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Sugar Overload: Dashboards that Yield Insights Not Headaches

Posted by Blair Bailey

Thu, Jun 29, 2017

simple froyo.png

Back in the old days (2002?) if you wanted a frozen treat—you ordered from the nice person at the TCBY counter, paid your money, and went on your way. Then Red Mango came to town and it was a game-changer. Now instead of someone else building my treat, I had total control—if I wanted to mix mango and coffee and throw some gummy bears on top I couldI didn’t though, I’m not a monster.

Of course, there was a downside—sometimes I’d walk away with a $15 froyo. Sometimes, there is such a thing as “too much of a good thing”. As a data manager, knee-deep in interactive data viz, I know this applies to dashboards as well as dessert. 

When starting a dashboard from scratch, there’s the same potential to go overboard, but for different reasons. Like flavors and toppings, there are many viewer design and build directions I could take. Will the dashboard be one centralized page or across multiple pages? What types of charts and tables should I use? What cuts should be columns and which should be filters?

The popular platform, Tableau, has so many options that it can often feel overwhelming. And aside from design, Tableau lets users deep dive into data like never before. With so many build options and data mining capabilities at our fingertips, what’s a designer to do?

Forget the gumdrops and jalapeño flavored yogurt—I encourage our clients to go back to basics and ask:

Who is the dashboard for? The content and design of a well-made dashboard should depend on its purpose and end user. The dashboards I create in my spare time (yes, it’s also a hobby !) are very different than the ones I build for clients.  For example, a deep-in-the-weeds analyst will need (and appreciate) very different functionality and design than a C-suite level user would. An analyst interested in deep-dives may need multiple filters and complex tables to cut the data every which way and investigate multiple scenarios, whereas a c-suite level needs a dashboard that answers their questions quickly and directly so they can move forward with business decisions.

It may be tempting to add flashy charts and lots of filters, but is it necessary? Will adding features help answer key business questions and empower the end user, or will it overwhelm and confuse them?

Here's a snippet from a dashboard that an executive could glean a good amount of insight from without feeling overwhelmed:

AffinID sample_simple.jpg

What will they use it for? Depending on what business questions the client is trying to answer, the design around specific types of dashboards may vary. For example, a brand health tracker dashboard could be a simple set of trending line charts and callouts for KPIs. But it’s rare that we only want to monitor brand health. Maybe the client is also interested in reaching a particular audience. So as the designer, I'll consider building the audiences in as a filter. Perhaps they want to expand into a new market. Divide your line charts by region and track performance across markets. Or maybe they need to track several measures over time across multiple brands, so rather than clog up the dashboard with lots of charts or tabs, you could use parameters to allow the user to toggle the main metric shown.

When in doubt, ask. When I plan to build and ultimately publish a dashboard to Tableau Public, I consider what elements will keep the user engaged and interested. If I’m not sure of the answers I force politely ask my friends, family, or co-workers to test out my dashboards and provide honest feedback. If my dashboard is confusing, boring, too simple, too convoluted, awesome, or just lame, I want to know. The same goes for client-facing dashboards.

As a data manger, my goal is to create engaging, useful data visualizations. But without considering who my end user is and their goal, this is nearly impossible. Tableau can build Pareto charts, heat maps, and filters, but if it doesn’t help answer key business questions in an intuitive and useful way, then what’s the point of having the data viz?

Just because you can mix mango and coffee together (and even add those gummy bears on top), doesn’t mean you should. Like TCBY and Red Mango with their flavors and toppings, Tableau offers infinite data viz possibilities—the key is to use the right ingredients so you aren’t left with a stomachache (or a headache).

Blair Bailey is a Data Manager at CMB with a focus on building engaging dashboards to inform key business decisions and empower stakeholders. Her personal dashboards? Less so.

Topics: advanced analytics, integrated data, data visualization

Passive Mobile Behavioral Data – Part Deux

Posted by Chris Neal

Wed, Aug 10, 2016

Over the past two years, we've  embarked on a quest to help the insights industry get better at harnessing passive mobile behavioral data. In 2015, we partnered with Research Now for an analysis37824990_thumbnail.jpg of mobile wallet usage, using unlinked passive and survey-based data. This year, we teamed up with Research Now once again for research-on-research directly linking actual mobile traffic and app data to consumers’ self-reported online shopper journey behavior.

