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EP 68 - Survey Says
13 Plays1 month ago
We’re inundated with data: Survey requests, analytics reports, low cost sensors, AI data mining, and much more. But there’s a big difference between data and insight. This episode explores the processes and roles data plays in our lives, our organizations, and for our customers.
For more info & to download a free PDF of today's episode notes, visit: www.chriskreuter.com/CDWI
Join the Kreuter Studios mailing list: https://mailchi.mp/810367311f3d/ksb
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Transcript
Introduction to Chris and the Podcast
00:00:08
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On Chris deals with it, I talk about the frameworks and methods I use to clear personal, creative, and professional roadblocks. My goal is to help others bridge the gap between where they're at now and what they want to achieve. If you're new to the show, I'm an engineer, writer, parent, game designer, leader, and reader who leverages that experience to develop creative solutions to problems.
00:00:30
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an AI statement that all elements of this episode are products of the author, Chris Kreuter, and made without the use of any AI tools.
Data vs Insight: Episode Overview
00:00:38
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Welcome to episode 68 of Chris deals with it. Survey says, we're inundated with data, survey requests, analytics reports, low cost sensors, AI data mining, and much more. But there's a big difference between data and insight. This episode explores the processes and roles that data plays in our lives, our organizations, and for our customers.
Active vs Passive Data: Collection and Challenges
00:01:00
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I've broken the episode into four focus areas, active versus passive data, utilizing data, hidden information, and data's limitations. So let's start with number one, active versus passive data. I define active data as information gathered by actively engaging with a customer, product, or service. So examples here could include prompting a user to provide feedback in a website pop-up,
00:01:25
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emailing a survey form to a customer, the happy sad buttons in public restrooms, opinion polling on a street corner, and manual logging of interaction information by an employee. This type of collection is an interruption to the customer. It's a request for their time, possibly in addition to what they've already invested in a product or service. Are we hounding them with multiple emails, such as reminders to leave feedback? I know I personally get annoyed by this practice.
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Are we tracking how many of these requests actually result in usable feedback? Has there been enough time or experience with the material we're asking them for review? For example, I don't ask someone if they like my book a day after they purchase it.
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What is the expected bandwidth of our customers? Are we asking busy customers to fill out a 10-minute email survey after a one-time experience in our store? Are we setting up a feedback session with a customer partner we've worked with for years? They're very different experiences.
Effective Data Usage: Sensitive to Quantitative
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And how personable is your request for that feedback? At the end of the day, who's parsing this information? Is it the employee who directly sold or provided that product or service? Is it some third-party marketing research firm well after the fact?
00:02:36
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Contrary to active data is passive data, and this is information that requires no direct action or interruptions of your customers or processes. Examples could include software usage data, automated bug reporting, data gathered by passive sensors and or cameras, and gathering customer data at the point of sale. I also use this definition for things where customers go out of their way to provide data, such as a Google review, website feedback form, or unsponsored media posts.
00:03:04
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There is some risk of decoupling data gathered passively from our customer's reality, since we only have our view from the data, not their personal opinion. And the availability of active and or passive data sources is highly dependent on the products and or services being offered.
Stakeholder Consultation and Data Efficiency
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So let's talk about utilizing data.
00:03:26
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With all this data, we need to be mindful of some key factors. Sensitive data. These are things like credit card information, social security numbers, passwords. You have to understand the laws that are governing its storage. How risky is storing the data that you're collecting? Proprietary data. How essential is this information to your company's success? What would happen if that data ended up in the hands of your competitor? And there's qualitative versus quantitative data.
00:03:54
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qualitative data can be tricky, opinion-based assessments that can be difficult to codify. On a scale of one to 10, how do you like this podcast? quantitative data would be an actual metric based on something that's measurable. How long did they listen to the podcast? And I'll come back to that example a little bit later in the episode. Having a clear usable data structure is vital for success. You want to focus on maximizing the benefits to the end users and or customers who are going to benefit most from the insights gained. You want to consult with them on what would help their aspect of the business or improving their experience with your products or services.
00:04:32
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Consider the impact of data at all levels of your organization. What gets measured gets managed. And who owns the results of the data being analyzed? Will they have personal reviews, compensation, success or failure, be measured with the data? If so, you better be confident in the quality of the data and put in steps to regularly review its efficacy. For those gathering the data, you want to try to avoid double work or entry whenever possible. If you make it onerous to gather data, you might get inconsistent results, especially during busy periods.
