As fashion becomes more and more data-driven, companies are focusing on acquiring information on their customers. The idea is that the more we know about our users, the more likely we are able to hone and fine-tune our messages and products to their liking.
As fashion is always evolving, whenever a brand needs to invest in developing a new collection or new accessories, looking at what is currently meeting customers’ demands and expectations or looking back to what has been successful in the past is a very effective way to navigate future seasons.
This is why data has become so relevant in decision-making, and why it has become so valuable, it is basically a currency.
For as much as brands need to collect and – in some cases – invest in acquiring customer data and market information, there are often some unexpected challenges that can make data interpretation very difficult.
In this post, we’ll be looking at some of the most common hurdles that need to be overcome whenever marketers and analysts are looking into customer research to find insight into their future brand strategies.
Data is expensive.
First and foremost, it’s important to take into account how expensive can data be. If we look at the three categories of information that can be collected from the market we are quick to realize that the more we want to know, the more it will cost us.
- Descriptive Data. This data typology is the most accessible and cheap. Descriptive data allows profiling our customers in terms of where they live, what language they speak, how old they are, what job they have, what’s their family status etc. This data is also referred to as demographic, as it lists a series of facts about the lives of our customers. For as much as this information is easy to acquire (often through online questionnaires or surveys), it is also the least useful, as knowing the demographic features of our customer population is not going to help us identifying clear behavioral patterns and habits connected to our target audience.
- Psychographic Data. Psychographic data is a typology of data that relates to our customers’ beliefs and values. Psychographic data is connected to what our audience likes and dislikes, what goals are they setting for themselves and for their family, what do they believe in. Collecting this information is more challenging, because, even if it can be done remotely through online surveys, questions tapping into psychographic behavior are both hard to write and difficult to answer. In some cases what can help is running in-person interviews, but this requires a big investment of time and money. On the other hand, understanding the value system, your customers believe in can be very useful, and allow the brand to develop cause-marketing campaigns which allow brands to understand what social issues are concerning and in need of attention from the fashion industry players.
- Behavioural Data. This data set is the most useful of all, as it is able to describe the customer’s purchase journey by focusing on the problem that the customer is trying to solve. If you focus on the problem, it can be much easier to make the product or service you are offering enticing and valuable for your audience, as your value communication will be perfectly aligned with your audience’s needs. At the same time collecting this data can be very expensive because, in order to identify patterns of behavior and habits, a variety of research approaches are necessary: from observations to interviews, from case studies to action research. At the same time identifying the problem with a clear focus on what can be done to solve it, is the ultimate weapon to communicate effectively with your customers.
So how much data is enough? Do we always need to find more information, or can we set a line? This is what we’re going to discuss in the next section of our post.
There could always be more data available.
How much is enough? This is a philosophical issue more than anything else. In decision-making, there can be a variety of circumstances that could easily lead us to put off a decision with the intent of collecting more information.
Ideally, the more information we have, the less or a margin of error we can expect to have in our forecasts and decisions.
This however is not a productive or realistic approach in the context of decision making.
On the contrary, in business, it is very valuable to think about decision-making as an imperfect process.
In many cases, despite our efforts to collect data and further evidence, when it comes to social sciences (like the ones connected to running a business) there will never be a point of certainty. Decisions need to be made within the constraints and limits of the information we have. The perfect answer, a minute too late, is not actionable.
This is a common bias, leading business managers to focus on data collection rather than on analyzing data and squeezing every single insight from it.
Decisions are time-sensitive and the more time and money you spend collecting data the less time you’re spending in implementing a strategy.
But what happens when instead of collecting the necessary information, we are too fast at drawing conclusions? That’s something that can easily happen because of our bias. We’ll address this issue in the next section of our post.
We Fill the Gaps with a Biassed Interpretation.
For as much as we strive to be comprehensive in our data collection, we will always deal with incomplete data. This is ok, the world is complex and not everything fits into a spreadsheet.
At the same time, whenever we’re dealing with incomplete datasets, we tend to fill the missing data with our own projections and assumptions.
This is a big mistake because we end up influencing the interpretation of the information according to our experience of the world, which is the exact opposite of objective analysis.
Interpretation will always be subjective to some extent, but we need to try and make sure that the way we read and interpret information is not too reflective of our pre-established vision, or in other terms what is usually called ‘wishful thinking.
A way to help us move away from subjective interpretation is by adopting the “Jobs to Be Done” theory. This theory allows us to move away from the customer and focus on the underlying problem, that led the customer to seek a solution in the market.
If you’d like to read more about this theory, here’s an article where we discuss it in more depth: Why Jobs to Be Done Matters for Your Business.
In the next section of the post, we’re going to take a look at why focusing on Jobs to Be Done theory and behavioral analysis can be a way to steer clear of too much wishful thinking.
We focus too much on the customer and too little on the Job.
As introduced in the previous paragraph, because of our natural biases it can be really difficult to use market data to profile and segment our customers correctly. Information can be interpreted in hundreds of different ways, and any reading of data is as good as any other.
This is why market research when is too concerned with understanding the customer may end up being completely fruitless.
On the other hand, focusing on understanding the problem or the situation that the customer is looking to solve by purchasing our products can lead to better insights.
According to Jobs to Be Done there are simply three types of jobs that customers need to get done:
- Functional Jobs. These are the types of jobs that are usually connected with solving a practical problem. In the case of fashion, these functional jobs are covered by mass-market products or commodities which have the purpose of being cheap and practical.
- Social Jobs. These are the types of jobs that are usually connected to social status and using fashion as a social currency. The social function of fashion products is usually connected to the idea of either standing out of the ordinary and making oneself noticed, or alternatively showing belonging to a group or fitting in with a clique.
- Emotional Jobs. These jobs are connected with emotional rewards, connected with the ownership of products that allow customers to fully dive into the identity, legacy, and legend of a brand or product. These jobs are usually the ones connected with luxury and exclusivity.
What is the job that our customer is looking to get done? What kind of product would he hire to do it?
By focusing on the problem, the solution can be much clearer, if we focus on the customer, it will be difficult to come up with any evidence that shows a connection between descriptive data and market behavior.
This is because when reading data, it’s important to understand where data can provide clarity, and where data can instead lead to confusion and frustration.
There you have it. Now that we’ve covered the subject in sufficient depth it’s time to draw a few conclusive remarks.
As we’ve seen from our post, decision-making is challenging even when it’s data-driven. The four types of mistakes we described in this article are some of the most common. It’s very important to find a balance between conducting research to understand a problem, but then spending the right amount of time in developing a solution.
The mindset that is pursued in data collection is what separates academic scholars from business practitioners. The former tend to focus on understanding the problem in-depth, the latter on solving it for their business.
In this sense, it’s important to find the right tradeoff when investing time and money to address our business issues, so that the search for a solution does not become a bigger problem in itself.
It is undeniable that many fashion companies are now looking at data as the future of the fashion business, as we discuss in this post: Why Data Science Has Become the Hottest Trend in the Fashion Industry.
If you’d like to learn more about data-driven decision-making in fashion, don’t hesitate to look into our blog, where you can find a broad selection of free sources connected to the business of fashion and the arts. Enjoy!