GreenBook Blog provides original insight into the challenges faced by the market research industry today. Contributors from both sides of the table share their expertise and offer unique perspectives on a wide variety of issues, both strategic and tactical.
How well do Super Bowl ads drive customers to spend money or do some kind of proactive and positive behavior towards the advertised brand?
By Michael Wolfe
According to Advertising Age, a 30-second Super Bowl ad in 2017 cost about $5 million. That puts total Super Bowl 51 ad spend near $385 million. It seems like many Super Bowl ads focus on gaining viewers likability and it often appears to be a popularity contest. The key question, however, is how well do these ads drive customers to spend money or do some kind of proactive and positive behavior towards the advertised brand. That is the issue we wish to explore here.
Using Advertising Benchmark’s ABX copy test scores, the overall results for 2017 Super Bowl commercials were nothing to brag about. In fact, using standard ad effectiveness criteria, 2017 ads were a disappointment, at best. Overall scores of the last 5 Super Bowls also generally fall short of ad norms.
The chart below summarizes ABX ad effectiveness scores for 65 ads. Overall, 58% of these ads scored at or above normative levels. As shown, unfortunately, there were some very low scoring ads. Ads with a political message, such as the 84 Lumber ad, did not fare that well.
The ABX ad testing system is based on a survey of a nationally representative panel. The major components making up the ABX index are:
Awareness/brand linkage. Was the brand advertised correctly recalled?
Message clarity. Was the primary commercial message understood?
Brand reputation shift. Did viewing the ad change brand reputation perceptions?
Message relevance. Was the ad message deemed relevant to the customer?
Call to action. Did the ad elicit any positive actions or intentions, including purchase intent?
Although not part of the ABX score algorithm, was the ad liked or disliked?
If we look at the key criteria or drivers of ad effectiveness, below shows that, while Super Bowl ads do very well on “likeability” and generating “buzz”, these didn’t fare so well on key action points and particularly on such critical measures as “purchase intent”
In sum, the key insight here is that popularity and likeability do not always translate to effective actions from the customer. Clearly, funny, cool and emotional ads can be good ads, but focusing on winning a popularity contest does not always translate into effective marketing, which stimulate customers to do some positive action towards a brand.
Periscope By McKinsey and their new Insights Solutions practice area are moving firmly into the research industry with a next gen offering that merges the best of high-end strategic consulting and comprehensive data-driven solutions.
Historically consulting firms such as PwC, Deloitte, Accenture and McKinsey have fallen more into the “research client” bucket than “supplier” category; like most ad agencies, although they might conduct research on behalf of their clients, as industry publications or even as branded syndicated offerings the research was generally sourced to external partners and it wasn’t a “tip of the spear” defined product offering. As the insights industry has fragmented and evolved over the past decade, we’ve seen a gradual blurring of the lines between both agencies and consultancies as it related to insights, especially in the use of social data or other to help measure and predict on behalf of clients.
Concurrently, many research providers have struggled to re-position themselves as data-driven strategy consultancies (and even a couple making the leap to creative agencies) with various degrees of success, but overall it’s been a challenge for traditional research suppliers to move up market. Also, many new data consultancies have emerged who challenge all the existing players by focusing on high end analytics, “Big Data”, and various aspects of business intelligence.
Of course during all of these changes the advent of insights technology, especially DIY and automation across the data collection and analytical spectrum have emerged, many powered by the big tech companies or social platforms, and gone from strength to strength, further accelerating and empowering disruption.
Due to these shifts I (and others) have long predicted that the industry might end up looking something like this:
Recently I had the privilege to get to know the folks at Periscope By McKinsey, and I am more now firmly convinced than ever that the general outline above is not just where we are headed, but where we are already.
All of that is to set the context for today’s post; an in depth interview with the senior leadership at Periscope By McKinsey where we explore what I consider to be a major sign that the industry has fundamentally shifted and is falling firmly into a new structure.
McKinsey has always conducted research, but with Periscope by McKinsey and their new Insights Solutions practice area they are moving firmly into the research industry with a next gen offering that merges the best of high-end strategic consulting and comprehensive data-driven solutions. In this interview with Brian Elliott, Ph.D. CEO & Managing Partner Periscope By McKinsey & Oliver Ehrlich Partner McKinsey & Company a Global leader of the Insight Solutions Suite we dive deep into what they are doing. how they view their role in the insights space ecosystem, and what the future holds.
