Before we go into the technicalities of how to improve the KPIs above, there is a main concept to keep in mind when creating and optimising a Messenger bot built for customer acquisition purposes:
You need to create consistent ad-to-bot experiences.
The ad and the bot needs to be conceived together, since there is no way you can start to improve the bot if the ad is not consistent with it. You would simply receive poor traffic that you will not be able to optimise for.
We have learned this the hard way, when the first tests were not producing the expected results. And the first signal of it was a really low welcome message conversion rate (i.e. only a few people interacting with the Facebook ad started to engage with the bot).
To understand the reasons why it was happening we did multiple UX tests with the bots, asking users to go through the whole funnel and tell us what they were expecting, step after step. It clearly emerged that the first reason people dropped was that what they received on the chat was not what they were expecting.
And this happened because who was doing the ad (the tester) was different from who was creating the bot (us).
That´s when we understood that, before even building the bot, we needed to think at the whole funnel together, beginning from the ad (and as a consequence we stated designing ads together with the bot — using this super cool tool for ad mockups).
Having said that, we can now go more into the details of what to look at when improving the different steps of the funnel.
1. Optimising the acquisition KPI (= cost per click)
These are the key questions we ask ourselves when the ad is not performing as intended (i.e. CPC consistently out of the expected range).
- Are you using Messages objective campaigns (campaign level optimisation)?
In our tests, they have proven to perform consistently better than other campaigns, including conversion campaigns.
- Are you using the right audience (ad set level optimisation)?
This has revealed to have a huge impact on the performance of the ad, as it happens in any other type of Facebook campaigns. The only aspect worth mentioning here is that a good optimisation technique is the following: after that a good number of conversations has been collected, you can create lookalike audiences to target people similar to the ones that already chatted with your bot. And this works pretty well.
- Are you using a low effort call-to-action (CTA) (ad level optimisation)?
In our tests, CTAs implying high potential effort for the user (e.g. “Send message”) performed worse than low effort ones (e.g. “Learn more”).
2. Optimising the activation KPI (= welcome message conversion rate)
These are the key questions we ask ourselves when the welcome message is not performing as intended (i.e. conversion rate consistently below 25%).
- Is your welcome message content consistent with the ad text and image?
As highlighted before, the main reason for bot acquisition campaigns to fail is that the ad and bot experience have not been thought together. Align the content of the ad with the one of the welcome message.
- Are you your asking a low effort question?
The welcome message role is essentially to ask the user to opt in the conversation with the bot. As a consequence you want to minimise friction as much as possible. And the way the message is phrased has a big impact. As michael highlights in his article, low effort asks work well, especially if they are in the form of rhetorical questions. Examples could be “Do you want to start?” or “Do you want to receive a free coupon code?”.
3. Optimise the conversion KPI (= lead conversion rate)
Being the concept of lead different from company to company, it is difficult to abstract lessons on how to optimise this KPI. Please keep that in mind when reading the key questions we ask ourselves when the lead conversion rate is not performing as intended (i.e. conversion rate consistently below 25%).
- Is the conversation building enough trust for the user to leave its contact details?
While carrying out our tests we realised something we had not expected before. We initially moved from the design assumption that shorter bots would have performed better than longer ones, since users would have gone through less steps.
But these short bots were not performing as expected, and removing questions produced even worse results. When we run UX tests we started receiving comments like the following:
“Feels like it hasn’t been enough questions to give out email address”
“How is this gonna provide me a personal quote with such a limited number of information given?”
It paradoxically seemed that users expected many questions before they could consider the bot reliable and decide to provide their personal details. In other words:
The reality is indeed that the bot funnel of a lead generation bot looks more like the one you can see below.
- Are you explaining why you are asking for the personal information?
Before you ask for an email or a phone number, is always a good practice to explain why you need such information and what will happen after the user submits it, including when they will be contacted, by whom and for which reason (e.g. “We will send you a custom quote”, “We will book a visit in our apartments for you”).
- Are you providing any incentive?
It is a good practice to provide an incentive for the user to leave his personal details, such as a free quote, sample or a high quality content. This can be allowed already at the ad level and emphasised on the welcome message, but should ultimately product gains at the lead conversion level.