We sat down with Mark Petersen, the Digital Product Manager for Woods Office, part of the Nordic real estate leader Nrep. Nrep is an investor, innovator and all around untraditional company in many ways. Their mission is to drive real change in real estate, for the people and the planet. So Nrep is not only a real estate company, it’s a company which strives to always add value to the communities in which it operates.
Nrep partnered with Magic Feedback for their Woods Office business. Woods’ challenge laid in understanding the needs and requirements of potential customers. Utilising Magic Feedback’s native forms helped them receive direct, high quality feedback from potential customers and automated all their analysis, saving the workload of one student worker. This enabled them to move faster than ever before in launching product improvements, it allowed them to understand their customers better and contributed to a 200% increase in website conversion rates.
Mark, you mention Nrep’s focus and commitment to adding value. How does Woods approach that in the communities in which it operates?
We do have a firm belief that we should add value when we do the investments. And in order to do that we should solve problems, for the customers, for the users, for the micro communities and in order to do that a broad set of competences is needed. From digital to construction and sustainability.
What specific problem brought you to Magic Feedback?
In Woods we invest, build and operate office spaces, with the intention of creating offices, where people want to go to work Monday morning.
In order to do that, of course, we want to be close to the customer, we want to understand the customer and be very customer centric. But at the same time, we also really want to move fast and we want to be light and we want to make quick decisions, which is a little bit contradictory in some ways.
From my previous experience, I knew the value of understanding the “why” and the need to customize the experience to deliver value. But I also knew that this can be and often is pretty time-consuming to really dive into. It often takes a lot of data and a lot of different data points in order to really form strong convictions on behalf of the customers. And we have this ambition to do that, but we also try a little bit to put it on steroids, so to say, because we want to do it very fast.
Our challenge was that – we have close ties and we really understand our existing customers pretty well. We interact quite a lot with them and have a good understanding of them, but what we didn’t have before Magic Feedback was an automated way to get a more real time, qualitative input from potential customers. And we really needed that, to be able to really get to understand the customer better.
What was your approach to solving that challenge, to understanding your potential customer better?
Before we didn’t really get much more than the basic information on the lead form, for potential customers converting on the website. And what Magic Feedback has done is to extend the contact form on the website with a couple of optional questions. Where we actually get to understand potential customers and what is important to them.
One of the reasons why we actually decided to go with Magic Feedback is their expertise on how to actually approach the customer centricity and research. There is a quote from David Ogilvy, “the trouble with market research is that they don’t think what they feel, and then they don’t say what they think and then they don’t do what they say”. And I think that is quite true. So when you ask them what they want, they’ll tell you a faster horse. so that oftentimes the problem with market research, in my opinion, is that you cannot ask directly. You cannot ask the specific question for the answer you want to get because simply the customer doesn’t really know.
And very importantly, for me, this is very much the same mindset that Magic Feedback has. You cannot just ask for a functionality and then if the customer tells you: “Yes, I would like that”. Then you can take that for granted and know that you will succeed. If you implement that you have to apply interpretation and you have to apply your own kind of empathy and mindset on to what the customer is actually saying. And then come up with what you believe is then a really good solution.
Magic Feedback has also been really good here and actually helped us to not ask too many questions. They also helped us to formulate the questions. So we ended up actually asking the customers something that is natural for them to answer, something that they actually know, which can build a strong foundation for further analysis.
As you said earlier for your potential customers, it’s both important to understand their needs as best as you can, but also move as fast as you can. Did Magic Feedback also help with that?
With the risk of sounding a little bit arrogant, I think we do have a pretty good understanding of our customers. We stay in touch with them and talk a lot with them, on top of that the customer facing team is really experienced. So often we have these ideas where we have a pretty strong conviction, that it will provide value. On these we always execute really fast, and most of the time we are right.