We asked over 1,000 shoppers, across a variety of Black Friday/Cyber Monday categories, a standard set of purchase journey survey questions immediately after the event, then again after 30 days, 60 days, and 90 days. We then compared their self-reported online and mobile behavior to the actual mobile app and website usage data from their smartphones. 

The results deepened our understanding of how best to use (and not use) each respective data source, and how combining both can help our clients get closer to the truth than they could using any single source of information.

Here are a few things to consider if you find yourself tasked with a purchase journey project that uses one or both of these data sources as fuel for insights and recommendations:

  1. Most people use multiple devices for a major purchase journey, and here’s why you should care:
    • Any device tracking platform (even one claiming a 3600 view) is likely missing some relevant online behavior to a given shopper journey. In our study, we were getting behavior from their primary smartphone, but many of these consumers reported visiting websites we had no record of from our tracking data. Although they reported visiting these websites on their smartphones, it is likely that some of these visits happened on their personal computer, a tablet, a computer at their work, etc.
  2. Not all mobile usage is related to the purchase journey you care about:
    • We saw cases of consumers whose behavioral data showed they’d visited big retail websites and mobile apps during the purchase journey but who did not report using these sites/apps as part of the journey we asked them about. This is a bigger problem with larger, more generalist mobile websites and apps (like Amazon, for this particular project, or like PayPal when we did the earlier Mobile Wallet study with a similar methodological exercise).
  3. Human recall ain’t perfect. We all know this, but it’s important to understand when and where it’s less perfect, and where it’s actually sufficient for our purposes. Using survey sampling to analyze behaviors can be enormously valuable in a lot of different situations, but understand the limitations and when you are expecting too much detail from somebody to give you accurate data to work with.  Here are a few situations to consider:
    • Asking whether a given retailer, brand, or major web property figured into the purchase journey at all will give you pretty good survey data to work with. Smaller retailers, websites, and apps will get more misses/lack of recall, but accurate recall is a proxy for influence, and if you’re ultimately trying to figure out how best to influence a consumer’s purchase journey, self-reported recall of visits is a good proxy, whereas relying on behavioral data alone may inflate the apparent impact of smaller properties on the final purchase journey.
    • Asking people to remember whether they used the mobile app vs. the mobile website introduces more error in your data. Most websites are now mobile optimized and look/ feel like mobile apps, or will switch users to the native mobile app on their phone automatically if possible.
      • In this particular project, we saw evidence of a 35-50% improvement in survey-behavior match rates if we did not require respondents to differentiate the mobile website from the mobile app for the same retailer.
  4. Does time-lapse matter? It depends.
    • For certain activities (e.g., making minor purchases in grocery store, a TV viewing occasion), capturing in-the-moment feedback from consumers is critical for accuracy.
    • In other situations where the process is bigger, involves more research, or is more memorable in general (e.g., buying a car, having a wedding, or making a planned-for purchase based on a Black Friday or Cyber Monday deal): you can get away with asking people about it further out from the actual event.
      • In this particular project, we actually found no systematic evidence of recall deterioration when we ran the survey immediately after Black Friday/Cyber Monday vs. running it 30 days, 60 days, and 90 days after.

Working with passive mobile behavioral data (or any digital passive data) is challenging, no doubt.  Trying to make hay by combining these data with primary research survey sampling, customer databases, transactional data, etc., can be even more challenging.  But, like it or not, that’s where Insights is headed. We’ll continue to push the envelope in terms of best practices for navigating these types of engagements as Analytics teams, Insights departments, Financial Planning and Strategy groups work together more seamlessly to provide senior executives with a “single version of the truth”— one which is more accurate than any previously siloed version.

Chris Neal leads CMB’s Tech Practice. He knows full well that data scientists and programmatic ad buying bots are analyzing his every click on every computing device and is perfectly OK with that as long as they serve up relevant ads. Nothing to hide!

