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> Find out which qualitative techniques would be most beneficial depending on the stage of your start-up.
> Identify opportunities to conduct quantitative research without treating questionnaires as a Swiss army knife.
> Learn how you can benefit from business intelligence.
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When

November 17, 2022; 1 to 1:50pm Eastern

The event has passed. Please enjoy the recording!

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Speaker

Annie Pettit, PhD CAIP FCRIC is the Chief Research Officer, NA, at E2E Research. She is a research methodologist who specializes in participant engagement, data quality, and innovative methods. She has spoken at conferences around the world, educating researchers about the best ways to conduct research that is valid, reliable, and actionable. She is also Chair of the Canadian ISO Standards Committee (ISO 20252). Most importantly, she’s an avid ukulele player.

Marketers and researchers use the word ‘analytics’ to describe many different things that can be done with digital data. Without a common understanding, it can be easy to misinterpret what a client actually needs and end up assigning project tasks to the wrong people, costing jobs inaccurately, and not meeting client expectations.

In this post, we’ll take a deep dive into the different interpretations this word can have to ensure that both clients and suppliers are on the same page when it comes to extracting relevant insights from from myriad datasets about buyers, brands, and businesses.

Before we get into the details, you might appreciate this short introduction to data analytics from The Career Force on YouTube.

Types of Data

First of all, let’s look at some types of data that business leadership, marketers, brand managers, and researchers have access to in order to better understand consumer and market enigmas.

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Primary research data

Primary research data is generally considered ‘small data. They’re easily stored in traditional spreadsheets like Excel and the files are small enough to be emailed without getting stuck in your outbox or flagged as spam. These data tend to represents people’s opinions and perceptions about various topics asked of them in a quantitative questionnaire, or a qualitative interview or focus group.

• Ad hoc survey or interview data: Often under 1000 records and under 100 variables. Normally focused on one brand or topic. Qualitative datasets converted to quantitative formats may have fewer records but much more, or much larger, variables.
• Tracker survey data: When gathered across multiple brands or countries, may be up to 50 000 records and a couple hundred variables. Normally focused on one product category though they may shift in focus from time to time.

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Business data

Business data is often created in passing – as something happens in the company, a physical or digital record is created. Created and stored over years and in many disparate formats, these records are used to fulfill customer requests, manage employees, or keep track of product development. In many cases, these data are left lying around, ignored on servers, collecting virtual dust, and not leveraged for the insights that lie within.

• Employee data: Records of retention, satisfaction, reviews, salaries, promotions, complaints, departments and more can be transformed and standardized as variables for statistical analysis.
• Customer data: This is where we start to use the phrase “big data.” Transactional data reflecting purchases, SKUs, prices, times, dates, and more can come in datasets of millions or trillions of records with thousands of variables. Click-stream data gathered from websites can be exponentially more massive as every tiny movement and action made by a finger, pen, or mouse on digital screens is tracked. These data are already collected in standardized datasets and ready to be reformatted or transformed into specialized datasets for analysis.
• Business data: Executives are often most interested in these data – revenue, costs, finances, operations, inventory, supply chain, & logistical data. These data, also usually available in standardized datasets, are often summarized from individual level data but are even more valuable at the individual level.
  

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Secondary research data

Secondary research data is all-encompassing. It can include any type of primary research or business data that were collected for some other purpose, whether by yourself, someone else at your company, or someone at a different company. As such, you might have access to small survey datasets, massive transactional datasets, or compiled and summarized datasets. In addition to the primary and research data already described, it could include:

• Third party data: A huge range of data types and sizes can be purchased from third parties that create, curate, and collate many sources of data, potentially terabytes of individual or summary level data.
• Social media data: Originally created to communicate a specific message to a specific person (or persons), social media data can be gathered and used for purposes other than originally intended. These data may include information about brands, people, and companies, date, time, geography, sentiment, and more. It may need to be transformed and standardized but a wealth of insights exist here as well.

Types of Analyses

There are three categories of analytics and skill-sets that might be required in the course of a research project. 

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Standard analytics

Most quantitative market researchers have a broad understanding of the theory and application of statistics. They know when and why to apply certain types of analyses to achieve specific research goals. Specifically, they have a lot of experience interpreting massive data tabulation files and running standard survey analyses to help us identify patterns and understand what happened and why.

