In case you missed it, customers’ expectations have changed.

The way we communicate with them has changed, and Intercom users are leading the way. Customers and prospects like communicating via Intercom chat. It’s efficient, modern, and conversational.

Integrating a customer feedback program with Intercom takes customer experience to the next level. Having all your customer experience data gathered, viewed, and managed in Intercom sets you up to build high-quality relationships with your customers.

Easy, breezy, and code-free option: Surveys in Intercom chat & email

Wootric gives Intercom users two code-free ways to survey customers – in Intercom chat and via email. The survey responses then flow back into Intercom records to view for follow-up.

Wootric Surveys is an Intercom Messenger app, available now in the Intercom App Store, that lets you send surveys within the Intercom Messenger chat bubble. From your customers’ point of view, sending Wootric surveys through Intercom makes sense — that is where they are used to getting communication from you.

Alternatively, you can survey your customers via email. Download the free Wootric email survey template (you’ll find it in Wootric settings) and upload it into Intercom.

Whether you are surveying in Intercom chat or via email, you can create auto-messages based on Intercom rules and attributes, or send a survey out to individual customers manually.

Here are the top 5 use cases for the Wootric-Intercom integration to unify your customer feedback program.

Target a specific customer segment with an NPS survey campaign

To better understand the sentiment for each your customer personas, set up a campaign-style auto message with a Net Promoter Score survey in Intercom.

For example, you may choose to target all of your enterprise customers, or all of your self-service customers. Perhaps you want insight into customers in the EU, Asia, or South America. You could also choose to survey customers who have been with you for 6 months.

With Intercom and Wootric, you can send a survey can be based on any group of customers that you can define in Intercom.

Target customer surveys based on a product or service milestone

Gather feedback after product or service milestones. The feedback you gather at customer journey points can help you prioritize improvements that will increase customer retention. Here are two popular examples:

• Deploy a Customer Effort Score (CES) survey after a product milestone is achieved

If you are trying to understand customer sentiment around installation processes, onboarding, or other product milestones, the Customer Effort Score is the survey for you. Measuring customer effort gives you insight into pain points or friction that customers may experience while using your product or service. Asking “how easy was it to __” will help you quantify ease and give you wide-ranging feedback. You may find that your processes (e.g. installation, getting started, etc.) are easy enough, but documentation is difficult to find. Customers may end up going to Customer Support because they are frustrated about finding answers and instructions on their own.

 As for when to send a CES survey, you may choose to send a survey via auto message for folks who have logged into your platform after completing onboarding. You could choose to send an email survey to users who have exported their data to another platform to see if the process is easy enough. Surveys can be triggered based on any event you are tracking in Intercom, giving you a plethora of options.

• Send a Customer Satisfaction (CSAT) survey to ask about product features

Customer Success and Support aren’t the only teams that will benefit from surveys via Intercom.

This channel is a great way to survey customers about different product features as they use them. For example, you can choose to deploy a CSAT or “PSAT” survey to all users who have used a feature X number of times or 30 days after upgrading to a new feature set.

The feedback you get from asking the Customer Satisfaction question can be valuable to your product development team and product managers.

Make sure everyone is covered

• Send a survey in a live conversation

“Oops, I didn’t mean to score you a 2!”  Sometimes a customer will make a mistake or get distracted and fail to click “submit” when they see your survey.

Now, you can manually insert a fresh survey into the conversation. This lets you fill in the gaps that automated campaign-style messages may leave.

• Hear from more customers by sending an in-app survey to people who don’t respond to email surveys

If you’ve been surveying your customers via email, you may find that a good portion of them go unopened. Other email surveys may be opened and the surveys don’t get filled out because folks think “Oh, I’ll get to that after I finish up this other thing!”, then get distracted and forget about your survey.

Cover your bases and reach customers where they’re already working by sending these users another survey via Intercom messenger.

The set & forget option: Wootric in-app surveys

One challenge with Intercom auto-messages is that they are not recurring. If you want to survey your customers say, every 90 days, the best way is to install the Wootric code snippet on your web application (or use the SDK for you mobile app). Then you can easily configure your sampling requirements and survey cadence in your Wootric settings.  The best part is that survey responses collected this way will still flow into Intercom records for follow-up action.

See survey feedback & respond to customers in Intercom

Consolidating all of your customer experience data in one platform isn’t just efficient. It provides your customer-facing agents with vital context as they interact with users without having to shift from one platform to another.

For example, your Customer Success Managers will be able to see each company’s survey responses, including user comments.

Your Support and Success agents can manage the follow up with customers in the same platform that they view users’ survey responses. The Wootric integration can be configured to automatically open a conversation when a survey response is received. This can prompt team members to reach out to personally to thank the customer or follow up on any issues. Or, set up auto-messages to response to customers based on their score. For example, trigger an email that asks happy customers to write a review of your product. 

With Intercom, customers know where to expect communication from you and know exactly where to reach out for help. Using Wootric and Intercom together is a convenient way to close the loop with customers, letting them know you value their feedback.

Do you have a data silo problem?

• Do customers complain of having to explain everything about their business to sales, and then to customer success, and then again to customer support?
• Is customer support hearing about the same issues, over and over again, that aren’t being addressed by product?

Those are just two of the most frequent symptoms of data silos. Here are some more, reported to us by our friends at Segment.

• Inability to answer complex questions about your customer journey.
• Inability to quantify the impact of a given campaign against down-funnel, often offline conversations (like Salesforce lead status updates).
• Inability to affect targeting criteria in a given channel based on interactions that occurred in another (ie. you’re spamming users across channels when they’ve already converted or signaled their preferences in another.

