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Why “Walk Forward Analysis” is still unreliable and useless!
ATTENTION: THIS THREAD IS OUTDATED
Please go to http://datfra-expert-advisor.com/ in order to find the final result of all the research!
Hello, I am Darwin and today I want to talk about the limitations that walk forward analysis suffers from. This is my 3rd article, so if you do not know how WFA works, please read the other 2 (you can find them here on the forum).
The target audience of this article is everybody that deals with ExpertAdvisors and Backtesting / Walk Forward Analysis
Some of you might already have seen a few posts of me where I talk about some research I do in the fields of Trading System Analysis (in the course of writing a meta-algorithm that can build, analyse and trade strategies on its own). The goal is to write an algortithm that is so powerful that it can take every EA and, due to in-depth analysis, tell you how and when to trade it in order to make profit, no matter how good or bad the underlying EA is
So here is a new article in which I would like to lay down some insights that I could get in the process of writing this algorithm (DATFRA – Darwins Algorithmic Trading Framework)
Well, lets begin. My first concern is that the design of Walk Forward Analysis is, in its nature, unrewarding and not the kind of analysis a trader wants.
Also, I claim that the results of a WFA are more or less random, and if a system works well after a successful WFA, then not because the test was successful, but because the trader designing the system did a good job.
In this article I do not yet want to show how this problems can be solved, I just want to demonstrate that they exist. In my next article I will explain how I think this all can be solved in an elegant way.
The fundamental design problem
Walk Forward Analysis is designed to evaluate a trading construct you give to it.
This construct consists of:
* Trading System (eg an Expert Advisor)
* Market/Timeframe (eg “EURUSD / H4”)
* System’s Parameter ranges (eg “Moving Average Period from 50-150”)
* Optimisation (In Sample) Timespan (eg “Optimise on 2 years of data”)
* Forward Trading (Out of Sample) Timespan (eg “Forward Trade for one month”)
* Preferred characteristic (eg “forward trade the candidate with highest profit”).
So all this has to be pre-determined by the trader, out of intuition, and not based on true facts and data. But god, these are the most important decisions, how should one “guess” them?!
And then, WFA will only be able to tell you if this construct would have worked in the past or not, thats it.
So in order to find the best trading construct, you have to use trial&error and repeat WFA step multiple times. This would then, step by step, even lead to the worst case, your “unseen” out-of-sample tests would slowly become “known” in-sample data and the whole advantage of WFA over backtesting would fade away completely.
This design related problems are already showing that WFA can not be the end of the road in terms of system analysis.
In a perfect world, you should give the analysis algorithms only the trading system and the market/timeframe, no other parameters. And then, the algorithm should tell you the best choices for all the other parts of the trading construct, based on data and facts, not the other way round.
Side Note: it should NOT just tell you how to trade your systems, it should give you the possibilities to look into the system’s characteristics on your own. You should never be forced to trust any algorithms without the possibilities to check it’s findings!
This is very, very important. It is not very much of value to evaluate a single trading construct, but it is a gamechanger if you can look into your strategies in a way that would allow you to just “see” how they work and what trading construct will work best (More on this in my next article)
Even worse: Unreliable results because of lacking data
Ok, so even if a trader could come up with a good trading construct out of intuition/knowledge, WFA would still be a more or less random thing. But first, let’s make a rough calculation:
An example trading system and a small estimation of its parameterspace-size
So, a system that enters trades based on a Moving Average Crossover and RSI Indicator, and exits them using a different Moving Average Crossover has at least 5 Parameters (2×2 for MA-Periods + RSI Threshold). It’s 6 if you take into account the StopLoss.
Let’s say the “fast” Moving Average Periods can be 10-50 and the “slow” ones 50-250, the RSI threshold can be 1-100 and the StopLoss 50-150 pips (this is no real system, just an example!)
So this system can already be traded in 40*200*40*200*100*100 different ways. That is 640 billion (640.000.000.000), which is quite a huge number.
One might question my exact example strategy, but can not question the millions or billions of possible parameter combinations, even for small systems.
