Understanding, Influencing & Predicting Consumer Behaviour

Exploring Digital Psychology & Psychographics

Photo by Andrea Piacquadio from Pexels

Consumer behaviour, as the name implies, is simply the way consumers like yourself, and myself, behave. Over time, many of the best companies have come to understand the importance of understanding, influencing (in an ethical way), and predicting consumer behaviour when building products or services. This can also be summed up in ‘digital psychology’ and ‘psychographics’.

TL;DR

1. It takes time to understand consumer behaviour.
2. It takes time, risk, and serious innovation, to influence or change consumer behaviour.
3. It takes time, confidence, and knowledge, to predict consumer behaviour, and most importantly, act on it.

Understanding Consumer Behavior

It makes sense to understand how consumers behave. It’s important when building products and services. This is the reason why data, as a commodity, is said to now be more valuable than many natural resources. Understanding consumer behaviour is simply understanding what people want; why they want it, when they want it, and how they want it.

This applies across the board, not just in the tech industry. As a restaurateur (a person who owns/manages a restaurant), for example, understanding consumer behaviour will help you identify how to optimize your restaurant to become more successful. You’ll know what to sell, what not to sell, when to sell it, what time to open, what time to close, and so much more. This is besides the fact that you’ll see a significant increase in your revenue and a decrease in operating costs.

To understand consumer behaviour, you simply watch what people do. In the aforementioned example, the restaurateur simply studies what items on the menu are being ordered and when they’re being ordered. They can also study foot-traffic (# of customers), analyzing what time and day people visit the restaurant the most. They could even analyse where people come from, the % of people that return frequently, and whether people come in groups or not. With these data points, they can make decisions that are conducive to the success of the restaurant. There’s actually a role dedicated to this type of work, called Data Scientist. Data Scientists study…well, data. You might not find Data Scientists in restaurants, but it’s been a growing field across multiple industries for the past several years, most especially in technology, but also in Energy, Hospitality, Retail, and much more.

As I mentioned, understanding consumer behaviour is simply watching what people do. For digital products and services like a mobile/web application or website, there are many tools out there that help watch what consumers do on your product; Hotjar, Google Analytics, and FullStory, to name a very few. There are more advanced tools that help understand consumer behaviour for a variety of purposes. And of course, large companies build their own Artificial Intelligence models that help with understanding the behaviour of their customers.

Another way to understand consumer behaviour is to look at trends. For example, reports show that in recent times, more people are using TikTok above Google Search when searching for things. What can you get from this trend? Well, a few things:

1. More people are leaning towards video content than traditional search.
2. People find bite-sized content more digestible.
3. People believe it’s easier to watch a video than read an article.

This is the reason YouTube (Shorts), Facebook (Reels), Instagram (Reels) and apparently, even Twitter, have begun integrating short-form video content on their platforms. They notice a trend in consumer behaviour and then adapt. The same thing happened with the now desolate Clubhouse and audio-first communities.

Identifying trends and analysing them helps you make decisions that are conducive to your product/service/business.

We currently shipped TopSpots, a search engine for top spots to eat and drink in your city. When building this, we built it according to consumer behaviour of searching. You’ll notice the biggest difference between this search engine and others is the results page. We only show the top 7 spots, rather than thousands of available spots. Why? Well, when you search for something online, you’re not looking for every single option, but rather, the best, hence the reason you don’t get to the 2nd page of google search results. There’s even a feature on our roadmap that was entirely ideated around the video-content trend mentioned above. These are trends in consumer behaviour that we harness. So can you, in whatever you’re doing. As I said, this applies across the board, not only in the technology industry.

It’s easier to build a successful business, product or service when you understand consumer behavior.

Influencing Consumer Behaviour

To influence consumer behaviour, you must first understand it, thoroughly. This is basically influencing how people think & behave. It’s ridiculously difficult to do. Many have tried and failed. However, when successful, the success is monumental.

