Top 12 Benefits of Chatbots: Comprehensive Guide [2018 update]

No one can know benefits of chatbots better than potential users of chatbots. So that’s where we started, the above list is based on Drift’s 2018 State of Chatbots Report. We added a few more points to categorize benefits of chatbots clearly.

Benefits to Customers

  • 1- 24 hour availability: While this is clearly a huge benefit, highlighting this risks creating backlash when bots are down due to security issues or maintenance
  • 2- Instant answers
  • 3- Consistent answers: Talking to a customer service rep, a customer has no assurance that other reps are also providing similar, consistent responses. If a customer service rep is not helpful, a customer could be tempted to try calling again to see if the next rep is better.
  • 4- Recorded answers: Talking to a customer service rep, a customer gets no record of the conversation and most people would prefer not to record their conversations. However, a customer can take a screenshot whenever she likes, to remember the conversation or to challenge an answer provided by the bot.
  • 5- Instant transactions: Actions like changing or querying records are almost instantaneous for bots.
  • 6- Endless patience: While customer reps and customers sometimes lose their patience, that’s something bots are yet incapable of.
  • 7- Programmability: Since bots are on digital platforms where people spend majority of their waking hours working, bots can be used to automate common tasks such as arranging meetings, providing advanced search functionality

Benefits to Companies Read more

In-depth Sales Analytics Guide: Best Practices, Applications [2018]

Unlike marketing, sales has always been numbers driven and now with the explosion of data and computational power, sales analytics has become central to any large sales organization

What is sales analytics?

Sales involves making many decisions with limited data. Sales analytics helps uncover insights and increasingly recommends the best decisons to sales reps and managers.

Source: HBR

Relevant data sources include most data used by marketing departments such as account level and lead level digital history and preferences along with rich data on all sales rep interactions such as calls logs and emails. Read more

2018 AI predictions: Summary of top AI experts’ predictions

Since the beginning of the year, PwC, CEO of pymetrics, Gil Press  published predictions on the direction AI will take. We read them all and couldn’t resist adding our predictions and categorizing the predictions:

Mega-trends that will shape AI in 2018

The news cycle is full of AI, research centers being opened, re-organizations, new research findings and tabloids peddling that robots will kill us all tomorrow. Reading a different type of news everyday, it is easy to lose track of what is really happening. What are the major trends? Read more

Challenges of implementing an AI solution

Deloitte survey identifies top challenges by corporations applying AI in their businesses

Judging from the numbers above which are from Deloitte’s 2017 State of Cognitive survey, it seems that only a tiny minority (6%) of the corporations are having a smooth ride with AI. We found the survey results realistic and combined them with our experience with companies that reached out to us regarding advice on their AI solutions. We think there are 2 classes of issues

Issues with building own AI solutions

Lack of business alignment

Identifying business cases for AI applications requires managers to have a deep understanding of current AI technologies, their limitations and the current processes of their division. As with any nascent field, lack of AI know-how in management is hindering adoption in most cases. Read more

Limitations of AI: Data hungry, opaque, brittle, self-reliant systems

Though we preach that AI investments can transform businesses, we are also not naive in our beliefs in AI’s current capabilities. Most modern AI systems suffer from common issues highlighted by respectable publications that we will collect here:

Reliance on large volumes of data

Impacts deep learning algorithms. Sadly, even when data is available, it’s likely to suffer from bias.

Research on one shot learning is an attempt to solve this problem.

Reliance on labeled data

Limits supervised learning algorithms to relatively few problems where labeled data is either available or where the solution is so valuable that companies invest in preparing semi-manually labeled data. Read more