Hi PH'ers - Happy to have Sherlock hunted! We're very excited to release Sherlock - our latest offering for product teams looking to understand how their users are engaging with their product.
With SHERLOCK, you create a custom engagement algorithm for your product by scoring each one of your events based on its "engagement weight" and Relay delivers you a ranked list of your users ordered by their engagement. It's completely flexible and allows you to see how user engagement shifts over time. You can also dive into detailed profiles with more info about each user's overall engagement.
Early users are getting almost immediate value, so we're really excited to open it up to a wider base.
Happy to answer any questions...fire away!
@dskaletsky Very cool idea Derek! Love that you can use a slider to carefully weight each event, and that it uses Segment so it works with tons of analytics tools out of the box.
@imakestrides Thanks, Kyle. You bring up an important point - Segment. We are huge Segment fans and built this MVP off of Segment exclusively. So...it's an important point - you can only use Sherlock via a Segment integration right now.
For the last few years I see that customer engagement calculation is automatically done/calculated by more and more SaaS services. I understand that you can "actually" define your engagement KPIs to have a summated metric - in fact a sum is really avoided and not suggested in metric world, as it's only a number and doesnt represent much value.
But the main problem still remains: Ok, I have a list but what am I going to do with all those users? Send them a push notification? Offer a discount? Provide a means to dig further by sending out drip marketing messages?
What are your plans with this issue?
@gorkemcetin Hi Gorkem - great comments. I agree. The raw number is fairly useless by itself. But it does provide the basis upon which you can compare users and get the ranking. Also, we are adding more time-based functionality soon which will show trending in the raw score which gives it more context.
But, totally, what do you do with this information is where the rubber meets the road. our main product - knowtify.io - is an engagement messaging platform, so Sherlock is a piece of an overall "User Engagement" play. For the Sherlock MVP, we allow you to export your ranked lists to CSV so you can at least have the data to take action on...
@sheppyc Hi Chris - kind of a longer story - I'd be happy to connect with you offline on it (derek@knowtify.io). Short answer is that we thought that this take on analytics could deliver a lot of value to companies/teams that didn't need the messaging piece. They will all be tightly integrated shortly (we have another product called Relay which allows you to connect user events to Slack - https://www.producthunt.com/tech...).
I love the simplicity of this and totally get the need; it's so easy to get bogged down with metrics. I haven't used the product yet, but will take a look when I get time. Great call using Segment.
One question for @dskaletsky do you also provide inverted results? For example, I'd be more interested in 'what users DIDN'T get engaged and of those users what 'DIDN'T they do?' etc..
Another cool feature (if not there already) would be "Quick WIn Alerts" showing me what features I should increase visibility on because of their clear engagement correlation.
@cpresc Hey Chris - great questions/suggestions. I'll invite co-founder @duilen to answer as well. We don't provide 'inverted results' today - but it's a great suggestion. We have a lot of plans for automatically surfacing insights and this could definitely be a piece of that...
@dskaletsky@cpresc I'd love to crack the nut on unengagement analytics. To me, that's where the value is. Sure there are things you can do with highly engaged users. But the value of knowing when someone stops engaging and why is huge.
To get there, we can do a few simple things. The easiest would be to allow filtering by users who are trending down and sort by users who are trending down more than others. From there, it would be great if you could dive in and compare the difference between current usage and recent usage. For example, say you've got a tool that lets users order breakfast. One day you decide to redesign the tool to make it more powerful. Frank, a loyal user, doesn't understand the new 'improvements' so he stops using the tool and goes back to his old breakfast routine.
So if Frank isn't performing an important action, you'd instantly be able to see him in the negative trending users and see that he is suddenly not ordering breakfast. From there, customer success can reach out to him and solve the problem before he churns.
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