They focus mostly on:

• Types of data: Primary data, usually quantitative survey data
• Types of analyses: Correlations, t-tests, chi-square, means, standard deviations, ANOVAs, descriptive and diagnostic statistics
• Analysis tools: Menu driven SPSS, Excel, data tabulations
• Outputs: PPT reports, static Excel reports

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 Questions to Find Out If This Is The Goal

• Will the analyses focus on details from the data tabulations?
• Do you need insights beyond what is covered in the data tabulations?
• Do you need anything beyond descriptive statistics like means, standard deviations, and box scores?

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 Possible Research Questions

• What parts of the shopper experience do my customers like the most?
• What problems does my product help consumers solve?
• What types or segments of shoppers prefer my brand and how can I tailor my products and marketing for them?
• How many people are aware of my brand and where does it sit in the competitive brand space? – A case study

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Advanced analytics

Advanced analytics are usually conducted by people who have specialized training and expertise in statistics. They are experienced with non-standard and special cases of statistical tests that can’t be determined from data tabulations. Advanced analytics can help us understand what happened, why it happened, and predict what is likely to happen next.

They focus mostly on:

• Types of data: Primary research data, small business datasets, biometrics data
• Types of analyses: All of the standard analytics plus linear / logistic / multiple regression, conjoint, MaxDiff, TURF, factor analysis, cluster analysis, segmentation, discriminant analysis, perceptual mapping, special cases of standard analytics, predictive analytics, forecasting, and more
• Analysis tools: Menu or script driven SPSS, SAS, R, Python
• Outputs: PPT reports, static or dynamic Excel reports, user-guided dashboards, simulators

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 Questions to Find Out If This Is The Goal

• Do you need to segment people or products into groups?
• Do you need to predict purchases or forecast sales?

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 Possible Research Questions

• What is the highest price we can charge without making our customers feel ripped off while also earning as much profit as possible?
• What do my customers’ spending patterns look like?
• What is the best set of flavors for my products that won’t result in cannibalization? – A case study
• What is the potential size of my market given various product feature scenarios?
• Which employees are most likely to stay with our company for a long timer, thereby reducing costs of recruiting and training?

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Business Analytics/Intelligence

Answering business intelligence questions to improve strategic decision making and create a competitive advantage normally requires advanced expertise in both statistics and data management. That skill set is often described as data science. Of course, for maximum effectiveness, you would also want this person to have extensive experience with marketing and consumer data.

These experts focus mostly on:

• Types of data: Big data, business data, transactional data, logistics, employee data, real-time or near-time data
• Types of analyses: All standard and advanced analytics, plus data transformation and manipulation, data fusion, data mining
• Analysis tools: Python, R, SAS, SQL, machine learning, AI
• Outputs: PPT reports, static or dynamic Excel reports, user-guided dashboards, simulators

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 Questions to Find Out If This Is The Goal

• Do you need to combine different types of data from multiple sources?
• Do you need to make sales or logistics predictions in real-time?

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 Possible Research Questions

• Why are we unable to keep warehouses stocked with the right products at the right time?
• Where are we dropping the ball with our processes and logistics, and how can we solve small problems before they become big problems?
• How can we increase our efficiency to improve our overall profitability?
• When a customer has selected a single product, what other products would they be most interested in?
• Can we drop a rarely purchased product without causing our highest value customers to switch retailers?
• How can we ensure optimal inventory for every SKU using existing business data? – A case study

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What’s Next?

There’s a lot of overlap among various analytical techniques and objectives. One project may require only standard analytics whereas another may require all of them. However, once the research problem and the available datasets are clearly defined (not as easy as you’d think!), your analyst will know which techniques and software are best suited to uncover your answers.

If you’re ready to gather top quality insights about your buyers, brands, and business, please do email your project specifications to our research experts using Projects at E2Eresearch dot com. We’d love to help you turn your enigma into enlightenment!