What do all of these silo symptoms have in common? They all damage customer experience, and they all result from data not being shared between teams and departments.

Three main causes of data silos

Data silos are isolated islands where information sits, visible to just one or a few people. Usually, the cause of data silos isn’t some greedy information hog, unwilling to let anyone see his or her hoard of numbers. It’s nothing so Dickensian. Here are the main reasons they exist.

1. Structural

Businesses that have been around through multiple owners, leaders and ideologies typically have incompatible systems in place from various eras and incarnations. Older software or apps that haven’t been updated or replaced probably don’t play well with others. Whereas newer data collection and analysis programs have built-in capacities to share information with other apps, older systems don’t. Or, they don’t do it automatically. If no one is tasked with disseminating the information, it doesn’t get shared.

2. Social

Maybe teams aren’t rewarded for sharing, or required to share information. Or, maybe there is a data hoarding person or group who keep data to themselves to maintain a sense of power and control. But usually, it’s a case of ‘this is the way we’ve always done it’ resistance to change. Having a ‘silo mentality’ in your business makes it difficult or impossible to quickly spot opportunities and take advantage of them, because when information isn’t shared, you can’t make fast, informed, data-driven decisions.

3. Vendor lock-in

Maybe it’s not you, it’s them. The software vendors. Yes, even software-as-a-service applications can effectively ‘trap’ businesses within their platforms by requiring heavy investments in special training, or they may lack native integrations or an open API. In either case, they make it difficult to switch information over to other apps.

Breaking down these data silos requires a lot of effort and commitment. Structural causes require an overhaul of all or most of your existing systems; social causes may take a company-wide initiative to improve company culture; and vendor lock-in-related causes are, by nature, tricky to remedy.

So before we get into how to break down data silos, let’s look at why it’s worth all of the time, effort, and investment.

What you stand to gain by breaking silos down

One of the biggest threats data silos pose to companies is blocking customer success. Customer success depends on everyone in the company being aligned behind the same data-informed vision of the target customer – their needs, wants, challenges and desired outcomes.

But that alignment depends entirely on sharing information across the entire organization, not just once, but continuously, to facilitate collaboration between sales, marketing, customer success and customer service (at minimum). When customer-facing departments run entirely separately from each other, it’s the customers who pay the price.

When customers run into trouble, they have to repeat themselves as they’re bounced around from agent to agent.

If a loyal customer was unhappy with the last order, s/he will feel pestered and aggravated when a clueless sales rep tries to upsell them.

Of course, it’s not only customers who suffer – nobody benefits from data silos! A 2016 brief from Forrester observed the high rates of “misaligned performance metrics, lack of clarity around lead scoring (and definitions)” and other misunderstandings between marketing and sales that leaves “sales ops in the middle to make sense of the chaos.”

Another Forrester statistic is “less than 1% of leads in B2B ever become customers,” which means businesses are wasting money on marketing that doesn’t work, salespeople are wasting time on leads that will never convert, and – when you have data silos, marketers might not even know what they’re doing wrong.

With some types of data, sharing is even more important because so many departments stand to benefit from having easy access to it. Voice-of-customer data, for example, is a must-have for marketing (for testimonials, ad/sales page/email copy, content ideas), sales (for upsells), and product (to optimize features).

The bottom line is: Breaking down data silos is an absolute requirement of creating the customer-centric culture customers want and companies need.

How to break those silos

“A customer-centric culture should be the North Star and guiding principle for tearing down the silos [between marketing, sales, and customer service]… Before joining Salesforce, I spent 12 years running global engineering and also serving as a [chief marketing officer]. Silo busting was how I spent most of my time. I realized that I had to try to align different areas of the business, and the only way to do that was to silo-bust.”

– Vala Afshar, chief digital strategist at Salesforce

First, diagnose what is causing your silo problem using the 5 Whys cause and effect analysis.

The idea is to find the root cause of the surface problem. The surface problem, for example, might be that marketing isn’t qualifying leads before passing them on to sales. The reason for that might be that marketing isn’t sure what the success indicators are for leads who convert. The reason for that might be because that data is stopped up – it’s kept by sales.

We’re already at the third ‘why’ question and we’ve just gotten to the middle problem of the data silo.

The answers to ‘why’ #4 and ‘why’ #5 will reveal the core cause that’s creating the silo in the first place.

Why use the 5 Whys? Because you might find that a data silo isn’t the root of the problem, or that the reason for the silo isn’t what you think it is. There may, in fact, be an underlying issue that runs deeper than investing in a new data gathering and analysis program can fix.

Second, get management buy-in.

Once you’re armed with the problems the data silo creates, as well as a thorough understanding of the underlying issues contributing to those problems, take your findings to management. You’ll need total buy-in from the top to address those deeper issues and find a data-busting solution that works perfectly for your company.

To get that buy-in, you’ve got to present a strong case that freely shared information will help each individual department, and the entire organization, essentially offering them a unified vision. In addition to bringing up current problems free-flowing information can fix, also consider how it can aid your company’s long-term goals and department objectives.

Third, align behind your North Star (the customer)

It’s not going to be easy to change long-standing habits in your organization, so to do it successfully, you’ve got to have whole-company alignment behind the real purpose of your proposed changes: The customer.

Your customers will tell you what impact your changes are really having. But, you need a metric to track, so everyone can see that breaking down silos (and all the work and training that go into it) are worth the effort.