But thankfully, if we take into account that a lot of these parameter-combinations would behave very similar, we do not need to evaluate them all, but we need at least a meaningfull sample of it, like a few hundred thousand or a few million.
So, keep this huge amount in mind, even for small systems, because with every new dimension for our optimisation problem’s solution space (every new parameter) the amount of possible parameter-combinations grows exponentially.
Walk Forward Analysis – missed data during optimisation
Ok, now lets look at the first step of WFA, and the first problem: Missed data because of inefficient algorithm design and computing time concerns.
During optimisation step of WFA, the algorithm should, in a perfect world, evaluate all 640 billion combinations in order to determine which of them work best. Of course this is not possible, but a “meaningfull” sample (let’s say 500.000) would be feasible and _needed_ if we want to look at the “real” picture.
The problem is, due to limitations of WFA algorithms, optimisation has to be done in every single Walk Forward Window.
Let’s say we do a WFA on 10 years of data and our Forward Trading Timespan is 2 weeks: That makes 240 Walk Forward Windows. That means 500.000 tested parameter combinations per window would need 120.000.000 single simulations.
And then, remember that WFA relies on a trial&error principle, so you will most likely have to do this a few times.
You see? Evaluating the “real” picture would take very, very long, and therefore most WFA implementations are forced to only evaluate an very much cropped fraction of the actual parameterspace because it is not possible to evaluate the whole parameterspace (or a meaningfull sample of it) in a reasonably small timespan, because optimisation has to be done in every single WF-Window.
This means, WFA most likely does not evaluate 500.000 parameter combinations per window but only 10.000 or 50.000 or something like that. So eventually we already lose like 90% of all data in this step.
This is a problem that could be solved if the trader has lots of time for his/her analysis (which is not likely, especially based on the trial&error method), or with a more efficient design of these algorithms. Nevertheless, in praxis, this problem is ever-present.
For comparison: DATFRA, which is my private research project, only has to do one single simulation per parameter-combination, no matter how many WF-Windows it analyses. In the above example, that would already decrease the computing time by the factor 240.
Parenthesis: What kind of data do we look at when analysing trading systems, what is a “datapoint”
I will talk about “datapoints” and “data” quite frequently in this article and in my posts, so here is an explanation.
When analysing systems, it is always about a trinity of informations. Remember how WFA works:
So a datapoint, of which 1 is generated per Walk Forward window, consists of:
* The performance in the RED optimisation window
* The performance in the GREEN forward trading window
* The used parameter-combination for this specific test
So, in our example, a WFA would generate 240 of them, whereas 120million (500k * 240) would be possible for our example system. That should already give you headache.
Walk Forward Analysis – tons of missed data during forward trading
Ok, now lets look at the second step of WFA, and the second problem: Missed data because of _wrong_ algorithm design and computing time concerns.
Now remember, a meaningfull sample of our trading system’s parameterspace would be 500.000, and we have 240 WF-Windows. That would make a total of 120.000.000 optimisation-candidates. And out of this huge amount, a WFA algorithm takes the very best per window, 240 in this example.
That is 0,0002% of the total amount of all datapoints that we could use to describe/analyse this system and it’s ability to produce good forward trading results, based on good optimisation results.
And then WFA takes these few datapoints and claims it gives a somehow realistic view on a trading system’s performance / robustness.
Thats nonsense! You also would not judge a picture’s colour by looking at 1 pixel, would you?
A word about fluctuations and why the “very best” parameter combination is not meaningfull
You could argue that it is not important if we forward trade all 500.000 candidates per window, because we are only interested in the top performers, as they are the ones we trade in realtiy.
Well this argument would _only_ works if:
* We would ignore the
90% of data lost in the optimisation step
* The very best candidates would be meaningfull, which means that all candidates that are following (like the next 10 or 20 or 50, which is not much compared to 500.000) would behave in quite the same way.
But reality is different, the performance of the top candidates per window fluctuates quite much and taking the “very best” therefore leads to more or less random outcomes.