For example, Jesus (Yes, that same Jesus) was crucified, *partly* because of His influence on the people at the time. He brought a new way of living and thinking, but it seemed to be too much for the religious leaders who had grown accustomed to their archaic culture; they decided, “we don’t like this guy and His influence, so let’s kill Him”. The life Jesus brought to us is a life of freedom, joy, and peace which I personally experience daily, but still, the ingrained behavioural patterns of most people act as a deterrent to accepting this life. Anyway, I’m not here to preach to you (or am I? 😉). The point is, it’s difficult to influence (or change) behaviour.

Apple is another example. When Apple introduced the first iPhone back in 2007, many articles were released, even by well-respected people, about how the iPhone was going to fail. People made this assumption because they believed consumers were already used to physical keyboards (which they were), and having a full touchscreen was insane. Despite this, Apple doubled down on their plans with the iPhone. We know how that ended. Apple was able to influence the behaviour of millions of people to adopt this new technology, from what the consumers were used to (physical keyboards). The reason this was successful was because of the sheer innovation behind the plan. I remember watching the Ad back in High School. Seeing Maps, Music and more. This was 10x better than the status quo (same as the life Jesus brought), so it was a no-brainer for many consumers.

Now, that’s physical products. What about software? Influence is even more prevalent in the software world. The reason digital Ads exist, in the way they exist, is because companies like Meta (or Facebook) and Google, understand consumer behaviour, thoroughly; and because they do, businesses are willing to pay them to help them place ads in the right places. As a matter of fact, if you’re in the US, Facebook can tell if you’re a Democrat or Republican based on your activities. For example, a report indicated that there’s an 81% chance you’re a Democrat if:

• You ‘liked’ a Hello Kitty picture on Facebook AND…
• Live in the East Coast of the US (New York, Boston, or Virginia)
• You’re female
• You’re between 30–49

This is called ‘Supervised Learning’, which means, the use of numeric data to predict and understand data points. For example, a surge in Uber rates is partly determined by the number of cars available at the time, in your area. It’s basically math.

If our restaurateur above wanted to influence consumer behaviour towards a new product — say, lobster — she could place that product (lobster) in a particular part of the menu. She could also have her waiters mention the lobster option while taking orders, or analyse the sitting arrangement in the restaurant and strategically place a picture or symbol of the lobster item in a place where people who visit the restaurant see it, desire it and then order it. This is one way she would’ve influenced consumer behaviour. Mcdonald's uses a similar strategy to sell fries and other items with their burgers, by simply asking “would you like some fries with that?”. Their strategy is called cross-selling, or upselling.

In one of my previous posts, I spoke about how I was able to build a personality profile on someone I don’t follow on Twitter. I found out their birthday, religion, friends, the preferred mode of transport, and the city (+ area) they live in. This was done only after a few minutes of scanning her profile. The more time I spend on that profile, the more I’ll understand her behaviour, and the better I understand her behaviour, the better I can influence it. (Btw, these are not my intentions. Lol).

Some companies, however, are able to do this at scale. The more cat videos you watch on YouTube, the more cat videos will be suggested to you; And when you go on Google Search or Instagram, the more Ads for cat food/toys/shampoo you’ll see. The algorithm has learned your behaviour and is now trying to influence it to make purchase decisions based on the knowledge it has garnered from your activity. It’s pretty straightforward.

To influence consumer behaviour, you must first understand it, thoroughly. For consumers, your behaviour cannot be influenced if your behaviour cannot be understood; however, life can be inconvenient if your behaviour isn’t understood.

Predicting Consumer Behaviour

Just like influencing consumer behaviour, predicting behaviour also requires in-depth knowledge and understanding of the consumer. Predicting consumer behaviour is being able to determine what consumers will do in the near or far future. When Amazon launched amazon.com, it predicted that consumers — with the rise of the internet — will do more of their shopping online. Amazon started selling books online, but quickly ventured into CDs and now, significantly more items. When you study their acquisitions, you’ll notice Amazon is venturing beyond eCommerce; they’re looking to get into the daily lives of their customers to better understand & predict consumer behaviour. In August, I wrote about how consumers inevitably work with companies to provide data points that help with this prediction.

Let’s look at a bit of the technical structure behind this.