We call this a “North Star metric,” like Net Promoter Score (NPS). When you see NPS scores rise, proving that customers are indeed happier (so happy they’re willing to recommend you to a friend or colleague), it’s proof positive that what you’re doing makes a difference.

Fourth, find the right tools.

Better tools lead to better collaboration, and what you’ll want to look for are data gathering and analysis tools that integrate with your CRM software (which will also solve the vendor lock-in problem, if that’s the source of your silo).

This is going to be your “single source of truth” database. Salesforce is a perfect example.

It’s key to make sure that data is shared with various functional systems of record so everyone has what they need at their fingertips. At Wootric, for example, we sync customer/prospect data from our product, Intercom (for Success) and HubSpot (for Marketing) to Salesforce – and from the Wootric survey platform, we integrate with Slack, Intercom, Salesforce, and HubSpot.

For us, this means:

• The way we put NPS into Intercom so that if a customer reaches out about a conversation, someone can see the entire history of that customer.
• You could have a different conversation with a promoter than someone who ‘dinged you’ the last time – having that context shifts the conversation.

Segment Product Manager Chris Sperandio says customers come to his company for better alignment through data.

The key is the desire to align all of their departments around a shared customer context. The way they achieve this is ensuring each department’s tools are running on a common data set. This way, they can run more cohesive campaigns and they can operationalize their insights and predictions.

Fifth: Invest in cross-functional training – together.

Once you have diagnosed your core problems, obtained management buy-in, and choose a metric that measures progress, and have the right tools – it’s time to bring everyone together for training.

Not only will everyone need training on how to use the new tools, they’ll also need training on how they can best work together to create better customer experiences through sharing information. Silo-busting is a multi-team effort, but when teams have traditionally been kept separate and sovereign, it can be a challenge to build bridges and relationships.

Try hosting a meeting with everyone to establish a shared understanding of each team’s goals, challenges and pain points.

Then, have everyone get together to find areas where insights and abilities from one person can help another person with their challenges and goals.

Finally, have everyone fill out a “communication builder” questionnaire that asks:

• Basic contact information: phone/email/Slack etc.
• What is their job title/function?
• When and how do they prefer being contacted (ie. by phone before noon, or via email – but not available on weekends for immediate response).

This step sets up co-workers for success by setting expectations and letting everyone receive requests and information in the way that works best for them.

Alternately, you might consider creating a cross-functional “tiger team” who ‘owns’ the progress of the North Star metric (like NPS) and has a C-suite sponsor who helps them get things done.

Collaborative training is a good start, but will need to be nurtured over time as the human tendency is to fall back into familiar behavior patterns. To help break those patterns, you might even consider physically moving people so employees from different teams work next to each other, building relationships.

What is Empathy?

Empathy is the ability to understand and share the feelings of another. For Product Managers looking to improve customer experience (CX), that definition translates to doing more than understanding the user’s pain points, but also looking at the emotional landscape of what it’s like to use the product – when it is working, and when it isn’t working.

Empathetic Product Managers ask themselves:

• How does using the product make the customer feel?
• How does the customer want to feel when using your product? What would be the best possible emotional outcome for them?
• How do I ensure the product developers understand and take the customers’ needs into consideration in their process?

The answers to those questions affect every facet of business, from acquisition to retention. It’s how, through CX, you can generate rapid growth through word-of-mouth recommendations, and sustain your success with customers who never want to leave.

Tying Empathy into CX

Empathy is a soft skill, and while those are typically difficult to measure, the effects of empathetic product development can be seen in every CX metric: Customer satisfaction (CSAT), Customer Effort Score (CES) and Net Promoter Score (NPS).

Sinead Cochrane, Senior Product Researcher at Intercom, wrote “For product teams, empathy building activities such as observing research or doing customer support is often not considered ‘real work’. However, product teams that consistently keep customer needs in mind are able to maintain and evolve their products in ways that won’t negatively impact the user experience.”

For her, empathy for product departments means “When a customer tells you something is broken, you are able to imagine the impact it’s having on the job they’re trying to get done,” and, “you realize the emotional impact the problem is having on that person.”

But I think we can go further than just recognizing the emotional impact of problems. That’s scratching the surface of what having empathy for customers can mean for producing superior customer experience.

Because empathy shouldn’t be reduced to realizing customers feel bad when a product isn’t working for them. A whole new world opens up when you also consider how you can design your product, updates, and expansions to enhance positive emotions as well.

Here are the questions that lie at the heart of empathetic product management:

• How can you get more emotionally in sync with your customers?
• Which are the most important negative emotional outcomes to manage?
• Which emotions should you seek to heighten (and how?)

To answer these questions, try these empathy building exercises.

Empathy Building Exercises for Product Managers and their Teams

1. Listen actively to discover underlying needs and emotional motivations

Often relegated to customer service and customer success departments, ‘active listening’ to find out why your customers use your product and what they really want to achieve is very important. You can’t get the depth and honesty of answers by just sending out a survey – this works much better if you do phone, Zoom or in-person interviews. In fact, Roman Pichler recommends product managers meet real users on a regular basis. You may find that your assumptions of why customers use your product aren’t accurate, or don’t tell nearly enough of the story.

“At first, our assumption was that they wanted to make more money. That often was true, but frequently we heard something different. Many simply wanted to maintain the business but run it more efficiently so they could have more free time (we heard about golfing on Fridays more than once). Others wanted to build a sustainable business they could pass on to their son or daughter.” – Jim Semick, Founder & Chief Strategist at ProductPlan

To get down to customers’ real motivations, ask open-ended questions beginning with “why” and “how.” Then make sure to record their answers in their own words (you can hand those assets to your copywriters for later use).