Here are some examples, I plotted the forward trading performance of the best (left) and the next 4 candidates of some random strategies I created and evaluated with DATFRA. Most of the analysed WF Windows looked like these:
These were just a few examples to illustrate my point of view, I could show hundreds or thousands of them.
So, for the real picture, you would AT LEAST need to evaluate a few hundred of the top candidates, not just one, as it does not show the “real” picture. It’s performance is more or less random!
A perfect analysis algorithm would evaluate every single candidate that made at least 1$ profit during optimisation. That would give the real picture and most likely 1000 or 10.000 as many datapoints than what a WFA gives.
Here are some more examples, this time I plotted the overall WFE (red) and the WFE of single windows (green) of some random strategies I created and evaluated with DATFRA.
WFE (Walk Forward Efficiency) is a measurement that compares in-sample and out-of-sample performance and is used as THE statistic about system robustness in WFA (google for it if you want to know more about it)
This clearly shows the flucutating nature of the results a WFA generates, and that the end result is not really telling much about your expected live trading performance.
Btw: To keep the plot scale in limits I did map all points > 2.5 to 2.5 and all points A word about feasibility
Please do not think I only talk about grey theory here “as it is not possible to do this kind of simulations in a short enough amount of time anyway”.
If the algorithm is designed well, one would not need a single further simulation in order to determine forward trading profit and not a new optimisation procedure for each WF-window.
So for the used example, DATFRA can generate 34.000.000 “Optimisation=>Forward Trade datapoints” in
24 hours and on a mid-end PC (8GB Ram, quadcore 3GHZ).
Still not 120millions, sure, but compared to 240, I think its a very good result.
So it IS feasible to analyse a system with such a level of insight, even on today’s hardware.
To everyone claiming that backtesting strategies does not work: Well, in its current form it does not, but if you look at enough data, it does, and it can aid a trader in taking funded decisions.
To everyone using backtests/WFA: It does not work that way, you can never rely on your analysis results, and if your EAs/Trading Strategies make profit, then not because of the good tests, but because you did a very good job designing them!
In about 1-2 weeks I will post my next article, in which I will explain how an ultimative state-of-the-art system analysis algorithm works and what can be done with it. You will be stunned, promised!
“Are you just trying to sell stuff?”
People keep asking me this whenever I post stuff.
No, I post this because I want to discuss my concepts and thoughts with other advanced traders. The side benefit is the educational effect for everyone that is willing to learn more about algotrading.
And yes, I am developing an algorithm that is based on the concepts that I explain in my articles (especially the next one) and that is able to solve the issues discussed here. Well, basically I have already developed it, it’s in first alpha version at the moment and works great.
But I am developing this for my private usage, so No, I am not here trying to sell you stuff, as most of the people reading this will not have the chance to purcase it.
It will only be sold to a few people, just enough so that I can fund my own trading accounts (I am young and therefore need the money ).
Most likely I will limit the amount of copies sold or only sell to expert traders or only to companys or charge enough money so most ppl won’t want it or sell copies in silent auctions or . Well, I do not know yet how it will work out, I can just say that I will keep it private to a small circle of happy few, so do not read this article with the bias of “this guy just wants to sell me stuff”, thanks.
PS: As always, just add me on Skype if you want to discuss further and/or want to have more informations about DATFRA: Darwin-FX is my SkypeID.
Six of My Online Stores Failed. One Didn’t. Here’s What I Learned.
Five years ago when I first started selling online, all I wanted was a mentor to help me achieve success.
Unfortunately, with my measly $30k salary and the average mentors’ sky-high five-figure rates, I knew I couldn’t afford one.
So, I had to learn how to succeed the hard (and super slow ) way. I was left with no choice but to learn from experience.
This is basically what I’d tell my 25-year-old, bright-eyed self if I could go back in time.
Six of My Online Stores Failed. One Didn’t. Here’s What I Learned.
1. Ignore the Headlines, It’s Not Easy
Hey you, heard you just quit your job to start a business. That’s exciting. But maybe you shouldn’t have jumped ship from that 9 to 5 just yet. I know there’s a ton of passive income blogs out there showing people making six figures , but that stuff just doesn’t happen overnight. You’re about to build your first business. The odds of achieving success your first try are stacked against you.
