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What factors predict the success of a Steam game? (An analysis)

What factors predict the success of a Steam game?

I've seen quite a few discussions, comments and questions on /gamedev about what determines a game's success. How much does quality matter? Is establishing market awareness before launch the only thing that matters? Does a demo help or hurt? If your game has a poor launch, how likely is it to recover? Is it possible to roughly predict the sales of a game before launch?
In preparation for my game's launch, I spent a lot of time monitoring upcoming releases trying to find the answer to these questions. I compiled a spreadsheet, noted followers, whether it was Early Access or not, and saw how many reviews it received in the first week, month and quarter.
I'm sharing this data now in the hopes that it helps other developers understand and predict their games' sales.
First some notes on the data:
Game Price Launch Discount Week Guess Week actual 3 Month 3 Month/week Followers Early Access Demo Review Score
Pit of Doom 9.99 0 7 27 43 1.592592593 295 Y N 0.8
Citrouille 9.99 0.2 16 8 12 1.5 226 N N
Corspe Party: Book 14.99 0.1 32 40 79 1.975 1015 N N 0.95
Call of Cthulhu 44.99 0 800 875 1595 1.822857143 26600 N N 0.74
On Space 0.99 0.4 0 0 0 4 N N
Orphan 14.99 0 50 0 8 732 N N
Black Bird 19.99 0 20 13 34 2.615384615 227 N N
Gloom 6.99 0 20 8 17 2.125 159 N N
Gilded Rails 5.99 0.35 2 3 7 2.333333333 11 N Y
The Quiet Man 14.99 0.1 120 207 296 1.429951691 5596 N N 0.31
KartKraft 19.99 0.1 150 90 223 2.477777778 7691 Y N 0.84
The Other Half 7.99 0 2 3 27 9 91 N Y 0.86
Parabolus 14.99 0.15 0 0 0 16 N Y
Yet Another Tower Defense 1.99 0.4 20 22 38 1.727272727 396 N N 0.65
Galaxy Squad 9.99 0.25 8 42 5.25 3741 Y N 0.87
Swords and Soldiers 2 14.99 0.1 65 36 63 1.75 1742 N N 0.84
SpitKiss 2.99 0 3 1 2 2 63 N N
Holy Potatoes 14.99 0 24 11 22 2 617 N N 0.7
Kursk 29.99 0.15 90 62 98 1.580645161 2394 N N 0.57
SimpleRockets 2 14.99 0.15 90 142 272 1.915492958 3441 Y N 0.85
Egress 14.99 0.15 160 44 75 1.704545455 7304 Y N 0.67
Kynseed 9.99 0 600 128 237 1.8515625 12984 Y N 0.86
11-11 Memories 29.99 0 30 10 69 6.9 767 N N 0.96
Rage in Peace 12.99 0.1 15 10 42 4.2 377 N N 0.85
One Hour One Life 19.99 0 12 153 708 4.62745098 573 N N 0.81
Optica 9.99 0 0 2 3 1.5 18 N N
Cybarian 5.99 0.15 8 4 18 4.5 225 N N
Zeon 25 3.99 0.3 3 11 12 1.090909091 82 Y N
Of Gods and Men 7.99 0.4 3 10 18 1.8 111 N Y
Welcome to Princeland 4.99 0.1 1 15 55 3.666666667 30 N N 0.85
Zero Caliber VR 24.99 0.1 100 169 420 2.485207101 5569 Y N 0.73
HellSign 14.99 0 100 131 334 2.549618321 3360 Y N 0.85
Thief Simulator 19.99 0.15 400 622 1867 3.001607717 10670 N N 0.81
Last Stanza 7.99 0.1 8 2 4 2 228 N Y
Evil Bank Manager 11.99 0.1 106 460 4.339622642 8147 Y N 0.78
Oppai Puzzle 0.99 0.3 36 93 2.583333333 54 N N 0.92
Hexen Hegemony 9.99 0.15 3 1 5 5 55 Y N
Blokin 2.99 0 0 0 0 0 10 N N
Light Fairytale Ep 1 9.99 0.1 80 23 54 2.347826087 4694 Y N 0.89
The Last Sphinx 2.99 0.1 0 0 1 0 17 N N
Glassteroids 9.99 0.2 0 0 0 0 5 Y N
Hitman 2 59.99 0 2000 2653 3677 1.385978138 52226 N N 0.88
Golf Peaks 4.99 0.1 1 8 25 3.125 46 N N 1
Sipho 13.99 0 24 5 14 2.8 665 Y N
Distraint 2 8.99 0.1 40 104 321 3.086538462 1799 N N 0.97
Healing Harem 12.99 0.1 24 10 15 1.5 605 N N
Spark Five 2.99 0.3 0 0 0 0 7 N N
Bad Dream: Fever 9.99 0.2 30 78 134 1.717948718 907 N N 0.72