Rather than my usual video interview, we approached this as a podcast type interview, and then added a few slides as background to help illustrate the points we cover in the discussion. Think of this as a bit of a private webinar, where Brian, Oliver and I go back and forth to set the context of the Periscope By McKinsey offering within the broader industry. It’s surprising and revealing in many ways and falls into the “must listen” category.
Here is an embed of the interview:
This isn’t shoehorning research into strategy consulting; this is a bottom up, highly productized and fully baked research offering. From the most basic of data collection needs to the most advanced integrated strategic analytics and everything in between, Periscope By McKinsey has it. Perhaps most surprising is their Insights Solutions suite, which embraces automation and agile approaches for primary research to deliver cost and speed efficiencies one wouldn’t normally associate with a high end strategy consultancy.
To be clear, this isn’t a developing solution. This group is already a large players with 400+ Marketing and Sales analytics specialists, 35 distinct research techniques, 100+ sector-specific capabilities and 50+
unique data sources available. And based on their investment in rolling out into the research industry, they are absolutely committed to building on their success and becoming a major force in the industry.
Here is a bit from their website to show the breadth of their Insights Solutions suite offering:
Agile Insights consists of a leading edge research design and execution capability that is paired with subject matter expertise to ensure that research is truly fit-for-purpose to address key business needs. Our 4 distinctive services for companies across consumer and B2B sectors include:
Survey (quantitative research):Get the facts about individuals’ beliefs and behaviors
Growth potential surveys
Customer Decision Journey Score Card
Other modular surveys
Speak (qualitative research):Talk to and observe individuals in real time
Digital UX testing
Scrape: See what people are doing and saying online
Social brand equity
Social media customer engagement
Pricing & assortment insights
Scan: Scan data on a specific market, segment, or category to get rapid, targeted insights
Digital Opportunity Scan
And of course, all of this fits into a larger system as you would expect from a company like McKinsey. Again, their website describes it best:
…Our services break down into four offerings:
Agile Insights: Our team conducts interviews, creates surveys, and analyzes social and online data to better understand the consumer.
Applied Insights: Our proprietary tools and services help companies identify potential market growth areas, assess product pricing and assortment, and uncover sales and marketing improvement opportunities.
Intelligence Flow: We use embedded insights such as data feeds, industry surveys, and managed analytics to provide ongoing support that helps companies stay ahead.
Insights Boost: We create a custom program to transform the way your organization develops insights, conducts market research, and uncovers revenue growth opportunities.
Many traditional MR suppliers may be reading this with trepidation, and perhaps rightfully so for some, but as Brian and Oliver point out they are also very happy to partner when that is what is in the best interests of the client or the business as a whole. That is a difference between some of the other large MR firms and not only is it likely a competitive advantage for Periscope By McKinsey, but it is also a real opportunity for a variety of research suppler companies to develop a new network of partnerships around them.
The Periscope by McKinsey team will be at IIeX Europe next week in Amsterdam and likely at many other GreenBook events this year so anyone who is interested will have a chance to engage with them directly. In the meantime, stay tuned to see what they and other disruptive players are up to; this ride is just beginning!
Visualization of social networks is now coming online to make sense of Big Data and convey the results of analyses through emerging, open-source programs.
By Michael Lieberman
Mathematical analysis can tell us a lot of what is happening now. A great example is a Social Network Analysis Map of “@Nordstrom” I ran last Friday, February 10.
The graph represents a network of 5,293 Twitter users whose recent tweets contained “”@Nordstrom””, or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 5,000 tweets. The network was obtained from Twitter on Friday, 10 February 2017.
There are six major types of Twitter maps produced by Social Network Analysis. One type is called a Polarized Network. This pattern emerges when two groups are very split in their opinion on an issue. Two or more dense clusters form with little interconnection. Generally one sees polarized Twitter maps for divisive issues such as women’s rights in the Arab world or a hotly contested gubernatorial race in, say, Texas.
The most effective method of reading the map is to observe how hashtags cluster within the software. With our map we see three major groups forming. Below is a summary of how the dominant hashtags in the three largest groups cluster.
It is evident that G1 are mildly anti-Trump, perhaps media and those not thrilled with the new administration. G2 are the anti-Trumps. Hashtags in G3 are, evidently, supporters of the President and Ivanka’s line of clothing. The software captures every individual tweet in an Excel file. It is possible to drill down, if the client asks, with text analytics.
