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.