Underworld Ascendant 29.99 0.15 200 216 288 1.333333333 8870 N N 0.34
Reentry 19.99 0.15 8 24 78 3.25 202 Y N 0.95
Zvezda 5.99 0 2 0 0 0 25 Y Y
Space Gladiator 2.99 0 0 1 2 2 5 N N
Bad North 14.99 0.1 500 360 739 2.052777778 15908 N N 0.8
Sanctus Mortem 9.99 0.15 3 3 3 1 84 N Y
The Occluder 1.99 0.2 1 1 1 1 13 N N
Dark Fantasy: Jigsaw 2.99 0.2 1 9 36 4 32 N N 0.91
Farming Simulator 19 34.99 0 1500 3895 5759 1.478562259 37478 N N 0.76
Don't Forget Our Esports Dream 14.99 0.13 3 16 22 1.375 150 N N 1
Space Toads Mayhem 3.99 0.15 1 2 3 1.5 18 N N
Cattle Call 11.99 0.1 10 19 53 2.789473684 250 Y N 0.71
Ralf 9.99 0.2 0 0 2 0 6 N N
Elite Archery 0.99 0.4 0 2 3 1.5 5 Y N
Evidence of Life 4.99 0 0 2 4 2 10 N N
Trinity VR 4.99 0 2 8 15 1.875 61 N N
Quiet as a Stone 9.99 0.1 1 1 4 4 42 N N
Overdungeon 14.99 0 3 86 572 6.651162791 77 Y N 0.91
Protocol 24.99 0.15 60 41 117 2.853658537 1764 N N 0.68
Scraper: First Strike 29.99 0 3 3 15 5 69 N N
Experiment Gone Rogue 16.99 0 1 1 5 5 27 Y N
Emerald Shores 9.99 0.2 0 1 2 2 12 N N
Age of Civilizations II 4.99 0 600 1109 2733 2.464382326 18568 N N 0.82
Dereliction 4.99 0 0 0 0 #DIV/0! 18 N N
Poopy Philosophy 0.99 0 0 6 10 1.666666667 6 N N
NOCE 17.99 0.1 1 3 4 1.333333333 35 N N
Qu-tros 2.99 0.4 0 3 7 2.333333333 4 N N
Mosaics Galore. Challenging Journey 4.99 0.2 1 1 8 8 14 N N
Zquirrels Jump 2.99 0.4 0 1 4 4 9 N N
Dark Siders III 59.99 0 2400 1721 2708 1.573503777 85498 N N 0.67
R-Type Dimensions Ex 14.99 0.2 10 48 64 1.333333333 278 N N 0.92
Artifact 19.99 0 7000 9700 16584 1.709690722 140000 N N 0.53
Crimson Keep 14.99 0.15 20 5 6 1.2 367 N N
Rival Megagun 14.99 0 35 26 31 1.192307692 818 N N
Santa's Workshop 1.99 0.1 3 1 1 1 8 N N
Hentai Shadow 1.99 0.3 2 12 6 14 N N
Ricky Runner 12.99 0.3 3 6 13 2.166666667 66 Y N 0.87
Pro Fishing Simulator 39.99 0.15 24 20 19 0.95 609 N N 0.22
Broken Reality 14.99 0.1 60 58 138 2.379310345 1313 N Y 0.98
Rapture Rejects 19.99 0 200 82 151 1.841463415 9250 Y N 0.64
Lost Cave 19.99 0 3 8 11 1.375 43 Y N
Epic Battle Fantasy 5 14.99 0 300 395 896 2.26835443 4236 N N 0.97
Ride 3 49.99 0 75 161 371 2.304347826 1951 N N 0.74
Escape Doodland 9.99 0.2 25 16 19 1.1875 1542 N N
Hillbilly Apocalypse 5.99 0.1 0 1 2 2 8 N N
X4 49.99 0 1500 2638 4303 1.63115997 38152 N N 0.7
Splotches 9.99 0.15 0 2 1 0.5 10 N N
Above the Fold 13.99 0.15 5 2 6 3 65 Y N
The Seven Chambers 12.99 0.3 3 0 0 #DIV/0! 55 N N
Terminal Conflict 29.99 0 5 4 11 2.75 125 Y N
Just Cause 4 59.99 0 2400 2083 3500 1.680268843 50000 N N 0.34
Grapple Force Rena 14.99 0 11 12 29 2.416666667 321 N Y
Beholder 2 14.99 0.1 479 950 1.983298539 16000 N N 0.84
Blueprint Word 1.99 0 12 15 1.25 244 N Y
Aeon of Sands 19.99 0.1 20 12 25 2.083333333 320 N N
Oakwood 4.99 0.1 32 68 2.125 70 N N 0.82
Endhall 4.99 0 4 22 42 1.909090909 79 N N 0.84
Dr. Cares - Family Practice 12.99 0.25 6 3 8 2.666666667 39 N N
Treasure Hunter 16.99 0.15 200 196 252 1.285714286 4835 N N 0.6
Forex Trading 1.99 0.4 7 10 14 1.4 209 N N
Ancient Frontier 14.99 0 24 5 16 3.2 389 N N
Fear the Night 14.99 0.25 25 201 440 2.189054726 835 Y N 0.65
Subterraneus 12.99 0.1 4 0 3 #DIV/0! 82 N N
Starcom: Nexus 14.99 0.15 53 119 2.245283019 1140 Y N 0.93
Subject 264 14.99 0.2 25 2 3 1.5 800 N N
Gris 16.9 0 100 1484 4650 3.133423181 5779 N N 0.96
Exiled to the Void 7.99 0.3 9 4 11 2.75 84 Y N
Column Explanations
For the columns that are not self-explanatory:

Question 1: Does Quality Predict Success?

There was a recent blog post stating that the #1 metric for indie games' success is how good it is.
Quality is obviously a subjective metric. The most obvious objective measure of quality for Steam games is their % Favorable Review score. This is the percentage of reviews by purchasers of the game that gave the game a positive rating. I excluded any game that did not have at least 20 user reviews in the first month, which limited the sample size to 56.
The (Pearson) correlation of a game's review score to its number of reviews three months after its release was -0.2. But 0.2 (plus or minus) isn't a very strong correlation at all. More importantly, Pearson correlation can be swayed if the data contains some big outliers. Looking at the actual games, we can see that the difference is an artifact of an outlier. Literally. Valve's Artifact by far had the most reviews after three months and had one of the lowest review scores (53% at the time). Removing this game from the data changed the correlation to essentially zero.
Spearman's Rho, an alternative correlation model that correlates rank position and minimizes the effect of huge outliers produced a similar result.
Conclusion: If there is correlation between a game's quality (as measured by Steam review score) and first quarter sales (as measured by total review count), it is too subtle to be detected in this data.

Question 2: Do Demos, Early Access or Launch Discounts Affect Success/Failure?

Unfortunately, there were so few games that had demos prior to release (10) that only a very strong correlation would really tell us anything. As it happens, there was no meaningful correlation one way or another.
There were more Early Access titles (28), but again the correlation was too small to be meaningful.
More than half the titles had a launch week discount and there was actually a moderate negative correlation of -0.3 between having a launch discount and first week review count. However it appears that this is primarily the result of the tendency of AAA titles (which sell the most copies) to not do launch discounts. Removing the titles that likely grossed over a $1 million in the first week reduced the correlation to basically zero.
Conclusion: Insufficient data. No clear correlation between demos, Early Access or launch discount and review counts: if they help or hurt the effect is not consistent enough to be seen here.

Question 3: Does pre-launch awareness (i.e., Steam followers) predict success?

You can see the number of "followers" for any game on Steam by searching for its automatically-created Community Group. Prior to launch, this is a good rough indicator of market awareness.
The correlation between group followers shortly before launch and review count at 3 months was 0.89. That's a very strong positive correlation. The rank correlation was also high (0.85) suggesting that this wasn't the result of a few highly anticipated games.
Save for a single outlier (discussed later), the ratio of 3 month review counts to pre-launch followers ranged from 0 (for the handful of games that never received any reviews) to 1.8, with a median value of 0.1. If you have 1000 followers just prior to launch, then at the end of the first quarter you should expect "about" 100 reviews.
One thing I noticed was that there were a few games that had follower counts that seemed too high compared to secondary indicators of market awareness, such as discussion forum threads and Twitter engagement. After some investigation I came to the conclusion that pre-launch key activations are treated as followers by Steam. If a game gave away a lot of Steam keys before launch (say as Kickstarter rewards or part of beta testing) this would cause the game to appear to have more followers than it had gained "organically."
Conclusion: Organic followers prior to launch are a strong predictor of a game's eventual success.

Question 4: What about price?

The correlation between price and review count at 3 month is 0.36, which is moderate correlation. I'm not sure how useful that data point is: it is somewhat obvious that higher budget games have larger marketing budgets.
There is a correlation between price and review score of -0.41. It seems likely that players do factor price into their reviews and a game priced at $60 has a higher bar to clear to earn a thumbs up review than a game priced at $10.

Question 5: Do first week sales predict first quarter results?

The correlation between number of reviews after 1 week and number of reviews after 3 months was 0.99. The Spearman correlation was 0.97. This is the highest correlation I found in the data.
Excluding games that sold very few copies (fewer than 5 reviews after the first week), most games had around twice as many reviews after 3 months as they did after 1 week. This suggests that games sell about as many copies in their first week as they do in the next 12 weeks combined. The vast majority of games had a tail ratio (ratio of reviews at 3 months to 1 week) of between 1.3 to 3.2.
I have seen a number of questions from developers whose game had a poor launch on Steam and wanted to know what they can do to improve sales. While I'm certain post-launch marketing can have an effect on continuing sales, your first week does seem to set hard bounds on your results.
Conclusion: ALL SIGNS POINT TO YES

Question 6: Does Quality Help with a Game's "Tail"?

As discussed in the last question while first week sales are very strongly correlated with first quarter, there's still quite a wide range of ratios. Defining a game's Tail Ratio as the ratio of reviews after 3 months to after 1 week, the lowest value was 0.95 for "Pro Fishing Simulator" which actually managed to lose 1 review. The highest ratio was 6.9, an extreme outlier that I'll talk about later. It is perhaps not a coincidence that the worst tail had a Steam score of 22% and the best tail had a Steam score of 96%.
The overall correlation between the Tail Ratio and Steam score was 0.42.
Conclusion: Even though there is no clear correlation between quality and overall review count/sales, there is a moderate correlation between a game's review score and its tail. This suggests that "good games" do better in the long run than "bad games," but the effect is small compared to the more important factor of pre-launch awareness.

Question 7: Is it possible to predict a game's success before launch without knowing its wishlists?

While I was compiling the data for each game, sometime prior to its scheduled launch date, I would make a prediction of how many reviews I thought it would receive in its first week and add that prediction to the spreadsheet.
The #1 factor I used in making my prediction was group follower count. In some cases I would adjust my prediction if I thought that value was off, using secondary sources such as Steam forum activity and Twitter engagement.
The correlation between my guess and the actual value was 0.96, which is a very strong correlation. As you can see in the data, the predictions are, for the most part, in the right ballpack with a few cases where I was way off.
Based on my experience, multiplying the group follower count by 0.1 will, in most cases, give you a ballpark sense of the first week quarter review count. If a game doesn't have at least one question in the discussion forum for every 100 followers, that may indicate that there are large number of "inorganic" followers and you may need to adjust your estimate.
Conclusion: Yes, with a few exceptions, using follower data and other indicators you can predict first week results approximately. Given the strong correlation between first week and quarter sales, it should also be possible to have a ballpark idea of first quarter results before launch.

Final Question: What about the outliers you mentioned?

There were a few games in the data that stood out significantly in one way or another.
Outlier #1: Overdungeon. This game had 77 group followers shortly before launch, a fairly small number and based solely on that number I would have expected fewer than a dozen reviews in the first week. It ended up with 86. Not only that, it had a strong tail and finished its first quarter with 572 reviews. This was by a wide margin the highest review count to follower ratio in the sample.
Based on the reviews, it appears to basically be Slay the Spire, but huge in Asia. 90% of the reviews seem to be in Japanese or Chinese. If anyone has some insight to this game's unusual apparent success, I'm very curious.
This seems to be the only clear example in the data of a game with minimal following prior to launch going on to having a solid first quarter.
Outlier #2: 11-11 Memories Retold. This game had 767 group followers shortly before launch, ten times as many as Overdungeon. That's still not a large number for even a small indie title. It had a fair amount going for it, though: it was directed by Yoan Fanise, who co-directed the critally acclaimed Valiant Hearts, a game with a similar theme. It was animated by Aardman Studios of "Wallace and Gromit" fame. Its publisher was Bandai Namco Europe, a not inexperienced publisher. The voice acting was by Sebastian Koch and Elijah Wood. It has dozens of good reviews in both gaming and traditional press. It currently has a 95% positive review rating on Steam.
Despite all that, nobody bought it. 24 hours after it came out it had literally zero reviews on Steam. One week after it came out it had just 10. Three months later it had demonstrated the largest tail in the data, but even then it had only climbed to 69 reviews. Now it's at about 100, an incredible tail ratio, but almost certainly a commercial failure.
This is a solid example that good game + good production values does necessarily equal good sales.