Podcasts about Business Intelligence

• Analytics Power Hour with Michael Helbling, Tim Wilson, and Moe Kiss, Apple Podcasts
• Analytics on Fire with Mico Yuk, PlayerFM
• BIFocal – Clarifying Business Intelligence, Spotify
• Data Science at Home with Francesco Gadaleta, Spotify
• Data Stories: A podcast on data visualization with Enrico Bertini and Moritz Stefaner, Spotify
• Hub & Spoken: Data | Analytics | Chief Data Officer | CDO | Data Strategy, PlayerFM
• Making Data Simple by IBM, Apple Podcasts
• Raw Data By P3 Adaptive with Rob Collie, PlayerFM
• The PolicyViz Podcast with Jon Schwabish, Apple Podcasts

Business Intelligence and Data Analytics Conferences

• SAS Hackathon: Winter, 2022
• The Global CDAO: Driving a Global D&A Strategy: Beverly Hills, January 30 to February 1, 2022
• Gartner Data & Analytics Summit: Florida, March 14 to 17, 2022
• Big Data & Analytics Summit: Toronto, March 22 to 23, 2022
• Informs Business Analytics Conference: Texas, April 3 to 5, 2022
• CIO’s Data & Analytics Summit: April 13 to 15, 2021
• SIAM International Conference on Data Mining (SDM22): Virginia, April 28 to 30, 2022
• Data Innovation Summit 2021: Stockholm, May 5 to 6, 2022
• TDWI Data Conference: Chicago, May 8 to 13, 2022
• Data Summit Connect: Boston, May 17 to 18, 2022
• SAS Innovate: San Diego, April 11 to 13
• TIBCO Analytics Forum: June 13 to 15, 2022
• DATA 2022: Lisbon, July 11 to 13, 2022
• SIAM Annual Meeting (AN22): Pennsylvania, July 11 to 15, 2022
• Industrial Conference on Data Mining (icdm): New York, July 13 to 17, 2022
• Real Business Intelligence: September
• SAS Explore: Fall, virtual
• Chief Data & Analytics Officers Exchange: September
• The 8th IEEE International Conference on Data Science and Advanced Analytics: Portugal, October 6 to 9, 2021
• Chief Data & Analytics Officers (CDAO), October 12 to 14, 2021
• BigData & AI: Toronto, October 13 to 14, 2021
• The Data Science Conference: Boston, October 14 to 15, 2021
• DataScienceGO: California, October 15 to 17, 2021
• Open Source Analytics (OSA CON): November 2, 2021
• Marketing Analytics and Data Science: Tennessee, November 15 to 17 2021
• Business Intelligence & Analytics Conference, London, November 14 to 17, 2022
• Disney Data & Analytics Conference: Florida, November 30 to December 1, 2021

When you’re immersed in data and numbers every day, all day long, it’s easy to forget that numbers can be intimidating. However, built with care and purpose, real-time dashboards are a great way to help non-technical people feel more comfortable with numbers and encourage them to dig into real-time data without feeling overwhelmed.

Regardless of how comfortable people are with numbers, everyone needs to understand and analyze their KPIs and critical data points to make more informed decisions that will result in business growth. As with any tool, there are good reasons to choose one data presentation tool over another.

With that in mind, let’s first consider under what circumstances dashboards are preferable and second, how to set up an actionable dashboard that people will want to use.

Optimal Use Cases for Digital Dashboards

Huge Sample Sizes

No one likes a long, PPT report. But when sample sizes are huge, forcing a potentially massive set of results into a short static report can minimize the potential of the data you so carefully collected. Think about it in terms of a global report covering a brand 15 different countries. It doesn’t make sense to write 15 reports that are each 20 pages. However, it does make sense to capture high-level global insights in one report and then provide dashboard access to the nuanced results within each country.

• Trackers that accumulate thousands of records on a daily, weekly, monthly, or quarterly basis
• Global point-in-time studies covering many SKUs, languages, and countries
• Transactional/purchase datasets covering hundreds of SKUs, hundreds of retailers, and millions of individual, consumer purchases

Time-Dependent Reporting

Whether it’s tracker data from the last 6 months or historical, business records from the last 6 years, dashboards can help you consolidate terabytes of data into meaningful chunks. Discover insights that have been hidden in the data because the data wasn’t previously reviewed with a certain question in mind or because year-over-year data wasn’t previously available.

• Monitor brand health and campaign effectiveness year-over-year
• Monitor seasonal employee satisfaction and engagement.
• Review the past, monitor the present, and predict the future

Access Real-Time Insights

When you’ve waited 4 weeks since the start of a project, 2 weeks since it went in field, and you still have to wait 2 more weeks until tabulations and a draft report is ready, you know the power of accessing real-time data. Dashboards can be the answer to quick insights, particularly when a problem appears seemingly out of nowhere!