That doesn’t mean you’ll never make your dreams happen. Because you’ve got heart and I know you won’t quit. But you haven’t built up any real experience yet. I think you might’ve underestimated how much work is going to have to go into this. A lot of people will tell you to hustle. But hustle just doesn’t cut it. You need to be obsessed with this business. Even more obsessed than you were with Ryan Gosling when The Notebook came out.
You’re going to be building a lot of businesses over the next few years. Some will fail, others will flourish. But entrepreneurship is a patience game. Don’t put all your expectations in your first store. And most importantly, don’t give up after that first one fails . The secret to success is to keep building upon your experience. And so that means, keep on building. You didn’t need to quit your job to build something. It’s a lot easier to start it by building on the side.
2. Don’t Pay for Something You Can Get for Free
Hey you, heard you just spent $2,500 on online courses, business books , and conferences. That’s a whole lot of dough to spend on education. Sure, you’ll learn some valuable stuff in everything you read and watch. But there are plenty of great online resources that are completely free that you can check out too.
Have you checked out Shopify Academy , which breaks down how to successfully build a dropshipping business for free? Or watched one of Shopify’s short but impactful webinars ?
There are also millions of great blogs out there. If you read enough of them, you’ll suss out the great content from the crud. Plus, YouTube can also be a great place to learn more about starting an online store. Free education isn’t bad education. It might be a bit more work to organize. But you’d save some money that could be reinvested into your business.
3. Don’t Create More Than One Store at a Time
Hey you, heard you launched four different stores this week. Does that mean your first store immediately took off? No? Then, why the heck are you launching another store?
You’re getting overly excited about the building process, but that’s not the part that makes money. I think you should consider zeroing in on one store.
I know, I know, you don’t know which store will succeed best. But by building several stores at once, I can already predict which one will fail. You’re not going to like the answer though, because it’s all of them.
If you want to win, you need to have focus. Building one store is actually a lot of work. It’s more than just adding products to your website, more than creating a Facebook ad , and more than occasionally posting on social media. You need to get obsessed with building your store’s blog to keep your acquisition costs low, post on social several times a day, and experiment with ads/emails/products. Those three things alone will eat up more than enough of your time. And they’re definitely not the only things you need to do.
4. Don’t Neglect the Most Important Thing
Hey you, you’ve been in marketing roles for a few years now, but I’ve noticed that you’ve been neglecting that very thing. Success in ecommerce basically comes down to choosing the right product and marketing it. You’re not going to make any money if you never get your product out to the right people.
Why are you suddenly so afraid of marketing your store? It’s okay if your first few ads failed. Mine did too. You just need more practice. How about you try putting some content on that blog of yours? Have you tried sending your first email out to your list? I see that it’s been growing steadily, kinda like that plant you finally remembered to water!
How about you reach out to some influencers or fan pages and pay for a shout-out. It’s at least worth a try. In the meantime, don’t forget to keep experimenting with ads. I know it can be a lot of work in the beginning. But practice makes perfect!
5. You’ve Got to Get Your Finances in Check
Hey you, heard you’re breaking even on all your shout-outs from that one influencer. So, why do you keep working with them?
Just because you’re getting sales doesn’t mean you’re on track to building a successful business. You need to consider profitability too. Take a look at all your costs and how much money you’re bringing in to make sure you’re actually making money. Because it sounds like you’ve got a major loss right now.
Are you charging enough for your products to account for your expenses? It might be time to try charging at a 3-5x multiple of your product cost.
Have you been trying lower your marketing costs? Because that could really help, too. Maybe it’s time to try affordable, long-term strategies like SEO instead of expensive, short-term wins like advertising and influencer marketing, especially if you’re selling products in a steady niche . Don’t spend money you don’t have. Once you’ve built up some cash flow, reinvest that money back into your business.