Final notes:
The big take-aways from this analysis are:
Thanks for reading!
submitted by justkevin to gamedev [link] [comments]

Can Chatbots be Intelligent?

Can Chatbots be Intelligent?
Businesses devise a billion ways of wooing customers, every day. If a chatbot can be a useful accomplice toward that end, why not give it a try? Afterall, who wouldn’t want a tool that can hold an intelligent conversation with customers, make them feel comfortable and bind them to your business.
Is it possible?
Recall that memorable scene from the award winning 2003 film, Lost in Translation, where an aging American actor, Bob Harris (played by Bill Murray), is on a set in Tokyo to shoot a whiskey commercial. The director, Yutaka Tadokoro, begins instructing Bob in Japanese, and the slapdash interpreter fails to capture the meaning—namely, it gets lost in translation. The process bogs down, and the commercial is a disaster.
You don’t want human-to-computer interactions to end up that way, right? But one-way communications prove to be too exasperating to users. People give up on trying to get a machine understand their intentions in a few clicks and presses. There’s that missing vibe, that interactive component in any human-computer engagement; and it’s the main reason a vast majority feels they must adapt to the technologies they use, rather than technology adapting to them.
https://preview.redd.it/mzxagl6zwrd11.jpg?width=220&format=pjpg&auto=webp&s=49da6f90e91dc9686b28c337b159b74c7f6dd3bf
Enter 2018, and we have artificial intelligence (AI)-driven chatbots that are revolutionizing human-computer interactions just the way the humans want it. Chatbots today are more adaptive to the way people speak and mimic their emotions to the nearest binary. 2018 is paving the way for a great chatbot innovation.
Meanwhile, developers are working tirelessly to bring in new consumer experiences to market. For example, once WhatsApp opens to bots next year, it will unlock direct access to over one billion new users. Chatbots are continuing to push the envelope of new technology further.
To reckon with, a chatbot isn’t an additional handle on your website or a fancy add-on. It’s the need of the hour for every business that’s flourishing or aspires to flourish. In a market that’s fiercely competitive, customers expect to receive accurate information quickly enough to make a decision. As a business owner, you need to cater to that need. If you don’t have funds to recruit more people to answer all the questions customers throw at you, then deploying a smart chatbot can rescue your business in that case.
https://preview.redd.it/vpuz6mb1xrd11.png?width=800&format=png&auto=webp&s=f615929b9f190e38afe38c3d59ba084dbfc9747b
But then intelligence also matters as it determines the kind of tasks or conversations your chatbot can handle. Needless to say, if you have a clear set of activities preconceived in your mind, you can build awesome customized bots.
Let’s take you through a short read about 5 important things that can make a chatbot intelligent.
1. Bots need to understand human conversations:
The bot needs to be quick and intelligent enough to understand the context of the conversation happening in real time. It’s about sense and sensibility, in conversations.
Normal human conversations are replete with instances of switching over context while talking, while at work - resuming a task, discarding the current task and switching to a newer one, or in general hold a task while the other is being executed and work on follow on. Human conversations tend to switch between contexts and variables (intents and entities), often combining multiple things into one.
Sample this response to a flight booking bot for example, "My Destination? San Francisco. But how's the weather over there?"
What should be the bot’s response here - capture the entity and continue booking or check the weather before that?
In this case, chatbots need to
  • have context switching abilities to handle interruptions smartly and provide full control to developers in defining the experience
  • capture unattended interruptions from a conversation flow and keep them accessible
  • be equipped with human conversations and have the ability to hold and resume a dialog for a certain amount of time and execute the tasks in sequential order, and especially while understanding human emotions
You may argue that a bot is after all a machine and cannot absorb emotions, but all said and done, it also depends partly on how much capability you build into it. So, it must be clever enough to filter the feelings of the customer. The bot needs to understand, analyze and respond based on the human emotion. For instance, if a customer messages an online shopping portal saying, “Your service is amazing, the delivery of items are always 2 to 3 days delayed”, none can miss the biting sarcasm intended in the statement. But if the bot isn’t developed to cater to this sort of sentiment, it may end up answering in a horribly awry manner.
https://preview.redd.it/a3p88148xrd11.png?width=800&format=png&auto=webp&s=f589d16a1d1407018f26c460404a69def2a4cf52
Intelligent bots will have: Sentiment analysis, context switching, hold and resume feature.
2. Standardization and uniformity in bot utterances
It’s important to remember that a chatbot must give vibes closest to humans as much as possible. The way humans carry the stamp of their personality and style, bots too need to be enabled to do that. When asked about something, a bot must respond in a particular way and pattern that sounds like a human. This warms the usecustomer and makes him feel at ease during the conversation with a chatbot.
“You must have direct connection with your customers as part of your brand’s identity, even more than your website that doesn’t seem to have an identity, this will have a personality.”- William Meisel
Thus, chatbots need to
  • understand and remember the user context - make all user information available in a single location and accessible
  • store the user profile with information like first name, last name and make it accessible to all the systems for the convenience of the user.
  • remember what a specific user talks to a specific bot, in an enterprise scenario it needs to keep certain features such as prompt for ‘Password length’ / ‘folder for HR information, constant for all the employees in the enterprise
It’s important for a chatbot to keep a current task which is being executed in an active mode and store information.
As a corollary, customers appreciate and connect with the support executives (call support executives/shopping store helpers) who can remember their preferences, can validate their purchases, help them with more information on products, and basically give importance to them while attending to their queries. For example, in a Forex platform the currency against each country is maintained constant across all systems for everyone to access. The platform tends to store the first and last name of the customer, their last transaction and their payment options.
Chatbots now have the responsibility to standardize their understanding of a customer and respond to them accordingly, whether in the manner of communicating or the speed with which they resolve their query. Chatbots need to converse with customers to extract this information and keep up to their pace.
https://preview.redd.it/flps4l6bxrd11.png?width=800&format=png&auto=webp&s=dee88672cef176ed92778e962a9029543ee6cbd9
Intelligent bots will have features like: Small talk, Bot user session, Enterprise context, User context and User session
