS and Sentiment over time.

Systemise

Improving your customer experience is cyclical, not linear. That’s why the final stage is to systemize the process. Launch another NPS survey with the next cohort of customers. Take note of the improved score and report continuously in an easy and digestable way to share result company wide. Be sure to set up instant notifications of low scores so you can react in real time to a drop in NPS.

Remember to measure continuously over time to ensure a constant flow of data as it’s the only way to get the magical moments and optimise your customer experience.

  • Sam Frampton

    Growth Marketing at Chattermill - A.I for better customer experience

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A Deep Dive into Surveys: Open Ended Vs. Close Ended

by Sam Frampton on 10 Oct 2018

Survey questions come in two main varieties: open-ended and close-ended.

Usabilla and Chattermill Survey Deep Dive

Survey questions come in two main varieties: open-ended and close-ended. When creating excellent online surveys, it’s critical you make proper use of both question types and know exactly when to use each one across customer touchpoints and behaviors.

The Questions Closed-ended questions

Close-ended survey questions give a limited set of answer options. As such they don’t allow the respondent to provide a unique or unanticipated answer, rather they offer specific feedback about a focused area.

Examples of close-ended questions are:

  • Dichotomous or two-point questions: (e.g yes or no)
  • Multiple questions: (A, B, C, D or E)
  • Checkboxes
  • Drop down
  • Scaled questions: that is, making use of rating scales (e.g semantic differential)
Open-ended questions

Open-ended questions are those which require more thought than a simple one-word answer. There are no predefined answers, and respondents can create their own answers. The answers could come in the form of a list, a few sentences or something longer such as a speech, paragraph or essay.

Writing a good open-ended research question can be a tricky balancing act. It should be designed so it prompts users to provide useful information and elaborate responses that are free of restraint.

Examples of open-ended questions include:

  • Completely unstructured questions: openly ask the opinion or view of the respondent
  • Word association questions: the participant states the first word that pops into his/her mind once a series of words are presented
  • Thematic Apperception Test: a picture is presented in which respondents explain their point of view

Close-ended questions

Strengths:

  • Demographic studies: These are used if a manager wants to decipher the demographic breakdown of their store visitors, which could include, age, gender, marital status, and employment status and are easily answerable as questions. The manager could determine a profile of their typical customer.
  • Measuring KPIs: Understanding how many of your customers who rate their experience with your brand as “extremely” or “very” satisfied or are likely to recommend your product in an NPS survey is an excellent barometer to understand your business’s health. The conclusive nature of the data is easily quantifiable making it particularly useful to prove the statistical significance of results and measure over time.

Weaknesses:

  • Deep understanding of topic: The researchers must have a clear understanding of the topic before close-ended questions are designed otherwise they will have insufficient answer options for respondents to select. For example, if I asked the question “What mode of transport did you use to get to work today? Car, Bus or Walk.” I would mistakenly miss out ridesharing or cycling.
  • Lazy responses: Respondents with no opinion on the research question may answer anyway. It prevents researchers from further exploring the meaning of the responses.
  • Stop the conversation: When you ask closed-ended questions, you may accidentally limit someone’s answers to only the things you believe to be true. Worse, closed-ended questions can bias people into giving a certain response.
  • Multiple questions required for insight: If you want insight on more than one specific area, you’ll need to add additional questions for each new area.
Open-ended survey questions

Strengths:

  • Richer insight: Open-ended questions allow respondents to include more information, including feelings, attitudes, and understanding of the subject. This allows researchers to better access the respondents’ true feelings on an issue. Closed-ended questions, because of the simplicity and limit of the answers, may not offer the respondents choices that actually reflect their real feelings. Closed-ended questions also do not allow respondents to explain that they do not understand the question or do not have an opinion on the issue.
  • Cut down response error: Open-ended questions cut down on two types of response error; respondents are not likely to forget the answers they have to choose from if they are given the chance to respond freely. Also, open-ended questions simply do not allow respondents to disregard reading the questions and just “fill in” the survey with all the same answers (such as filling in the “no” box on every question).
  • Reveal the unexpected: Due to the nature of open-ended questions, they facilitate an unlimited number of answers that may reveal some unexpected insights and novel answers.