The interesting finding is this: Why has Nordstrom, a chain of luxury department stores usually found in upscale malls, now become a symbol of resistance against the new administration? We think we know the answer. What is interesting is that the results show up clearly when we run a Twitter map. Is upside down the new normal?
Visualization of social networks is now coming online to make sense of Big Data and convey the results of analyses through emerging, open-source programs. This kind of analysis is not limited to Twitter, but also can be applied to other social media data, megadatasets, consumer sales data from, say, a major supermarket, Walmart or survey data. It is a great new tool that, together with our analytic skills, we can deploy to give our clients the story.
A good use of this tool is if a major brand launches a new advertising campaign. By running a Twitter network map every day for 30 days, we can gauge the penetration of the message, which hashtags are going viral over time, how they are clustering, and what is the trending message.
Palm tree visualizations perfectly align visual elements with the core underlying structure of the data.
By Tim Bock
Palm trees are my favorite visualization. They look great. They are easy to understand. There is no other visualization that is as effective at decomposing performance across multiple dimensions.
Palm trees perfectly align visual elements with the core underlying structure of the data. This encourages our brain to separate out additive effects from interactions – without our brains even needing to know what these terms mean!
A great looking, easy visualization to show multiple dimensions in your data
The palm trees below represent the concerns American travelers have about different destinations. The tallest palm tree is for Egypt. It has the worst performance (i.e., the most concerns). Hover your mouse over the fronds of the palm tree, and you will see the breakdown of these concerns.
Safety is clearly the top concern, although there are lots of other important concerns. Compare China. China has many issues in common with Egypt, but Not being understoodis a bigger concern, and Safety is less of a concern.
If you want to play around with the the examples used in this post, or create Palm Tree Plots of your own, you can do so by clicking here.
There is no better way to visualize data with multiple dimensions
I am making a bold claim here. However, it is a claim I can back up.
There is a standard best-practice visualization for data with multiple dimensions: a line chart (I explain the logic of this below). Check out the line chart version of the same data, shown below. Yes, if you dig it is the same data and shows the same patterns. However, your brain has to work really hard to extract them.
Perfect alignment between visual elements and underlying structure of data
The visual elements of a palm tree plot mirror the structure of a two-way analysis of variance, and allow us to visualize the type of conclusions typically obtained via formal statistical testing: separation of the two main effects, and the interaction.
Main effect 1. Three visual elements represent the average level of concern by country. 1. The height of the palm trees. 2. The average length of the fronds. 3. The order of the palm trees. In the example, it is easy to see that Egypt has marginally more concerns than China, and the UK and Australia are close to tied.
Main effect 2. Three visual elements encode information regarding the frequency of the concerns. 1. The ordering of the concerns in the legend. 2. The ordering of the fronds (clockwise from midnight). 3. The average length of each type of frond. In our example, reproduced below, we can readily see that Safety is a huge concern, whereas Boredom is largely irrelevant. Although gross patterns can be seen here, this is the weakest aspect of the palm trees(e.g., length of frond is used to encode the prevalence of concerns, but some users will make inferences from area). For this reason, such information is better represented via a separate visualization.
Interactions. This is where the palm trees have no equal. The statistical literature generally recommends using line charts to represent interactions. Line charts only really work for very simple data sets. The palm trees are so much better for bigger examples, such as this one. Our brains are primed to quickly spot differences in shapes. This allows us to quickly see the relationships between concerns and countries. We can readily see that, for example:
The UK and Australia are, in terms of concerns, essentially identical: people are concerned about cost, but little else.
People are also concerned about Cost with France, but Friendliness and Not being understood are also factors (which is a bit sad for France, as my own experience is that these have not been real issues when traveling in France for many years).
With Mexico, concerns relate to Safety, Health, and Cleanliness.
If you want to play around with the examples used in this post, or create Palm Tree Plots of your own, you can do so by clicking here.
Kantar Tops MRS League Tables – The Market Research Society has published its analysis of the top 100 UK MRX firms: they grew 6.7% year over year (2015 over 2014), with 71% of agencies experiencing growth. The largest three firms are Kantar (WPP), Wood Mackenzie Research & Consulting, and Dunnhumby.
TheBest Data Scientists Get Out and Talk to People – Writing for the Harvard Business Review, Thomas Redman points out, “Great data scientists know they have to understand the strengths and weaknesses in the data in great detail.” Three keys: See how the data is collected; learn the full context; integrate these findings into your work.