• Identify problematic business practices and roadblocks from transactional or logistics data in real time
• Catch consumer-reported problems in social media data or tracker data before they become full-blown crises

Mine for Insights

It’s impossible to anticipate every possible, meaningful analysis prior to writing a report. With a user-guided dashboard, you can check hunches, test wild scenarios, and discover insights that were secondary (or tertiary) to the original research questions or that weren’t obvious at the time of writing. And, these analyzes can be done even by those who don’t have access to or knowledge or SPSS, SAS, or the original data tables.

• Dig into to data beyond the original research objectives
• Uncover serendipitous insights that would never otherwise be discovered

Reach Multiple Audiences

Most written reports are tailored for a single audience. But we know that research data is invaluable to many groups of people. With an interactive dashboard, each user can focus on the level of detail that will help them make the best decisions in their role, and all of them can be using the same raw data source for a consistent message.

• Sales/Marketing Team: Dashboards can help you understand the performance of individual salespeople, track the pipeline and conversion, understand marketing campaigns. All of this will help them understand how they are performing and where they need to direct their efforts.
• Brand Managers: Brand managers rely on analytical dashboards to track campaigns, product development, customer satisfaction, and more. Dashboards help them track key metrics and spot and resolve issues before they become much bigger problems.
• Operations Managers: Operations managers rely on operational dashboards to track purchase behaviors, discover logistical roadblocks, and improve processes.
• Decision-Makers: CEOs need a strategic dashboard with KPIs across all departments to track company goals, visualize new trends, and inform future strategies – all in one place.

Fuse Data from Multiple Sources

If you’ve ever struggled through 3 reports written by 3 different people in 3 different formats and tried to consolidate trends and themes, you know how valuable inputting all that data into one dashboard can be. Save time and confusion by incorporating website analytics, transactional data, survey tracker data, and customer support data into one place to reveal holistic, company-wide insights.

• Merge transactional and survey data for a holistic picture of customer
• Merge employee engagement data and sales data for a holistic picture of the business

Detailed Building Blocks for a User-Friendly, User-Guided Dashboard

After you’ve decided that an interactive, user-guided data dashboard is the right reporting tool for your research, then you need to actually build that dashboard. Here are a few key tips to keep in mind during the development process.

• Choose play: People want to play, even adults! Dashboards don’t have to be boring just because they’re designed for business professionals. Incorporate pleasing designs and interactive filters that encourage play and discovery. A playful dashboard is a used dashboard!
• Choose clean data: Don’t assume that all data is good data, and that all data can be immediately dropped into a dashboard. Check all of the data for errors, both manual and systematic, before loading it into the dashboard and letting users work with it. Make sure it’s clean, complete, and compliant. Don’t let the data lie to users.
• Choose the most important data: Yes, you can have a dashboard with 100 filters and 50 pages. But will they all be used? As the dashboard creator, you know which variables are of key importance. Focus on those so that users don’t get distracted by incidental data.
• Choose actionable data: If you know that you can never act on a certain issue, then it’s a waste of time, space, and users’ cognitive power to include it in a dashboard. Focus on data that people can and will act on to improve the business.
• Choose the right charts not the pretty charts: The purpose of a dashboard is not to include one of every type of chart. The purpose is to choose charts that are best suited to the data being shared. If that means one page has 5 line charts and no bar charts or pie charts, then so be it. Clarity is key.
• Choose accessibility: Sometimes, accessibility is easy. Make sure to use large fonts, comprehensive labels, indicators that can be differentiated in both black/white and color, generous spacing, and large clickable areas. Consider whether your audience has unique accessibility needs due to a disability. Even better, consult with an accessibility expert.

Types of Market and Consumer Insight Dashboards

No matter what kind of dashboard you need, you will be available to find a solution. If you can focus on your audience and your goal, you’ll be able to properly distinguish between three major categories of dashboards.