Weaknesses:

  • Harder to extract insight from unstructured data: Despite being the richest source of feedback, it is the hardest to interpret accurately at scale without a tool to analyze the topics and sentiment within each response.
  • Larger item of non-responses: Greater amount of thought, response time and effort is needed to complete the question.
  • Generalized responses: Questions may be too general for some respondents who then lose direction. Different people will also give differing levels of detail when answering.
Combining close-ended and open-ended questions

When designing a survey it is often advantageous to combine open and close-ended questions. A well-known example of a survey that uses both types of questions is the Net Promoter Score.

Close-ended questions provide the data that can be measured over time.

An open-ended question will give you the insights as to why the customer gave their rating.

Combining both question types will provide the valuable data you need to make informed business decisions. Gaining a better understanding of your customer’s experience and pinpoint areas for improvement.

Usabilla & Chattermill Solution Partnership

Collecting feedback via Usabilla gives you valuable insights regarding your customer’s journey. We understand that continued feedback may be challenging when it comes to analysis, especially to process data in a way that scales as data volumes grow.

In order to get the most out of your collected data, Usabilla collaborates and integrates with Chattermill. Chattermill helps you to get a better understanding of the most important topics your customers talk about using AI to interpret theme and sentiment analysis across unstructured feedback.

With the Usabilla-Chattermill integration, we tailor each area of feedback to how your business runs and ensure that we go into as much detail as is required for your business to effectively gain the insight you need so you can make the right decisions.

  • Sam Frampton

    Growth Marketing at Chattermill - A.I for better customer experience

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US Open 2018: Can Amazon Ace Live Sports Broadcasting?

by Sam Frampton on 17 Sep 2018

Using the power of AI to interpret theme and sentiment within each and every customer review we set out to find answers.

Amazon US Open 2018

The 2018 US Open came to a close this past Sunday. With one returning champion and a new women’s champion it was a tournament full of controversy, both on and off the court. Despite spending $40 million on a five-year deal Amazon failed to ace it’s first exclusive coverage of a live sporting event. Tennis fans across the UK flocked to Amazon after they snapped up UK rights to the US Open. What followed was a deluge of complaints, with many users leaving one or two-star reviews.

The negative feedback amongst users will no doubt come as a surprise to many. With its relentless focus on customer experience, Amazon has grown into a trillion dollar company by being the ‘Earth’s most customer-centric company.’ After securing 20 matches a season of Premier League football from 2019-2022, the company has set its sights on the brutal arena of live sportscasting for the long term. As Amazon gets serious about streaming live sports, we look at where Amazon needs to improve if they are going to conquer this new market.

Using the power of AI to interpret theme and sentiment within each and every customer review we set out to find answers.

Here’s what we found: https://app.chattermill.io/d/232b8d77-d340-4296-8273-f81254307790

  • Sam Frampton

    Growth Marketing at Chattermill - A.I for better customer experience

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The Slack Growth Hack: Making Word of Mouth Work

by Sam Frampton on 5 Sep 2018

How to grow your company by doing one simple thing

Slack's Growth in Daily Active Users

The company has been on a growth tear over the last few years. The cloud-based technology company has capitalised on the pent-up demand for a workplace communications tool and has become the tech darling of Silicon Valley. It recently passed 8 million daily active users with approximately 70,000 businesses paying for their services.

The company now has a valuation of $5.1 billion after a recent funding round by SoftBank. The Slack CEO says the company ‘should end up as big as Microsoft’. The company has now grown so big it’s beginning to draw a competitive response from the tech giants like Microsoft who recently unveiled a new version of Teams. So how did Slack go from tech upstart to Silicon Valley royalty? The answer. Through word-of-mouth marketing tracked by NPS.

This post is about making word of mouth work for you.

It’s no secret that the most credible form of advertising comes straight from the people we know and trust. According to the Nielsen Global Trust in Advertising Report, 83% of respondents from 60 countries say they value recommendations from friends or family.

As Andrew Chen writes in an essay on scaling growth if you create experiences that your users love and they engage with your product, you can achieve major “word of mouth” growth driven by a high Net Promoters Score. Turning more customers into promoters boosts the number of times your company is recommended. This, in turn, fuels growth and is one of the few ways to reach hundreds of millions of users or revenue at minimal cost to you.

Identify what experiences turn customers into Promoters

Have you visited Slack’s wall of love” by First Round Review highlights:

  • Search Capabilities: When the search user experience has been raised so high by Google. Slack knew they needed a seamless user experience where users could find documents and conversations with ease.

  • Synchronization: When a user leaves their desktop and switches devices to a phone Slack knows where every person in every conversation leaves off.