6. Choose a Niche With a High Search Volume
Hey you, I finally created my first successful store. Remember how that narrow niche you were in resulted in so few sales? I decided to do the opposite and found success. Woo-hoo!
The strategy was to choose a niche so big that it had trending products within it. That way, I could benefit from having a niche that would stand the test of time. However, since it has trending products, I can also benefit from the sudden rush in sales from selling something trendy.
When choosing a niche, choose something with high search volume. I chose yoga which had 1.83 million monthly searches. You don’t have to choose the same thing. All you need to do is find something that gets hundreds of thousands or millions of searches a month. Then, you need to experiment with different products to find out what your home run best-seller is. But even after your best-seller has slowed down in popularity, you’ll at least still have a niche that will continue to have other best-sellers. You just need to be willing to always be on the lookout for them.
7. Sell Impulse Buy Products You Can’t Find in Stores
Hey you, remember when fidget spinners were all the rage? Do you remember what killed those online sales? It was because you could buy them anywhere.
What’s the secret to selling a winning product? First, you need to make sure it’s an impulse buy product. Second, you need to make sure it’s not mainstream enough that someone could just buy it locally.
My best-selling product was a beach blanket, and boy did they sell like hot cakes. Thing is, no one was able to find them at a local store, so when they saw my ad they’d impulse buy from our website without doing any further research.
Sure, occasionally people would mention that they found it on Amazon cheaper. But that didn’t stop me from getting almost 11,000 orders (we were one away from hitting that number).
So what does an impulse buy product look like? Imagine you’re scrolling through your feed and suddenly you stop. What do you see? You see colors that pop on the screen, instantly catching your attention. The person in the photo reminds you of you. The product is different than anything you’ve seen. It’s something you’d never realized you’d ever want or need. But as soon as you see it, you know you need to have it.
8. You’re Not Going to Realize You’re on The Right Track Until You’re Deep Within It
Hey you, you know that bestseller on your store? I know you don’t see it yet, but that product is going to bring in hundreds of thousands of dollars in sales. I know the $2k in sales it’s made so far doesn’t seem like much right now. But you need to keep experimenting with your marketing. Keep testing different ad sets with Facebook ads. Try retargeting your blog traffic. Keep posting pictures on social media. Get some influencers to take photos you can use in exchange for a free product.
The truth is sometimes you can be on the right track and have no idea. You might be too early in the process. That $2k month you have will eventually turn into a $70k+ month. But the thing is you need to keep executing, marketing, and getting your products in front of customers to ever get to that level. A few months from now, you’ll start to see that you’re on the right track. But for now, all you need to do is focus on getting that product in front of even more people.
9. Start with One Channel and Expand
Hey you, it’s me again. Remember how you used to try a little bit of everything to see what would stick? Well, with this new store, I experimented with the opposite strategy and it’s been a game-changer.
When it comes to marketing, instead of trying to master every social platform and marketing tactic, focus on one to start. Let’s face it, you’re doing this alone and have enough on your plate. You’ll eventually expand into other channels, but focus on one for now.
If you want a higher engagement and click through rate, try Instagram. If you want to run ads on an impulse buy product, master Facebook. Marketing search based products, play the long game and optimize your website and write a TON of blog content for your niche.
Choosing a strategy can help you find success, but it’s going to take a few months for you to have an impact. So pick a channel, study your competitors’ strategies on that channel meticulously, post often, and get better over time. You’re going to suck in the beginning, but hey we all do.
10. Try Retargeting Blog Traffic
Hey you, remember the days when you’d constantly struggle to get your first sale? I learned this new trick, tried it out, and it worked!
Content marketing can be a powerhouse at driving traffic for a website. So start building out your blog with a long-term strategy in mind. Write content around certain keywords several times a week.
