4 reasons data scientists are falling in love with bots

Companies have invested over $4 billion to create bots and analyze the resulting data, which is now being generated by millions of users around the globe. Far from being a specialized subset, bot data contains all of the hallmarks of user behavior data that’s been collected since the dawn of the internet, but it’s more than just conversions, demographics, and engagement.

Read on to learn how much more value bot data can provide, beyond answering wtf is that?

1. Data is awesome. Bot data is data

Bots provide data streams that are inherently social and optimized for feeding machine learning algorithms and still include the same invaluable information inherent to any digital user process. For example, bots on a platform like Facebook Messenger can capture user demographics, traffic rates, sales conversions, or any activity or API request. As a result, the botmaster can identify promising market segments, use A/B testing to optimize for conversion, or track traffic and sales patterns. For Messenger bots, Facebook is encouraging developers to track analytics through their app insights platform. App metrics such as segments, gender, platforms, and more, as seen below, can be captured through the user profile API.

2. Do you know anyone who speaks human? Language data is inherently social

Users interact with bots via human language, an incredibly rich data type that can be used in many applications. In fact, processing natural language data is projected by Markets and Markets to produce over $16 billion in value over the next five years.  One of the unique values of language data is its capacity to provide emotional context. That’s why, in basic NLP, words or phrases can be associated with negative or positive feelings — making it easier to estimate net promoter scores or optimize user experiences.

Furthermore, language-based bot interactions are inherently social, as bots capture data from users in precisely the online environments where users typically interact with humans. Messaging apps now eclipse social media in terms of users and growth, so bots living on Facebook, WhatsApp, Slack, and other messaging apps have instant access to the rich language data of a growing user base.

3. ‘Daisy, Daisy, give me your answer, do.’ Bots can learn from their data

Like the world’s most famous heuristically programmed algorithmic computer, bots are designed to learn from their observations. The turn-based query/response pattern of bot interactions, coupled with the potential millions of varied users and contexts, provides fertile data for machine learning algorithms that allow bots to self improve. Content and outcome data for every bot and human statement in a conversation is recorded and, over time, responses will pile up in the thousands, or hundreds of thousands, providing raw data to optimize a predictive model.

For example, let’s say your fantasy football bot asks for a user’s favorite team and then responds with either “Brady Cheats” or “Free Brady.” As Patriots fans respond with expletives to “Brady Cheats” and everyone else responds with a “Ha. Yes,” the bot can learn to use “Free Brady” for Patriots fans, or New England geolocations, and “Brady Cheats” for everyone else. The same principle can be applied to outcomes such as closing a sale, where probabilities for a close can be attached to specific responses.

Furthermore, bots can learn new phrases from humans. For example, if a bot says, “How are you?” and humans who respond with “You mean how am I right meow?” end up purchasing the most wares, the bot data will include both the new phrase as well as the probability of a positive outcome. Thus, a sales-oriented bot with basic A.I. may respond with its own “Right meow” to select users, learning vocabulary and idioms outside of its initial programming.

4. Terabytes of bot data are waiting

As the bot industry explodes, there’s a ripe opportunity to generate novel ways of applying data science techniques to this new, rich data stream. The amount of data you can capture from your bot — and how you use that data — mirrors the amount of data you can capture from any digital-to-human computer interaction, whether that’s a point-of-sale event or a customer interacting with your website or playing a web-connected video game.

Using machine learning techniques coupled with the simple language-focused, call-response bot interface, you can train your bot to use its data to get smarter. Bots will soon be teaching themselves exactly how to optimally leverage the big data revolution. So if you’re not generating and analyzing bot data already, right meow might be a good time to start.