Write Right: Convincing Content – Isabel Gautschi of Cascade Insights provides concrete tips including this gem: “Don’t subject your audience to stream-of-conscious business blogging.”
Why Machine Learning is Meaningless – Tom H.C. Anderson of Odin Text gives two examples that can be labeled “machine learning” even though they different dramatically in complexity.
Note: This list is ordered by the relative measure of each link’s influence in the first week it debuted in the weekly Top 5. A link’s influence is a tally of the influence of each Twitter user who shared the link and tagged it #MRX, ignoring retweets from closely related accounts. Only links with a research angle are considered.
The UK’s largest market research event, The Insight Show, is celebrating 20 years on March 8-9, 2017 in London
By Stewart Tippler
With Brexit’s immediate and continual impact on the value of Sterling, the US Dollar and EURO have become much stronger in comparison. The UK has always been the second market globally (after the US) for market research and insight, but with the collapse of the £ it’s now looking like an even more attractive proposition. The skills and experience is in London, in particular for international research, but in a language you speak and now at knock-down prices to non-Brits!
So, it’s never been a better time to travel to London to take full advantage of leading agencies and suppliers covering the best in research, insight and data analysis. What is more, the UK’s largest market research event, The Insight Show, is celebrating 20 years on March 8-9, 2017 in London.
The show has two days packed with thought-provoking content, an exhibition showcasing more than 100 leading suppliers, 50 sessions of live content, and lots of networking – all free of charge!
On Day 1, the Show will open with a CEO debate on the big issues and latest trends affecting the industry, including Ipsos Mori’s Ben Page, Kantar’s Bart Michels and Flamingo’s Kirsty Fuller. Day 2 will include a panel debate involving some of the best young minds in the industry as well as an International Stage showcasing research studies from around the world.
The larger Headline Stage features the ten winning papers from the Insight Show’s call for papers. The International Stage, in partnership with ESOMAR, will feature speakers from around the globe to offer unique perspectives into how research is being delivered across countries. At the end of the second day, Strive Insight will present their Best Paper Winner from ESOMAR’s 2016 Congress: ‘Pushing the frontiers of the eyewear business.’
These two new stages help make up a total of four stages spread over the two days. The Showcase Stage is anticipated to be another big hit, with supplier presentations covering the latest topics, themes and case studies across multiple areas of research and insight. Finally, the client only AURA conference offers high-level engaging content exclusive to AURA’s corporate researcher members.
Of course, it wouldn’t be the biggest market research event in the UK without some decent networking. So, there are several opportunities to chat with industry experts, whether it’s during the Research Club Party on the first night, or the multiple happy hours at the venue in Olympia.
In addition to free entry to the Insight Show, you’ll also get entry to MWLive, located right next door. Learn more about who is attending and review the content line-up by visiting www.insightshow.co.uk.
Oh, and while you’re over there, take advantage of the new currency exchange rate to see London’s most sought after attractions for less than ever before!
At IIeX EU in 2016 the winner of the Insight Innovation Competition was Heartbeat.ai. Now in honor of the next round of the IIeX EU Competition next week we touch base with Lana on the Heartbeat journey, where they are now and where they are going.
At IIeX EU in 2016 the winner of the Insight Innovation Competition was a unique emotionally-based text analytics solution led by our first female CEO winner, Lana Novikova of Heartbeat.ai. Now almost a year later, and in honor of the next round of the IIeX EU Competition next week, I decided it would be interesting to touch base with Lana on the Heartbeat journey, where they are now and where they are going.
First, a bit about their SaaS platform in their own words:
True deep insights can’t be found among scores and scales. Numbers have a definite role in research, yet only words and actions can point to our true hearts’ desires. Like raw diamonds, words often need skilled work to transform into a genuine sparkle.
Survey open-ended comments that contain deep insights into human emotions, needs and motivations, often get discarded. Manual data coding is time consuming and labour intensive, pricey and inconsistent from study to study. HEARTBEAT brought together market research, psychology, neuroscience, data analytics, and technology to enable researchers to find and polish these “insight diamonds” – quickly, affordably, and consistently.
You’ll notice the use of almost poetic language in their description, which is absolutely “on brand” for Lana and her team; they approached the development of their platform not as an exercise in technology advancement but as an effort to combine myriad disciplines to address a need to create scalable “understanding” tools. Market research was one example of use cases, but the needs of healthcare, public policy, education and other categories drove their thinking as much as anything else.