• Quick: When budgets are tight, timelines are short, and you still need a user-driven tool to investigate data and discover insights, try a quick and cheap dashboard. They may not have the swoopy transitions or endless bonus features but you can still get the basic functionality you truly need to analyze a few waves of tracker data or a multi-country study. Our Raven dashboards are one example of a quick and competitively priced dashboard.
• Comprehensive: For most people, the middle option works best. With tools like PowerBI (cost-effective for Microsoft users) and Tableau (super-speed with massive datasets), most medium to large datasets can be nicely transformed into easy to use, attractive dashboards.
• Custom: The sky is the limit! With tools like .NET and Python, you can have the dashboard of your dreams. Filter real-time transactional, survey, and logistics data into one dashboard. Forecast future sales given consumer opinion scores and live purchase data. Plan more timely deliveries of the SKUs they actually want.

What’s Next?

Once you’ve decided to use a dashboard, the sky is the limit. Focus on your needs not your wants, and you’ll end up with a dashboard that will help you gather insights into your buyers, brands, and business, and create a successful future.

Are you ready to build a quick, comprehensive, or custom dashboard that helps you communicate more effectively with a wide range of key stakeholders? E2E’s Raven dashboards are competitively priced even for small projects.  Email your project specifications to our research experts using Projects at E2Eresearch dot com.

Learn more from our case studies

• Automating Distribution of BFSI Performance Metrics – An automated reporting case study
• Creating a Financial Portfolio Health Review Report – A credit card business analytics case study
• Building a Strategic Business Plan Grounded in Industry Data – A pharmaceutical data analytics case study

Learn more from our other blog posts

• New! Raven Dashboards for Inquisitive Minds
• 8 Engaging Question Types to Improve Participants’ Survey Taking Experience
• Trackers Suck. Here’s how to fix them.
• 8 Reasons to Invest in a Hybrid DIY Market Research Team
• (Not-So) Secret Strategies for Great Market and Consumer Research

Research Objective

• A US-based manufacturer of personalized gift items with markets in more than 10 countries across North America, Europe and Middle East needed SKU-level demand forecasting to ensure all orders are met while maintaining optimal inventories.
• Over 3000 SKUs across 8 product platforms with a 3-month lead time needed to be tracked.

Scope & Methodology

• A framework was developed to accommodate weekly re-forecasts based on SKU-level actuals-to-date for repeat customers, varied product platforms, new products, etc.
• It was designed to learn from historical trends and project future demand.
• A weekly ‘Early Warning System’ to predict excess and stock-outs was also built.

Value Delivered

• The forecasting tool helped reduce obsolete inventory at the end of the year which freed up working capital and reduced waste.
• It also reduced ‘out-of-stock’ rates leading to increased customer satisfaction and revenue, in part due to preventing unnecessary attrition.

Check out other case studies

• Fine Tuning New Personal Care Products with JAR Analysis – A sensory IHUT case study
• Modeling a Consumer Response Hierarchy to Guide Brand Strategy – A personal care survey analytics case study

Research Objective

• A senior management team at a financial company needed a high-level, portfolio health review report to draw valuable high level insights, and identify unusual trends over time for their credit card portfolio.

Scope & Methodology

• The client made a range of business intelligence data available including:
  • Account level financial information
  • Credit bureau data for risk profile of accounts
  • Transactional account data

Value Delivered

• The data helped the client better understand historical and future trends related to consumers’ financial behaviors. They were better able to understand where their potential risks were.

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Check out other BFSI case studies

• Automating Distribution of BFSI Performance Metrics – A BFSI automated reporting case study
• Customer Behavioral Segmentation to Support Targeted Marketing – A BFSI data analytics case study
• Removing Customer Barriers to Auto Insurance Renewals – An insurance survey case study

Research Objective

• A leading food company wanted to understand the sales impact of various trade schemes.
• They wanted to deconstruct sales in order to differentiate the impact of various schemes (lift analysis) and to understand how price, promotion, schemes,and competition activity impacted sales.

Scope & Methodology

• Created multiple regression models for key SKUs to determine the impact of trade promotions, slab based discounts, internal cannibalization, competition activity and seasonality on their overall sales
• Calculated sales lift & ROI for each promotion to help optimize budget allocation

Value Delivered

• A scheme simulator was built that allowed the business team to align their trade promotion calendar to meet budget constraints and achieve the desired sales lift for their portfolio.
• The simulator now guides future investment decisions.

Check out other retail case studies

• Consumer Perceptions Towards Online Ordering – A retail survey + desk research case study
• Launching a Fast Food Franchise In A Competitive Market – A retail survey + competitive analysis case study
• Improving Operational Effectiveness at a CPG Company – A retail B2B survey case study