  • Simple File Sharing: User of Slack interact with the product for hours a day. By adding the simple ability to share files and images quickly by dragging and dropping providing shortcuts and productivity hacks.

In short, allowing customers to leave feedback in their own words paints a detailed picture of what triggers a positive or negative experience. With this knowledge, you can make confident decisions on what to amplify and what to fix. As you do so, you will start to see more of your customers turning into Promoters. Slack does not stop at making you their user. They want you to shout about how awesome Slack is on every corner.

NPS can help identify your brand’s advocates, drive your product roadmap, alert you to customers in danger of churning, and which customers you should approach for stories. Ask your biggest evangelists for reviews, tweets or customer videos. It’s really easy to reach out and tap into that evangelism. Slack CMO (2014-2017), Bill Macaitis

They use NPS as the most important metric. No wonder, it’s one of the fastest growing company in the world. Below are some tried and tested techniques to get your customer talking.

Step 1. Run a Net Promoter Score (NPS) survey:

You can’t achieve something unless you measure it. Some companies use smart tracking and referral codes to estimate the viral coefficient. But only a few businesses grow through clear social media channels. Most word of mouth happens somewhere else, where it’s hard to measure. Think coffee shop, conference, meetup, canteen and many other situations. For some products, people just won’t be into sharing their experience with strangers. Mint.com is an excellent example of a strong word of mouth growth without much true virality. People loved Mint. It had a great Net Promoter Score, and it solved a real problem for people. On the other hand, anonymous sharing app Secret spread like wildfire in 2014 reaching 15 million users only to shut down a year later because virality only accounted for the “hot new thing factor”, not the usefulness of the service.

To help us measure true long-term growth potential, we turn to Net Promoter Score. NPS is a simple survey asking your customers what the likelihood is of them recommending your business on a scale of 0 to 10. And before you ask, yes, this is an 11-point scale. And no, it does not make any sense. Such is life.

Based on their answers, it then categorises your customers into one of three groups: Promoters (9–10), Passives (7–8) or Detractors (0–6). The more Promoters you have the more customers you will acquire through recommendations.

Net Promoter Score was introduced in 2003 by Bain & Co as “the one number you need to grow”. It is a number that, roughly speaking, tells you how much your customers like you. It is calculated as follows:

## NPS = % Promoters (9–10) — % Detractors (0–6)

A good NPS is typically considered anything above 70%, with over 85% being outstanding. This is however a very rough guideline, the real value of NPS comes from understanding how to improve it within the context of your own company.

Step 2: Collect feedback

Make sure you follow up with an open ended question of why the customer gave the score they did.

The challenge here is to collect enough quality comments as to why respondents gave the recommendation score they did. You can also ask your customers to tell you what would it have taken them to give you a higher score.

Customers can often tell you fascinating things about your business that you would otherwise miss. For more information on some of the best tools to collect feedback read our customer experience technology stack guide.

Step 3. Identify what experiences turn customers into Promoters

Slack is genuinely obsessed with customer feedback. Every day their team reads through heaps of emails and comments, forwarding product suggestions to the management team (who also love reading this stuff). Great as this approach is, it is hard to scale.

“Every CEO should be able to answer this question: what are the top 3 reasons why people recommend and do not recommend your brand?”

Bill Macaitis, CMO, Slack

An alternative solution to manually reading customer comments is to get a computer to do it. Chattermill.io for example, has specialist software that can detect sentiment and themes in customer feedback with precision. It can literally tell you what your customers are saying.

In short, allowing customers to leave feedback in their own words paints a detailed picture what triggers a positive or negative experience.

With this knowledge you can make confident decisions on what to amplify and what to fix. As you do so, you will start to see more of your customers turning into Promoters.

Step 4. Rinse and repeat

A lot of companies collect NPS but don’t utilise it to full potential. The reason you should be using it is not because Bain said so, but because it gives you a pulse of your business. What good is a pulse that is collected once a year. Twice a year? NPS just as your other KPIs should be collected regularly, as often as possible. There is nothing stopping you from collecting it daily if you have enough customers. If not, consider weekly or monthly.

The trick is obviously not to spam your customers but also not to spend all your time building surveys, sending emails, maintaining spreadsheets with response data etc. Again, an automated solution makes life much easier. At Chattermill, we hook directly into your CRM or email marketing tool and make sure a sample of your customers gets surveyed every day / week / month.