The result is a uniquely powerful, beautiful, and elegantly simple solution that can be used by novices and experienced pros alike, with an API based model that allows their tech to be plugged in to power or augment many different complementary platforms.
Here is a bit more from their website:
Heartbeat text analytics app automates the segmentation of self-reported emotions and feelings…into 10 primary and 100 secondary emotion groups. Our app is unique, scientifically sound, simple to use and affordable. Our results are accurate and 100% transparent. It will give you:
Automated categorization of emotion and feeling words and phrases
Unprecedented granularity and precision of emotion segmentation
A beautiful interactive data visualization with analytics and filters
According to the most recent GRIT report, text analytics continue to grow in use within research, with a variety of solutions available and some amazing work being done to prove use cases. Heartbeat’s niche is the deep profiling on non-conscious levels and that is exciting indeed since they are helping to bridge the gap between behavioral and computer sciences.
Lana was kind enough to sit down with me for a catch up on what’s been happening with Heartbeat since they won the Competition at last year’s IIeX EU; it’s a fun and enlightening conversation, and Lana has an amazing story to boot. The work they are doing within MR is fantastic, but some of the other projects they are working on outside of this space, for instance as a tool for addiction treatment, are truly fascinating. Check it out and I’m sure you’ll agree.
Look for Heartbeat to be showcased at several events this year along with many other emerging companies doing amazing work on the forefront of research!
The tagline: “New ideas. New approaches. New connections.” fits the event like a glove. As I wrote last year, the IIeX is a very forward-thinking event with a focus on introducing you to the new ideas changing the world of insights.
Coming well-prepared is key. The event being two days long, packed with over 100 presentations and over 300 people in attendance. In order to make the most of this much-anticipated event, I would like to give you three points of advice:
Pick the right sessions and tracks. The quantity of information and the speed at which it is delivered makes this event quite a different experience, so make sure to study the agenda beforehand. If you can find the right tracks, IIeX will provide you with some very valuable new and innovative insights.
The new Client Disruptive Innovation Showcases, which includes three of the most future-focused brands in the world to host their own sessions, seems to be a good starting point – here Heineken, Unilever, and IIF will showcase their roster of innovative insights providers.
Make the most of the networking opportunities. Although this one seems to be a bit of an open door, IIeX truly is full of opportunities to connect and people who want to do so. The crowd is incredibly diverse; client-side attendees who are looking for new insights or new research partners, tech companies looking for partnerships and suppliers looking to expand their network.
IIeX gives you the opportunity to find your new competitive advantage, new offering or that one client you are looking for. Make sure to study the list of attending companies or sign up for the IIeX community to reach out to those MR professionals you want to meet before the event.
Book a hotel and stay the night. When in Amsterdam you should make the most if it! Connect with your clients, partners or people you just met at IIeX and spend the night strolling the canals, have a drink or dinner at one of the best hotspots Amsterdam has to offer. This is a great way to expand your network or existing relationship.
It’s time to re-examine and perhaps even re-frame our thinking about the generations.
By Michael Wood
Editor’s Note: GreenBook remains steadfast in our commitment to grow the industry through innovation. One startup we’re enthusiastically supporting has as its mission to do just that: “Get research funded.” Collaborata, through leveraging the sharing economy for our industry, is currently funding more than 20 projects at www.collaborata.com, with new projects being funded and added each week. One of those newly featured projects is unique in its critical relevance to virtually all consumer brands, as it aims to reframe how marketers and researchers should think about generations, based upon the changing times. To launch this study, what follows is a post by one of the lead researchers, Michael Wood of 747 Insights.
Millennials have been in the spotlight almost since the day they were born. As a nation, we’ve watched them grow up with admiration, adoration, and perhaps even a touch of resentment. Early on, they were referred to as the next “Great Generation.” And, as predicted, they’ve continued to make their mark in the world, the workplace, and marketplace. Consumer brands and organizations of all types are adjusting and readjusting to meet Millennials’ ever-evolving needs and wants — as consumers, employees, and citizens. Today, Millennials in this country are 75 million strong and account for fully one-third of the U.S. workforce.
Over the years, much has been studied, written about, and reported on the tremendous impact Millennials have had on all aspects of our culture. Youth culture has become pop culture. Gone are the days of our fashion and their fashion, your music and my music, our entertainment and theirs. Today, it’s just fashion, music, and entertainment. Simply put, there are more lifestyle similarities than differences among the generations than ever before.