But if you’re looking for immediate returns, write an article featuring quotes from influencers in your niche. For example, if you’re selling yoga products, you’d write an article like “25 Inspiring Yoga Quotes from B.K.S. Iyengar.” Since Iyengar started a type of yoga but is no longer living, you can’t reach out to him. However, you can reach out to social media followers and those who mention Iyengar yoga in their posts. Before you reach out to them, set up a Facebook pixel on your website. Then, create a Facebook retargeting ad for $5. After that, you’ll want to reach out to groups that cater to that influencer. Since your article seems like a fun read, is relevant to the audience, and doesn’t mention your products in it, people are more likely to share it. And that retargeting ad will end up having a crazy high ROI for you because you’re retargeting content instead of a product. Plus, since you’re reaching out to niche influencers, you know that you’re driving the right type of traffic back to your website. This strategy will help you get your first few sales.
11. Optimize Your Product Page
Hey you, you know how you love designing stores? Well, I found a way you can do that but in a way that actually helps you get more sales.
You can have the best ad in the world, but if your product page sucks (considering that it’s also your landing page) you’ll be met with a $0.00 in your sales report. I’ve been testing different ways to optimize your product page. You know countdown timers? Well, they definitely work. I accidentally removed it from my store yesterday because I thought it looked unprofessional. It resulted in my lowest sales day this month! And immediately after I added it back, sales skyrocketed.
We also decided to add Spin A Sale as an exit intent and it’s helped skyrocket our email list. Sales also started rolling in from people using the discount codes. Doing these two things helped us us better optimize our store for conversions. And the sales have been growing astronomically.
12. Email Customers After The Sale
Hey you, after mastering Facebook ads a bit, I finally tried something different: email. We’re still bootstrapping so we decided not to email our massive list. Instead, we decided to only email people who’ve already bought from us once. We sent them these exact email campaigns and made an extra $6504.82. Pretty impressive considering they were all repeat customers.
Since our list was small, we were able to get by with the cheapest email plan keeping our costs low. And since they had already bought from us before, they were less hesitant to buy from us again. The coolest thing, though, is that we usually showed them other styles of the exact same product. So they were buying duplicates of what they had already originally purchased. Since we knew they purchased our beach blankets, we showed them more, and they bought more too! So don’t forget to give your customers more of what they like rather than trying to introduce them to something totally different.
13. Making Your First Hire
Hey you, guess what? I just hired our first two freelancers. My sales have been skyrocketing and I can’t keep up with all the work. My first hire is helping by posting on social media and our second hire is helping by processing orders for our store. How cool!
There was a very minor bump that happened. My new social media hire shared some offensive body shaming content on our page. I had an important talk about inclusivity and his content has been improving ever since.
He helped us grow to over 50k Facebook followers . When managing new hires, remember that they start out just like you did. Remember all those mistakes you made when you were starting out? Well, they make mistakes because they’re just starting out too. Be patient, act like a mentor, and lead by example.
14. Order Products from Different Suppliers
Hey you, before you start selling products, there’s something you should alway remember to do: order product samples . I don’t care if you sell 30k products on your store. You should never sell anything to a customer without knowing what they’re getting. Today you might look at your store as a way to make money online. However, once you start making six figures, you’re going to wish you had put more care into your store’s branding and reputation. You’ll minimize negative feedback and returns by ordering a sample of each product. Ask yourself these questions: Does the product look like the picture? Would I feel guilty selling this product? Does this product work? Will this product’s clothing size fit my target audience? The plus side is that you can also take your own product photos to better optimize your product pages.
15. Don’t Give Up
Hey you, the real reason I’m sharing all these lessons with you is because I know some days you’ll feel like giving up. Entrepreneurship is hard. Even when I experienced my biggest wins, it’d sometimes follow up with an upsetting loss. There are moments when you’ll feel proud of what you’ve accomplished. And other times you’ll be embarrassed to answer the question, “So how’s your business doing?”
But all those mistakes adding up bring you so much closer to becoming a greater success. So don’t be afraid to fail. And don’t be ashamed of your setbacks. They’re all short moments in the grand entrepreneurial experience. One day you’ll look back and think to yourself, “I was so crazy for trying that. But it sure was one helluva a ride.”
You’ll never regret trying but you’ll always regret holding yourself back. So take risks. But most importantly, have fun building.
