Are organizations lacking customer understanding?
How well does your organization know its customers? The 2023 Q Report asked respondents to elaborate on their feelings regarding their company’s customer understanding and while many respondents say their company knows its customers well enough, some more nuanced open-ended answers stuck out.
Some feel their organizations don’t understand their customers or have a stable understanding of their market’s customers.
Not nearly as well as we should. Too often product and other people rely on anecdotal information vs. hearing/listening to the voice of the customer. In some areas, they know their customers well, but in too many areas they really know very little about them.
Not very well given that it is my job to understand its customers and I'm tasked with other things.
We are still a product-driven company and most stakeholders believe a) our cars will sell themselves or b) they know their customers. But as Mark Ritson always says: You are not your customer.
We are getting there in our understanding. I would say we are lukewarm right now in regards to our own customers since the function is still new. What we lack currently is stronger market understanding – we really do not understand the market's customers at all.
Others say their organizations believe they know their customers when in reality, they are falling short.
Overall, we know our customers well but we suffer from confirmation bias and using small or anecdotal evidence to validate preconceived notions about what we think we know about our customers. We also have blind spots when it comes to prospects and former customers.
That's a loaded question – many think they know the customers, based on a couple, but our customers' needs have changed and assumptions about their needs are falling flat.
I would say we have a basic understanding and good routines for learning more but it does not seem we know our customers intimately.
Pretty well, and I think our strong NPS and CSAT scores prove that, but as with many companies, I feel most departments can become "silos" at times and it hinders them from being able to see the customer journey on a broader level.
Meanwhile, other respondents indicate that their organizations know their customers’ wants and needs and cite the methods they have implemented to better understand them.
Pretty well. We use a custom donor segmentation to help us profile our donors behaviorally and psychographically.
Very well. We conduct projects throughout the year as needed to prepare for future trends, in addition to rolling research which maintains our understanding of the ever-changing components of our industry.
We have ongoing anonymous surveys as well as focus groups where we invest a lot in knowing our customers.
Regardless of whether organizations know their customers, most agree they could know them better and are planning to act on it.
We know demographics and behaviors but not what drives their hearts and minds. Because of this we are currently running a needs-based segmentation study.
We know our customers relatively well but we need to know our prospects better in order to grow.
Our company knows how customers feel about our own products/brands but we have a larger opportunity to understand the consumer in general in order to build empathy within the organization.
We have a strong understanding of current users but still opportunity to learn about potential users.
Some of the largest barriers companies face when improving their understanding of customers include lack of resources (40%), lack of provable ROI (15%), low response to customer research (12%), lack of C-level support (11%) and lack of internal faith in the insights department (6%). Only 7% say the available tools for understanding customers are not effective.
Respondents shared some thoughts on the marketing research or listening tools available, their value and whether any additional capabilities are necessary. They feel that the tools are effective but that their organizations lack the manpower needed to effectively analyze findings.
Yes, effective. But understanding data and extracting insights and mapping it to solve business challenges is not happening as consistently as expected. Team is overwhelmed with data and hard to make sense most of the times.
Listening tools we have in place are very effective; we lack the tools and/or manpower to take the data and turn it into valued information (open text answer analysis, for instance).
It is less about the tools than it is insights professionals making the learnings relevant for a broader stroke of stakeholders.
Every day my consumer insights department is getting better and better at knowing who our customers are. Figuring out products that are available to us that we can use is our biggest challenge – doing research with a limited number of personnel. Not enough hours in the day.
While research tools are nice to have, they are difficult to use due to the security risks associated. It is challenging to ask the right questions while sticking to the budget and upholding security standards.
My struggle is that I need something that is defensible to federal clients (who have concerns about data privacy) but is also lean and nimble enough that I’m not soaking my budgets with several thousand-dollar costs outside of labor. We’re contractors, so those are things we have to “eat.” The time spent on less-efficient but cheaper tools can sometimes be offset by the fact you can bill that time.
They are effective but could be better. We often can't ask about what we want to because it's sensitive or unreleased information and there are leak risks, so we generalize and it's not as specific as we'd like.
Somewhat effective but open to great risks associated with bots, fraud. We need better representation of our customer base and the market that's quick and cost-effective but also with controls building to reduce the risks of fraud.
Some respondents hope AI will improve the tools available to create more efficient processes.
The bots and spam in online quant research are getting harder and harder to detect and filter out; it would be great to have a way to filter and clean the data rapidly and effectively.
We (the industry) seem to be getting there. I'd still like to see more options for online platforms where analysis, insights generation and reporting can become quicker through AI automation. It would be great to be able to spend less time reviewing responses and more time reviewing potential insights.
There needs to be an easier way for marketing researchers not versed in SQL or Python to append customer, transactional and operational data to survey data collected for richer insights. It's a steep learning curve and I'm hopeful generative AI can help with this challenge.
I think there are a lot of great tools to deeply understand consumers but it does take time to do it AND communicate effectively, in a story-telling way, across the organization. I strongly believe that AI will enable us to accelerate and improve.
Regardless of the implementation and effectiveness of listening tools, some respondents say their organizations lack the human touch.
We use a lot of online techniques, which is good but missing some in-person depth. I'd prefer we use more camera-on sessions instead of text chat but that is out of budget usually.
No, it’s become too digitally focused, we’re losing in-person deep dives.
The tools are adequate with the exception of coding and understanding open-ended questions. No one bothers to have a person read them, which is really what you need to understand what consumers are saying. Most text analytics fail at true understanding.
The tools are there. For us it is the brain that is missing. And the willingness to understand that people are not machines.
Methodology
The Q Report work life and salary and compensation study of end-client/corporate researchers is based on data gathered from an invite-only online survey sent to pre-qualified marketing research subscribers of Quirk’s. The survey was fielded from May 24 to July 10, 2023. In total we received 1,969 usable qualified responses of which 707 were from end-client researchers and used for this end-client report. An interval (margin of error) of 2.17 at the 95% confidence level was achieved for the entire study. (Not all respondents answered all questions.)