2. Use your own product

Empathy is often described as ‘putting yourself in someone else’s shoes’ – and there’s no better way to do this for a product manager than to actually use the product, just like any user would. You’ll empathize with users’ frustrations as you experience your own product’s shortcomings and hopefully find moments where it’s possible to create more delight.

But always keep in mind – you are not the average user. You’ll still need to listen to your users to get a complete picture of how they feel, and what problems they perceive as being severely aggravating.

3. Share verbatim comments

Someone, somewhere, is tracking customer experience metrics, sending out surveys, and collecting the answers. That someone might even be you. When reading users’ written responses, don’t just look for problems to solve and ignore the positive comments. Read them for emotion and see what conclusions you can draw about what people are feeling, and want to feel.

Pick a few relevant verbatim comments to bring to the rest of the product team. Reading these comments often helps engineers and designers feel the same joy or frustration as their users. This new emotional understanding will help you evangelize CX as a priority with everyone.

4. Mine your qualitative data and quantify customer sentiment

Those open-ended response answers are a goldmine for user research that can alert you to problems – and give you hints into the customer’s emotional state of mind. However, once you are getting more than a hundred comments a month, seeing the forest for the trees can be a difficult exercise. Qualitative feedback is notoriously tough to quantify, but it is now possible and easy to quantify sentiment with the help of machine learning.

AI-powered platforms, like InMoment CXInsight™, automatically sort your customer comments into themes while simultaneously assigning positive or negative sentiment. This provides you with a big picture understanding of how customers feel about your product and why. Categories of feedback vary by business sector and business model–payment processes & delivery for e-commerce, perhaps, while UX and usability may surface for SaaS products. Quantifying the sentiment of what your customers are talking about can help you track emotional trends over time. Presenting this kind of data alongside verbatim comments connects customer emotion with real business consequence.  

5. Set empathy KPIs

What gets measured gets done, and adding empathy into your product development work is no different. The KPIs for empathy may look a little different than your typical performance indicators, but the good news is: They’re not difficult to get. You’ll find key performance indicators like NPS, CES and CSAT are a good start, and comments in the open-ended questions can give you insight into the metric. Start identifying what kinds of ratings and qualitative answers correlate to genuinely happy customers – and frustrated customers likely to churn.

6. Chart out an empathy map

You’ve done your user journey, but even though it’s part of the buyer persona building process, you may not have done an empathy map.

• What your user sees – on competitors’ websites, common visuals in their industries, maybe what they enjoy watching or reading
• What your user says – how they measure success, what they say they want, what they say about your product
• What your user hears – what their influencers are saying, not just about your product, but about their jobs and what constitutes success, what they enjoy, what they don’t like about their experiences with your competitors, etc.
• What your users think and feel – worries, aspirations, what they really want, what really annoys them

Notice how the empathy map includes business/industry-specific observations, but also branches out into the user’s personal life and larger environment. People are not their jobs – or even their ‘jobs to be done.’ For true empathy, you have to look at the whole person.

This is a great activity to get other teams involved in – consider hosting a meeting with Customer Success, Sales, Marketing and Customer Service for a wider scope of insights.

Activities involving multiple teams help to build a shared understanding of your customers’ experiences that can strengthen the whole company.

7. Add happy moments to your Customer Journey Map

You’ve probably mapped out your customer/user journey, but you probably didn’t include this: Happy moments. See if you can take your old customer journey map and mark the points where positive, fun, delightful things happen. Can’t think of any? Then you have some serious CX work to do!

And of course, also note points where you’ve observed friction, difficulties, and problems, and address those in the order of biggest impact + easiest to implement.

8. Work in Customer Support for an afternoon

Whether that means answering the live chat questions, picking up the phone, or monitoring your product’s customer Slack channel, try out being the Customer Support agent for an afternoon to and put yourself on the front lines! There’s no better way to find problems than to let customers tell you exactly – and in great detail – what they are. And they’ll likely throw in how frustrated it makes them feel too.

9. Build a prototype to test your emotional hypotheses

By now, you probably have a few ideas on how you can improve the customer experience, and it might be time to test those theories. Create a prototype for a select group of qualified users to try (and react to). And, if possible, have them test the prototype in a testing facility that allows you to observe their reactions as they use your product.

If there is a Golden Rule for empathy, it’s a simple one: Forget your assumptions and be genuinely interested and curious about what people are feeling (not just what they’re doing) while using your product. Empathy is a learned skill that needs practice so don’t forget to try out these empathy exercises on a frequent basis for enhanced customer experience.  

To quote Maya Angelou: “People will forget what you said, people will forget what you did, but people will never forget how you made them feel.”

Data is the beating pulse of business, but customer data is more like DNA. Customer data, if we’re using it right, directs how we grow and what we develop. But what happens if that customer data becomes corrupted by our own bias?

We can’t grow or develop in the ways we need to.

But what is bias exactly? Where does it come from?

The most prevalent bias is, perhaps, confirmation bias – seeking out data that confirms our existing beliefs.

In an early study of confirmation bias, young children were asked what features in a sports ball are important to the quality of a player’s serve. Some said size, others said material, some dismissed color as a factor – but once they’d made up their minds, they failed to acknowledge evidence that was contrary to their theory – or explained away evidence that didn’t fit.

But what’s worse, especially for those of us using data to steer our businesses, is that confirmation bias caused them to not generate alternate theories unless someone asked them to. They missed exploring and finding other possibilities.