The secret to building a successful online store is to keep building. You’re going to have some epic failures under your belt. You’re going to make some big, fat mistakes. You’re going to lose some money. But all those lessons and skills you learn along the way build up on top of each other, making you a better entrepreneur. Eventually, you learn what makes money and what doesn’t. Whether you’re currently working a 9 to 5 job or working on a completely different side hustle , don’t discredit the skillset you build up. Your future success is built from the skills you learn from everything you experiment with and every job you ever hold.
When it comes to running an online store what’s been your biggest mistake and biggest win? Comment below!
Why You Can’t Really Trust Negative Online Reviews
Research suggests that people heed negative reviews more than positive ones — despite their questionable credibility.
By Caroline Beaton
June 13, 2020
The Great Wall of China has more than 9,000 Google reviews, with an average of 4.2 stars. Not bad for one of the most astonishing achievements in human history.
But you can’t please everyone.
“Not very tall. Or big. Just sayin. I kinda liked it. Sort of,” wrote one ambivalent visitor of the structure, which stretches thousands of miles . Another complained, “I don’t see the hype in this place it’s really run down and old … why wouldn’t you update something like this? No USB plug ins or outlets anywhere.” Someone else announced that he’s “Not a wall guy. Laaaaaaaaammme.”
Even Shakespeare can’t escape the wrath of consumer scorn. One reviewer on Amazon awarded Hamlet just two stars: “Whoever said Shakespeare was a genius lied. Unless genius is just code word for boring, then they’re spot on. Watch the movie version so you only waste two hours versus 20.”
It’s no wonder why we live and buy by online reviews: The Washington Post recently reported that a third of American adults use a computer or phone to buy something at least once a week — “about as often as we take out the trash.” Last December, 75 percent of Americans said they would do “most of their holiday shopping on Amazon,” according to CNBC’s “All-America Economic Survey.”
We use reviews to vet our options. In 2020, the Pew Research Center found that 82 percent of American adults say they sometimes or always read online reviews for new purchases. And more than two-thirds of regular review readers believe that they’re “generally accurate.”
Marketing data indicates that negative reviews in particular dramatically influence our buying behaviors. But research on the biases and demographics of online reviewers — and our own, often errant interpretations — suggests that our faith in reviews is misguided.
Why we care so much about negative reviews
There are many more positive reviews online than there are negative ones, studies show, which creates a scarcity of negative reviews that we associate with value.
For instance: In a data sample from Amazon, just 4.8 percent of reviews with a verified purchase were rated one star, whereas 59 percent had five stars, according to a study published in 2020 by The Journal of Marketing Research and led by Duncan Simester, a marketing professor at the M.I.T. Sloan School of Management.
“The infrequent nature of negative reviews may help to distinguish them from other reviews,” Dr. Simester wrote in an email. We consequently pay more attention to them.
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We also think of negative reviews as windows into what could go wrong. Is this camera’s memory card going to go kaput in the middle of my honeymoon? Are these socks scratchy? Dr. Simester pointed out that people may see negative reviews as more informative, and therefore more valuable, than positive ones because they highlight defects — even if they’re not actually more accurate.
“We want to feel secure in our decision-making processes,” said Lauren Dragan, who analyzes consumer feedback as the audio tech products reviewer at Wirecutter, a New York Times company that reviews and recommends products. We use negative reviews to understand our risk and reduce our losses, studies show.
Plus, after reports that five-star reviews are frequently fake, people may depend on negative reviews more than positive ones because they see them as more trustworthy.
Online reviews are less trustworthy than we think
The credibility of all reviews — even real ones — is questionable. A 2020 study published in The Journal of Consumer Research looked at whether online reviews reflected objective quality as rated by Consumer Reports. The researchers found very little correlation.
Reviews are subjective, and the tiny subset of people who leave them aren’t average.
People who write online reviews are more likely to buy things in unusual sizes, make returns, be married, have more children, be younger and less wealthy, and have graduate degrees than the average consumer, according to Dr. Simester’s 2020 study. Online reviewers are also 50 percent more likely to shop sales, and they buy four times more products.
