Case Study - MongoDB: increased new leads by 70% in three months.

Chatbot system:Drift

Key stats:

Increased net new leads by 70%

Increased total messaging response by 100%

MongoDB was having a lot of success with live chat, but like all humans, their salespeople were limited by things like "time" and "space." They couldn't significantly increase the number of conversations they were having without significantly increasing the size of their team.

As their director of demand generation puts it:

"We needed a messaging tool that could scale with our business and increase the volume of our conversations, leading to the increase of our pipeline and Sales Accepted Leads (SALs)the metrics my Demand Generation team are measured on."

MongoDB let Drift's Leadbot ensure that their sales reps only talked to the people who were most likely to buy. And with Drift's meeting scheduler, people didn't have to play phone tag to make an appointment.

For MongoDB, automating lead-qualifying conversations allowed them to have more conversations, and automating the scheduling process let them turn more of those conversations into leads.

Q1. Identify and describe what type of business chatbot MongoDB has implemented. What is the main challenge that pushed MongoDB to adopt this technology?

Case Study - MongoDB: increased new leads by 70% in three months.

Chatbot system:Drift

Key stats:

Increased net new leads by 70%

Increased total messaging response by 100%

MongoDB was having a lot of success with live chat, but like all humans, their salespeople were limited by things like "time" and "space." They couldn't significantly increase the number of conversations they were having without significantly increasing the size of their team.

As their director of demand generation puts it:

"We needed a messaging tool that could scale with our business and increase the volume of our conversations, leading to the increase of our pipeline and Sales Accepted Leads (SALs)the metrics my Demand Generation team are measured on."

MongoDB let Drift's Leadbot ensure that their sales reps only talked to the people who were most likely to buy. And with Drift's meeting scheduler, people didn't have to play phone tag to make an appointment.

For MongoDB, automating lead-qualifying conversations allowed them to have more conversations, and automating the scheduling process let them turn more of those conversations into leads.

Q2. What are the two main objectives of this specific chatbot? What is the most important metric for the Demand Generation team at MongoDB?

Case Study - MongoDB: increased new leads by 70% in three months.

Chatbot system:Drift

Key stats:

Increased net new leads by 70%

Increased total messaging response by 100%

MongoDB was having a lot of success with live chat, but like all humans, their salespeople were limited by things like "time" and "space." They couldn't significantly increase the number of conversations they were having without significantly increasing the size of their team.

As their director of demand generation puts it:

"We needed a messaging tool that could scale with our business and increase the volume of our conversations, leading to the increase of our pipeline and Sales Accepted Leads (SALs)the metrics my Demand Generation team are measured on."

MongoDB let Drift's Leadbot ensure that their sales reps only talked to the people who were most likely to buy. And with Drift's meeting scheduler, people didn't have to play phone tag to make an appointment.

For MongoDB, automating lead-qualifying conversations allowed them to have more conversations, and automating the scheduling process let them turn more of those conversations into leads.

Q3. What security measures can MongoDB implementin order toensure that its chatbot is safe for its customers? Listand describetwo such measures.