Facebook uses A.I. to increase engagement. Why not do the same in ManyChat?
RallySeller is the best all-around chatbot flow to accompany your landing page or offer. Using A.I., optimal prompts will be selected that people are more likely to click on. Warmer leads will then be directed to the next step (e.g. "checkout" or "sign up" page). Give your customers an enterprise level experience with RallySeller.
Just by loading FAQs from their website into RallySeller, a recent CuriousityStream affiliate campaign obtained a 2x increase in clickthrough and call-to-action taps.
Click here for more results achieved with RallySeller as part of the marketing mix.
The usefulness of A.I. will become evident quickly as thousands of different combinations are tested.
Q-learning keeps track of successful interactions with leads in order to get the maximum cumulative value (longer conversations and more clickthrough).
Selection of prompts such as “Any discounts?” or “How does it work” are made to increase the likelihood of cumulative results in a dialog.
Place RallySeller on your product page to help move people through the consideration phase.
Or use paid ads to send RallySeller leads.
By loading persuasive statements into the agent, optimal dialogs will be constructed after a learning phase consisting of exploring different dialogs with customers and re-using successful techniques.
RallySeller has quickly found some winning dialogs that resulted in 4-5 long message sequences AND call-to-action taps “Sign me up” for an online streaming service. On the bottom, 0 call-to-action taps were obtained with very few dialogs.
Get unlimited access to RallySeller and quickly install your template in minutes. Tap below to get started with Persuasive A.I. $0 due today.
Get Started FreeStep-by-step process:
2. Fill out this Google Sheet with your 16 persuasive selling points which will be loaded onto the chatbot
3. Download and install the RallySeller template for ManyChat Pro --
some slight adjustments can be made, such as adding pictures or changing the wording on the call-to-action buttons
4. Login and view your credentials to input into the template
5. Test out the new sequence or conduct your own training
6. Have support reset Rally's memory once testing is complete, or you can choose to go live*
7. Monitor learning progress charts in real-time
*All interactions teach the bot what messages work best, so this optional reset step can be used if you want a clean slate -- it's also possible that your testing period is a controlled experiment where you send subscribers to the bot you know are making the selections you want to begin the learning phase
Please contact support if you have any questions.
Beyond finding the best, most engaging messages, the chatbot also learns to increase the length of the conversation by selecting the best message sequence, also finding the best point in the dialog to include the call-to-action button.
For example, if one message such as “How does it work?” was consistently getting a good response, but was repeatedly followed by the customer ignoring the next message then the sales agent would detect that “How does it work?” may be a conversation killer – it is seeking to optimize for length of the dialog, and button taps on the call-to-action.
The sales agent uses the reinforcement method of machine learning to explore and adapt to its environment (customer interaction) – this allows for winning strategies to be repeated while new ones can also be continually tested. Since customer preference can be unpredictable this can help find a balance between exploring new ways to entice customers vs. exploiting previously found successful strategies.
This “convergence” point is when the optimal behavior of the agent has been achieved, though at any time the “explore more” strategy can be turned up.
The agent holds 16 persuasive statements about the product or service divided into 2 categories: Attribute and Non-Attribute.
See this example of the selling points to provide in order to load Rally.
1) Product Attribute persuasive statements (for high prior knowledge customers), contain specific details for example:
“The i7-6700 incorporates the HD Graphics 530 IGP operating at 350 MHz and a turbo frequency of 1.15 GHz. This chip supports up to 64 GiB of dual-channel DDR4-2133 memory.”
(in response to the prompt: “What are the specs?”)
2) Non-Attribute persuasive statements (for low prior knowledge customers), contain details about the general benefits or how it will make them feel, for example:
“You might think it's the number of cores, but for the most part desktop Core i5 processors have four cores, just like i7s.”
(in response to the prompt: “Is i7 faster than i5”)
These statements can be written in the form shown above or using an argumentation framework with common arguments or “barriers to purchase” (in place of question prompts) being refuted by counter-arguments such as “Is the CPU slow?” followed by a persuasive argument #1 above – the 16 persuasive statements can also be a mix of question/answer and argument/counterargument – the agent will find the best messages.
At the start, the agent will try to determine if the customer is high prior knowledge or not.
By dividing the persuasive statements into groups with the maximum conversation length of 4-5 messages there are tens of thousands of possible dialog trees.
This number includes messages with or without the call-to-action. It may be determined that a customer segment prefers to get right to the action and taps “Sign me up” or “Order Now” in message #2, or perhaps they need a little more nurturing.
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