Fundamently people flock to popular, recommended things. Get more of your customers to become Promoters, the best advertising space you can get.

  • Sam Frampton

    Growth Marketing at Chattermill - A.I for better customer experience

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The Ultimate Guide to Net Promoter Score | Chattermill

by Sam Frampton on 4 Sep 2018

What You Need to Know about the Net Promoter System

The Ultimate Guide to NPS | Chattermill Introduction

Do you know how Net Promoter Score can transform your business? A method of measuring customer loyalty by sorting them into detractors, passives and promoters. It has helped many companies remain in the top ranks of their industries. Loyal, passionate customers stay longer, spend more, contribute suggestions and sing the praises of your company to their friends.

Using the Net Promoter Score (NPS) to measure customer loyalty has become industry best practise. That’s why the team at Chattermill has put together a guide to help you get started with NPS.

In this guide, we will cover everything you need to know about NPS to get started and help your company grow.

What is Net Promoter Score? The Definition

Net Promoter Score (NPS) is a seemingly simple yet effective way for companies to track promoters and detractors to produce a clear measure of their performance through its customers eyes. NPS is built on the basis that every company can divide their customers into three distinct buckets - promoters, passives and detractors and customers are categorised based on their response to the standard NPS question - “How likely is it that you would recommend us to a friend?”.

Using data and a disciplined process, it has been proven that companies with higher NPS scores achieve long term growth and NPS leaders grow, on average, at double the pace of competitors.

To track the performance of company growth, take the percentage of customers who are promoters and subtract the percentage who are detractors.

Net Promoter Score Calculation

Answers to the question are scored on an 11 point scale (0-10) and ranked on an index ranging from -100 to 100 to gauge customers overall satisfaction with a company’s product or service and customer loyalty.

Responses can be defined into three distinct clusters that represents different attitudes, sentiment and economic value.

Promoters (scored 9 - 10):

Promoters are your biggest fans. They actively advocate your product on your behalf, bringing in the majority of referrals, and are far more likely than any group to remain customers. Their Customer Lifetime Value (CLV) is far greater than any others.

Passives (scored 7-8):

Passives are satisfied for the time being, but can defect at any time. Their referral rate is as much as 50% lower than promoters, and those referrals are of far less quality. Their CLV is also usually less than half that of promoters.

Detractors (scored 0 - 6):

Detractors are unhappy customers and account for more than 80% of negative word-of-mouth opinion. They have the highest rates of churn and defection and harm your company’s reputation, putting off new customers.

The End Result

Your Net Promoter Score is the percentage of promoters minus the percentage of detractors.

The Net Promoter Score is a simple and straightforward metric that can be shared throughout the company with every function and team. You can also track by product, store, team, geography and more to focus on the goal of improving customer experience.

If you have more detractors than promoters the score will be negative and likewise positive for more promoters than detractors. Lower Net Promoter Scores can be indicators of harmful customer experiences leading to potential losses of revenue, whilst higher Net Promoter Scores suggest a stronger performing business.

The median NPS score is just 16 and typically remain quite low; depending on the industry.

Economics of NPS

Striking the balance of promoters and detractors through Net Promoter Scores is clear. Promoters will actively advocate your business on your behalf, repeatedly purchase and refer you to friends. They not only bring in the most revenue, but are also most likely cheaper to manage than detractors.

Detractors will cost you money both in terms of damaging your brand and also the resources required to deal with their complaints. They are also more than likely to not purchase repeatedly.

The Net Promoter Score accounts for between 20% and 60% of organic growth for companies and on average the industry leader’s NPS is twice that of its competitors.

It has also been found that promoters are more than 6x likely to forgive, are more than 5x as likely to repurchase and 2x more likely as detractors to recommend a company.

Calculating NPS is just the start

NPS is far more than a score. Also following up your NPS question asking customers for the reasons why they left their score with an unstructured and open-ended form allows you go beyond the score to identify the root causes driving promoters, passives and detractors experiences.

Scores will tell you what happened; feedback tells you why, allowing you to build feedback into part of their daily systems to amplify the factors improving customer experience and nullify the largest negative driver to create a fully closed loop customer experience process.

Throughout the guide we’ll walk you through everything you need to become an NPS pro, right the way through from collection, analysis and insights. Now we’ve discussed how to calculate NPS it’s time to move on to best practise when sending an NPS survey.

Net Promoter Score: Types of surveys

Once you’ve decided to survey your customers to find your NPS, you have a few different surveying options.