“Very few people write reviews. It’s about 1.5 percent, or 15 people out of 1,000,” Dr. Simester said. “Should we be relying on these people if we’re part of the other 985?”
What’s more, reviews are often capricious and circumstantial. For example, the sentiment of travelers’ reviews hinges on their companionship. A study published last fall in Electronic Commerce Research and Applications, looking at 125,076 online reviews, found that people traveling with significant others wrote the most positive reviews, followed by those traveling with friends or family. Reviewers traveling alone or for business were the most negative. Our experiences change depending on our expectations, travel expertise and who we’re with.
People’s motivations also taint their neutrality. Take TripAdvisor’s “Super Contributors,” whose reviews tend to be more negative than those by less active members, according to a forthcoming study from Ulrike Gretzel, a communications professor at the University of Southern California and the director of research at Netnografica. Having formed identities around being expert travel reviewers, Super Contributors may “write more critically to appear more professional,” Dr. Gretzel said. Nevertheless, consumers disproportionately value and trust reviews professing expertise.
Put simply, we should distrust online reviews “because emotions are involved,” Ms. Dragan said.
Another reason to be wary is roughly one in 15 people review products they haven’t actually purchased or used, according to Dr. Simester. These “self-appointed brand managers” write speculative, unsolicited negative reviews to offer the company “feedback.” The problem is consumers are bad at determining which reviews are based on actual experiences and which aren’t, said Dr. Simester. “We are easily fooled.”
Get savvier about how you read reviews
Still, reviews can be helpful gauges when you’re buying stuff — so long as you keep in mind all the caveats around them.
First, weed out the most polarized perspectives. People are much more likely to write reviews if they have extreme emotions about something, said Eric K. Clemons, who teaches information management at the University of Pennsylvania’s Wharton School. This is why you see so many rave reviews and so many rancorous ones.
Even people who don’t initially have strong feelings often develop them in response to survey questions — something called the mere-measurement effect.
“We are socially conditioned to give answers when someone/something asks us a question,” Dr. Gretzel wrote in an email. So if we don’t have a pre-existing, well-defined opinion, we make one up.
When you’re reading reviews, try to find ones that are closer to the median, Ms. Dragan advised. She deliberately looks at three-star reviews first because they tend to be more moderate, detailed and honest. Unfortunately, research suggests that most of us instinctively do just the opposite: We prefer extreme reviews because they’re less ambivalent and therefore easier to process.
Second, ask yourself: “Is this person like me? Are the problems mentioned ones I care about?” For example, Dr. Simester recently bought a pair of ski pants online. He read the reviews and most people liked them, but one guy didn’t. “It turned out his body shape wasn’t the same as mine,” Dr. Simester said, so he disregarded the review.
Dissecting people’s preferences can be useful even if you don’t agree with them. Dr. Clemons, an I.P.A. fan who uses RateBeer.com, said, “If a Scandinavian who really likes lagers complains that a beer tastes way too hoppy, that may mean I should buy it.”
Finally, pay attention to contextual details and specific facts rather than reviewers’ general impressions and ratings. The number of stars someone selects often has “very little to do with” their review text, Dr. Gretzel said. People have different rating standards, and written explanations are inherently more nuanced.
Focusing on the most thorough reviews may also protect against getting duped by fake ones. In experiments where Dr. Gretzel and her collaborators presented both real and fake reviews, readers distinguished between the two better when reviews were longer.
And if you’re still not sure whether a review is fake, scan the reviewer’s profile. Dr. Clemons said that “someone who’s paid to write reviews probably isn’t doing a lot of writing under the same name.” His own research omitted reviews from profiles containing fewer than 10 reviews, “and that took care of a lot of paid nonsense,” he said.
All that said, real reviewers are usually genuinely trying to help: Research consistently shows that people are most motivated by helping others make decisions.
“They feel that they have benefited from other people’s reviews, so they want to give back,” Dr. Gretzel said. “They think it’s for the greater good.”
Caroline Beaton is a freelance writer and producer who sends a monthly newsletter about science and society. Sign up to receive it here .
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