3. Making the complex conversation sound simple
Chatbots are expected to break the complex structures of conversations into simpler tones and bring to a logical conclusion. Here’s where ‘Artificial Intelligence’ comes into play. Among the many types of chatbots, the most common ones are task specific that cater to a specific job, with pre-loaded answers and information. These type of chatbots have the ability to gather data from the internet, previous company database and other sources. Therefore, these bots are able to reply to diverse queries.
The intelligent bots, in addition, have the potential to mold the conversation the way the customer wants and guide him towards a specific solution. In an office setup, it’s common for a conversation like, “Hey Lisa, set up a meeting with Phani if he’s free”, to be handled between a Boss and Secretary. To enable that, the chatbot needs to first look up the calendar of ‘Phani’, find a suitable time for a meeting in sync with the Boss’s schedule and then reschedule the meeting. Chatbots thus need to break up complex sounding conversation into simpler nuances and then execute the task sequentially and logically.
Intelligent bots can also break down the conversation to its essence and action items. Let’s look at a very common scenario: ‘Customer tries to book tickets for 14th August, confirms on the choice of airlines, origin and destination and navigates to the next page, but feels that the pricing is very high. The customer then asks the bot to check for ‘15th August instead’. Here, based on the situation, the chatbot is acting and will be able to display the new prices by changing the date of journey.
https://preview.redd.it/bzfoasiexrd11.png?width=800&format=png&auto=webp&s=67d134343093daf3d239c9bcfd0b315fb687d333
Intelligent bots will have features like: Amend Entities, Planning.
4. Adapting to human utterances
In the context of human–computer communication, forming assumptions about what a system can do and understand is problematic for most people. In turn, forming assumptions about how users will “talk to” the system is also likely to be problematic for system developers. The potential for variability in how users will communicate with a system is enormous and has been dubbed “The Vocabulary Problem.”
An intelligent chatbot can not only handle queries smartly and remembers them through the session, but also learns new things with every conversation that happens, saves them and uses them appropriately for future instances.
In a human conversation and especially over voice, there are bound to be
  • expectation of elaboration or confirmation (“can you hear me?”, “I do not follow”)
  • request for repeat of sentences (“ I’m sorry I couldn’t hear that, can you please repeat it again?” “Sorry, can you repeat?”)
  • pauses (“can you please hold? [pause] thank you!”)
  • interruptions (“the number is 212-” “sorry can you start over?” )
The simplest thing to do when writing responses to command and inquiry utterances in a conversational UI is to get straight to the point: respond with facts. That’ll remove a lot of the ambiguity and simplify the dialogue.
It’s up to the intelligent chatbot to adapt to the way the human responds - with the referential context (or) pauses (or) specific context (or) synonyms (or) repetition (or) abbreviations (or) variations in dialect. The chatbot needs to map it pre-contextually. But like their human counterparts, chatbots’ conversational skills determine whether they earn you seamless, scalable transactions or just another horde of pissed-off customers. This needs a lot of training by the chatbot to help continue the conversations to the logical ending.
https://preview.redd.it/u5g63jovxrd11.jpg?width=1581&format=pjpg&auto=webp&s=1f394c933c28c1079cf290156de09f285b2054d1
Intelligent bots will have features like: Sync, Repeat, Interruptions and Pauses.
5. When bots are kept simple
Although AI chatbots’ task is complicated and they need to be built up that way, yet the effort should be made to keep it simple. They need to be comprehensive yet detailed. A customer initiating a conversation with a chatbot might already be troubled due to some poor service related issue, hence it’s better not to irk him further with complex interaction. The bot should be answering the already irked usecustomer in a most precise way possible without confusing the person further. It’s easy to figure out if you are talking to a bot or a human. Make sure the customer knows that they are talking to a bot by welcoming them with some sort of welcome message. Nobody likes being told the same thing over and over again, so why do chatbots keep doing it? Bots should detect when they’re about to repeat a previously given answer and switch strategies. If the answer didn’t resolve the user’s needs before, repeating it certainly won’t either. From the user interface, to the dialog flow the experience should be pleasant, and information given to the user needs to be valuable and crisp.
Twitter also provides the option to give your bot a custom name for different sections of the bot, which can be of use. It’s important to show what the chatbot is capable of doing with Quick Replies. The customer needs to be a guided stepwise within the conversation and with enough accessible options to choose from.
Lastly, there must always be a way to end the conversation with the bot and switch to a human agent. Many bots today include a Quick Reply to “Speak to an Agent”. Certain actions, such as open-ended visual search, are challenging to complete in a messaging environment. In those situations, bots can route to a website or app to help the user complete goals they couldn’t execute within the context of chat.
https://preview.redd.it/gc1j3z4sxrd11.jpg?width=578&format=pjpg&auto=webp&s=331b9de9fcfa0c26ddf34eeb93783e909ecab6cf
Intelligent bots will have features like: Simple UI, Simpler steps, Agent Handoff
In a nutshell, a chatbot must be programmed to not just provide optimum solutions to problems, but also converse with customers in an engaging manner. The interaction must be exciting and the bot must appear to be curious enough to answer all queries. People prefer lively interactions and a chatbot needs to meet that expectation.
https://preview.redd.it/erd22jzpxrd11.jpg?width=1505&format=pjpg&auto=webp&s=d83e0de6283b47b1210a912d26e23dd8023d3afc
For example, there are bots aligned with online shopping portals that can actually sense your liking and disliking. They can cancel orders for you accordingly and order the stuff that you actually want. Businesses are now moving way ahead than what anyone had ever thought of earlier. If we have an amazing concept like messenger or Kore.aiBots Platform, then why not use them to the full extent. Their proficiency in collecting massive data in a short period of time can be used to forecast upcoming business. You know it better how to get edgy with this interesting concept. The more you experiment with chatbots, the more you would get to know the wonders you can create with these little machines.
Some of the Global 2,000 companies and large enterprises are using Kore.ai Bots Platform to build their chatbots. How about you?
To get everything you need to build and deploy intelligent, enterprise-grade chatbots — without unnecessary complexity, click on Build your first BOT.
To ask questions, get tips, learn and grow with Kore.AI developer community, click on Ask questions on Developer Community.
Also Read on : Chatbots (of) the Future
Thank You
Phani Marupaka
LinkedIn| Tweet at : @phani_teja
submitted by PhaniTeja4 to u/PhaniTeja4 [link] [comments]