Relational surveys

A relational survey can be sent at any time, and it’s a good way to get a finger on the pulse of the relationship between your customers and your business. Relational surveys should be sent out often — a quarterly schedule is ideal.

However, while regular surveying keeps you up-to-date on customer feedback and loyalty, surveying your customers on such a regular schedule risks “survey fatigue,” which can lower your overall response rate. It’s important to make sure your customers know you value their survey responses — some options are sending thank you emails or offering perks or discounts for customers who complete relational NPS surveys. It’s also important to only survey as often as you have time to analyze the feedback, respond to your customers and create an action plan based on their surveys (we’ll cover this in more detail in a later chapter).

Transactional surveys

A transactional survey is sent after an event, such as soon after a new customer purchases your product or service, or after the resolution of a support ticket. The timing is a little easier with transactional surveys than with relational surveys — just make sure to send the survey before too much time has passed after the event that triggered it. Some good guidelines are:

  • 0-24 hours after a support ticket is resolved
  • 0-3 days after a product is purchased, if the product can be used right away
  • 7-10 days after a product is purchased, if the product needs to be shipped.

While transactional surveys aren’t as likely to cause “survey fatigue,” they also give a narrower picture of your NPS, since they relate to specific events and not your business as a whole.

Irrespective of the type of survey you choose, if you can pass any additional data you have for your customers into your survey emails, such as the product they ordered, location and length of time as a customer, you can then begin to piece together the opinions of different segments of your customers to understand their similarities and differences too.

Surveying the right number of customers

To get a clear picture of your NPS, you need a good sample of responses that represents your entire customer population. This can be tricky to get, but the bottom line is that the more responses you can get, the better your data will be.

It takes some complex math to get the exact number of responses will be representative of your business’ customer base (if you’re really into statistics, you can check that out here), but a good rule of thumb is to assume that only 15 percent of the customers you send surveys to will actually respond, which means you need to send out a minimum of 1,700 surveys to get 250 responses, what’s generally considered a good sample for calculating NPS.

Nailing your survey format

You want to maximize your response rate, so first and foremost, respect your customers’ time by keeping surveys short and sweet. Ask the standard NPS question (“How likely is it that you would recommend us to a friend?”) and then ask why. That’s it!

You should also aim to send out surveys, particularly B2B, during the work week. Avoid Fridays and weekends, since most attention is being paid to email Monday through Thursday.

Lastly, the most important thing you can do to maximize survey response is to make sure your customers feel heard. Respond to their feedback. Use it to build an action plan.

What’s next?

In the next section of the guide, we’ll talk about what to do once you actually receive that feedback you’re looking for, and how to optimize your response for every type of customer: promoter, detractor or passive.

Theme vs. Sentiment: Organizing your analytics

The first tool you can use to organize your NPS survey information for analysis is tagging, and two ways you can tag your feedback is by tracking themes and sentiments.

Themes

A theme is simply feedback around a recurring topic; for example, shipping times, support tickets or prices. You may already have an idea of some themes just from reading through feedback you’ve already received. Or, as you begin to tag your feedback, you’ll likely start to see themes naturally emerging. One potential difficulty with tracking themes is how many different ways there are to give feedback around one theme; for example, if your theme is “shipping times,” you may see feedback like “fast shipping,” “lightning quick” and “arrived on time.” There are so many different ways for people to refer to the same theme, which can make theme tagging labor intensive.

Sentiment

Another way to organize analytics is by the sentiment of the customer. Sentiment is more closely related to NPS because, broadly, it means assigning a metric to a piece of feedback that details how positive or negative that feedback is.

Approaches to text analytics

There are a number of ways to analyze your customers’ feedback, each with pros and cons.

Rule-based approaches

Rule-based analytics rely on sets of rules to determine which keywords found in customer feedback are positive and which are negative, and then determine a cumulative score for the feedback based on how many positive and negative words were found. Since rule-based analytics can function only within the boundaries of their existing rules, they work well when the scope of what they’re analyzing is narrow.

Excel Macro approaches

It’s possible to use Excel macros to collect rule-based analytics. Keywords can have values attached that denote how positive or negative they are, and then Excel will assign an average value to each piece of feedback depending on the number of positive and negative keywords found. This method is limited because keywords must be manually entered and assigned value.