But do these commonalities also apply to these generations’ defining attitudes, values, and beliefs? These are the attitudes that drive consumer behavior and typically evolve with far greater nuance. What if there is a truly a “Millennial mindset” that spans the generations and is shared by the older Boomers and Xers and the younger members of Generation Z? Do the generational differences we marketers have relied on as cornerstones of targeting efforts still hold up? Are long-held beliefs about and characterizations of these cohorts, upon which so much of our marketing is based, really accurate? Are Boomers still the most socially outspoken? Are Xers the most cynical? Are Millennials over-inflated with self-esteem? And, is Generation Z the most open and tolerant?
Together, with a number of leading brands across sectors, we believe it’s time to re-examine and perhaps even re-frame our thinking about these generations. What are the values and attitudes of each generation? And, where is the overlap that unites these cohorts based on a common mindset to which brands can resonantly speak?
Generational theory says that we’re shaped by the political, social, and economic events during our formative years. So, how do cohorts evolve and shift based not on their age, but on external forces, especially when the change is tumultuous, as we’re witnessing today?
That’s what we’re going to find out: How these generations are changing and how we, as marketers, might need to re-frame our thinking about these four big U.S. cohorts in the post-Obama era. We all need these insights, so we can each apply them to our businesses. That’s why we’re launching “Generation Nation” — a series of studies beginning with the initial release of “Values and Attitudes” — that will reveal the true essence of each generational cohort. We welcome your participation in helping to shape this research. Please click on the links below to learn more about the study and how you can participate.
Innovation needs to improve the predictability and reliability of our research – it does not have to be new, flashy, sexy, or disruptive.
By Dr. Stephen Needel
The annual GRIT report should be required reading for marketing researchers; it provides a current snapshot of what senior players in our business are feeling about things. That said the reader should take a big grain of salt to go along with the red herring that winds its way through the report. The red herring is innovation, described as a holy grail with magical powers that should be self-evident. It is my contention that innovation for the sake of innovation is foolish. It only appears to be effective because so many buyers are enthralled with all things new and shiny, rarely digging beyond the surface validity of a lot of new techniques. Innovation needs to improve the predictability and reliability of our research – it does not have to be new, flashy, sexy, or disruptive.
The “innovate or die” movement persists, even though research has shown this to be untrue (e.g. Getz and Robinson, 2003). Yes, I know about the buggy whip company who didn’t see cars coming and Netflix changed the game much to Blockbuster’s consternation. Amazon brought us a new way to shop and one day their financial performance might justify what they do and how they do it – or not. Walmart’s latest challenge will certainly put a dent in Amazon’s growth estimates. Now take a look at all the “innovative” tools marketing researchers have developed. Are there any that blow you away?
In the GRIT report, John McGarr makes a critical point. He says, “MR providers need to keep in mind that no client owes the industry a trial of new methods for the sake of being innovative.” Some will argue that we should expect buyers to try new things when they are (sorry about this) faster, better, or cheaper. I’d take the position that faster is fine, as long as it is at least as good and has a similar value proposition. I’d take the position that better is always better, even if it is not as fast and not as cheap – you should pay more for a better answer. I’d take the position that cheaper alone almost always has a hidden cost, usually in bad design, sampling, or survey construction.
The marketing research industry needs innovation, but the innovation needs to be directed at solving research problems. Here’s an example – we have a problem with predicting new product performance, as good as many models are. When our new product failure rates are over [70%, 80%, 90% – pick your number] we clearly do not understand the shopper dynamics beyond basic trial and repeat analyses. When was the last time we saw a new or better way to do our new product forecasting that was validated?
Instead of solving research problems, we pretend that automation and DIY are the innovations most needed because they make the research process less expensive. These types of innovations are technical innovations, but not problem-solving innovations. We miss the point – it’s not the price tag that matters, it’s the quality of answers we are able to give our clients. Of course budget limitations play a part in purchasing decisions. But, and it’s a big but, a methodology that is meaningfully more accurate will always be worth the cost. Greg Archibald, in summing up the report, says, “Over the next few years, we are going to see a continued focus on improving tools and methodologies…” I’d respectfully disagree – I don’t think we, as an industry, are very focused on improving our tool kit but rather we are trying to come up with the next new sexy thing to sell.
Innovations such as automation and AI are great for the business of marketing research, but that only provides a trivial benefit for our end-client. They need us to do our job better, they will pay us to do that, and we need to innovate with that in mind.