There are other types of bias too, including:

Algorithmic bias – When the data used to teach an AI machine learning system reflects the implicit values of the humans involved in collecting, selecting and using that data. You might remember the 2015 uproar around Google’s image recognition AI algorithm that auto-tagged photos of black people as gorillas? Yes, that happened. And in 2009, Nikon’s image recognition algorithms consistently asked Asian users if they were blinking.

Survivorship bias – When the data analyzed only comes from success stories.

Sample bias – When the population you collect data from doesn’t accurately reflect the population you’re trying to learn about.

Avoiding bias when gathering, analyzing and acting on data is impossible. Bias creeps in with assumptions, instincts, guesses, and ‘logical’ conclusions – and mostly, we don’t even know they exist until someone without those particular biases point them out.

But, while we can’t escape biases, we can try our best to account for them when we collect, analyze and interpret data.

How to fight bias in your data

In Forrester’s The illusion of insights recording, Forrester Vice President and Research Director Sri Sridharan makes three recommendations to reduce bias in data.

She says to “triangulate insight” by using multiple methods of arriving at an insight, and cross-validation. For example, pairing behavioral data with feedback from customer surveys to see if you arrive at the same or similar answers.

Her second piece of advice is to create a “self-correcting system of insights” that connects customer data with an effective action to create a closed loop of action, learning, and optimization. Essentially, this means testing the data by taking action and iterating based on how well you succeed in addressing the issue.

Tracking a ‘North Star’ metric like NPS or CSAT over time can be very helpful in confirming whether the changes you make are having the desired effect.

Sri’s third piece of advice is to “show your work to build trust” both internally and with customers. Your customers will be quick to correct you if your insights don’t hold true for them – and you have the bonus of showing them how hard you’re working to make sure they have what they need to succeed.

But there is also the potential for bias to happen before any of these fixes can be made – especially in Customer Discovery.

Bias in Customer Discovery, Before You’ve Even Gotten to the Data

Bias in whom you ask

Who you survey, interview or meet with can bias your results. This is called “sample bias” – but it can also turn into confirmation bias. Sample bias happens when some members of the intended population are less likely to be included than others. Think of all of the different segments of users you have – what would happen if you only surveyed one of those segments? You would get responses that don’t work equally well for all of your customers.

This can slide into confirmation bias if the population you select is more likely to give you the answers you want to hear.

And, there’s also the risk of “survivorship bias,” if the people you’re surveying are the customers who are still with you, rather than the users who have churned. Current users are much easier to collect data from, and while they can give you important insights, they can’t tell you why your churned customers left.

Bias in how you ask

How you frame questions can have a dramatic effect on the responses. In fact, by the wording you use in a survey, or even your tone of voice in a phone interview or facial expressions in an in-person interview, you can effectively steer the conversation to deliver exactly the answers you’re hoping to hear. Many of the words we use have positive or negative associations that cause people to react accordingly.

Biased question: How much do you like the color blue? (This presupposes they like the color blue at all)

Unbiased question: How does the color blue make you feel? (A much more neutral phrasing)

Or, if you aren’t specific enough about the information you want, you risk confusing your respondent and getting answers that aren’t at all helpful. Unless you have a professional market researcher on staff, you may want to stick with established questions like NPS and CSAT.

Bias in what you ask first – and last

The order of the questions you ask can also bias your results, and you’ll need to review your question order carefully to make sure the sequence doesn’t cause biased responses. Typically, you should ask general questions before specific ones, ask positive questions before negative ones, and ask questions about behavior before questions about attitude.

Bias in when you ask

Holidays and the summer months, when families often take their vacations, can be problematic for both response rates and sample bias. For example, if you send a survey during religious holidays, you’ll likely get different responses rates from different groups of people, who may or may not be taking that time off.  Be aware of your timing, including if you’re sending surveys during deadline rushes, before or after holidays, or other significant patterns that may affect who responds and how they respond. To be on the safe side, don’t send your survey at the same time every year – send a few, at different times, to get the most accurate feedback.

Objective Data Leads to More Accurate, More Valuable Insight

Sherlock Holmes famously tells Dr. Watson that he never forms a theory before he gathers all of the facts. But he’s much better at disassociating himself from the results than most of us are (and he’s fictional). Bias has a way of seeping into our results, and how we view and react to our results. But, when we put bias-countering measures in place, like gathering data from different sources, using different types of data, and checking our work through a process of action and iteration, we can get to the truth in the end.

PART 1

At Wootric we collect hundreds of thousands of NPS, CSAT and CES  survey responses every week. We do this across different industries and product categories. Our customers then use our various integrations such as Salesforce, HubSpot, and Slack to route this feedback to relevant teams. Some of these customers like DocuSign and Grubhub have huge user base. This means even with a conservative sampling strategy they get hundreds of pieces of feedback daily.

The Challenge of Analyzing Qualitative Feedback

The quantitative aspects of feedback — NPS, CSAT scores, for example — are relatively easy to aggregate and analyze. It is the qualitative comments that provide rich insight into customer experience, but analysis of unstructured feedback is hard. Someone could read each piece of feedback one by one, but having a human read each comment obviously does not scale. If you can’t or don’t review what customers are telling you, then why have CX program to begin with? This is where machine learning saves the day.