But then there are also areas where we are in doubt. And I think for those cases in order to come up with good ideas we really want to understand the customers’ problems and their needs and what is important to them. In essence to help them and provide value, and to do it fast. That is what Magic Feedback is very much enabling. Because we get these inputs on a daily basis, directly from potential customers and we have Magic Feedback helping us analyze the data. Enabling us to much faster come up with ideas on how to improve the website or our marketing efforts or actually also the actual built products.
Whas has been the most impactful insight from this analysis?
I’m a pretty big fan of the Kano model, where you have a kind of a matrix and then basically you’re trying to plot some different customer needs. In that model, on one axis, you have customer satisfaction and on the other you have your feature functionality.
Out of this what you end up having is a classification of factors. For example, you have hygiene factors which have diminishing value if they are not implemented well, but they will never really end up adding value. And then you have all of the spoken needs. So essentially that’s everything that the customer can actually tell you about and the more you give them of that the better and the more value they will get. This is also correlated with the price of implementation.
Cost is a good example, the more the customer values the most central location, the more they are willing to pay for it. And then I think the tricky part is often the unspoken needs. All of these features and functionalities, where you can actually not really go out and ask the customers about it. I think that one thing that Magic Feedback has really done is it has really concluded, on our belief, on the spoken needs. So it’s actually really, really good to really provide those inputs on that. But what is also quite interesting is that we see some of those unspoken needs in the analysis as well. We also see them come to life and it’s because it is a little bit more like analysis and also understanding. I believe it is actually helping us in plotting these different needs in the Kano model and it does that really well.
Talking about the unspoken needs, in one piece of feedback that came in today the person stated ‘vibes’ as decision criteria, and then he went on explaining what specific vibe he is after.
Yeah, I think that vibe, atmosphere is a good example of one of the more unspoken delights, actually. It’s one of those needs where it is also quite hard to show on a website. And it’s quite hard to explain, and It’s much easier to feel when you get there. Yeah. So I think that one is actually a really good example.
The data inside Magic Feedback has been accumulating over the last several months, so now when we look at it, I think also not surprisingly, we see that the most important factor is simply price. And then of course, the follow-up question is, what does price actually mean? Because it can mean different things right, there is square meter, price per seat, price per office, total cost, etc. But I think also, when you look at accumulated, we also get a pretty good overall understanding of what is really important for the customers.
What are the next steps from these accumulated insights?
What we’re trying to do is think about the customer journey, So what is important in the various phases of the customer journey. And we see that also here we can use Magic Feedback. Because we now have some pretty strong hypotheses, about when you’re going out looking for an office. How the journey actually starts for most people.
And I think you’re back to price, it is one of the very important aspects to begin with and location is also super important. Just if you’re looking for a home for yourself, And then you have a budget from the bank and you cannot really exceed that because there simply isn’t any more money. And also maybe you have some location where you want to live if you’re young, then there is some criteria that is determining that and if you’re older and have kids, then often there is a school or friends or whatever you want to stay close to. And then there is also the size thing, where you have to be able to fit and also probably you don’t need too much space. In the same way we really try to double down and understand that, build the same picture but for the search for a new office.
And with that understanding improve both our findability and discoverability when it comes to offices. And how can we make that journey easier? And then when you get a nudge and move further down, and you have checked all these ‘fundamental boxes’. What then? What becomes important for you then? I think then we start to see that things like vibe and atmosphere get really important, right? These are the insights we are unlocking with Magic Feedback, and once we understand these things we can then see how we can enable both on the website itself but also when we do a showing, focus on exactly the right things in the right moment, when the customer cares about. So in other words, we are constantly evaluating and optimizing that journey, that hypothesis with the use of Magic Feedback. Trying to challenge what we do today and see if we can do better.
Aligning with your motto: “to make people want to go to work Mondays”, is it even more vital to understand what qualities they value in an office so they choose the right one, so making sure all these qualities are shown and highlighted from the first encounter.