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Topics: advanced analytics, mobile, passive data, integrated data

Upcoming Webinar: Passive Mobile Behavioral Data + Survey Data

Posted by Chris Neal

Mon, Jul 13, 2015

mobile research, mobile data collection, The explosion of mobile web and mobile app usage presents enormous opportunities for consumer insights professionals to deepen their understanding of consumer behavior, particularly for “in the moment” findings and tracking consumers over time (when they aren’t actively participating in research. . .which is 99%+ of the time for most people). Insight nerds like us can’t ignore this burgeoning wealth of data—it is a potential goldmine. But, working with passive mobile behavioral data brings with it plenty of challenges, too. It looks, smells, and feels very different from self-reported survey data:

  • It’s big. (I’m not gonna drop the “Big Data” buzzword in this blog post, but—yep—the typical consumer does indeed use their smartphone quite a bit.)
  • It’s messy.
  • We don’t have the luxury of carefully curating it in the same way we do with survey sampling. 

As we all find ourselves increasingly tasked with synthesizing insights and a cohesive “story” using multiple data sources, we’re finding that mobile usage and other data sources don’t always play nicely in the sandbox with survey data. Each of them have their strengths and weaknesses that we need to understand in order to use them most effectively. 

So, in our latest in a series of sadomasochistic self-funded thought leadership experiments, we decided to take on a challenge similar in nature to what more and more companies will ask insights departments to do: use passive mobile behavioral data alongside survey-based data for a single purpose. In this case, the topic was an analysis of the U.S. mobile wallet market opportunity. To make things extra fun, we ensured that the passive mobile behavioral data was completely unlinked to the survey data (i.e., we could not link the two data sources at the respondent level for deeper understanding or to do attitudinal + behavioral based modeling). There are situations where you’ll be given data that is linked, but currently—more often than not—you’ll be working with separate silos and asked to make hay.

During this experiment, a number of things became very clear to us, including:

  • the actual value that mobile behavioral data can bring to business engagements
  • how it could easily produce misleading results if you don’t properly analyze the data
  • how survey data and passive mobile behavioral data can complement one another greatly

Interested? I’ll be diving deep into these findings (and more) along with Roddy Knowles of Research Now in a webinar this Thursday, July 16th at 1pm ET (11am PT). Please join us by registering here

Chris leads CMB’s Tech Practice. He enjoys spending time with his two kids and rock climbing.

Watch our recent webinar with Research Now to hear the results of our recent self-funded Consumer Pulse study that leveraged passive mobile behavioral data and survey data simultaneously to reveal insights into the current Mobile Wallet industry in the US.

Watch Now!

Topics: advanced analytics, methodology, data collection, mobile, webinar, passive data, integrated data

Find Multiple Uses for Your Internally Generated Data

Posted by Megan McManaman

Tue, Nov 17, 2009

In recent years, it has become easier for companies to collect information on their operations, clients and prospects. Credit cards, online tracking, loyalty programs, utilization reports, and other metrics are integral parts of the business landscape that fill servers and databases.

But when was the last time you critically reviewed the reports and analysis you receive from your internally collected data? Which of your business decisions could be supported by extracting additional insight from the data (internal or customer) that you already have?

More and more we are being asked to apply advanced analytics and critical thinking to data collected in the course of business operations. In doing so, weve been able to help in a number of ways:

1) Damage Control:  

 A hotel company wanted to predict the reduction in value (if any) from a customers exposure to lower- performing locations in its network. Using recent advancements in Customer Lifetime Value analysis (far beyond regression-based models) and thinking, we concluded whether underperforming locations were reducing the brands value by undermining customer connection.

2) Determine Best Practices:

 A services company with over 1400 locations wanted to share best practices for driving improvements to the bottom line. Using CHAID and Latent Class segmentation, we examined their internal data (e.g., number/type/wages of employees, customer volume, rate paid, how booked, etc.) to prioritize opportunities to reduce spending (with minimal impact) or increase investment (with maximum impact). They then could determine what elements of a locations success could/should be replicated across the organization.

3) Localization/inventory control:

Successful and insightful location managers know what sells, to whom, and when. Some patterns may not be as obvious particularly when managers are looking across multiple locations but by diving into the actual transactional data, we are able to put concrete numbers in front of managers that can support or change their intuition and drive more fact based decision making.

 

Topics: data collection, big data, integrated data