Here is a concrete example of problem we needed to solve:

We set out to solve this multi-label classification and  topic sentiment analysis problem for our customers. We knew that doing it at scale would require some sort of automation using machine learning (ML) and natural language processing (NLP).  Machine learning requires lots of training data — in our case it meant we needed thousands of survey responses manually labeled into different categories. Luckily our customers have had the ability to manually categorize survey responses in our dashboard.  In the last 3+ years, they have categorized thousands and thousands of comments. Fortuitous, but sometimes we would like to think we saw it coming. 😉 At this point, we have significant volume of training data, now we just needed to find the right machine learning algorithms to take a stab at the problem.

Sentiment Analysis

To get our feet wet in machine learning ecosystem, we thought we would start with sentiment analysis because there has been tons of research on this problem over last few years and there are several open source solutions claiming to solve this. We started with Stanford NLP library because it was and is still actively worked on and lots of great research papers have come out of this group. It took us about a week to get our heads around concepts of Tokenization, Lemmatization, Named Entity Recognition (NER), PoS tagging, Dependency parsing, Coreference Resolution, word embeddings and finally to embed the library into a REST API framework. The results were okay but not great for our use case.  We **think** it’s because feedback comments belong to different domain — these are responses to questions as opposed to news article, blogs and tweets. Most of feedback is short, ranging from a couple of words to couple of sentences. This does not give enough “context” for algorithms to find the sentiment. An alternative solution would have been to train our own model using Stanford NLP library but we did not have engineering resources and bandwidth at that point in time. But we had something working. It was a baby step, but a step in the right direction.

Categorizing Feedback Comments

For feedback categorization, we also first looked into a hosted service and open source libraries. The most compelling, trainable and easy to get started solution at that point in time (mid 2016) was Google Cloud Prediction API where you upload a CSV of training data and in few minutes you get a model and REST API to make a prediction. This sounded like a tailor-made solution for us. After all, we had lots of training data that our customers had manually labeled. We were able to quickly format our responses and training labels in order to meet the Prediction API requirements, and saw some initial results.

Results were better for some sets of feedback but horrible for other sets. Dissecting further we realized that the customer feedback set where prediction was more accurate belonged to DevOps, Data Analytics and similar developer centric and data analytics.  There was less success with feedback from e-commerce or other consumer-centric SaaS products. This made sense because most of our customers who spent their time manually labeling feedback had offerings catered to developers — such as New Relic, Docker etc. The bigger downside of Prediction API was that it was a black box so we did not have any ability to tune algorithms. Ironically, Google decided to deprecate Prediction API in favor of their Cloud ML Engine, driving us to improve our internal, customizable prediction methods instead.

Our experiments with Stanford NLP libraries and Google Prediction API gave us a good understanding of the complexity of problem we were tackling, provided more awareness of the ML ecosystem — people and research labs to follow, research papers to read — and finally helped us better understand the nuances of building machine learning models. It’s not as simple as having some training data, copy pasting some code from open source libraries and voila you have a ML solution. There are lots of hype and noise around what ML can do and how to go about doing it right.

At this point, we concluded that there was no shortcut and that we had to invest time focusing on high quality training data, going through various research papers, trying the algorithms in research papers using our training data and have nobs ( i.e. hyperparameters) tuned for our use case.

Delivering insights to customers

In April 2018, after six months in beta, we launched v1 of our product CXInsight™. The platform enables our customers to import and analyze customer feedback from any source. To date, we have analyzed 200,000+ comments pertaining to a wide spectrum of product categories and types of feedback — PaaS, SaaS, E-commerce, Mobile App reviews, employee reviews, social media, etc.

Of note, most of the data we’ve analyzed so far was originally collected using our competitors’ survey platforms. This is sweet validation of our goal: Regardless of how and where you collect customer feedback, Wootric gives you the best analysis.   

In our next series of blog articles, we will talk about how we have used and are using:

• Bag of Words with Naive Bayes and SGD Classifier (Part II)
• word2vec with PCA, Logistic Regression, SVM and SGD Classifier
• Bidirectional LSTM
• CNN
• Custom word embeddings
• Productizing — DevOps around ML
• UX and UI
• Online learning and Human in the loop

Our goal has never been to build a generic text categorization tool. Rather, our focus is to build the best customer feedback analysis platform. We are also aware that our system is never going to be 100% correct so we have made it easy for our customers and our own team to be human in the loop.

I would be remiss not to thank Stanford NLP, Richard Socher, Lukas Biewald, Sebastian Ruder , Google ML research whose research papers, blogs, tutorials, videos and guidance have directly or indirectly helped us build CXInsight product.

Please stay tuned to our Engineering Blog for next series of articles on this topic.  The Hacker News discussion is here.

Should enterprises build their own customer feedback software? After all, they’ve got the engineering talent and resources to take it on.

If you’ve got the resources to do it, creating such tools can be tempting, but more often than not, these solutions are trouble to build and maintain.

Why Companies Choose to DIY

Forget “to be or not to be”.

For businesses facing software decisions, it comes down to “build vs buy”. It’s always a balance between finding immediate solutions to problems and considering long-term growth.

Here are a couple of the tempting advantages of building software solutions for yourself:

• “Anyway you want it, that’s the way you need it” – Journey

When you build something for yourself, it will solve all of your problems in exactly the way you wish. The dashboard will look exactly how you want it to look. The functions will pull from exactly the data you want it to pull from. If your business has specialized needs, a custom solution is functionally ideal.

• Guaranteed compatibility with everything you already use

Your company has a suite of software that it’s already using. When the data in one software can’t be read by your system of record, people end up typing notes in manually, or other time-consuming methods to get important information recorded for everyone else in the organization. Building software for yourself means you can guarantee compatibility with everything you already use, and if you think ahead enough, compatibility with software you intend to acquire.