As I mentioned above, companies are using tools to better understand, influence and predict consumer behaviour, simply because there are just way too many data points to analyse manually. This is where Machine Learning comes in. There are 2 ways a machine can learn;

1. Structured (or Supervised) Learning
2. Unstructured (or Unsupervised) Learning

Structured Learning is basically working with numerical data. So, for example, a Machine Learning model takes numeric data from a Google Sheet and analyses it.

Unstructured Learning is the analysis of non-numerical data. In this case, the machine recognizes patterns, reviews, and decisions, and then works with those patterns to understand and predict behaviour.

Both structured and unstructured learning are replicas of what humans do, hence the reason it’s humans teaching the machines. I wrote a brief thread about this when I saw it at the Post Office.

As I mentioned, there are tools that can be used to determine and predict mental health, income level, and more. To predict consumer behaviour, machines aren’t only watching the things you consciously do, but also, the things you subconsciously do. Psychologists claim that 80% of the decisions we make are made subconsciously. I don’t know if I believe that, but I do know that to a certain degree, we’re not making 100% of our decisions with 100% of our consciousness.

That’s the reason you keep checking your fridge every 10 minutes, looking at the same items, hoping to see something new. This is the same reason why you can close a social media app, like Twitter, go to another app (or even put the phone down), and then open the social media app immediately after.

But you know what? The rate you open that app can decrease significantly if you simply put it in another folder/location. Why? Because most of the time you open it, you do it subconsciously. You know exactly where to tap on the screen; you do it so fast that your mind doesn’t even keep up with the speed of your fingers.

The example I shared above, regarding the post office, is a great example of making predictions based on behavioural patterns. The attendant noticed a pattern and made decisions based on that pattern.

One downside of predicting consumer behaviour, as an individual, is that you may seem like a mad man/woman at the time of your prediction, depending on how far into the future, your prediction is.

The key to benefiting from consumer prediction is acting on that prediction. It’s one thing to predict consumer behaviour, but it’s a different thing to act on it. It’s risky and may cost you money and time, initially, but it’ll eventually pay off.

To predict how consumers will behave in the future, you have to look at how consumers behaved in the past, and are behaving in the present. For example, it’s highly unlikely that a platform like Facebook will ever be built again. Why? Because presently, consumers are veering towards a new mode of content and socialising. As a matter of fact, even a platform like Twitter may never be built again. This prediction is based on how people behave now and have been behaving over the past couple of years.

Another way to predict consumer behaviour is to analyse things that you’re confident will not change. At Venlab, for example, we’re building business infrastructure for entrepreneurs based on the confident assumption that:

1. People will always need a legal entity to build large businesses
2. The government will always require businesses to file their taxes
3. People will always need a financial structure to run a successful business

Because we know these things won’t change, we’re able to predict that consumers will need a simplified way of accomplishing these tasks in the future. Especially the younger generation.

As I’ve mentioned, predicting consumer behaviour only comes after understanding consumer behaviour. The more I know about what you do on a daily basis, the more I know what you’ll do on a daily basis. (Read that again).

This is why research is important in many areas when trying to sell/build something new. For example, Product Managers, when doing research, don’t ask people “hey, would you use this product if we built it?”. That’s a terrible question. Instead, they ask questions to determine the current lifestyle of the person they’re interviewing, then they find out whether the product will fit into that lifestyle or not.

I could ask you “Would you pay $30/month for a personal assistant that helps you with your daily tasks and errands?” You might reply with an exuberant ‘yes’ because the idea of an assistant sounds good, but then when I build the app, you don’t use it, because it doesn’t fit in with your current behaviour. However, if I asked you “What do you do on a daily basis?” I’ll be able to identify the problems in your life and then predict whether you’ll use this app I’m building.

Understanding consumer behaviour is a prerequisite of predicting consumer behaviour.

Conclusion

The ability to understand, influence and predict consumer behaviour is important for any business, regardless of industry. When you understand consumer behaviour, you’ll be able to make better decisions, accurate projections, and avoid wasting resources.

Selah.