Yeah, yeah, completely. But also, here it’s a little bit tricky because there is a divide between the decision maker and then all of the employees that are often not really part of the decision of the office selection. So the real question is – are both employees and employers happy. And of course, we have to cater for both, right. Because if only the employees are happy and they are loving going to work Monday morning, but the decision makers are not then they churn. And if the opposite is true then probably also, they might churn, right? So there is also this balancing act of understanding both segments.
And where would you say the biggest impact has been on understanding the customer after you implemented Magic Feedback?
In regards to impact and results, what I’m really happy about is that with Magic Feedback, we are getting around 15-20% of all of our leads providing additional, valuable information, which is super useful.
I can say we went from 0 to 1, in terms of the qualitative feedback we get. We are now constantly getting feedback about the potential customers and their needs. Before the only feedback we were getting was from our sales team, who are in dialogue with our customers every day. So we were getting information, but it was second hand, I had to go and talk to them to find out what the customers said. Now I am getting that information directly from the customers or potential customers.
So that is really good and I think the biggest impact it has had so far is the help it has on our roadmap, mainly on the website. Magic Feedback automatically highlights problems out of the feedback coming in. There we have been able to see that some of these problems have been related to the early stages of the decision making process. That we have had some issues with helping the customers really find or evaluate whether what we have on the website is matching their needs. So we decided to really double down on that, to double down on findability. As a result we have seen a huge spike in a lot of our engagement metrics on the website, but also on our conversion rate.
So in the last three to four months, when we have really doubled down on this and our conversion rate has increased by 200%. This together with the spike in our engagement metrics indicates to me a lot, because a lot more time is being spent on the website, because we also see that the amount of events and clicks is increasing together with the conversion rate. So to me it indicated that also on the quantitative side, that customers are actually now finding what they are looking for, and they are finding it relevant to take the time to actually find what they are looking for. At the same time we see that these problems, and the tendency of them appearing in Magic Feedback is also decreasing with the same speed as the metrics going up.
So it is really helping us in prioritizing as well as triangulating the data points and getting a stronger conviction of what we are doing. And if it is actually providing value to the customers or not.
You have been on the journey with Magic Feedback for a few months, do you have any plans for the future to expand out to more parts of the customer journey?
Yes, we have lots of plans and ideas and also frequent talks with you guys. Right now, very specifically, we are working on using the new campaign system. We are looking very much forward to trying that out. We want to understand better what is important to our customers in the office context, and how our website is supporting that and where specifically, we should improve even more. And I am really looking forward to that. And also the feedback will go directly into the Magic Feedback Dashboard, because I think there is an advantage in using this tool which both gathers the data but also gives us the problems with AI automatically. This is really taking off some workload from my shoulders, which is very nice.
Then also very specifically, at the moment we have used the data a lot for improving the website. But we are also starting to use it in our marketing efforts, to also translate with that, and both come up with ideas on the experiments to run in marketing but also to have feedback going both ways.
And then also as I said before, we are looking into how we can use the data and the insights on our office buildings, to come up with potential ideas on how to improve the services that we offer.
You are saying that this is taking some workloads from your shoulders. Were you the one that was doing the analysis until you automated it?
Yes, and no. So we have some really bright student assistants that have a lot of stuff on their plate as well. But I’ve used them quite a lot to do some of these analyses. And I’ve also done it myself, a little bit. Back to the problem here of speed, right? We have a lot of stuff we want to do and we want to move really fast. So I think you can say the problem has been that we have not been able to do as much analysis as we probably would have wanted to. So what Magic Feedback does is it kind of skips some of those levels and some of those workloads by analyzing the data, right? And presenting to us the more likely conclusion from the analysis and the synthesis, that is super helpful.
How has speeding up the analysis enabled you to do even more than before?
I think it’s enabled me to make some decisions that I wouldn’t have been able to make before, because I simply didn’t really have the time or didn’t prioritize doing the analysis. Now it’s served on a silver platter more or less. We have this deliberate focus on speed. We rather want speed over really, delving into the details, to be exactly, right. I think in that case Magic Feedback is really helping.