00:05:05
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And as the data embedded already in your processes, adding extra data collection steps could slow or hinder your staff's ability to get their core job functions completed.
Uncovering Hidden Data and Avoiding Unreliable Sources
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And you might think that data analytics tools require expensive software and solutions and specialized training. While those certainly can help, there are some amazing sources out there and many tools are actually democratizing analytics. There are some great no to low code solutions that can help you gather and connect data and produce very useful reports. This would include Microsoft's Power BI, ah Tableau, which is now owned by Salesforce, Airtable, Google Data Studio,
00:05:44
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ah SQL or SQL database managers. Even Notion, one of my favorite apps, is getting into the data reporting game. Let's change tack a little bit and talk about hidden information. Are there sources of data within your existing tools that can enhance your business intelligence? A retail store might have a Bell device at the front door that alerts staff when people come and go. These devices can often be sources of data on the number of people coming and going. With timestamps, you could use the measure of foot traffic by time of day and day of week.
00:06:13
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You could further tie this into sales data for that same time period to find correlations. For example, are people buying more often and in larger amounts when the store is less busy? Explore other methods to utilize data you already have. For example, you might be keeping track of regular customers using your point of sale system. But are you reviewing this data to gauge engagement with these repeat customers? How often are they shopping? What's trending from their purchases? Are there discernible patterns that can be found in that data?
00:06:44
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A lot of organizations are gonna have multiple disconnected systems for various functions. It might be possible to pull data from these various systems and connect that data for further insight. These linkages are called data keys. And these can be valuable tools for enhanced analytics. You can create a whole, it's called a schema that you can create around all these different data sets interconnected to provide very very valuable insight. For example, you might have a ah CRM or customer relationship management tool or a pipeline system for customer management, but another one that actually manages the customer orders and deliveries. A customer account number would likely be the data key that connects the data from these two systems. And this would allow you to create combined reports showing customer metrics alongside sales and delivery history, a one-stop overview of a customer.
00:07:35
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Be wary of data sources from online tools like reviews. In some cases, many of these can actually be bots. And listening to this data too much can skew results or hide more realistic, actionable data. Or to put it another way, are you
Data Limitations and the Power of Storytelling
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measuring the right metrics that have the potential to provide a better customer experience?
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I mean, what is data unless we're using it to actually make for a better customer experience, right? So I'll go back to this example of my podcast analytics. If I were just to go offer reviews I find online, I'm only gonna get a few touch points on people's opinions. These are people who took extra time going out of their way to provide feedback.
00:08:12
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and It's possible some of those are bots, but regardless, the total number of reviews is a very small subset of the total listeners on any given episode. More relevant to me is data on how long customers are engaged with the episode. If people are tuning out within the first minute, it could signal my content isn't engaging enough. And thank you for listening this long.
00:08:34
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Real quick, I want to touch on data's limitations as well. For all of this talk about data, it's limited. Data can't run your business, nor should it. Consider the analytic skill set of those who are receiving this information. Are you providing too much detail? Not enough? You can set up reports that focus on the first level data, which might be enough for some certain staff, but then provide opportunities to click further into the results to gain second and even third level insights.
00:09:01
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And it's really important to be clear on the vectors to action that your data will have on your organization. Your marketing team and sales teams are both focused on growing the business, but they may need to utilize the data in different ways. Marketing is all about creating buzz around a product or service, while sales is working to convert that buzz into paying customers. Now these could often utilize similar data sets, but the stories they tell from that data will often differ.
00:09:28
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This kind of storytelling is critical. Humans are not data. We're emotional, rational beings that buy through a combination of trust, relationship, and stories. So
Conclusion and Listener Engagement
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how are you going to drive human action with data? So today's quote is from Kayleen Bradley's wonderful debut novel, The Ministry of Time.
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Ideas are frictional, fractional entities, which wilt when pinned to flowcharts. Ideas have to cause problems before they cause solutions. And with that, have a great day.
00:10:09
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If you feel that Chris dealt with it, I'd appreciate your support of the show by sharing it with someone who might benefit. Ratings on your favorite podcast player are also helpful in growing the audience. Visit chriscroiter dot.com for free downloadable PDFs with notes and resources from today's episode, sign up for the CDWY mailing list, or to send in your problems or requests for future shows. That's C-H-R-I-S-K-R-E-U-T-E-R dot.com, or use the link in the show notes.