Unfortunately, these benefits will only bring value if you can spread out the significant cost of building custom software (time, energy, and resources) over a large number of clients and your engineers’ time isn’t better spent on other projects. Let’s face it, a customer feedback solution for Customer Success/ Customer Support is unlikely to be a priority for your product team.

Building your own software is expensive and getting a high enough ROI on this kind of project is difficult. Add in the ongoing costs associated with maintaining what you’ve built and buying a solution becomes very appealing.

Why You Should Buy Customer Feedback Software

Besides being incredibly labor and resource intensive, trying to build a solution requires months and months of brainstorming, planning, and coding. If the in-house solution isn’t properly and thoroughly planned out, with input from multiple functional teams, this can actually create more headaches and manual processes in the long term. Even worse, if the tool does not add value to the employees that it was built for, it could go unused.

When it comes to surveying your customer base, experts have already thought out a vast number of details, building standard settings and customizable options based off of best practices. There is a reason why customer feedback is a whole industry, and that is because rigorous methodology is paramount to actionable insight.

Customer feedback software creators like Wootric have developed and iterated a variety of features to make starting and running a robust feedback program convenient and valuable. These tools automate gathering feedback and surfacing insight, which can be sent out for action. Buying customer feedback software gets both immediate and long-term value out of a customer feedback program:

Automated Sampling

If you’ve ever gone through the trouble of listing out, segmenting, and randomly sampling your users/customers, you know how tedious this task can be.

Multi-channel survey solutions – that reach your customers via email, in web products, and via text – help you automatically survey the appropriate random sample to capture different segments of your customer base. You can get feedback from both decision makers who do not log-in to the platform very often via email surveys, and feedback from daily end-users via in-app surveys.

Wootric’s standard settings allow you to survey your customers with two different methods. You can keep the flow of feedback constant and random, avoiding various biases that may sneak in if you are not aware of them. This method gives you a daily pulse of feedback, usually Net Promoter Score (NPS), which provides a good sense of user sentiment on any given day, and can show you trends over time.

You can also send surveys based on completion of different events. For example, you may want to send out a Customer Satisfaction (CSAT) survey after a support ticket is closed, or trigger a Customer Effort Score (CES) survey after a customer completes onboarding. Implementing these three micro-surveys at various customer journey touchpoints will get you a holistic “Trifecta view” of your accounts.

With both of these methods, you have the power to change the time frame dictating eligibility to take a survey and what percentage of users/visitors we sample, including the option to survey less than 1% of the customer base, if necessary.

Automated Safe Guard: Intelligent Throttle

It’s always important to have safety features. Customers are already inundated with information every day. You don’t want to add to that annoyance by sending the same survey to them over and over again in a short period of time.

Avoiding survey fatigue requires having separate controls for a slew of different situations that enterprise feedback software companies have thought out and prepared for. These include control over how often any individual will see a survey and how often individuals can respond to the same question.

For example, after one of your customers takes an in-app survey, that customer will not be shown another survey for another 90 days. You can change the number of days between surveys to suit your needs. You also have control over the number of days between surveys for people who decline your surveys.

All of these settings can be manipulated for each of the survey delivery channels that Wootric provides, as well as for each type of survey (i.e. NPS, CSAT, or CES) you choose to send. For Voice of the Customer programs using multiple delivery channels, Wootric has cross-channel safety features so customers don’t feel overwhelmed by your surveys popping up everywhere they turn.

If you decide to base your surveys off a triggering event, our survey throttle prevents customers from being bombarded with the same satisfaction survey in a short amount of time. While it is standard to have this throttle on, this can be overridden if you want every single triggering incident to fire off a new survey.

Auto-tagging and Segmentation for Insight

A Voice of the Customer feedback program doesn’t stop at just gathering feedback. The key to success is in the insight and action that happens after you’ve gathered customer feedback. If your engineers build a way to gather feedback but that data ends up sitting in a silo, unorganized, then you will never realize any value.

Tagging and segmentation features in enterprise customer feedback solutions aim to make sorting and analyzing survey responses easy and insightful.

Different customer segments will have different needs and therefore different feedback. The segmentation feature in software platforms like Wootric enables you to analyze CX metrics like Net Promoter Score by customer properties. You can pass various properties, like geographic region, or persona, to drill down to specific segments and understand what’s important to your different types of customers.

Tagging is an incredibly powerful tool when it comes to dealing with qualitative feedback. Frequency analysis lifts trending topics out of customer comments, and various teams can find relevant feedback with a single click. 

For example, a product team can view all comments under a feature request tag and prioritize the most frequently requested ones from the highest value customers.

Tagging can be done manually for companies receiving smaller quantities of responses. For companies overwhelmed with feedback, expertly built tools like Wootric can save you time and effort.

Check out our guide to auto-tagging for more benefits and ideas on how to start.

Integrations & Webhooks: Break Down Data Silos & Trigger Workflows

With native integrations and webhooks, you can achieve some of the same benefits of building your own software, i.e. automated workflows among platforms and a consolidated overview of important account information.

Switching back and forth between platforms disrupts workflow. With that in mind, Wootric has built a host of native integrations such as Slack, Salesforce, Gainsight, and Hubspot, to get customer feedback into the hands of those who can act on it like Customer Success, Product, Marketing & Sales. For other apps, Wootric can connect via incoming and outbound webhooks or Zapier.

This means you can push Wootric’s data out onto the platform of your choosing, and Wootric can listen for instructions to fire a survey based on events from whatever app you choose. The possibilities for data exchange are endless. Best of all, this sharing happens in real-time, so your information will always be up-to-date.

