An Analysis of the Raw Data From 21 Clients and Over 22 Million Website Visitors
By Lise Reddick, VP Product at Lift AI
On average, Drift customers using Lift AI get 9x more conversations that turn into pipeline opportunities.
Lift AI is game-changing.
So, how did we calculate the 9x number?
Lift AI Tracks Key Metrics to Perform
As with any marketing technology, Lift AI tracks metrics on each customer install so that we can measure results and seek improvements. The data for this analysis was pulled directly from Drift’s platform, including information such as:
- Number of visitors scored
- Number of chat invitations (or “sends”) presented
- The nature and number of each playbook deployed
- The number of accepted conversations
- The number of conversations converted into opportunities
With this information in hand, we can review the analytics for our customers to measure results, find optimizations, and continuously learn.
The Raw Data From 21 Clients and Over 22 Million Website Visitors
Below is a table representing the metrics averaged from 21 Drift clients and including over 22,366,102 million website visitors.
The above two rows show what an average customer’s results looks like without Lift AI, and what it looks like with Lift AI. As you can see, at a high level, customers using Lift AI get 9x more conversations turned into pipeline, measured by the difference between 0.4% without Lift AI and 3.7% with Lift AI: 3.7/0.4 = 9.25.
Below is a breakdown of figures from the websites that do use Lift AI. The segments are determined by Lift AI which uses a machine-learning model to assign each visitor a “buyer intent” score in real-time.
Typically, we segment visitors by high, medium, or low buyer intent. This determines how likely the visitor is to convert if they’re engaged with the right experience. The right experience in this case means customizing Drift playbooks to engage each buyer intent segment accordingly. You can’t use a “one size fits all” approach for conversational marketing and expect to get strong results - rather, you should engage visitors based on their intent.
For example, a visitor with high buyer intent should automatically deploy a live sales agent playbook (if they’re online). Our study revealed that 2,635 high intent visitors engaged in a conversation with a live sales agent playbook converted into an opportunity 11.7% of the time!
A medium intent visitor might deploy a “qualification” chatbot playbook which seeks to move visitors up to a live agent for conversion. A low intent visitor might get a support or nurturing chatbot which directs visitors to a key content piece or similar. Each playbook is then customized depending on if live agents are online or not, for maximized results.
By creating Drift playbooks based on Lift AI intent, you can engage high intent visitors in real-time to convert them using online live agents, while nurturing and supporting the medium to low intent visitors using automated tools like chatbot playbooks.
We Stand By Our Numbers
We created Lift AI to help get better results for our Conversational Marketing clients, for whom we offered a range of services to increase the efficiency and efficacy of their implementations. As conversational marketing experts, we worked on a “pay for performance” model - where our clients would only pay us if we achieved the results we promised.
Over the course of many years, gaining an incredibly deep understanding of conversational marketing and conversions, we realized that there must be a better way to engage visitors in chat based on their own unique intent. That’s when Lift AI’s machine-learning model was born.
We still stand by our numbers and our data today and include a performance guarantee in many of our agreements.
If you’d like an even deeper analysis of the results, we’d be happy to walk you through them - you can contact us here to learn more.