AI approaches

AI, or machine learning approaches, are data driven and use existing rules to assign sentiments to feedback, but also to predict outcomes and create new rules. As long as the AI has a sizeable input from which to draw its predictions, this approach tends to result in extremely accurate feedback analysis at scale.

Why analyze your NPS feedback?

As you can see, implementing a system to analyze you NPS feedback isn’t easy or quick. But going beyond your NPS score gives you the opportunity to make changes that address the specific themes that impact your NPS, which means a direct path toward improving your NPS overall.

When and how to survey

You already know that it’s important to keep a constant finger on the pulse of your NPS, because it gives you a constant baseline idea of whether more of your customers are happy with your product or service than not. That means surveying your customers frequently to keep an eye out for changes and trends.

Best practices say you should be sending out relational surveys — general NPS surveys that can be conducted at any time — to your customers on, ideally, a quarterly schedule. Additionally, transactional surveys — surveys sent out after and related to an event, like a product purchase or the resolution of a support ticket — should be sent out within hours to days after the event that triggered them.

That’s a lot to keep track of.

Sending out all those surveys at the right times could be a full time job, or even more. But instead of manually sending them to your customers, you can automate your survey process to handle all the sending for you. It’s an easy way to make sure NPS surveys are going out when they should, and always on time. That way, you know you’re never missing any opportunity to gauge how your customers are feeling about their experiences, and your NPS data is always up to date.

Sharing your results

But there’s more to streamlining your NPS analytics process than just automating your surveys. In order to get the most out of your NPS and analytics, they need to be seen by the right eyes within your company. That’s why you need a system for sharing your results with all the stakeholders in your company.

And that doesn’t just mean C-level executives. That means people across departments and at different levels. For the entire company to move cohesively toward an effective and shared CX strategy, everyone needs to be on the same page, which means everyone needs to have access to shared NPS data, preferably in real time.

Achieving that means selecting a platform that not only automates your NPS surveying, but also collects, sorts and stores the data in a format that’s accessible for your entire team. Once you have the right platform in place, this is a job that gets done automatically, meaning more company time and effort can be put toward using that data to make your CX stand out.

Time to Monetize

You’ve surveyed your customers, analyzed their feedback, and determined a customer-centric path forward. But what’s the next step? Why have you put all this work into making changes based on your customers’ feedback?

Well, because good CX can increase your bottom line. We’re in a new era, where customers expect stellar experiences when they interact with businesses. And businesses are using that change to monetize more than just customer transactions — they’re monetizing customer relationships. Here’s how.

Learn, iterate and improve by putting data into action

One of the easiest ways you can monetize your customer relationships is by building brand loyalty amongst your users. According to Amy Gallo, in an article for the Harvard Business Review, acquiring a new customer is anywhere from five to 25 times more expensive than keeping an existing one. Creating loyalty can reduce your churn rate and make sure you keep those existing customers. But creating the kind of loyalty that will keep your costs low means providing truly excellent CX. Your customer feedback is your most important tool in creating the CX your customers want. It’s also important to understand the key drivers of loyalty, and then use that knowledge to improve your CX.

But retaining loyal customers is only one piece of the monetisation puzzle. Even though it’s more expensive to acquire new customers, you have to for your business to grow. The key is to keep your existing customers and acquire new ones, and you can do that through happy, engaged customers who act as brand ambassadors. Getting high quality insights from your CX feedback will reveal patterns over time that can be fed right back into your product development roadmap. Giving the people both the products and the experiences they want will make them NPS promoters for your product. They’ll spread it through their social circles, driving more revenue. On the other hand, though, negative feedback should be a wakeup call telling you it’s time to change your product or CX to keep customers happy and build those positive relationships.

Finally, take a good, hard look at your customer onboarding process. This is a pivotal moment in the CX, and a great customer onboarding experience can be the difference between a user churning, or becoming a loyal, active user of your brand, product or service. The feedback you get from customers should direct you to any pain points that exist in the onboarding process, especially if you’re sending out transactional NPS surveys immediately following onboarding (and if you’re not doing this, start now). It may also be helpful to reverse engineer your onboarding process, especially if you have a truly great product. Offer a free trial or some other way for users to get inside your product before completing their signup. Once they realize the product is a must-have, they’ll be more than happy to complete onboarding.

Monetising your customer feedback is the fastest way to see quantifiable results from the work you’ve put into designing a stellar CX. Finally, all that hard work has paid off.

  • Sam Frampton

    Growth Marketing at Chattermill - A.I for better customer experience

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