Spend Your Time Acting on Insight Immediately

When it comes to build versus buy, there is great peace of mind that comes with buying an enterprise feedback management platform. You’ll have experts guide you through the set-up, listening to your company’s specific needs. You can get started immediately, reaping the rewards of a stellar customer feedback program now, including higher customer retention, happiness, and company growth.

The Customer Success Analyst has evolved to be the go-to person for all the data – or as Marketo put it in their Linkedin job ad, “the primary deliverable of the Customer Success Decision Analyst is to convert our Customer Success operation at Marketo into a highly data-driven business where we can measure, analyze and optimize every aspect of our engagement with our customers.”

This includes data like:

• Feature usage patterns
• Maturity scores
• NPS results
• Voice of customer qualitative feedback
• Customer journey mapping
• Customer experience metrics
• Capacity models

Among all of the hats that CSM’s wear, the number-crunching, data-heavy, quantitative analyst hat is one of the most time-consuming. But because of the data-savviness this role demands, CS analysts also hold the keys to unlocking incredible potential when your business is scaling up.

The CS analyst role isn’t *just* about collecting data for dashboards and reports (and basing recommendations on that data) though. It complements the Success Operations role, which builds new tools and processes to scale CSM’s everyday activities. As the person navigating multiple platforms for data on a day-to-day business, CS Analysts know how information flows and who needs what information.

For one of Wootric’s customers, Chorus.ai, CS Analysts also take ownership of the technical onboarding process for new or upgrading customers, ensuring “a smooth implementation, including initial and ongoing training for customers.”

It’s a prime position from which to watch for opportunities to make big impacts on the success of customers – and the success of the company. That’s the subtextual expectation: By being in charge of the data, the CS Analyst knows how to use it to find untapped value.

What does a CS Analyst need to know?

Experience working with large amounts of data (SQL, Python or R) and with survey and analysis tools (Wootric, Tableau, etc.) are must-haves, but the most important qualification is having taken that data and used it to produce actionable insights.

Analysts are data story-tellers. They work with the numbers and provide context for them, creating reports to recommend strategic options and solutions. A listing for a CS Analyst position at Salesforce described one of the responsibilities as “assist in developing and delivering presentations for senior executives”, which requires strong public speaking and presentation skills.

If a company struggles with data silos, CS Analysts must bridge the gap between teams. Not only must analysts overcome the technical issues of compatibility, but they need to possess strong internal communication skills to overcome any organizational walls that may be contributing to the data isolation.

While CS Analysts own the quantitative facet of Success, a customer-centric mindset and empathy for the humans behind the numbers distinguish a great analyst from a good one. These soft skills help analysts frame their analysis to produce long-term, customer-centric solutions that support CSMs to retain customers.

What does this role look like in real life?

For some companies, the CS Analyst position can be a foot-in-the-door to Customer Success.

Anthony Enrico, VP of Customer Success at Emailage, created the Analyst position because his “CSMs were being asked to spend enormous amounts of time compiling reports and the opportunity cost of spending time deepening relationships and loyalty with customers was too great.” As a leader within the organization, Anthony was also doing a lot of work with these reports, when his time was clearly better spent working on strategy, escalations with his CSMs, and focusing on new business opportunities with the company.

So Anthony hired Bryan Mehrmann, now a CSM at Emailage. Bryan was originally brought on as the first CS Analyst to support the CSM team. Bryan compiled and sent out daily reports on customer usage trends to identify and correct anomalies as early as possible. He took detailed revenue projection reports and distilled them for the C-suite for their weekly use. Bryan took on more responsibilities as time went on.

Working together, Anthony and Bryan shaped the role as it is today. As for how the position fits into the CS team, Analysts can be promoted to full CSMs after they’ve achieved a comprehensive understanding of the product, metric drivers, and relationships with Emailage’s customers.

On the other hand, CSMs may choose to specialize in VoC data analysis like Customer Success Analyst Tim Dressel at Qualer. For him, there’s the usual collection and analysis of customer data in spreadsheets, but also a lot of room for innovation and collaboration. If he sees a red flag in the metrics, he leads investigations into those customer issues, working with his cross-functional team (and collaborating, at times, with Qualer’s Head of Technology) to make sure customers’ needs make it into the software they develop.

How do you know if you need a Customer Success Analyst on your team?

Customer Success Managers are often being pulled in five different directions at once, and when that happens, they sacrifice time on one task for another. Not only does Customer Success provides data and insight crucial to their own day-to-day, but they are the go-to team for reports for the C-suite.

Customer Success needs data. Data is at its core. So if your Customer Success team doesn’t have time to live and breathe data, you may be at the tipping point to bring in an analyst who can parse the numbers for you. This is especially important for scaling processes when anecdotal experiences have to give way to metrics.

For some companies, bringing on an Analyst to Customer Success may happen by incorporating a company-wide business analyst into the team and transitioning them into a full-time Success Analyst. Depending on the company, Customer Success may not need an Analyst until their team is four or five CSMs.

The most common theme among companies looking to hire a CS Analyst is major growth.

For example, a Wootric customer that recently started trading publicly, DocuSign, decided to add the Customer Success Analyst position as they accelerated their growth.

Analysts (& their data) are a CSM’s best friend

For Customer Success, the best way to prove value, whether it’s to senior management or to a customer, is with numbers and context. Having a role dedicated to creating robust reports to highlight value and propose inventive, data-backed solutions is an excellent way to help your current CSMs be the best that they can be at scale.