Chatbots have a lot to offer brands, marketers and e-commerce merchants in terms of data. On the consumer side, chatbots forge deep relationships with users, enabling access to key user data that you can use to offer them highly targeted, relevant content. On the business side, chatbots’ conversational UI lets analysts make sense of broad swaths of data at a glance using natural language.
The data afforded by chatbot analytics can be a huge help in iterating products and services to make them better—which includes improving your consumer-facing chatbot, too. Spurred by the chatbot craze, the world of chatbot analytics has grown rapidly. Let’s look at chatbot analytics trends from before until now.
The Early Days: Traditional Chatbot Engagement Analytics
At their most basic, chatbots provide simple, traditional metrics like any other digital platform. Without sophisticated bot analytics platforms, bot makers were limited to metrics such as how many users would use a chatbot, which times were most active, and retention rates. These metrics aren’t so different than what you would receive for, say, a web page.
One could always read a history of conversations with a chatbot, though until more recently tools weren’t available to the masses that made organizing this information easy.
The Rise of Chatbot Analytics on an Individual Level
As bots became tied to social media platforms—such as with the Facebook Messenger—chatbot developers are now empowered to analyze and manage data on a personal, individual level. This includes measuring helpfulness through metrics like how many conversations a user will have (with the amount of conversation steps for each) and identifying a bot’s most active users.
While traditional analytics helped early bots measure increase or decrease in use, analytics at the individual level helps to forge relationships on a deep level with users. Chatbot developers can now meet users’ needs better by identifying:
- Response fails and bottlenecks in unique conversations
- Patterns in user behavior
- Best times for re-engaging (great for broadcasting to individual users)
Going a step above traditional analytics by assessing engagement on an individual level means the difference between personalized one-to-one communication and broadcasting to wide swaths of your audience.
Real Time Chatbot Engagement Analytics for Optimization, Personalization & Flow
Presently, chatbots are becoming more realistic and lifelike, helping users find the information they need with a touch of personality. Now that chatbots are relatively simple to set up, brands are jumping on the trend to further extend their brand voice through their bots’ personalities. Presenting an entertaining, engaging personality is a great way for bots to set themselves apart from competition.
As brands try to make their bots more engaging and personal, they will need to look further into real time engagement. Through A/B testing randomized responses, chatbot developers can find which phrases perform best. Developers may also optimize conversation flow by watching conversations in real time. When discussion hits a snag, a human agent can jump right in to fix things.
Real time analytics also lets you switch up responses on the fly as new trends, memes and current events affect your community or industry.
What’s in Store: Predictive Chatbot Analytics
Big data caused a huge shift in personalized content and marketing on social media. One of the biggest chatbots analytics trends is using predictive analytics to make sense of all that data. Predictive analytics lets you identify what users want before they need to ask, and can help brands better personalize results for hyper-niche demographics.
Predictive chatbot analytics may provide a revolution in content marketing and ecommerce. As chatbot analytics continue to grow more sophisticated, brands will become better able to truly understand their audience as individuals, and will be empowered to better recommend products, services and content relevant to them.