Can Chatbots be Intelligent?

Can Chatbots be Intelligent?
Businesses devise a billion ways of wooing customers, every day. If a chatbot can be a useful accomplice toward that end, why not give it a try? Afterall, who wouldn’t want a tool that can hold an intelligent conversation with customers, make them feel comfortable and bind them to your business.
Is it possible?
Recall that memorable scene from the award winning 2003 film, Lost in Translation, where an aging American actor, Bob Harris (played by Bill Murray), is on a set in Tokyo to shoot a whiskey commercial. The director, Yutaka Tadokoro, begins instructing Bob in Japanese, and the slapdash interpreter fails to capture the meaning—namely, it gets lost in translation. The process bogs down, and the commercial is a disaster.
https://preview.redd.it/6vqwiux3urd11.jpg?width=220&format=pjpg&auto=webp&s=fd5151869d3e932a32f56fc969406633cd3ba623
You don’t want human-to-computer interactions to end up that way, right? But one-way communications prove to be too exasperating to users. People give up on trying to get a machine understand their intentions in a few clicks and presses. There’s that missing vibe, that interactive component in any human-computer engagement; and it’s the main reason a vast majority feels they must adapt to the technologies they use, rather than technology adapting to them.
Enter 2018, and we have artificial intelligence (AI)-driven chatbots that are revolutionizing human-computer interactions just the way the humans want it. Chatbots today are more adaptive to the way people speak and mimic their emotions to the nearest binary. 2018 is paving the way for a great chatbot innovation.
https://preview.redd.it/27twgl16urd11.png?width=800&format=png&auto=webp&s=c2a83408e1f5a9495428fff1f4cc0414d30b8d84
Meanwhile, developers are working tirelessly to bring in new consumer experiences to market. For example, once WhatsApp opens to bots next year, it will unlock direct access to over one billion new users. Chatbots are continuing to push the envelope of new technology further.
To reckon with, a chatbot isn’t an additional handle on your website or a fancy add-on. It’s the need of the hour for every business that’s flourishing or aspires to flourish. In a market that’s fiercely competitive, customers expect to receive accurate information quickly enough to make a decision. As a business owner, you need to cater to that need. If you don’t have funds to recruit more people to answer all the questions customers throw at you, then deploying a smart chatbot can rescue your business in that case.
But then intelligence also matters as it determines the kind of tasks or conversations your chatbot can handle. Needless to say, if you have a clear set of activities preconceived in your mind, you can build awesome customized bots.
Let’s take you through a short read about 5 important things that can make a chatbot intelligent.
1. Bots need to understand human conversations:
The bot needs to be quick and intelligent enough to understand the context of the conversation happening in real time. It’s about sense and sensibility, in conversations.
Normal human conversations are replete with instances of switching over context while talking, while at work - resuming a task, discarding the current task and switching to a newer one, or in general hold a task while the other is being executed and work on follow on. Human conversations tend to switch between contexts and variables (intents and entities), often combining multiple things into one.
Sample this response to a flight booking bot for example, "My Destination? San Francisco. But how's the weather over there?"
What should be the bot’s response here - capture the entity and continue booking or check the weather before that?
In this case, chatbots need to
  • have context switching abilities to handle interruptions smartly and provide full control to developers in defining the experience
  • capture unattended interruptions from a conversation flow and keep them accessible
  • be equipped with human conversations and have the ability to hold and resume a dialog for a certain amount of time and execute the tasks in sequential order, and especially while understanding human emotions
You may argue that a bot is after all a machine and cannot absorb emotions, but all said and done, it also depends partly on how much capability you build into it. So, it must be clever enough to filter the feelings of the customer. The bot needs to understand, analyze and respond based on the human emotion. For instance, if a customer messages an online shopping portal saying, “Your service is amazing, the delivery of items are always 2 to 3 days delayed”, none can miss the biting sarcasm intended in the statement. But if the bot isn’t developed to cater to this sort of sentiment, it may end up answering in a horribly awry manner.
Intelligent bots will have: Sentiment analysis, context switching, hold and resume feature.
2. Standardization and uniformity in bot utterances
It’s important to remember that a chatbot must give vibes closest to humans as much as possible. The way humans carry the stamp of their personality and style, bots too need to be enabled to do that. When asked about something, a bot must respond in a particular way and pattern that sounds like a human. This warms the usecustomer and makes him feel at ease during the conversation with a chatbot.
“You must have direct connection with your customers as part of your brand’s identity, even more than your website that doesn’t seem to have an identity, this will have a personality.”- William Meisel
Thus, chatbots need to
  • understand and remember the user context - make all user information available in a single location and accessible
  • store the user profile with information like first name, last name and make it accessible to all the systems for the convenience of the user.
  • remember what a specific user talks to a specific bot, in an enterprise scenario it needs to keep certain features such as prompt for ‘Password length’ / ‘folder for HR information, constant for all the employees in the enterprise
It’s important for a chatbot to keep a current task which is being executed in an active mode and store information.
As a corollary, customers appreciate and connect with the support executives (call support executives/shopping store helpers) who can remember their preferences, can validate their purchases, help them with more information on products, and basically give importance to them while attending to their queries. For example, in a Forex platform the currency against each country is maintained constant across all systems for everyone to access. The platform tends to store the first and last name of the customer, their last transaction and their payment options.
Chatbots now have the responsibility to standardize their understanding of a customer and respond to them accordingly, whether in the manner of communicating or the speed with which they resolve their query. Chatbots need to converse with customers to extract this information and keep up to their pace.
Intelligent bots will have features like: Small talk, Bot user session, Enterprise context, User context and User session
3. Making the complex conversation sound simple
Chatbots are expected to break the complex structures of conversations into simpler tones and bring to a logical conclusion. Here’s where ‘Artificial Intelligence’ comes into play. Among the many types of chatbots, the most common ones are task specific that cater to a specific job, with pre-loaded answers and information. These type of chatbots have the ability to gather data from the internet, previous company database and other sources. Therefore, these bots are able to reply to diverse queries.
The intelligent bots, in addition, have the potential to mold the conversation the way the customer wants and guide him towards a specific solution. In an office setup, it’s common for a conversation like, “Hey Lisa, set up a meeting with Phani if he’s free”, to be handled between a Boss and Secretary. To enable that, the chatbot needs to first look up the calendar of ‘Phani’, find a suitable time for a meeting in sync with the Boss’s schedule and then reschedule the meeting. Chatbots thus need to break up complex sounding conversation into simpler nuances and then execute the task sequentially and logically.
Intelligent bots can also break down the conversation to its essence and action items. Let’s look at a very common scenario: ‘Customer tries to book tickets for 14th August, confirms on the choice of airlines, origin and destination and navigates to the next page, but feels that the pricing is very high. The customer then asks the bot to check for ‘15th August instead’. Here, based on the situation, the chatbot is acting and will be able to display the new prices by changing the date of journey.
Intelligent bots will have features like: Amend Entities, Planning.
4. Adapting to human utterances
In the context of human–computer communication, forming assumptions about what a system can do and understand is problematic for most people. In turn, forming assumptions about how users will “talk to” the system is also likely to be problematic for system developers. The potential for variability in how users will communicate with a system is enormous and has been dubbed “The Vocabulary Problem.”
An intelligent chatbot can not only handle queries smartly and remembers them through the session, but also learns new things with every conversation that happens, saves them and uses them appropriately for future instances.
In a human conversation and especially over voice, there are bound to be
  • expectation of elaboration or confirmation (“can you hear me?”, “I do not follow”)
  • request for repeat of sentences (“ I’m sorry I couldn’t hear that, can you please repeat it again?” “Sorry, can you repeat?”)
  • pauses (“can you please hold? [pause] thank you!”)
  • interruptions (“the number is 212-” “sorry can you start over?” )
The simplest thing to do when writing responses to command and inquiry utterances in a conversational UI is to get straight to the point: respond with facts. That’ll remove a lot of the ambiguity and simplify the dialogue.
It’s up to the intelligent chatbot to adapt to the way the human responds - with the referential context (or) pauses (or) specific context (or) synonyms (or) repetition (or) abbreviations (or) variations in dialect. The chatbot needs to map it pre-contextually. But like their human counterparts, chatbots’ conversational skills determine whether they earn you seamless, scalable transactions or just another horde of pissed-off customers. This needs a lot of training by the chatbot to help continue the conversations to the logical ending.
Intelligent bots will have features like: Sync, Repeat, Interruptions and Pauses.
📷
5. When bots are kept simple
Although AI chatbots’ task is complicated and they need to be built up that way, yet the effort should be made to keep it simple. They need to be comprehensive yet detailed. A customer initiating a conversation with a chatbot might already be troubled due to some poor service related issue, hence it’s better not to irk him further with complex interaction. The bot should be answering the already irked usecustomer in a most precise way possible without confusing the person further. It’s easy to figure out if you are talking to a bot or a human. Make sure the customer knows that they are talking to a bot by welcoming them with some sort of welcome message. Nobody likes being told the same thing over and over again, so why do chatbots keep doing it? Bots should detect when they’re about to repeat a previously given answer and switch strategies. If the answer didn’t resolve the user’s needs before, repeating it certainly won’t either. From the user interface, to the dialog flow the experience should be pleasant, and information given to the user needs to be valuable and crisp.
Twitter also provides the option to give your bot a custom name for different sections of the bot, which can be of use. It’s important to show what the chatbot is capable of doing with Quick Replies. The customer needs to be a guided stepwise within the conversation and with enough accessible options to choose from.
Lastly, there must always be a way to end the conversation with the bot and switch to a human agent. Many bots today include a Quick Reply to “Speak to an Agent”. Certain actions, such as open-ended visual search, are challenging to complete in a messaging environment. In those situations, bots can route to a website or app to help the user complete goals they couldn’t execute within the context of chat.
Intelligent bots will have features like: Simple UI, Simpler steps, Agent Handoff
In a nutshell, a chatbot must be programmed to not just provide optimum solutions to problems, but also converse with customers in an engaging manner. The interaction must be exciting and the bot must appear to be curious enough to answer all queries. People prefer lively interactions and a chatbot needs to meet that expectation.
https://preview.redd.it/ri36kv7aurd11.jpg?width=800&format=pjpg&auto=webp&s=b8d3f5a1bab5b31dc3617f4bb7645e88bf70f836
For example, there are bots aligned with online shopping portals that can actually sense your liking and disliking. They can cancel orders for you accordingly and order the stuff that you actually want. Businesses are now moving way ahead than what anyone had ever thought of earlier. If we have an amazing concept like messenger or Kore.aiBots Platform, then why not use them to the full extent. Their proficiency in collecting massive data in a short period of time can be used to forecast upcoming business. You know it better how to get edgy with this interesting concept. The more you experiment with chatbots, the more you would get to know the wonders you can create with these little machines.
Some of the Global 2,000 companies and large enterprises are using Kore.ai Bots Platform to build their chatbots. How about you?
To get everything you need to build and deploy intelligent, enterprise-grade chatbots — without unnecessary complexity, click on Build your first BOT.
To ask questions, get tips, learn and grow with Kore.AI developer community, click on Ask questions on Developer Community.
Also Read on : Chatbots (of) the Future
Thank You
Phani Marupaka
LinkedIn| Tweet at : @phani_teja
submitted by PhaniTeja4 to Chatbots [link] [comments]

Order Book Indicator Secrets To Make Better Trades! - YouTube Forex Fundamental Analysis - You Don't Need It - YouTube Depth chart explained  Order book visualized - YouTube Simple Trick to Understanding Order Flow in the Forex ... Buy/Sell Walls and Order Books - What You Need To Know ... RTAS Order Book Analysis How orders affect the order book - YouTube

Learn the different types of market analysis in forex and CFD trading. Explore fundamental analysis techniques, and adopt technical analysis tools to learn what to trade and when to trade it. OANDA uses cookies to make our websites easy to use and customized to our visitors. Cookies cannot be used to identify you personally. By visiting our website you consent to OANDA’s use of cookies in ... The 3 Best Order Flow Indicators For Forex Traders. posted on . The goal of an order flow trader is to make predictions about the future market price by thinking about how and when orders are going to come into the market from traders making decisions. This is primarily done via guess-work and from an understanding of how traders trade, but there are also some indicators you can use to aid you ... For doing a correct order flow analysis of the forex markets you have to use the real stock exchange data. Volumen and order flow which is provided by Forex Brokers is useless because it is depending on the liquidity provider. By using the volume of futures you get real data that is moving the markets. In the next sections, we will show you how to do it. Metatrader 4/5 order flow trading is ... We are pleased to present you the last part (move to previous part) of our guide "mastering the Order Book".. Here you’ll find some examples of the order book analysis. We would like to focus your attention on the complexity of the analysis: first, several (but not one) signals by the order book will be used, and, second, a technical analysis of the market will be also taken into account. Forex Order Book. The second sentiment analysis tool in our list is the based on broker clients positions order book indicator. The Forex order book indicator allows traders to view price levels where there are accumulations of stop loss orders. It clearly highlights on the chart, where the largest numbers of stop orders are sitting and gives you an edge when planning entry and exit levels ... Forex Trading Strategies Free PDF Trading Strategies. IFC Markets, 17 Pages. Let's Get to Know Forex Free PDF. Forex.com, 28 Pages. Make Forex Trading Simple Free PDF. Sona Matasyan, 12 Pages, 2013. MetaTrader4 (MT4) User Guide Free PDF. MetaQuotes, 65 Pages. The Little Book of Currency Trading: How to Make Big Profits in the World of Forex ... Order Book is one of the most efficient tools for market sentiment analysis. Download and try the demo version for MT4 now. FXSSI - Forex Sentiment Board Forex Chart Analysis; Forex Indicators; Forex Trading Tips; Trading For Beginners; Trading Strategies; Order Book. No Tags; Trading Strategies . An order book is a real-time, continuously updated list of buy and sell orders on an exchange for a specific financial asset, such as a stock, bond, ETF or currency. Each order shows the number of shares or dollar amount of the asset being bid or ... advantage over other types of market analysis, although as with anything in trading it does require a significant amount of training in order to learn how to use it effectively. Unfortunately for us, the order book is unavailable to use in the forex market, due the fact there's no centralized exchange where trading takes place. The closest thing we have to a similar order flow indicator would ... Order Book Dynamics. Analysis of the order book’s formation dynamics is an essential part of its analysis. Move the mouse cursor over the chart in DOM snapshots tool. You can notice in the left order book that volumes rise at particular levels, decrease at other ones and remain the same at some levels. One can conclude from it that if trades ...

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Order Book Indicator Secrets To Make Better Trades! - YouTube

This video is part of the Udacity course "Machine Learning for Trading". Watch the full course at https://www.udacity.com/course/ud501 Let's go in depth with depth charts. We have already seen how the price chart allows us to visualize the trade history, and we will now see how the depth cha... You were told from the start you needed Forex Fundamental Analysis to trade profitably. You were lied to. Here is why. Blog for this Video - http://nononsens... https://www.forexreviews.info - Video guide to understanding the basics of order flow in the Forex market and other popular traded markets. Works in nearly a... 🔔 Did you enjoy this video? SUBSCRIBE for more: https://www.youtube.com/c/nuggetsnews?sub_confirmation=1 📹 For more Resources & Content: https://nuggetsnews.... Let me know what you thought about this training in the comments! 👇SUBSCRIBE TO JAMESON'S YOUTUBE CHANNEL NOW!👇 http://bit.ly/jamesonbrandon #1 Way to Grow Y... I reveal a Forex secret: How we can find the order blocks with the help of a website - Duration: 19:53. The Forex Pipmanager 12,112 views

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